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| Groupon |
At Groupon we use Storm to build real-time data integration systems. Storm helps us analyze, clean, normalize, and resolve large amounts of non-unique data points with low latency and high throughput. |
| The Weather Channel |
At Weather Channel we use several Storm topologies to ingest and persist weather data. Each topology is responsible for fetching one dataset from an internal or external network (the Internet), reshaping the records for use by our company, and persisting the records to relational databases. It is particularly useful to have an automatic mechanism for repeating attempts to download and manipulate the data when there is a hiccup. |
| FullContact |
At FullContact we currently use Storm as the backbone of the system which synchronizes our Cloud Address Book with third party services such as Google Contacts and Salesforce. We also use it to provide real-time support for our contact graph analysis and federated contact search systems. |
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Storm powers a wide variety of Twitter systems, ranging in applications from discovery, realtime analytics, personalization, search, revenue optimization, and many more. Storm integrates with the rest of Twitter's infrastructure, including database systems (Cassandra, Memcached, etc), the messaging infrastructure, Mesos, and the monitoring/alerting systems. Storm's isolation scheduler makes it easy to use the same cluster both for production applications and in-development applications, and it provides a sane way to do capacity planning. |
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| Yahoo! |
Yahoo! is developing a next generation platform that enables the convergence of big-data and low-latency processing. While Hadoop is our primary technology for batch processing, Storm empowers stream/micro-batch processing of user events, content feeds, and application logs. |
| WebMD |
We use Storm to power our Medscape Medpulse mobile application which allow medical professionals to follow important medical trends with Medscape's curated Today on Twitter feed and selection of blogs. Storm topology is capturing and processing tweets with twitter streaming API, enhance tweets with metadata and images, do real time NLP and execute several business rules. Storm also monitors selection of blogs in order to give our customers real-time updates. We also use Storm for internal data pipelines to do ETL and for our internal marketing platform where time and freshness are essential. We use storm to power our search indexing process. We continue to discover new use cases for storm and it became one of the core component in our technology stack. |
| Spotify |
Spotify serves streaming music to over 10 million subscribers and 40 million active users. Storm powers a wide range of real-time features at Spotify, including music recommendation, monitoring, analytics, and ads targeting. Together with Kafka, memcached, Cassandra, and netty-zmtp based messaging, Storm enables us to build low-latency fault-tolerant distributed systems with ease. |
| Infochimps |
Infochimps uses Storm as part of its Big Data Enterprise Cloud. Specifically, it uses Storm as the basis for one of three of its cloud data services - namely, Data Delivery Services (DDS), which uses Storm to provide a fault-tolerant and linearly scalable enterprise data collection, transport, and complex in-stream processing cloud service. In much the same way that Hadoop provides batch ETL and large-scale batch analytical processing, the Data Delivery Service provides real-time ETL and large-scale real-time analytical processing — the perfect complement to Hadoop (or in some cases, what you needed instead of Hadoop). DDS uses both Storm and Kafka along with a host of additional technologies to provide an enterprise-class real-time stream processing solution with features including:
Infochimps has extensive experience in deploying its DDS to power large-scale clickstream web data flows, massive Twitter stream processes, Foursquare event processing, customer purchase data, product pricing data, and more. |
| Health Market Science |
Health Market Science (HMS) provides data management as a service for the healthcare industry. Storm is at the core of the HMS big data platform functioning as the data ingestion mechanism, which orchestrates the data flow across multiple persistence mechanisms that allow HMS to deliver Master Data Management (MDM) and analytics capabilities for wide range of healthcare needs: compliance, integrity, data quality, and operational decision support. |
| Cerner |
Cerner is a leader in health care information technology. We have been using Storm since its release to process massive amounts of clinical data in real-time. Storm integrates well in our architecture, allowing us to quickly provide clinicians with the data they need to make medical decisions. |
| Aeris Communications |
Aeris Communications has the only cellular network that was designed and built exclusively for machines. Our ability to provide scalable, reliable real-time analytics - powered by Storm - for machine to machine (M2M) communication offers immense value to our customers. We are using Storm in production since Q1 of 2013. |
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Flipboard is the worldʼs ﬁrst social magazine, a single place to keep up with everything you care about and collect it in ways that let reﬂect you. Inspired by the beauty and ease of print media, Flipboard is designed so you can easily ﬂip through news from around the world or stories from right at home, helping people ﬁnd the one thing that can inform, entertain or even inspire them every day. We are using Storm across a wide range of our services from content search, to realtime analytics, to generating custom magazine feeds. We then integrate Storm across our infrastructure within systems like ElasticSearch, HBase, Hadoop and HDFS to create a highly scalable data platform. |
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| Rubicon Project |
Storm is being used in production mode at the Rubicon Project to analyze the results of auctions of ad impressions on its RTB exchange as they occur. It is currently processing around 650 million auction results in three data centers daily (with 3 separate Storm clusters). One simple application is identifying new creatives (ads) in real time for ad quality purposes. A more sophisticated application is an "Inventory Valuation Service" that uses DRPC to return appraisals of new impressions before the auction takes place. The appraisals are used for various optimization problems, such as deciding whether to auction an impression or skip it when close to maximum capacity. |
| Ooyala |
Ooyala powers personalized multi-screen video experiences for some of the world's largest networks, brands and media companies. We provide all the technology and tools our customers need to manage, distribute and monetize digital video content at a global scale. At the core of our technology is an analytics engine that processes over two billion analytics events each day, derived from nearly 200 million viewers worldwide who watch video on an Ooyala-powered player. Ooyala will be deploying Storm in production to give our customers real-time streaming analytics on consumer viewing behavior and digital content trends. Storm enables us to rapidly mine one of the world's largest online video data sets to deliver up-to-the-minute business intelligence ranging from real-time viewing patterns to personalized content recommendations to dynamic programming guides and dozens of other insights for maximizing revenue with online video. |
| Taobao |
We make statistics of logs and extract useful information from the statistics in almost real-time with Storm. Logs are read from Kafka-like persistent message queues into spouts, then processed and emitted over the topologies to compute desired results, which are then stored into distributed databases to be used elsewhere. Input log count varies from 2 millions to 1.5 billion every day, whose size is up to 2 terabytes among the projects. The main challenge here is not only real-time processing of big data set; storing and persisting result is also a challenge and needs careful design and implementation. |
| Alibaba |
Alibaba is the leading B2B e-commerce website in the world. We use storm to process the application log and the data change in database to supply realtime stats for data apps. |
| Baidu |
Baidu offers top searching technology services for websites, audio files and images, my group using Storm to process the searching logs to supply realtime stats for accounting pv, ar-time and so on. This project helps Ops to determine and monitor services status and can do great things in the future. |
| Klout |
Klout helps everyone discover and be recognized for their influence by analyzing engagement with their content across social networks. Our analysis powers a daily Klout Score on a scale from 1-100 that shows how much influence social media users have and on what topics. We are using Storm to develop a realtime scoring and moments generation pipeline. Leveraging Storm's intuitive Trident abstraction we are able to create complex topologies which stream data from our network collectors via Kafka, processed and written out to HDFS. |
| Loggly |
Loggly is the world's most popular cloud-based log management. Our cloud-based log management service helps DevOps and technical teams make sense of the the massive quantity of logs that are being produced by a growing number of cloud-centric applications – in order to solve operational problems faster. Storm is the heart of our ingestion pipeline where it filters, parses and analyses billions of log events all-day, every day and in real-time. |
| premise.is |
We're building a platform for alternative, bottom-up, high-granularity econometric data capture, particularly targeting opaque developing economies (i.e., Argentina might lie about their inflation statistics, but their black market certainly doesn't). Basically we get to funnel hedge fund money into improving global economic transparency. We've been using Storm in production since January 2012 as a streaming, time-indexed web crawl + extraction + machine learning-based semantic markup flow (about 60 physical nodes comparable to m1.large; generating a modest 25GB/hr incremental). We wanted to have an end-to-end push-based system where new inputs get percolated through the topology in realtime and appear on the website, with no batch jobs required in between steps. Storm has been really integral to realizing this goal. |
| Wego |
About Wego, we are one of the world’s most comprehensive travel metasearch engines, operating in 42 markets worldwide and used by millions of travelers to save time, pay less and travel more. We compare and display real-time flights, hotel pricing and availability from hundreds of leading travel sites from all around the world on one simple screen. At the heart of our products, Storm helps us to stream real-time meta-search data from our partners to end-users. Since data comes from many sources and with different timing, Storm topology concept naturally solves concurrency issues while helping us to continuously merge, slice and clean all the data. Additionally with a few tricks and tools provided in Storm we can easily apply incremental update to improve the flow our data (1-5GB/minute). With its simplicity, scalability, and flexibility, Storm does not only improve our current products but more importantly changes the way we work with data. Instead of keeping data static and crunching it once a while, we constantly move data all around, making use of different technologies, evaluating new ideas and building new products. We stream critical data to memory for fast access while continuously crunching and directing huge amount of data into various engines so that we can evaluate and make use of data instantly. Previously, this kind of system requires to setup and maintain quite a few things but with Storm all we need is half day of coding and a few seconds to deploy. In this sense we never think Storm is to serve our products but rather to evolve our products. |
| RocketFuel |
At Rocket Fuel (an ad network) we are building a real time platform on top of Storm which imitates the time critical workflows of existing Hadoop based ETL pipeline. This platform tracks impressions, clicks, conversions, bid requests etc. in real time. We are using Kafka as message queue. To start with we are pushing per minute aggregations directly to MySQL, but we plan to go finer than one minute and may bring HBase in to the picture to handle increased write load. |
| QuickLizard |
QuickLizard builds solution for automated pricing for companies that have many products in their lists. Prices are influenced by multiple factors internal and external to company. Currently we use Storm to choose products that need to be priced. We get real time stream of events from client site and filters them to get much more light stream of products that need to be processed by our procedures to get price recommendation. In plans: use Storm also for real time data mining model calculation that should match products described on competitor sites to client products. |
| spider.io |
At spider.io we've been using Storm as a core part of our classification engine since October 2011. We run Storm topologies to combine, analyse and classify real-time streams of internet traffic, to identify suspicious or undesirable website activity. Over the past 7 months we've expanded our use of Storm, so it now manages most of our real-time processing. Our classifications are displayed in a custom analytics dashboard, where Storm's distributed remote procedure call interface is used to gather data from our database and metadata services. DRPC allows us to increase the responsiveness of our user interface by distributing processing across a cluster of Amazon EC2 instances. |
| 8digits |
At 8digits, we are using Storm in our analytics engine, which is one of the most crucial parts of our infrastructure. We are utilizing several cloud servers with multiple cores each for the purpose of running a real-time system making several complex calculations. Storm is a proven, solid and a powerful framework for most of the big-data problems. |
| Alipay |
Alipay is China's leading third-party online payment platform. We are using Storm in many scenarios:
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| NaviSite |
We are using Storm as part of our server event log monitoring/auditing system. We send log messages from thousands of servers into a RabbitMQ cluster and then use Storm to check each message against a set of regular expressions. If there is a match (< 1% of messages), then the message is sent to a bolt that stores data in a Mongo database. Right now we are handling a load of somewhere around 5-10k messages per second, however we tested our existing RabbitMQ + Storm clusters up to about 50k per second. We have plans to do real time intrusion detection as an enhancement to the current log message reporting system. We have Storm deployed on the NaviSite Cloud platform. We have a ZK cluster of 3 small VMs, 1 Nimbus VM and 16 dual core/4GB VMs as supervisors. |
| Glyph |
Glyph is in the business of providing credit card rewards intelligence to consumers. At a given point of sale Glyph suggest its users what are the best cards to be used at a given merchant location that will provide maximum rewards. Glyph also provide suggestion on the cards the user should carry to earn maximum rewards based on his personal spending habits. Glyph provides this information to the user by retrieving and analyzing credit card transactions from banks. Storm is used in Glyph to perform this retrieval and analysis in realtime. We are using Memcached in conjuction with Storm for handling sessions. We are impressed by how Storm makes high availability and reliability of Glyph services possible. We are now using Storm and Clojure in building Glyph data analytics and insights services. We have open-sourced node-drpc wrapper module for easy Storm DRPC integration with NodeJS. |
| Heartbyte |
At Heartbyte, Storm is a central piece of our realtime audience participation platform. We are often required to process a 'vote' per second from hundreds of thousands of mobile devices simultaneously and process / aggregate all of the data within a second. Further, we are finding that Storm is a great alternative to other ingest tools for Hadoop/HBase, which we use for batch processing after our events conclude. |
| 2lemetry |
2lemetry uses Storm to power it's real time analytics on top of the m2m.io offering. 2lemetry is partnered with Sprint, Verizon, AT&T, and Arrow Electronics to power IoT applications world wide. Some of 2lemetry's larger projects include RTX, Kontron, and Intel. 2lemetry also works with many professional sporting teams to parse data in real time. 2lemetry receives events for every touch of the ball in every MLS soccer match. Storm is used to look for trends like passing tendencies as they develop during the game. |
| Nodeable |
Nodeable uses Storm to deliver real-time continuous computation of the data we consume. Storm has made it significantly easier for us to scale our service more efficiently while ensuring the data we deliver is timely and accurate. |
| TwitSprout |
At TwitSprout, we use Storm to analyze activity on Twitter to monitor mentions of keywords (mostly client product and brand names) and trigger alerts when activity around a certain keyword spikes above normal levels. We also use Storm to back the data behind the live-infographics we produce for events sponsored by our clients. The infographics are usually in the form of a live dashboard that helps measure the audience buzz across social media as it relates to the event in realtime. |
| HappyElements |
HappyElements is a leading social game developer on Facebook and other SNS platforms. We developed a real time data analysis program based on storm to analyze user activity in real time. Storm is very easy to use, stable, scalable and maintainable. |
| IDEXX Laboratories |
IDEXX Laboratories is the leading maker of software and diagnostic instruments for the veterinary market. We collect and analyze veterinary medical data from thousands of veterinary clinics across the US. We recently embarked on a project to upgrade our aging data processing infrastructure that was unable to keep up with the rapid increase in the volume, velocity and variety of data that we were processing. We are utilizing the Storm system to take in the data that is extracted from the medical records in a number of different schemas, transform it into a standard schema that we created and store it in an Oracle RDBMS database. It is basically a souped up distributed ETL system. Storm takes on the plumbing necessary for a distributed system and is very easy to write code for. The ability to create small pieces of functionality and connect them together gives us the ultimate flexibility to parallelize each of the pieces differently. Our current cluster consists of four supervisor machines running 110 tasks inside 32 worker processes. We run two different topologies which receive messages and communicate with each other via RabbitMQ. The whole thing is deployed on Amazon Web Services and utilizes S3 for some intermediate storage, Redis as a key/value store and Oracle RDS for RDBMS storage. The bolts are all written in Java using the Spring framework with Hibernate as an ORM. |
| Umeng | Umeng is the leading and largest provider of mobile app analytics and developer services platform in China. Storm powers Umeng's realtime analytics platform, processing billions of data points per day and growing. We also use Storm in other products which requires realtime processing and it has become the core infrastructure in our company. |
| Admaster |
We provide monitoring and precise delivery for Internet advertising. We use Storm to do the following:
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| SocialMetrix |
Since its release, Storm was a perfect fit to our needs of real time monitoring. Its powerful API, easy administration and deploy, enabled us to rapidly build solutions to monitor presidential elections, several major events and currently it is the processing core of our new product "Socialmetrix Eventia". |
| Needium |
At Needium we love Ruby and JRuby. The Storm platform offers the right balance between simplicity, flexibility and scalability. We created RedStorm, a Ruby DSL for Storm, to keep on using Ruby on top of the power of Storm by leveraging Storm's JVM foundation with JRuby. We currently use Storm as our Twitter realtime data processing pipeline. We have Storm topologies for content filtering, geolocalisation and classification. Storm allows us to architecture our pipeline for the Twitter full firehose scale. |
| Parse.ly |
Parse.ly is using Storm for its web/content analytics system. We have a home-grown data processing and storage system built with Python and Celery, with backend stores in Redis and MongoDB. We are now using Storm for real-time unique visitor counting and are exploring options for using it for some of our richer data sources such as social share data and semantic content metadata. |
| PARC |
High Performance Graph Analytics & Real-time Insights Research team at PARC uses Storm as one of the building blocks of their PARC Analytics Cloud infrastructure which comprises of Nebula based Openstack, Hadoop, SAP HANA, Storm, PARC Graph Analytics, and machine learning toolbox to enable researchers to process real-time data feeds from Sensors, web, network, social media, and security traces and easily ingest any other real-time data feeds of interest for PARC researchers. PARC researchers are working with number of industry collaborators developing new tools, algorithms, and models to analyze massive amounts of e-commerce, web clickstreams, 3rd party syndicated data, cohort data, social media data streams, and structured data from RDBMS, NOSQL, and NEWSQL systems in near real-time. PARC team is developing a reference architecture and benchmarks for their near real-time automated insight discovery platform combining the power of all above tools and PARC’s applied research in machine learning, graph analytics, reasoning, clustering, and contextual recommendations. The High Performance Graph Analytics & Real-time Insights research at PARC is headed by Surendra Reddy<http://www.linkedin.com/in/skreddy>. If you are interested to learn more about our use/experience of using Storm or to know more about our research or to collaborate with PARC in this area, please feel free to contact<EMAIL_ADDRESS>|
| GumGum |
GumGum, the leading in-image advertising platform for publishers and brands, uses Storm to produce real-time data. Storm and Trident-based topologies consume various ad-related events from Kafka and persist the aggregations in MySQL and HBase. This architecture will eventually replace most existing daily Hadoop map reduce jobs. There are also plans for Kafka + Storm to replace existing distributed queue processing infrastructure built with Amazon SQS. |
| CrowdFlower |
CrowdFlower is using Storm with Kafka to generalize our data stream aggregation and realtime computation infrastructure. We replaced our homegrown aggregation solutions with Storm because it simplified the creation of fault tolerant systems. We were already using Zookeeper and Kafka, so Storm allowed us to build more generic abstractions for our analytics using tools that we had already deployed and battle-tested in production. We are currently writing to DynamoDB from Storm, so we are able to scale our capacity quickly by bringing up additional supervisors and tweaking the throughput on our Dynamo tables. We look forward to exploring other uses for Storm in our system, especially with the recent release of Trident. |
| Digital Sandbox |
At Digital Sandbox we use Storm to enable our open source information feed monitoring system. The system uses Storm to constantly monitor and pull data from structured and unstructured information sources across the internet. For each found item, our topology applies natural language processing based concept analysis, temporal analysis, geospatial analytics and a prioritization algorithm to enable users to monitor large special events, public safety operations, and topics of interest to a multitude of individual users and teams. Our system is built using Storm for feed retrieval and annotation, Python with Flask and jQuery for business logic and web interfaces, and MongoDB for data persistence. We use NTLK for natural language processing and the WordNet, GeoNames, and OpenStreetMap databases to enable feed item concept extraction and geolocation. |
| Hallo | With several mainstream celebrities and very popular YouTubers using Hallo to communicate with their fans, we needed a good solution to notify users via push notifications and make sure that the celebrity messages were delivered to follower timelines in near realtime. Our initial approach for broadcast push notifications would take anywhere from 2-3 hours. After re-engineering our solution on top of Storm, that time has been cut down to 5 minutes on a very small cluster. With the user base growing and user need for realtime communication, we are very happy knowing that we can easily scale Storm by adding nodes to maintain a baseline QoS for our users. |
| Keepcon | We provide moderation services for classifieds, kids communities, newspapers, chat rooms, facebook fan pages, youtube channels, reviews, and all kind of UGC. We use storm for the integration with our clients, find evidences within each text, persisting on cassandra and elastic search and sending results back to our clients. |
| Visible Measures |
Visible Measures powers video campaigns and analytics for publishers and advertisers, tracking data for hundreds of million of videos, and billions of views. We are using Storm to process viewing behavior data in real time and make the information immediately available to our customers. We read events from various push and pull sources, including a Kestrel queue, filter and enrich the events in Storm topologies, and persist the events to Redis, HDFS and Vertica for real-time analytics and archiving. We are currently experimenting with Trident topologies, and figuring out how to move more of our Hadoop-based batch processing into Storm. |
| O2mc |
One of the core products of O2mc is called O2mc Community. O2mc Community performs multilingual, realtime sentiment analysis with very low latency and distributes the analyzed results to numerous clients. The input is extracted from source systems like Twitter, Facebook, e-mail and many more. After the analysis has taken place on Storm, the results are streamed to any output system ranging from HTTP streaming to clients to direct database insertion to an external business process engine to kickstart a process. |
| The Ladders |
TheLadders has been committed to finding the right person for the right job since 2003. We're using Storm in a variety of ways and are happy with its versatility, robustness, and ease of development. We use Storm in conjunction with RabbitMQ for such things as sending hiring alerts: when a recruiter submits a job to our site, Storm processes that event and will aggregate jobseekers whose profiles match the position. That list is subsequently batch-processed to send an email to the list of jobseekers. We also use Storm to persist events for Business Intelligence and internal event tracking. We're continuing to find uses for Storm where fast, asynchronous, real-time event processing is a must. |
| SemLab |
SemLab develops software for knowledge discovery and information support. Our ViewerPro platform uses information extraction, natural language processing and semantic web technologies to extract structured data from unstructured sources, in domains such as financial news feeds and legal documents. We have succesfully adapted ViewerPro's processing framework to run on top of Storm. The transition to Storm has made ViewerPro a much more scalable product, allowing us to process more in less time. |
| Visual Revenue |
Here at Visual Revenue, we built a decision support system to help online editors to make choices on what, when, and where to promote their content in real-time. Storm is the backbone our real-time data processing and aggregation pipelines. |
| PeerIndex |
PeerIndex is working to deliver influence at scale. PeerIndex does this by exposing services built on top of our Influence Graph; a directed graph of who is influencing whom on the web. PeerIndex gathers data from a number of social networks to create the Influence Graph. We use Storm to process our social data, to provide real-time aggregations, and to crawl the web, before storing our data in a manner most suitable for our Hadoop based systems to batch process. Storm provided us with an intuitive API and has slotted in well with the rest of our architecture. PeerIndex looks forward to further investing resources into our Storm based real-time analytics. |
| eClick |
At eClick (an ad-network for most popular websites and publishers in Vietnam) we are building a real time platform on top of Storm which does parsing raw logs, crawling websites for social analytics, analyzing big data. This platform tracks impressions, clicks, conversions, user-behaviour, .. etc. in real time. We are using Kafka as message queue, and deploy in customized Storm Cluster. |
| Wayfair |
At Wayfair, we use storm as a platform to drive our core order processing pipeline as an event driven system. Storm allows us to reliably process tens of thousands of orders daily while providing us the assurance of seamless process scalability as our order load increases. Given the project’s ease of use and the immense support of the community, we’ve managed to implement our bolts in php, construct a simple puppet module for configuration management, and quickly solve arising issues. We can now focus most of our development efforts in the business layer, check out more information on how we use storm in our engineering blog. |
| InnoQuant |
At InnoQuant, we use Storm as a backbone of our real-time big data analytics engine in MOCA platform. MOCA is a next generation, mobile-backend-as-a-service platform (MBaaS). It provides brands and app developers with real-time in-app tracking, context-aware push messaging, user micro-segmentation based on profile, time and geo-context as well as big data analytics. Storm-based pipeline is fed with events captured by native mobile SDKs (iOS, Android), scales nicely with connected mobile app users, delivers stream-based metrics and aggregations, and finally integrates with the rest of MOCA infrastructure, including columnar storage (Cassandra) and graph storage (Titan). |
| Fliptop |
Fliptop is a customer intelligence platform which allows customers to integrating their contacts, and campaign data, to enhance their prospect with social identities, and to find their best leads, and most influential customers. We have been using Storm for various tasks which requires scalability and reliability, including integrating with sales/marketing platform, data appending for contacts/leads, and computing scoring of contacts/leads. It's one of our most robust and scalable infrastructure. |
| Trovit |
Trovit is a search engine for classified ads present in 39 countries and different business categories (Real Estate, Cars, Jobs, Rentals, Products and Deals). Currently we use Storm to process and index ads in a distributed and low latency fashion. Combined with other technologies like Hadoop, Hbase and Solr has allowed us to build a scalable and low latency platform to serve search results to the end user. |
| OpenX |
OpenX is a unique platform combines ad serving, a real-time bidding (RTB) exchange, yield optimization, and content valuation to deliver the highest revenue across every desktop, tablet, and mobile screen At OpenX we use Storm to process large amounts of data to provide real time Analytics. Storm provides us to process data real time to improve our Ad quality. |
| Keen IO |
Keen IO is an analytics backend-as-a-service. The Keen IO API makes it easy for customers to do internal analytics or expose analytics features to their customers. Keen IO uses Storm (DRPC) to query billion-event data sets at very low latencies. We also use Storm to control our ingestion pipeline, sourcing data from Kafka and storing it in Cassandra. |
| LivePerson |
LivePerson is a provider of Interaction-Service over the web. Interaction between an agent and a visitor in site can be achieved using phone call, chat, banners, etc.Using Storm, LivePerson can collect and process visitor data and provide information in real time to the agents about the visitor behavior. Moreover, LivePerson gets to better decisions about how to react to visitors in a way that best addresses their needs. |
| YieldBot |
Yieldbot connects ads to the real-time consumer intent streaming within premium publishers. To do this, Yieldbot leverages Storm for a wide variety of real-time processing tasks. We've open sourced our clojure DSL for writing trident topologies, marceline, which we use extensively. Events are read from Kafka, most state is stored in Cassandra, and we heavily use Storm's DRPC features. Our Storm use cases range from HTML processing, to hotness-style trending, to probabilistic rankings and cardinalities. Storm topologies touch virtually all of the events generated by the Yieldbot platform. |
| Equinix |
At Equinix, we use a number of Storm topologies to process and persist various data streams generated by sensors in our data centers. We also use Storm for real-time monitoring of different infrastructure components. Other few topologies are used for processing logs in real-time for internal IT systems which also provide insights in user behavior. |
| MineWhat |
MineWhat provides actionable analytics for ecommerce spanning every SKU,brand and category in the store. We use Storm to process raw click stream ingestion from Kafka and compute live analytics. Storm topologies powers our complex product to user interaction analysis. Multi language feature in storm is really kick-ass, we have bolts written in Node.js, Python and Ruby. Storm has been in our production site since Nov 2012. |
| Qihoo 360 |
360 have deployed about 50 realtime applications on top of storm including web page analysis, log processing, image processing, voice processing, etc. The use case of storm at 360 is a bit special since we deployed storm on thounds of servers which are not dedicated for storm. Storm just use little cpu/memory/network resource on each server. However theses storm clusters leverage idle resources of servers at nearly zero cost to provide great computing power and it's realtime. It's amazing. |
| HolidayCheck |
HolidayCheck is an online travel site and agency available in 10 languages worldwide visited by 30 million people a month. We use Storm to deliver real-time hotel and holiday package offers from multiple providers - reservation systems and affiliate travel networks - in a low latency fashion based on user-selected criteria. In further reservation steps we use DRPC for vacancy checks and bookings of chosen offers. Along with Storm in the system for offers delivery we use Scala, Akka, Hazelcast, Drools and MongoDB. Real-time offer stream is delivered outside of the system back to the front-end via websocket connections. |
| MercadoLibre |


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{% extends "base.html" %} {% block avatar %}
Welcome to the Recommender Room
, or Rec Room
for short.
The Rec Room
is a platform that provides students with valuable,
on-demand access to academic support services, traditionally offered only in-person, via its suite of recommender systems.
Read the paper
.
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{% endfor %} {% endif %} {% endwith %}


Title
dbnomics -- Stata client for DB.nomics, the world's economic database (https://db.nomics.world)
dbnomics provides a suite of tools to browse and import time series data from DB.nomics, the world's economic database (https://db.nomics.world). DB.nomics is a web-based platform that aggregates and maintains time series data from various statistical agencies across the world. dbnomics only works with Stata 14.0 or higher, since it relies on the secure HTTP protocol (https).
dbnomics provides an interface to DB.nomics' RESTful API (https://api.next.nomics.world/apidocs), allowing for the advanced filtering of data using Stata's native options syntax (see Examples). To achieve this, the command relies on Erik Lindsley's libjson backend (ssc install libjson).
Load list of providers dbnomics providers [, clear]
Load content tree for a given provider dbnomics tree , provider(PRcode) [clear]
Load dataset structure given a provider and dataset dbnomics datastructure , provider(PRcode) dataset(DScode) [clear nostat]
Load list of series for a given provider and dataset dbnomics series , provider(PRcode) dataset(DScode) [clear limit(int) dimensions_opt]
Import series for a given provider and dataset dbnomics import , provider(PRcode) dataset(DScode) [clear limit(int) seriesids(SERIES_list) sdmx(SDMX_mask) dimensions_opt]
Load one series via direct reference dbnomics use SERIES_code , provider(PRcode) dataset(DScode) [clear delimiter(char)]
options Description ------------------------------------------------------------------------- Main provider(PRcode) declare the reference data provider (e.g. AMECO, IMF, Eurostat) dataset(DScode) declare the reference dataset (e.g. PIGOT, BOP, ei_bsin_m) clear replace data in memory
For datastructure only nostat disable the parsing of series statistics (speeds up importing)
For series and import only limit(int) set the limit for the number of series to load dimensions_opt provider- and dataset-specific options to filter series (see Examples)
For import only seriesids(SERIES_list) input a comma-separated list to load specific time series sdmx(SDMX_mask) input an SDMX mask to select specific series (Note: not all providers support this option)
For use only delimiter(char) define the separation character (default: tab) -------------------------------------------------------------------------
Options +------+ ----+ Main +-------------------------------------------------------------
provider(PRcode) sets the source that contains the data of interest. The list of data providers is continuously updated by the DB.nomics team. To obtain the most recent list of active data providers, use the command dbnomics providers [, clear].
dataset(DScode) sets the source dataset for the time series of interest. A category tree containing all datasets associated with each provider(PRcode) can be obtained via the command dbnomics tree, provider(PRcode) [clear]. (Note: not all datasets available in the category tree may be accessible).
clear clears data in memory before importing data from DB.nomics.
+---------------+ ----+ datastructure +----------------------------------------------------
nostat disables the parsing of series statistics. By default, the command dbnomics datastructure , (...) also collects information on the number of time series associated with each dataset dimension value. Using the nostat option disables this additional parsing of data, speeding up the loading process.
+---------------+ ----+ series/import +----------------------------------------------------
limit(int) sets the maximum number of series to load. It limits the number of series identified by the dbnomics series or dbnomics import commands, resulting in faster download of information. A Warning message is issued if the total number of series identified is larger than the inputted limit(int).
dimensions_opt(dim_values) are provider(PRcode)- and dataset(DScode)-specific options that can be used to identify series. A list of dimensions can be obtained using dbnomics datastructure, provider(PRcode) dataset(DScode). For instance, assuming the dimensions of dataset(DScode) to be freq.unit.geo, accepted options for dbnomics series,(...) and dbnomics import,(...) are freq(codelist), unit(codelist) and geo(codelist). Each dimension_opt(dim_values) can contain one or more values in a space or comma-separated list, so as to select multiple dimensions at once (e.g., a list of countries). Note: dbnomics does not carry a validation check on user-inputted dimension_opt(dim_values); if a dimension_opt(dim_values) is invalid, dbnomics will likely return the message Warning: no series found. See Examples.
+--------+ ----+ import +-----------------------------------------------------------
Note: the options below are mutually exclusive and are alternatives to dimensions_opt(dim_values).
seriesids(SERIES_list) accepts a comma-separated list of names corresponding to series available in provider(PRcode) and dataset(DScode). Each series is then imported accordingly.
sdmx(SDMX_mask) accepts an SDMX REST query ("SDMX mask") containing dimensions to identify series for a provider(PRcode) and dataset(DScode) that supports this function. A list of dimensions can be obtained using dbnomics datastructure, provider(PRcode) dataset(DScode). Note: not all providers will support this feature. In such case dbnomics import, (...) may return a Warning: no series found error.
+-----+ ----+ use +--------------------------------------------------------------
delimiters("chars"[, collapse|asstring]) allows to specify other separation characters. By default, dbnomics use ... will assume tab-separated data. This option is generally not needed but accounts for potential future changes in the series specification of https://db.nomics.world.
This program has two main dependencies:
1) The Mata json library libjson by Erik Lindsley is needed to parse JSON strings. It can be found on SSC: ssc install libjson. 2) The routine moss by Robert Picard & Nicholas J. Cox is needed to clean unicode sequences. It can be found on SSC: ssc install moss.
After each API call, dbnomics stores significant metadata in the form of dataset characteristics. Type char li _dta[] after dbnomics to obtain important info about the data, e.g., the API endpoint.
Load the list of available providers with additional metadata: . dbnomics providers, clear /// (click to run)
Load the dataset tree of of the AMECO provider: . dbnomics tree, provider(AMECO) clear /// (click to run)
Analyse the structure of dataset PIGOT for provider AMECO: . dbnomics datastructure, provider(AMECO) dataset(PIGOT) clear /// > (click to run) Price deflator gross fixed capital formation: other investment 86 series found. Order of dimensions: (freq.unit.geo)
List all series in AMECO/PIGOT containing deflators in national currency: . dbnomics series, provider(AMECO) dataset(PIGOT) unit(national-currenc > y-2010-100) clear (click to run) 40 of 86 series selected. Order of dimensions: (freq.unit.geo)
Import all series in AMECO/PIGOT containing deflators in national currency: . dbnomics import, provider(AMECO) dataset(PIGOT) unit(national-currenc > y-2010-100) clear (click to run) ........................................ 40 series found and imported
Import single AMECO/PIGOT series: . dbnomics import, pr(AMECO) d(PIGOT) seriesids(ESP.<IP_ADDRESS>.PIGOT,SVN.3 > .1.0.0.PIGOT,LVA.<IP_ADDRESS>.PIGOT) clear (click to run) ... 3 series found and imported
Load one AMECO/PIGOT series: . dbnomics use SVN.<IP_ADDRESS>.PIGOT, pr(AMECO) d(PIGOT) clear (click to run) (2 vars, 60 obs)
Eurostat typically supports SMDX queries Import all series in Eurostat/ei_bsin_m related to Belgium: . dbnomics import, provider(Eurostat) dataset(ei_bsin_m) geo(BE) clear (click to run) ................ 16 series found and imported
Do the same using sdmx: . dbnomics import, provider(Eurostat) dataset(ei_bsin_m) sdmx(M.BAL...B > E) clear (click to run) ................ 16 series found and imported
dbnomics stores the following in r():
Local endpoint name of dbnomics subcommand
Simone Signore signoresimone at yahoo [dot] it


Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Serves 8
There’s nothing better than a juicy, crispy, lush Scotch pie – unless of course you add spices to it. The sweet, fatty taste of the lamb mince is paired with white pepper, nutmeg, coriander, cumin and some chilli. It’s fantastic for picnics or lunches.
Heat a large frying pan and toast the cumin seeds for a few minutes, then set aside. Heat the oil in the same pan and fry the onion, garlic, chilli, pepper and a good pinch of salt for around eight minutes, until there is no moisture left. Remove from the heat, stir in the toasted cumin seeds, ground mace (or nutmeg) and ground coriander. Leave to cool.
In a large bowl mix together the minced lamb, white pepper, fresh coriander, and the cooled spiced onion mixture until combined. Set aside, covered, in the fridge.
Preheat the oven to 200C/400F/Gas 6 and generously grease a 20cm/8in diameter loose-bottomed or springform round cake tin with lard.
To make the pastry, sift the flour and salt in a large bowl and make a well in the centre.
Put the milk, lard and 90ml/3fl oz of water in a saucepan and heat gently. When the lard has melted, increase the heat and bring to the boil.
Pour the boiling liquid into the flour, and use a wooden spoon to combine until cool enough to handle. Bring together into a ball.
Dust a work surface with flour and, working quickly, knead the dough briefly – it will be soft and moist. Set aside a third of the pastry and roll the rest out on a well-floured surface. Line the pie dish with the pastry, pressing it right up the sides until it pokes just over the top of the tin.
Add the filling into the pastry-lined tin bit by bit. As you reach the top, form a slight peak. Roll out the reserved pastry and top the pie with it. Pinch the edges to seal and trim the excess. Poke a hole in the top of the pie and insert a small tube made from aluminium foil to allow steam to escape.
Brush the top of the pie with a little beaten egg yolk, and bake in the preheated oven for 30 minutes (put a tray on the shelf below to catch any drips). Then reduce the temperature to 160C/325F/Gas 3 and cook for a further 1¼ hours until golden-brown. Leave to cool completely before refrigerating for two hours, or overnight.
Run a knife around the edge of the pie, remove from the tin and serve with chutneys, salads or pickles.


---
layout: post
title: What tools does Alec use?
tags:
- ego
- LinkedIn
- Linux
- Open Source Software
- Work Practices
status: publish
type: post
published: true
meta:
_edit_last: '451289'
---
After Perl Mongers last night there was a short discussion over tools. Here is my preferred list
- Vim editor
- Git version control (and GitHub to host my remote repos)
- Linux operating system
- Perl and CPAN for programming. Not a prefect language, but has wide applicability to the the types of problems I solve
- Google search engine
- Email (gmail)
- Bash and the UNIX command line tools (sort, find, grep etc etc)
- OpenOffice
- Paper journal/notebook (currently using a Moleskine, but I prefer a Miquelrius)


About delanyo agbenyo
I'm a fronted developer who loves solving problems with inclusive design systems. I love making cool things on the web.
My Role
My skills range from fronted design. I design simple interactions for digital services by using existing or creating new styles, components and patterns. I help discover user needs and transform them into solutions to meet those needs. I do that by challenging assumptions and prototyping ideas for testing.
I'm well concerned with the way things function as the way they look. My work covers user journeys and navigation as well as micro interactions. I make beautiful and working web software. I'm satisfied when my work is in the hands of users, delivering values.
Personal Life
In my personal life, I'm from Ve, a suburb of Hohoe, Ghana.
I am doing this web development stuff for a while now, because I desperately need new skills for prototyping startups. After several months of learning designs and development, I feel challenged and Now, am using my expertise, skills and knowledge creating cool things and helping others.
About this Site
This site has a good history as my personal blog and web directory. The site is design and maintained by me.
EXPLORE!


Component with just HTML content tags (no html, head, body etc).
A full HTML document including head and body tags.
Empty component, containing only whitespace.
1 page component, 2 page component, 3 page component.
Short document with image. Image should fit on one page.
Document with external stylesheet. Text should be white on black.
Component contains inline javascript to write a message to screen. If not blank, javascript has executed.


Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Serves 4
This slow-cooked Chinese-flavoured dish makes a lovely change from a stir-fry but is just as easy to prepare.
Equipment and preparation: For this recipe, you will need an electric slow-cooker, at least 3.5 litres/6 pints in capacity.
Heat the oil in a large, heavy-based frying pan over a medium heat. Add the onions and fry for 8-10 minutes, stirring regularly, until softened and lightly browned. Add the ginger, garlic and chilli flakes, stir well and cook for 2-3 minutes, making sure the garlic doesn't burn.
Season the beef all over with pepper, then sprinkle over the five-spice powder and mix to coat evenly. Add to the pan and fry for 3-4 minutes, turning the beef regularly until lightly browned all over.
Transfer to the slow-cooker. Pour over the stock and stir in the soy sauce. Cover and cook on high for 4 hours, until the beef is meltingly tender.
When the beef is cooked, whisk the cornflour with 1 tablespoon water to a smooth paste, stir in the honey. Add to the slow-cooker, cover and cook for 12-15 minutes, or until the sauce is glossy and has thickened slightly.
Serve the beef with steamed rice or boiled noodles and stir-fried vegetables.


convexHull Finds the convex hull of a point set
hull = cv.convexHull(points)
hull = cv.convexHull(points, 'OptionName', optionValue, ...)
Input
- points Input 2D point set, stored in numeric array
(Nx2/Nx1x2/1xNx2) or cell array of 2-element vectors (
{[x,y], ...}
).
Output
- hull Output convex hull. It is either an integer vector of indices or
vector of points. In the first case, the hull elements are 0-based
indices of the convex hull points in the original array (since the set
of convex hull points is a subset of the original point set). In the
second case, hull elements are the convex hull points themselves.
In case output is the hull points, it has the same type as the input.
Options
- ReturnPoints Operation flag. In case of a matrix, when the flag is
true, the function returns convex hull points (Mx2 matrix). Otherwise,
it returns indices of the convex hull points (vector of length M).
In case the input is a cell-array, when the flag is true, the function
return convex hull points (as a cell-array of points). Otherwise it
returns indices of points (vector of length M). default true
- Clockwise Orientation flag. If it is true, the output convex hull is
oriented clockwise. Otherwise, it is oriented counter-clockwise. The
usual screen coordinate system is assumed so that the origin is at the
top-left corner, x axis is oriented to the right, and y axis is
oriented downwards. default false
The functions find the convex hull of a 2D point set using the Sklansky's
algorithm [Sklansky82] that has O(N logN)
complexity in the current
implementation.
References
[Sklansky82]:
Jack Sklansky. "Finding the convex hull of a simple polygon".
Pattern Recognition Letters, 1(2):79-83, 1982.


| Adapter Part Number |
PC Port |
OWCOM Adapter Name |
OWCOM Adapter Ports |
Features |
|
DS9490B (iButton holder) |
USB |
{DS9490} |
USBX (where X is the port number from 0 up to 15 ) |
power delivery, overdrive, single iButton
holder |
|
DS9490R (RJ11-Out) |
USB |
{DS9490} |
USBX | power delivery, overdrive, RJ–11 out for
DS1402D-DR8 iButton holder |
| DS9480 (future product) | USB |
{DS9097U} | COMX (where X is the port number from 0 up to 15 ) |
USB-to-Serial-Port adapter,
power delivery, overdrive, RJ–11 out for DS1402D-DR8 iButton
touch-and-hold probe, EPROM write, led indicators |
|
DS1410E (legacy) |
Parallel |
{DS1410E} | LPTX (where X is the port number from 0 up to 15 ) |
power delivery, overdrive, dual iButton holder, DS2401 ID |
|
DS9097U-009 |
Serial |
{DS9097U} | COMX |
power delivery, overdrive, RJ–11 connector, DS2502 ID |
|
DS9097U-S09 |
Serial |
{DS9097U} | COMX | power delivery, overdrive, RJ–11 connector, no ID |
|
DS9097U-E25 |
Serial |
{DS9097U} | COMX | power delivery, overdrive, RJ–11 connector, EPROM write |
|
DS1411 |
Serial |
{DS9097U} | COMX | power delivery, overdrive, single iButton
holder |
|
DS9097, DS9097E, DS1413 (legacy) |
Serial |
{DS9097E} | COMX | legacy passive serial port adapters.
DS9097E has EPROM write capabilities. DS1413 is iButton holder |


MY_PRINT_DEFAULTS(1) MySQL Database System MY_PRINT_DEFAULTS(1) NAME my_print_defaults - display options from option files SYNOPSIS my_print_defaults [options] option_group ... DESCRIPTION my_print_defaults displays the options that are present in option groups of option files. The output indicates what options will be used by programs that read the specified option groups. For example, the mysqlcheck program reads the [mysqlcheck] and [client] option groups. To see what options are present in those groups in the standard option files, invoke my_print_defaults like this: shell> my_print_defaults mysqlcheck client --user=myusername --password=secret --host=localhost The output consists of options, one per line, in the form that they would be specified on the command line. my_print_defaults supports the following options. · --help, -? Display a help message and exit. · --config-file=file_name, --defaults-file=file_name, -c file_name Read only the given option file. · --debug=debug_options, -# debug_options Write a debugging log. A typical debug_options string is d:t:o,file_name. The default is d:t:o,/tmp/my_print_defaults.trace. · --defaults-extra-file=file_name, --extra-file=file_name, -e file_name Read this option file after the global option file but (on Unix) before the user option file. · --defaults-group-suffix=suffix, -g suffix In addition to the groups named on the command line, read groups that have the given suffix. · --no-defaults, -n Return an empty string. · --verbose, -v Verbose mode. Print more information about what the program does. · --version, -V Display version information and exit. COPYRIGHT Copyright © 1997, 2015, Oracle and/or its affiliates. All rights reserved. This documentation is free software; you can redistribute it and/or modify it only under the terms of the GNU General Public License as published by the Free Software Foundation; version 2 of the License. This documentation is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with the program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA or see http://www.gnu.org/licenses/. SEE ALSO For more information, please refer to the MySQL Reference Manual, which may already be installed locally and which is also available online at http://dev.mysql.com/doc/. AUTHOR Oracle Corporation (http://dev.mysql.com/). MySQL 5.5 02/26/2015 MY_PRINT_DEFAULTS(1)


Welcome to Resume Nation. Your one stop shop to create all the beautiful Resumes.
Resume building has never been these easier. Fill in the required information like your personal info, work experience, education, skills
and voilà! at the end of process you'll have your beautiful resume. Try yourself!


{%extends 'base.html'%} {% load bootstrap5 %} {%load static%} {%block content%} Therapi Home New Article Find Support Appointments @{{user.username}} Profile Sign out
By: {{article.author}}


VanillaAutoComplete
It's a simple AutoComplete in JS Vanilla
Options
| Property | Default/Type | Description |
|---|---|---|
| selector | string or object selector | Required selector for construct Calendar |
| onSelect | Callback function |
A callback function to capture the date selected
onSelect((data, element, event) => { }) |


GUN CRAZY(director: Joseph H. Lewis; screenwriter: MacKinlay Kantor from his story in the "Saturday Evening Post"/Lillard Kaufman is the uncredited Dalton Trumbo-he was blacklisted; cinematographer: Russell Harlan; cast: Peggy Cummins (Annie Laurie Starr), John Dall (Bart Tare), Berry Kroeger (Packett), Morris Carnovsky (Judge Willoughby), Anabel Shaw (Ruby Tare), Harry Lewis (Clyde Boston), Nedrick Young (Dave Allister), Trevor Bardette (Sheriff Clyde Boston), Anne O'Neal (Miss Sifert), Don Beddoe (Car Owner from Chicago), Harry Hayden (Mr. Mallenberg), Mickey Little (Bart Tare age 7), Russ Tamblyn (Bart Tare: age 14), Paul Frison (Clyde Boston: age 14), Dave Bair (Dave Allister: age 14), Robert Osterloh (Hampton Policeman), Stanley Prager (Clown), Virginia Farmer (Miss Wynn, Teacher), 1950)
Reviewed by Dennis Schwartz
Bart Tare (John Dall) has always been obsessed with the love of guns. He was raised by his older sister Ruby (Anabel), who bought him his first BB gun as a child. On a rainy night in the small-town in the southwest he lives in, the 14-year-old Bart (Russ Tamblyn) sees a beautiful gun in a hardware store window and smashes the window with a rock to get it, but he is caught by the sheriff (Trevor) when he stumbles in a puddle. The judge (Carnovsky) sends him to reform school, despite Ruby's plea for mercy, bringing up the point that he was raised without a father figure. He stays for four years there and then he joins the army. Other than that incident, Bart has shown no tendency for violence or breaking the law, he just feels good when he's shooting a gun, something he is an expert in. He feels compelled to always have a gun, as it makes him feel important. But in this expressionist-like opening scene, it is shown that Bart will have little chance of overcoming his obsession. There is even one point in the robbery where he stretches his arms out Christ-like, in a symbolic gesture of what is to come of the naive child as he reaches adulthood.
Returning home for the first-time since he was sent away to reform school, he meets again his childhood buddies, David Allister (Young), who is now a newspaper reporter, and Clyde Boston (Paul), who is now a sheriff, like his father, the one who arrested him. They both thought the army would have been a natural place for him to make a career in, but he tells them he got bored with showing soldiers how to fire a gun and plans to look for work with Remington.
The boys decide to go to a travelling carnival that is in town that night and when an Annie Oakley-type shooter from England, Annie Laurie Starr (Cummins), challenges anyone in the audience to a pistol-shooting contest, Bart accepts the challenge and wins. She talks her boss, a sleazy carnival operator, Packy (Berry) into hiring him, as the two fall in love on first sight. The intimacy that grows between the two infuriates Packy, who has his eyes on Laurie, holding over-her-head the knowledge that she killed someone in a St. Louis robbery, telling her he will go to the police if she doesn't continue to give him sex. When he grapples with her, Bart fires his gun at him and the two are fired. He decides to marry her that night, even though he was warned by a clown (Prager) at the carnival that he doesn't know anything about women-that she's a bad one.
It now becomes a road movie of a crime spree, or a love-on-the run film. Laurie demands the need for action and money, threatening to leave him if he can't live up to her expectations. He has fallen so much in love with her, that even though it isn't in his gentle nature to be a criminal, he goes along with her robbery schemes. They rob their hotel, a liquor store, and a gas station, but she wants more money. For another of their robberies, they carefully come up with a more developed scheme, as she hitches a ride from an older gentleman visiting California from Illinois (Beddoe) who is suggestively coming on to her, while the conversation takes place, she, in a casual manner pulls her pistol out of her purse and steals his Cadillac, using it for their next robbery so the cops can't get their real license plates, as they bound him in their car on the roadside. The robbery takes place in Hampton, where they rob the local bank dressed in their showbiz cowboy outfits and escape when she conks the policeman (Osterloh) who is standing in front of the bank, karate-style on the neck, knocking him out. The getaway scene is dramatically filmed from the backseat of their car. The crimes become very sexually stimulating to them, as she becomes more and more passionate with Bart, even as their escape is taking place.
After another harrowing robbery, their pictures appear in the newspaper, as Packett names them as the wanted criminals sought, even mentioning the killing she did in St. Louis, and their names go over the wire service, so that his hometown of Cashville is now aware of his crime spree. After spending some time on-the-run, they plan one more big robbery and have hopes of getting enough money from that one to retire from crime (at least he feels that way). The both get jobs in an Armour meat-packing plant in New Mexico and rob the payroll office, but this time the office manager, Miss Sifert (O'Neal), rings the burglar alarm in the middle of the robbery and Laurie plugs her. In their getaway, it also becomes necessary for her to shoot a company guard who is in their way. She tells Bart, that when she gets scared she uses her gun to kill.
The two make plans to split-up for a few months and then get together again, but they can't go their separate ways, having fallen too passionately in love with each other. In town, they are staying in a California hotel near the ocean and have made arrangements by bribing some officials to get across the border into Mexico. Bart hopes to use the money to buy a ranch in Mexico and maybe raise a family there. Celebrating their changing luck by going on some amusement rides and then dancing in the fairgrounds, they notice some federal agents have spotted the marked money from the robbery they were using and assume they are waiting for them back in the hotel, where they kept the stolen loot. They are now broke and on-the-run again, as they flee town by carjacking a cab and then riding the rails out of town.
With no place else to run to, they return to his small-town and force their way into Ruby's place. She has a few children and is not pleased to see them. The neighbors notice she has her blinds down and they inform Clyde about it, who brings Dave along with him to Ruby's place, suspecting he is there and that they can talk him into peacefully surrendering. The two desperadoes have no intention of surrendering, and after locking Ruby in the garage, they try to escape by going up a mountain road. When trapped overnight in a swamp, chased by dogs and a police posse, Dave and Clyde find them in the old hiding spot they used as youths. When Laurie tries to kill them, Bart shoots her, the only killing he does, and is shot in return by the sheriff, who mistakingly thinks he was shooting at them. The swamp was used because this low-budget film couldn't afford the money to pay extras and the swamp scene would require only a limited cast.
As film noir, this hard-boiled story is much superior to the more popular and more critically acclaimed "Bonnie and Clyde," which covered the same territory story-wise: amour fou- the running away together by lovers. Both films were based on the real lives of the infamous criminal couple. This fast-action film, with its typically notorious femme fatale depicted, is subversive to the nth degree. It celebrates the couple's disregard for the law and their insatiable appetite for their own desires, which causes them to disregard middle-class values, as they act with a sense of bold impropriety. This is in line with what The Nation's former iconoclastic film critic, Manny Farber, called many films such as this one, that were now accepted by the surrealists and the new brand of intellectual youths, revolting against the middlebrow critics who called this merely a B- "pulp" film. He complimented films of this kind by endearingly calling them, "perceptive trash." Whatever weakness the story had as it waivered between art and entertainment, and even if it was acted by not the most accomplished of actors, yet there was a raw innocence about it creating a special kind of appeal to those who could feel its lively pulse beating to their resentment of the establishment. It should be remembered that this film came out at a time before there was rock & roll, a civil rights demonstration, and a Vietnam War protest movement, that even if the film appears dated in certain spots when presently viewed, it still retains its revolutionary spirit of youth being forced to find their own identity in the world, as they profoundly and uninhibitedly search for love.
REVIEWED ON 1/20/2000 GRADE: A
Dennis Schwartz: " Ozus' World Movie Reviews "
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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Kevin Matite
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We Accept the love we think we Deserve.
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WITNESS LIST
Environmental Regulation Committee
February 26, 2013 - 10:30 AM or upon final adjourn./recess
Invited Testimony on Issues relating to Environmental Regulation
On:
Brymer, David (Texas Commission on Environmental Quality)
Garcia, Ramiro (TCEQ)
Moore, Patrick (Legislative budget board)
Niermann, Jon (OAG)
Smith, Tom smitty (Self; Public citizen)
Wisdom, Christina (Texas Chemical Council)
Registering, but not testifying:
On:
Sinclair, Bryan (Tceq)
1


Here is a list of available commands for Prompt.
| Command | Description |
|---|---|
| General Commands | |
| help | Retrieves this list of available commands |
| clh | Shortcut for clear-history. |
| cls | Shortcut for clear-screen. |
| clear-history | Empties the list of Prompt commands you have previously executed in this session. |
| clear-screen | Clears the Prompt console window, giving you a blank screen. |
| config | Configures the Prompt console. |
| goto | Navigates the browser to the specified page. |
| reload | Causes the browser to reload the page. |
| set-mode | Sets the DNN View Mode. For example: Edit or Layout. |
| Page Commands | |
| get-page | Retrieves details about the specified page. |
| list-pages | Retrieves a list of pages based on the specified criteria. |
| new-page | Creates a new page within the portal. |
| set-page | Sets or updates properties of the specified page. |
| delete-page | Sends a page to the DNN Recycle Bin. |
| restore-page | Recovers a page from the DNN Recycle Bin. |
| purge-page | Permanently deletes a page from the DNN Recycle Bin. |
| User Commands | |
| get-user | Retrieves details about the specified user account. |
| list-users | Retrieves a list of users based on the specified criteria. |
| new-user | Creates a new user record in the portal |
| set-user | Enables updating user properties and values such as Username, Email, etc. |
| delete-user | Deletes the specified user, but the user can still be recovered. |
| purge-user | Permanently deletes a user that has been marked as 'deleted'. |
| restore-user | Recovers a user that is marked as 'deleted' but has not been purged. |
| add-roles | Add one or more DNN security roles to a user. |
| reset-password | Sends a reset password email to the user. |
| Module Commands | |
| copy-module | Copies a module to the specified page. |
| delete-module | Permanently deletes a module instance and all referenced instances, or soft-deletes a module on a specific page. |
| list-modules | Retrieves a list of modules based on the specified criteria. |
| move-module | Moves a module to the specified page. |
| get-module | Retrieves details about a single module. |
| restore-module | recovers a module from the DNN Recycle Bin. |
| Host Commands | |
| clear-cache | Clears the server cache and reloads the page. |
| get-host | Retrieves information about the DNN installation. |
| new-extension | Installs an extension in the DNN instance. |
| restart-application | Restarts the DNN instance. |
| Portal Commands | |
| clear-log | Clears the Event Log for the current portal. |
| get-portal | Retrieves information about the current portal. |
| Role Commands | |
| get-role | Retreives details about the specified DNN security role. |
| delete-role | Permanently deletes the given DNN security role. |
| list-roles | Retrieves a list of DNN security roles for the portal. |
| new-role | Creates a new DNN security role for the portal. |
| set-role | Sets or updates properties of the specified DNN security role. |
| Scheduler Commands | |
| get-task | Retrieves details for the specified DNN Schedule item. |
| list-tasks | Retrieves a list of scheduler items based on the specified criteria. |
| set-task | Sets or updates properties of the specified scheduled task. |


---
layout: post
title: So what's the deal with Ajax?
tags:
- Software Development
status: publish
type: post
published: true
meta: {}
---
I have been spending the last few weeks working in an Ajax environment and whilst I am no Ajax expert there are some observations I feel compelled to make.
- The benefits are substantial for the end user of your software (interaction, features offered...) so it's worth doing.
- It's a pain to debug because you are spreading your code across multiple locations and languages.
- It's worth using a templating system (e.g. HTML::Mason) when writing web apps, with or without Ajax. It makes live a lot easier when generating mark up.
- The Ajax paradigm leads to a different architecture at the back end. Something like this:
- A module generates
- Some Basic HTML and place holders for data, but no data
- Lots of Javascript (but remember to use an Ajax library such as YUI)
- This first page loads and makes an Ajax call
- A second module on the server responds to your Ajax call and supplies data (or whatever)
- The web page is populated by the Javascript via the DOM and and can be used.
- The user interacts with your Javascript (and forms etc.)
- Your Javascript makes an Ajax call
- goto 3
So you can see what I mean about things being spread around. It's a lot harder to work with that a traditional form based approach.


Home
Book Flight
Reservations
Story
About us
name
where did you traveled
tell us briefly about your journey
please insert an image


constructing an image can result in actual image and a set
(hopefully empty) of errors occured in a process of decoding an
image, that do not prevent us from actually creating an image.
In order to construct a value with the given type you must
provide an implementation for marshaling functions,
comparison function and pretty-printing.


Maximum Likelihood estimation of censored regression (Tobit) models with cross-sectional and panel data.
| Version: | 0.5-26 |
| Depends: | R (≥ 2.4.0), maxLik (≥ 0.7-3) |
| Imports: | glmmML (≥ 0.81-6), sandwich (≥ 2.2-6), miscTools (≥ 0.6-11), stats (≥ 2.15.0), plm |
| Suggests: | AER, lmtest (≥ 0.9-27) |
| Published: | 2017-03-21 |
| Author: | Arne Henningsen |
| Maintainer: | Arne Henningsen <arne.henningsen at gmail.com> |
| BugReports: | http://r-forge.r-project.org/projects/sampleselection/ |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://www.sampleSelection.org |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | Econometrics, Survival |
| CRAN checks: | censReg results |
| Reference manual: | censReg.pdf |
| Vignettes: |
Censored Regression Models |
| Package source: | censReg_0.5-26.tar.gz |
| Windows binaries: | r-devel: censReg_0.5-26.zip, r-release: censReg_0.5-26.zip, r-oldrel: censReg_0.5-26.zip |
| OS X binaries: | r-release: censReg_0.5-26.tgz, r-oldrel: censReg_0.5-26.tgz |
| Old sources: | censReg archive |
| Reverse imports: | gravity |
| Reverse enhances: | stargazer, texreg |
Please use the canonical form https://CRAN.R-project.org/package=censReg to link to this page.


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This product is made available subject to acceptance of the EPICS open source license.
EPICS Version 4 provides efficient
storage, access, and communication, of memory resident structured data.
The EPICS V4 Normative Types are a collection of structured data types
that can be used by the application level of EPICS V4 network endpoints,
to interoperably exchange scientific data.
normativeTypesJava is the Java implementation.
It is one part of the set of related products in the EPICS
V4 control system programming environment:
relatedDocumentsV4.html
This is the 18-March-2016 version for the 0.2-development release of the Java implementation of normativeTypes.
RELEASE_NOTES.md provides changes since the last release. TODO.md describes things to do before the next release.


Below should be displayed webcomponent-page from Beta micro frontend (you need to open Beta in advance).
- webcomponent-page is lazy loaded webcomponent in Beta
- there should be ngBootstrap pagination and user-poll webcomponent inside webcomponent-page.


How to Use
- Add the AngularJS ngAnimate script to your project
- Reference the 'ngAnimate' module as a dependency in your app
-
Add one of the following files to your app:
- Add the classes in the 'Classes Applied' section to the element you want to animate


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import uk.gov.hmrc.apiplatform.modules.applications.domain.models.ApplicationId @import uk.gov.hmrc.thirdpartydeveloperfrontend.controllers @(applicationId:ApplicationId)(implicit applicationConfig: ApplicationConfig)
You must submit your application for checking. We’ll take up to 10 working days to check your application and issue production credentials.


-
Uncertain class SI units (definitions). More...
#include <boost/quan/si_units.hpp>
Go to the source code of this file.
Functions | |
| void | showAllUnits (const unit *u, ostream &os) |
| void | showSIunit (const unit *u, ostream &os) |
| void | showSIunits (const unit *u, ostream &os) |
| void | showNonSIunits (const unit *u, ostream &os) |
| void | showAllNonSIunits (const unit *u, ostream &os) |
| unsigned int | findUnit (const string multipleUnit, const unit *u, unsigned int &givenIndex, unsigned int &SIindex) |
| const unit & | findAnyUnit (const string multipleUnit, unsigned int &uNamesIndex, unsigned int &givenIndex, unsigned int &SIindex) |
Uncertain class SI units (definitions).
SI units names and multipliers.
Definition in file si_units.cpp.
| const unit& findAnyUnit | ( | const string | multipleUnit, |
| unsigned int & | uNamesIndex, | ||
| unsigned int & | givenIndex, | ||
| unsigned int & | SIindex | ||
| ) |
Definition at line 860 of file si_units.cpp.
| unsigned int findUnit | ( | const string | multipleUnit, |
| const unit * | u, | ||
| unsigned int & | givenIndex, | ||
| unsigned int & | SIindex | ||
| ) |
Definition at line 688 of file si_units.cpp.
| void showAllNonSIunits | ( | const unit * | u, |
| ostream & | os | ||
| ) |
Definition at line 663 of file si_units.cpp.
| void showAllUnits | ( | const unit * | u, |
| ostream & | os | ||
| ) |
Definition at line 599 of file si_units.cpp.
| void showNonSIunits | ( | const unit * | u, |
| ostream & | os | ||
| ) |
Definition at line 645 of file si_units.cpp.
| void showSIunit | ( | const unit * | u, |
| ostream & | os | ||
| ) |
Definition at line 618 of file si_units.cpp.
| void showSIunits | ( | const unit * | u, |
| ostream & | os | ||
| ) |
Definition at line 627 of file si_units.cpp.
| const int ampCurrentUnitCount = 3 |
Definition at line 361 of file si_units.cpp.
| const char* ampCurrentUnits[ampCurrentUnitCount] |
Definition at line 362 of file si_units.cpp.
| const int ampHourChargeUnitCount = 5 |
Definition at line 486 of file si_units.cpp.
| const char* ampHourChargeUnits[ampHourChargeUnitCount] |
Definition at line 487 of file si_units.cpp.
| const int candelaLumintensityUnitCount = 2 |
Definition at line 444 of file si_units.cpp.
| const char* candelaLumintensityUnits[candelaLumintensityUnitCount] |
Definition at line 445 of file si_units.cpp.
| const char* celsiusTemperatureUnits[cTemperatureUnitCount] |
Definition at line 392 of file si_units.cpp.
| const unit charge |
Definition at line 501 of file si_units.cpp.
| const double chargeToSIfactors[chargeUnitCount] ={1., 3600} |
Definition at line 496 of file si_units.cpp.
| const int chargeUnitCount = 2 |
Definition at line 478 of file si_units.cpp.
| const char** chargeUnits[chargeUnitCount] = {coulombChargeUnits, ampHourChargeUnits} |
Definition at line 498 of file si_units.cpp.
| const int chargeUnitsCounts[chargeUnitCount] |
Definition at line 499 of file si_units.cpp.
| const int coulombChargeUnitCount = 2 |
Definition at line 479 of file si_units.cpp.
| const char* coulombChargeUnits[coulombChargeUnitCount] |
Definition at line 480 of file si_units.cpp.
| const int cTemperatureUnitCount = 3 |
Definition at line 391 of file si_units.cpp.
| const unit current |
Definition at line 373 of file si_units.cpp.
| const double currentToSIfactors[currentUnitCount] = {1.0} |
Definition at line 369 of file si_units.cpp.
| const int currentUnitCount = 1 |
Definition at line 359 of file si_units.cpp.
| const char** currentUnits[currentUnitCount] = {ampCurrentUnits} |
Definition at line 371 of file si_units.cpp.
| const int currentUnitsCounts[1] = {ampCurrentUnitCount} |
Definition at line 372 of file si_units.cpp.
| const int cwtMassUnitCount = 3 |
Definition at line 127 of file si_units.cpp.
| const char* cwtMassUnits[cwtMassUnitCount] |
Definition at line 128 of file si_units.cpp.
| const int dayTimeUnitCount = 4 |
Definition at line 300 of file si_units.cpp.
| const char* dayTimeUnits[dayTimeUnitCount] |
Definition at line 301 of file si_units.cpp.
| const char** dimlessNoUnits[noUnitsCount] |
Definition at line 532 of file si_units.cpp.
| const int dimlessUnitCount = 6 |
Definition at line 507 of file si_units.cpp.
| const int dimlessUnitsCounts[noUnitsCount] |
Definition at line 539 of file si_units.cpp.
| const char* fahrenheitTemperatureUnits[fTemperatureUnitCount] |
Definition at line 400 of file si_units.cpp.
| const int feetLengthUnitCount = 5 |
Definition at line 202 of file si_units.cpp.
| const char* feetLengthUnits[feetLengthUnitCount] |
Definition at line 204 of file si_units.cpp.
| const int fortnightTimeUnitCount = 2 |
Definition at line 325 of file si_units.cpp.
| const char* fortnightTimeUnits[fortnightTimeUnitCount] |
Definition at line 326 of file si_units.cpp.
| const unit freq |
Definition at line 473 of file si_units.cpp.
| const double freqToSIfactors[freqUnitCount] = {1.0} |
Definition at line 470 of file si_units.cpp.
| const int freqUnitCount = 1 |
Definition at line 463 of file si_units.cpp.
| const char** freqUnits[freqUnitCount] = {hertzFreqUnits} |
Definition at line 471 of file si_units.cpp.
| const int freqUnitsCounts[1] = {freqUnitCount} |
Definition at line 472 of file si_units.cpp.
| const int fTemperatureUnitCount = 3 |
Definition at line 399 of file si_units.cpp.
| const int gramMassUnitCount = 6 |
Definition at line 106 of file si_units.cpp.
| const char* gramMassUnits[gramMassUnitCount] |
Definition at line 107 of file si_units.cpp.
| const int hertzFreqUnitCount = 2 |
Definition at line 464 of file si_units.cpp.
| const char* hertzFreqUnits[hertzFreqUnitCount] |
Definition at line 465 of file si_units.cpp.
| const int hourTimeUnitCount = 4 |
Definition at line 291 of file si_units.cpp.
| const char* hourTimeUnits[hourTimeUnitCount] |
Definition at line 292 of file si_units.cpp.
| const int inchLengthUnitCount = 5 |
Definition at line 212 of file si_units.cpp.
| const char* inchLengthUnits[inchLengthUnitCount] |
Definition at line 214 of file si_units.cpp.
| const char* kelvinTemperatureUnits[kTemperatureUnitCount] |
Definition at line 384 of file si_units.cpp.
| const int kTemperatureUnitCount = 3 |
Definition at line 383 of file si_units.cpp.
| const int lbMassUnitCount = 5 |
Definition at line 135 of file si_units.cpp.
| const char* lbMassUnits[lbMassUnitCount] |
Definition at line 136 of file si_units.cpp.
| const unit length |
Definition at line 261 of file si_units.cpp.
| const double lengthToSIfactors[lengthUnitCount] |
Definition at line 243 of file si_units.cpp.
| const int lengthUnitCount = 7 |
Definition at line 175 of file si_units.cpp.
| const char** lengthUnits[lengthUnitCount] |
Definition at line 251 of file si_units.cpp.
| const int lengthUnitsCounts[lengthUnitCount] |
Definition at line 254 of file si_units.cpp.
| const unit lumintensity |
Definition at line 456 of file si_units.cpp.
| const double lumintensityToSIfactors[lumintensityUnitCount] = {1.0} |
Definition at line 451 of file si_units.cpp.
| const int lumintensityUnitCount = 1 |
Definition at line 443 of file si_units.cpp.
| const char** lumintensityUnits[lumintensityUnitCount] |
Definition at line 453 of file si_units.cpp.
| const int lumintensityUnitsCounts[1] = {candelaLumintensityUnitCount } |
Definition at line 455 of file si_units.cpp.
| const unit mass |
Definition at line 168 of file si_units.cpp.
| const double massToSIfactors[massUnitCount] |
Definition at line 160 of file si_units.cpp.
| const int massUnitCount = 5 |
Definition at line 104 of file si_units.cpp.
| const char** massUnits[massUnitCount] |
Definition at line 153 of file si_units.cpp.
| const int massUnitsCounts[massUnitCount] |
Definition at line 157 of file si_units.cpp.
| const int meterLengthUnitCount = 5 |
Definition at line 176 of file si_units.cpp.
| const char* meterLengthUnits[meterLengthUnitCount] |
Definition at line 178 of file si_units.cpp.
| const int mileLengthUnitCount = 3 |
Definition at line 187 of file si_units.cpp.
| const char* mileLengthUnits[mileLengthUnitCount] |
Definition at line 188 of file si_units.cpp.
| const int minuteTimeUnitCount = 5 |
Definition at line 281 of file si_units.cpp.
| const char* minuteTimeUnits[minuteTimeUnitCount] |
Definition at line 282 of file si_units.cpp.
| const int molSubstanceUnitCount = 2 |
Definition at line 427 of file si_units.cpp.
| const char* molSubstanceUnits[molSubstanceUnitCount] |
Definition at line 428 of file si_units.cpp.
| const int monthTimeUnitCount = 3 |
Definition at line 308 of file si_units.cpp.
| const char* monthTimeUnits[monthTimeUnitCount] |
Definition at line 309 of file si_units.cpp.
| const unit noUnit |
Definition at line 547 of file si_units.cpp.
| const int noUnitCount = 2 |
Definition at line 508 of file si_units.cpp.
| const char* noUnits[noUnitCount] |
Definition at line 509 of file si_units.cpp.
| const int noUnitsCount = 6 |
Definition at line 531 of file si_units.cpp.
| const double NoUnitsFactors[noUnitsCount] |
Definition at line 543 of file si_units.cpp.
| const int ozMassUnitCount = 5 |
Definition at line 144 of file si_units.cpp.
| const char* ozMassUnits[ozMassUnitCount] |
Definition at line 145 of file si_units.cpp.
| const int perBillionUnitCount = 2 |
Definition at line 525 of file si_units.cpp.
| const char* perBillionUnits[perBillionUnitCount] = {"ppb","perBillion"} |
Definition at line 526 of file si_units.cpp.
| const int perCentUnitCount = 3 |
Definition at line 515 of file si_units.cpp.
| const char* perCentUnits[perCentUnitCount] = {"%", "percent", "pc"} |
Definition at line 516 of file si_units.cpp.
| const int perMilleUnitCount = 2 |
Definition at line 518 of file si_units.cpp.
| const char* perMilleUnits[perMilleUnitCount] = {"%%","permille"} |
Definition at line 519 of file si_units.cpp.
| const int perMillionUnitCount = 2 |
Definition at line 522 of file si_units.cpp.
| const char* perMillionUnits[perMillionUnitCount] = {"ppm","perMillion"} |
Definition at line 523 of file si_units.cpp.
| const int perTrillionUnitCount = 2 |
Definition at line 528 of file si_units.cpp.
| const char* perTrillionUnits[perTrillionUnitCount] = {"ppt","perTrillion"} |
Definition at line 529 of file si_units.cpp.
| const int pointLengthUnitCount = 4 |
Definition at line 223 of file si_units.cpp.
| const char* pointLengthUnits[pointLengthUnitCount] |
Definition at line 224 of file si_units.cpp.
| const double powersOfTen[maxPowerTen+1+maxPowerTen] |
Definition at line 78 of file si_units.cpp.
| const double rounds[maxRounds] |
Definition at line 97 of file si_units.cpp.
| const int secondTimeUnitCount = 5 |
Definition at line 271 of file si_units.cpp.
| const char* secondTimeUnits[secondTimeUnitCount] |
Definition at line 272 of file si_units.cpp.
| const char* SIprefixes[SImaxPowerTen+1+SImaxPowerTen] |
Definition at line 29 of file si_units.cpp.
| const char* SIsymbols[SImaxPowerTen+1+SImaxPowerTen] |
Definition at line 56 of file si_units.cpp.
| const unit substance |
Definition at line 437 of file si_units.cpp.
| const double substanceToSIfactors[substanceUnitCount] = {1.0} |
Definition at line 434 of file si_units.cpp.
| const int substanceUnitCount = 1 |
Definition at line 425 of file si_units.cpp.
| const char** substanceUnits[substanceUnitCount] = {molSubstanceUnits} |
Definition at line 435 of file si_units.cpp.
| const int substanceUnitsCounts[1] = {molSubstanceUnitCount} |
Definition at line 436 of file si_units.cpp.
| const unit temperature |
Definition at line 416 of file si_units.cpp.
| const double temperatureToSIfactors[temperatureUnitCount] |
Definition at line 407 of file si_units.cpp.
| const int temperatureUnitCount = 3 |
Definition at line 381 of file si_units.cpp.
| const char** temperatureUnits[temperatureUnitCount] |
Definition at line 411 of file si_units.cpp.
| const int temperatureUnitsCounts[temperatureUnitCount] |
Definition at line 413 of file si_units.cpp.
| const int thouLengthUnitCount = 4 |
Definition at line 234 of file si_units.cpp.
| const char* thouLengthUnits[thouLengthUnitCount] |
Definition at line 235 of file si_units.cpp.
| const unit Time |
Definition at line 350 of file si_units.cpp.
| const double timeToSIfactors[timeUnitCount] |
Definition at line 333 of file si_units.cpp.
| const int timeUnitCount = 7 |
Definition at line 269 of file si_units.cpp.
| const char** timeUnits[timeUnitCount] |
Definition at line 343 of file si_units.cpp.
| const int timeUnitsCounts[] |
Definition at line 346 of file si_units.cpp.
| const int tonMassUnitCount = 5 |
Definition at line 117 of file si_units.cpp.
| const char* tonMassUnits[tonMassUnitCount] |
Definition at line 118 of file si_units.cpp.
| const unit* unitps[unitsCount] |
Definition at line 585 of file si_units.cpp.
| const int unitsCount = 7 + 3 + 1 |
Definition at line 584 of file si_units.cpp.
| const int unknownCount = 2 |
Definition at line 555 of file si_units.cpp.
| const char* unknownunit[unknownUnitCount] |
Definition at line 556 of file si_units.cpp.
| const unit unknownUnit |
Definition at line 578 of file si_units.cpp.
| const int unknownUnitCount = 2 |
Definition at line 554 of file si_units.cpp.
| const char** unknownUnits[unknownUnitsCount] |
Definition at line 563 of file si_units.cpp.
| const int unknownUnitsCount = 2 |
Definition at line 562 of file si_units.cpp.
| const int unknownUnitsCounts[unknownUnitsCount] |
Definition at line 568 of file si_units.cpp.
| const double unknownUnitsFactors[unknownUnitsCount] |
Definition at line 573 of file si_units.cpp.
| const int yardLengthUnitCount = 3 |
Definition at line 194 of file si_units.cpp.
| const char* yardLengthUnits[yardLengthUnitCount] |
Definition at line 196 of file si_units.cpp.
| const int yearTimeUnitCount = 3 |
Definition at line 317 of file si_units.cpp.
| const char* yearTimeUnits[yearTimeUnitCount] |
Definition at line 318 of file si_units.cpp.


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This website is an individual effort that try to aggregate, analyse, and present useful data to Canadians. The idea comes out of the fundamental right of every person to have access to this information. Information is a key; let's use it for a better future.
Being a software developer is a privilege. As a way to give back, we need to use our knowledge to push for a better society. It is our duty to eliminate any technical difficulties that sit as an obstacle between people and information. Decisions that rely on facts usually yield to better outcomes. Let's set the data free !
Toronto, March 2015,
Jamil Shamy (jamilshamy.me)
All the data in this website is extracted from Open Government Data Sources (open.canada.ca). The data is governed by Open Government Licence - Canada
All contents of this website (aggregated data and it's presentation) is a copy right of www.transparent-canada.ca under the MIT License.
Copyright (c) 2015 transparent-canada.ca.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Some of the images on this website are governved by the Creative Common License. Credit: Alex (Flicker), Nottingham Trent University (Flicker), Jonathan Cohen (Flicker), haljackey (Flicker), Sean_Marshall (Flicker), davebloggs007 (Flicker), MSVG (Flicker), Brian L Burke (Flicker).


Tau Graphical Web Crawler
Starting URL
Recursion Limit
1
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3
Search Type
Breadth-first
Depth-first
Crawl!
Pages crawled:
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Site:


We not only work to earn our living, but still more we work as a way of sharing in God’s work of creation and in the work and struggles of the rest of humanity. Every sister contributes to the care of the house and garden according to her capacity. There is a rota for those who cook and another for answering the Front Door bell; parts of the house are shared out for the cleaning. Several others work in the garden. One or two are involved in the teaching and formation of new members. The Infirmarian and her helpers are closely involved with the care of the elderly and the sick. Each does what she can, just as each receives from the community what she needs, and this will be different for each person.
We have a small guest house where people can come to find rest and refreshment, sharing our prayer with us in our chapel and staying in simple, but attractive rooms. We try to share the gifts God has given us by enabling others to share our prayer in Chapel, and by praying constantly in response to people’s needs.
You can see round our guest house here.
One of our Sisters paints icons, a slow and prayerful work.
There are numerous crafts practised in the community, the fruits of which are on sale in our small shop. One sister turns wood, several others make garments on knitting machines. Others make candles, encaustic wax cards, as well as cards created and printed on our computer.
We also make vestments for liturgical use. There are albs, purificators, etc and many other designs than the ones pictured below.


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Getting StartedLorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s.
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Expert Help for any size of Heritage Project
Whatever your historic environment project – from undertaking listed building home improvements or saving a much loved community landmark to large infrastructure works and city centre regeneration, our team of heritage experts can tailor the perfect package of help and advice to ensure success.
| Combining years of experience with the latest insights, we cover planning and listed building consent, help with archaeology, finding the right funding, Conservation Management Plans, Heritage Statements and Impact Statements. |
Please get in touch to talk about how we can help you.
| ✉ |<EMAIL_ADDRESS>|
| 📞 | 078 3456 1626 |
| ☎ | 0114 230 7849 |


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AudioPlayer
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ItemType
MonitorType
MoviePlayer
MultiDriver
MultimediaControl
ProcessFiles
Product
ProductionLineMain
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ScreenSpec
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TODO : Favouritestores
Images of favourite Stores, these are the stores that are followed by the user. If user doesnot follow any, Default stores can be displayed. This is a horizantal Scroll


Tim Lewis
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Menu Items
| Name | Free Delivery | Price | Category | Action |
|---|---|---|---|---|
| Sandwich | Yes | Rs. 99.00 | Main Course | Add to Cart |
| Burger | No | Rs. 129.00 | Main Course | Add to Cart |
| Pizza | No | Rs. 149.00 | Main Course | Add to Cart |
| Name | Free Delivery | Price | Category | Action |
|---|---|---|---|---|
| Sandwich | Yes | Rs. 99.00 | Main Course | Add to Cart |
| Burger | No | Rs. 129.00 | Main Course | Add to Cart |
| Pizza | No | Rs. 149.00 | Main Course | Add to Cart |


If you are looking for help for mental health issues, certain therapists have been approved by the Center's HealthWorks Mental Health team. To be included in this list, therapists must affirm their support for same-gender sexual orientation as a normal way of life. Any concerns about individuals on this list may be directed to the Center's Mental Health HealthWorks Team.
One of our goals at the LGBT Center of Raleigh is to provide the best listing of all of the supportive resources that are available to our Community and present them on our website. We have maintained a free Community Resources website page for our Community since we started the Center. Thank you for being a part of that!
Affirming Religious & Faith Groups listed in this section are well known to be welcoming and affirming for people in the LGBTQ community. Members of these groups participate in the Center’s Affirming Faith Forum and other events in the community. To be included in this section, please come to a regular meeting of the Center’s Affirming Faith Forum and speak with one of the co-facilitators.
Several businesses have supported the LGBT Center of Raleigh and our community by including themselves in the Annual OutGuide. You can get a printed copy of the current OutGuide at the Center and other locations around the Triangle. The online versions are available on this website.