Event Location:
Marinus Ruppertgebouw, Universiteit Utrecht
Leuvenlaan 21
Utrecht, Utrecht, Netherlands
Abstract:
With the addition of R into SQL Server 2016, Microsoft have provided a few extra degrees of freedom for the standard SQL developer. You can now use the R language to wrangle, clean and collect external data, using libraries and functionality that simply wasn’t available to you before with regular T-SQL. Performing data enrichment at scale with SQL Server or even Microsoft R Server can provide extremely valuable new insights for your clients and start reaping the benefits of big data. In this session, Consolidata’s Oliver Frost shows you how to develop your own application for tapping into ‘dark data’. Ollie will demonstrate how to stream live tweets, perform aggregations in R and pipe the output to a Power BI dashboard, giving you a full end-to-end experience of the importance of learning some basic R code in 2016. This session is for anyone who is new to R and is interesting expanding their skill set beyond their comfort zone in SQL Server.
Speaker(s): Oliver Frost,
Abstract:
Does your application suffer from performance problems even though you followed best practices on schema design? Have you looked at your transaction log? There's no doubt about it, the transaction log is treated like a poor cousin. The poor thing does not receive much love. The transaction log, however, is a very essential and misunderstood part of your database. There will be a team of developers creating an absolutely awesome elegant design the likes of which have never been seen before, but the leave the transaction log using default settings. It's as if it doesn't matter, an afterthought, a relic of the platform architecture. In this session, you will learn to appreciate how the transaction log works and how you can improve the performance of your applications by making the right architectural choices.
Speaker(s): Lord Richard Douglas,
Abstract:
Building robust and resilient Data Platform solutions can be complex and costly, building out for both High Availability and Disaster Recovery. The Always On technologies incorporated in the Microsoft SQL Server stack help Data Platform Engineers to do just this. Creating solutions that span multiple locations, in an effort to protect the data that we are tasked to manage on a daily basis. Together we will walk through the architecture patterns, technology requirements, and configuration options that you need to know in order to build a solid Data Platform. Understanding the implementation differences between on-premises and cloud based deployments is important. Especially as there are specific requirements that need to be met for up-time SLAs to be applicable, as well as mitigating for region failures. All of these elements mean that it is more than just simple wizards to follow in order to have a solid, reliable, and robust SQL Server Data Platform.
Speaker(s): John Martin,
Abstract:
In this session, I'll provide an overview of your options when looking to migrate your SQL Server environments to the cloud from either on-site or from another cloud provider. We'll then go through potential cloud infrastructure options such as cloud implementations, compute offerings, and security. After that we’ll dive into in to an overview of the main cloud providers and providing some examples where they might be a good fit for various SQL Server environments; as well as what you might want to take into consideration during for your migration like your HADR strategy and if your SQL Server data would be better suited in another solution that your chosen cloud provider offers.
Speaker(s): Kevin Chant,
Abstract:
Relational databases has been around since decades. It uses a highly structured schema to store information in tabular form and are optimized to find answers about the data. One of the challenges where relational databases struggle with is to find answers on data relationships. Graph databases uses graph theory to store information in a collection of nodes and edges. Graph databases are optimized to find answers about the relationships. With graph data processing available in SQL Server 2017 we will get the best of both relational and graph databases in a single product. In this session the audience will learn about what is graph databases, why graph databases, and how to use the new graph data processing extension of SQL Server 2017.
Speaker(s): Raymond Sondak,
Abstract:
Introducing the new scripting language for tabular models. Before SQL Server 2016 tabular models was wrapped into a multidimensional constructs. TOM is the new native library for tabular - this makes it easy to maintain, modify and deploy your model. During this session I will go through and explain some examples and best practices on generating a SSAS tabular model by using the new TOM. I will spend some time showing and explaining a real world example on pushing measure creation and changes to the key business stakeholders and to ensure quick time to market. The last thing i will show is how you speed up your development free up up to 50% of the time you spend building tabular models with the simple and advanced features of Tabular Editor 2.0
Speaker(s): Bent Pedersen,
Abstract:
In Reporting Services 2016 Microsoft introduced support for mobile reports. They also released the Mobile Report Publisher for authoring and creation of mobile reports. Developers can create dashboards and KPI and publish them on Reporting Services in a very straightforward way. The downside is that Mobile Report Publisher offers simple features and settings for data visualization and parameterization. Nevertheless there are some poor-documented customizations users can apply to have a more flexible products and to determine objects visibility. Want to add parameters to a mobile report? Want to add drillthrough and navigation from one report to another? Want to design and import a custom map, for your visualizations? Want to implement security and filter visibility based on the user's grants? Want to apply a custom layout to your portal? In this session you'll find the solutions that best fit your needs based on real implementation in a large company.
Speaker(s): Andrea Martorana Tusa,
Abstract:
SQL Server 2016 introduces support for system-versioned temporal tables as a database feature that brings built-in support for providing information about data stored in the table at any point in time rather than only the data that is correct at the current moment in time. This session takes you behind the basics of Temporal Tables and answers - after a short introduction - the following questions: - How to handle the change of NULL constraints? - Can Temporal Tables be renamed and what happens to the System Versioned table? - What will happen if you change meta data (data length, data type, ...)? - What happens if you drop a column or add another column to the Temporal Table? - what about security for the system versioned table? - what about calculated columns in the temporal table? - ... You are interested in the answers to all the above questions (and more). This session will answer EVERY question about Temporal Tables.
 Let's elaborate on two topics you might have seen in my "From SQL to R and beyond" presentation, plus we'll check who obeys Benford's law and who doesn't. Here are the three subjects that this slightly code-heavy session will cover: • How to create dynamic T-SQL using R functions, for example to query your multi-company database • Visualizations in R based on shapefiles (those geographic polygons) and some choropleth technique • Applying Benford's law, which actually is not about crime and fraud, but may help you to detect them
Speaker(s): Thomas Hütter,
Abstract:
Performing exorcisms is a very difficult and time-consuming process. It isn’t always directly clear whether a person is truly possessed by an evil spirit or if we are dealing with a false-positive. What if we can accurately predict if someone is actually possessed before we start the exorcism? This can help us avoid spending valuable resources, like holy water, and avoids possible lawsuits if it turns out an exorcism wasn’t necessary. In this very special session, created specifically for the six-hundredth-and-sixty-six edition of SQLSaturday, we will use the power of Azure Machine Learning to find out if we can predict demonic possession and bring the worlds of data science and exorcisms closer together! We will go through all the steps for building a reliably machine learning model together. From analyzing the data to optimizing the model to increase the prediction accuracy. The end result? A machine learning prediction model that will help identify possible possession victims!
Speaker(s): Enrico van de Laar,
Abstract:
Imagine you could track down all dependencies between database and related objects. For example: From a KPI in a SSAS Tabular model to a Measure to a Calculated Column, ..., to Data Source View, To Database objects in a Data Warehouse and via a stored procedure or SSIS package to a Staging database or even to the queries into or exports from your source production systems. Then you could: - Automatically document your BI or database solution; - You could prove your data lineage; - You could generate a deployment script given the SSRS Report, SQL View or Tabular database your users are waiting for to get into production. But why use your imagination? Just come to see how I did it. I will demo it! This session is a mix of SQL and SSAS DMV's, T-SQL data models, build tools and some Powershell glue. My goal is to inspire you!
Speaker(s): Frans van der Geer,
Abstract:
Whether you are a developer, DBA, or anything in between, chances are you might skip some best practices when you write T-SQL. Unfortunately, many so-called “bad habits” aren’t always obvious, but can lead to poor performance, maintainability issues, and compatibility problems. In this session, you will learn about several bad habits, how they develop, and how you can avoid them. While we will briefly discuss advice you’ve probably heard before, like avoid SELECT * and be careful with NOLOCK, you will also learn some subtleties in SQL Server that might surprise you, how some shorthand can bite you in the long run, and a very easy way to improve cursor performance. By changing your techniques and ditching some of these bad habits for best practices, you will take new techniques back to your environment that will lead to more efficient code, a more productive workflow, or both.
Speaker(s): Aaron Bertrand,
Abstract:
There's a lot of buzz about containers in tech at the moment but not so much in the SQL Server world which I find odd because as SQL Server people this technology has benefits that we simply shouldn't be ignoring. My session aims to take attendees through what containers are and how they can benefit us. There are drawbacks which will also be covered but I'm hoping after this session, attendees will be able to go back to their respective companies and be able to evaluate container technology to determine whether or not it will be of benefit. Attendees will be taken through the following: - The definition of containers Virtual Machines vs Containers Container networking fundamentals Configuring Windows Server 2016 to run containers Installing the docker engine Pulling SQL images from the docker repository Running SQL Server containers Creating custom container images A real world implementation of containers
Speaker(s): Andrew Pruski,
Abstract:
When SQL Server 2016 was released, it offered a fantastic new feature with the Query Store. Long term, statistics based, query tuning became a reality. But what about the thousands of servers that aren't upgrading to SQL 2016 or newer? The open source project Open Query Store is designed to fulfill that need. This session will give a short introduction to the Query Store feature in SQL 2016 and then dive into the Open Query Store (OQS) solution. Enrico and William (the co-creators of the OQS project) will explain the design of OQS and demonstrate the features. You will leave this session with an understanding of the features of Query Store and Open Query Store, and a desire to implement OQS in your systems when you return to the office.
Speaker(s): William Durkin,
Abstract:
In this session (level 300-400) you will learn how SQL Server physically stores data in pages and how they are grouped together based on IAM -, PFS, GAM, SGAM pages and extents. Additionally we will have a look into partitions and allocation units and the different types of pages like in-row, row-overflow (row overflow pointer) and LOB. Alongside we will analyze the page header, payload applying the FixedVarFormat and the row offset array.
Speaker(s): Torsten Strauss,
Abstract:
SQL Server has a lot of difference execution plan operators. By far the most interesting, and the most versatile, has to be the Hash Match operator. Hash Match is the only operator that can have either one or two inputs. It is the only operator that can either block, stream, or block partially. And it is one of just a few operators that contribute to the total memory grant of an execution plan. If you ever looked at execution plans, you will have seen this operator. And you probably have a rough idea of what it does. But do you know EXACTLY what happens when this operator is used? In this 500-level session, we will dive deep into the bowels of the operator to learn how it performs. It is going to be wild ride, so keep your hands, arms, and legs inside the conference room at all times; and please remain seated until the presenter has come to a full stop.
Speaker(s): Hugo Kornelis,
Abstract:
Power BI and Power BI Embedded evolve weekly and monthly based on user feedback. In this session we will go through the most important changes to these tools. Serves both beginner as well as advanced users over Power BI.
Speaker(s): Jeroen ter Heerdt,
Abstract:
You know locking and blocking very well in SQL Server? You know how the isolation level influences locking? Perfect! Join me in this session to make a further deep dive into how SQL Server implements physical locking with lightweight synchronization objects like Latches and Spinlocks. We will cover the differences between both, and their use-cases in SQL Server. You will learn about best practices how to analyze and resolve Latch- and Spinlock contentation for your performance critical workload. At the end we will talk about lock free data structures, what they are, and how they are used by the new In-Memory OLTP technology that is part of SQL Server 2014.
Speaker(s): Klaus Aschenbrenner,
Abstract:
With the introduction of Microsoft Solution Templates, you can create an amazing Power BI Brand and Campaign Management solution leveraging Twitter data in just minutes. The solution includes several components, including a pre-built impressive Power BI Dashboard, backed by Azure Cognitive Services, Azure Logic Apps, App Service Plan, Azure Functions running C# code, and several other components. In this talk, we'll explore the Power BI Dashboard, getting started with the setup, and a deep dive into the technical components that power this solution. We'll also cover the pricing model and suggestions for right-sizing the components that I've used to help several customers with this amazing solution. Solution details can be found here: https://powerbi.microsoft.com/en-us/solution-templates/brand-management-twitter/
Speaker(s): Sam Lester,
Abstract:
With Azure Analysis Services (Azure AS) we have full set of PAAS and SAAS services like Azure SQL DWH, Azure SQL DB, Azure AS and Power BI for creating our BI solutions in the cloud. In this session I will introduce Azure AS and how we can use it together with other Azure services for creating complete BI solution in the cloud.
Speaker(s): Sergiy Lunyakin,
Abstract:
Creating a proper Tabular Model is essential for the success of your modern BI solution. If you set up the foundations properly, you will benefit when building the relationships, formulas and visualizations. Also your Self-Service BI users will understand and use the data model better. This talk guides you through the process of creating a Tabular Model. The session will be packed with very practical tips and tricks and the steps you should do to create a proper model. The session is based on “real life” projects, and will be backed with some theory. After this hour you will understand how to create a proper model, how to optimize for memory usage and speed, enhance the user experience, use some DAX expressions and to use the right tools for the job. You will go home with a very useful step-by-step-guide.
Speaker(s): Edgar Walther,
Abstract:
With the release of SQL Server 2017, Integration Services received some love with the addition of its very own Scale Out feature. In this session we'll see what kind of impact horizontal scaling has on your ETL's performance and how it performs compared to vertical scaling.
Speaker(s): Jan Van humbeek,
Abstract:
Embedding your R (and soon Python!) models in SQL Server enables you to add predictive capabilities to your applications and your analytics without adding expensive components or going outside your network via expensive API calls. In this demo-packed talk, you’ll see how you can go from a model built in R to making predictions on the fly in SQL Server 2016.
Speaker(s): Stephanie Locke,
Abstract:
You know locking and blocking very well in SQL Server? You know how the isolation level influences locking? Perfect! Join me in this session to make a further deep dive into how SQL Server implements physical locking with lightweight synchronization objects like Latches and Spinlocks. We will cover the differences between both, and their use-cases in SQL Server. You will learn about best practices how to analyze and resolve Latch- and Spinlock contentation for your performance critical workload. At the end we will talk about lock free data structures, what they are, and how they are used by the new In-Memory OLTP technology that is part of SQL Server 2014.
Speaker(s): Klaus Aschenbrenner,
Abstract:
Query Store is a new feature that got released on SQL Server 2016 version and is being improved over the last cumulative updates and on SQL Server 2017 vNext CTP's versions. It's a very useful and interesting feature that allow DBA's ( and non-DBA's :) ) to easily identify performance issues on queries and also allow us to fix it in a fast and simple way. Just the ability to compare a previous execution plan to a new plan is a huge step towards understanding what may be happening in our instance. We can even tell the optimizer which plan we want it to use. These were all either extremely difficult to do before and in some cases impossible to do. I want with this session to give you the insight and knowledge to get started using this new and wonderful feature that will change the way how you do performance tuning.
Speaker(s): Andre Batista,
Abstract:
How easy is it to hack a SQL Server? In this session we'll see examples on how to exploit SQL Server, modify data and take control, while at the same time not leaving a trace. We'll start by gaining access to a SQL Server (using some "creative" ways of making man-in-the-middle attacks), escalating privileges and tampering with data at the TDS protocol level (e.g. changing your income level and reverting without a trace after payment), hacking DDM, RLS, and more. More important, we'll also cover recommendations on how to avoid these attacks, and take a look at the pros and cons of new security features in SQL Server 2016. This is a demo-driven session, suited for DBAs, developers and security consultants. Disclaimer: No actual crimes will be committed. Please do not send agents to my house again.
Speaker(s): André Melancia,
Abstract:
There is a vast array of material in the SQL Server community that covers the in-memory engine in great detail, this session will cover some of the internals of the in-memory engine, however what it will also do is fill in the void as to common performance issues and how they can be solved with the in-memory engine, specifically: - Staging data as fast as possible - Turbo charging scalar aggregate functions - Implementing a scalable sequence - Overcoming the performance limitations of temporary tables - Making the critical path in your code base run faster
Speaker(s): Christopher Adkin,
Abstract:
SQL Server 2012 introduced an important extension to its integrated Full-text Search, namely the Statistical Semantic Search. In this session you will learn how to install and configure this new functionality, how to use it to extend your existing full-text search capabilities, and how to utilize it properly to improve your information management solutions. You will learn how to automatically extract key phrases from your documents, and how to find similarities between individual documents of your information corpus based on the key phrases that they contain.
Speaker(s): Matija Lah,
Abstract:
Microsoft initially released PowerBI online services which had restriction of developing dashboards and reports online only. Then came the PowerBI desktop for easy onsite development including the complex DAX logics. PowerBI support to SQL Server 2016 Reporting Services is a major breakthrough for any dashboards without any need to host in the Cloud. In this session, we will explore and compare the features availale on PowerBI Online Services and PowerBI On-prem SSRS 2016. This session also covers the PowerBI Roadmap for both on-prem and cloud solutions.
Speaker(s): Dr. Subramani Paramasivam,
Abstract:
Most database administrators know how to manage and deploy high availability solutions using SQL Server. Most also know parts of Windows clustering. But with SQL Server 2017 there is a old new kid on the block and it is called Linux. The SQL part might be the same, the operating system is certainly not. In this session we will take a look at high availability solutions on Linux and how they fit into SQL Server. How do you manage and deploy availability groups across your organization leveraging Linux. But also, what is new with SQL Server 2017 on Linux as well as Windows.
Speaker(s): Robert Hartskeerl,
Abstract:
Start from nothing and use Test Driven Development to write a PowerShell function that uses the Microsoft Cognitive Services API to analyse pictures. I will take you on a journey from nothing to a complete function, making sure that all of the code works as expected, is written according to PowerShell best practices and has a complete help system. You will leave this session with a good understanding of what Pester can do and a methodology to develop your own PowerShell functions
Speaker(s): Rob Sewell,
Abstract:
A lot of changes have come to Power BI. Do you understand how Premium fits in? What is an app workspace and why would you use it?  We will take a look at Premium, Apps and App Workspaces along with the changes to embedding. We will also see how Power BI Report Server fits into the picture.
Speaker(s): Adam Saxton,
Abstract:
SQL Server 2016 is full of new features and improvements. Some of them are "Killer" features like Query Store, Temporal Tables, R Integration, etc., which are discussed a lot and we always can get a lot of information about them. And in the same time, SQL Server 2016 have several fantastic features and improvements, which are more hidden from our sight. In this session, we will learn a lot about these features and improvements. Which features? This is a secret :) Come to the session and discover them with me!
 Enough of that, in this session we'll walk through a real SQL Server Machine Learning Services scenario from idea to code and from deployment to monitoring maintenance. Learn how you can easily roll out your own Data Science projects or support the data scientists and analysts at your company.
Speaker(s): Jan Mulkens,
Abstract:
Where do the estimated rowcount values come from? Look inside SQL Server’s distribution statistics to see how they are used to come up with the estimates. We’ll also discuss changes in the cardinality estimator in recent versions and look at some new metadata that gives us more statistics information. Goals: • Explore the output of DBCC SHOW_STATISTICS • Describe when the density information is useful • Look at some problem scenarios for which the statistics can’t give good estimates • Understand why cardinality estimation involves more than just the statistics
Speaker(s): Kalen Delaney,
Abstract:
Have you ever tried to migrate your On Premise DataWareHouse solution to a Azure SQL DataWareHouse? Azure SQL DW is a MPP system and requires a lot of changes to your current solution. What are the best practices to Migrate your solution with as little impact to users as possible? Why should you migrate to a Azure SQL DW? I did, and would like to share my experience. I will take you in this session through all the steps you have to take care off. The following points will be discussed Distribution keys, Replicated Tables, identity Colums, CTAS, scaling, pausing, monitoring and much more.
Speaker(s): Erwin De Kreuk,
Abstract:
If you want to mix traditional relational data with semi-structured data stored on Azure or Hadoop, then the out-of-the-box PolyBase functionality in SQL Server 2016 is one of the easiest ways to get started with this. In this session we first introduce the PolyBase architecture, then show how to setup and query PolyBase. This demo-rich session helps both developers and DBAs in understanding the potential and practical use of PolyBase.
Speaker(s): Nico Jacobs,
Abstract:
One hot topic with Power BI is security, in this deep dive session we will look at all the aspects of security of Power BI, from users, logging to where and how your data is stored and we even look at how to leverage additional Azure services to secure it even more.
Speaker(s): Kasper de Jonge,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
Contact: http://blogs.msdn.com/b/samlester
Sam Lester has spent the past 14 years working for Microsoft in various SQL Server related roles. After nearly 12 years working in the SQL Server development team in Redmond, WA, he has recently made the switch to a consulting role to work hands-on with European customers. While in the product group, Sam worked on numerous features including setup, migration, SSMS/manageability, Utility Control Point, Data-Tier Application Component, SSDT, Intellisense, amp; TSQL Debugger.
LinkedIn: http://www.linkedin.com/profile/view?id=270775013amp;trk=tab_pro
Contact: http://www.sqlmaster.de
Uwe Ricken is working with IT systems since the 90’s. The primary passion for developments with Microsoft SQL Server expanded in 2007 with his engagement as a DBA for Deutsche Bank AG in Frankfurt am Main. After 6 years of operational experiences as a DBA and over 14 years as a developer of complex database models he achieved the “Microsoft Certified Master – SQL Server 2008” certification which “was” the highest technical certification. In the same year he earned his first award as a Data Platform MVP for his support to the Microsoft SQL Server community in Germany and Europe. Uwe Ricken is a speaker on many international conferences and events and preferred topics are “Database Internals”, “Indexing” and “Development”.
LinkedIn: http://www.linkedin.com/pub/denis-reznik/3/502/234
Contact: http://reznik.uneta.com.ua
Denis Reznik is a Data Architect at Intapp, Inc. He has more than ten years experience in software development, database design, and performance tuning for cloud and on-premises solutions. In 2010 Denis became Microsoft Data Platform MVP and maintains MVP status today. Apart from his professional career, Denis actively participates in the development of the Ukrainian Data Community.
Kalen Delaney is a Data Platform MVP who has worked with SQL Server for more than 32 years. She has provided performance consulting services and taught advanced courses on SQL Server to thousands of people and organizations. In addition to her courseware development, Kalen has been writing about SQL Server for decades. She is the primary author of SQL Server Internals and the Inside SQL Server series. Kalen is also one of the primary editors for the Red Gate SQL Server Stairways tutorials.
LinkedIn: http://uk.linkedin.com/in/johnqmartin/
Contact: https://www.mssqltips.com/sqlserverauthor/291/john-martin/
John Martin is a Data Platform Engineer working in the financial sector, as well as Vice President Marketing for the PASS organization and currently a Microsoft Data Platform MVP. Previously John has worked as a product manager for SentryOne and a Premier Field Engineer with Microsoft UK. John has over a decade of experience working with SQL Server and the Microsoft Data Platform. Working as a DBA, developer and consultant for Microsoft, he has been lucky enough to see how best, and how not, to use SQL Server and the Data Platform effectively.
LinkedIn: http://pt.linkedin.com/pub/andre-batista/9/111/790/
Contact: http://klunkysql.info/
Andre Batista, with academic path in the course of Electronics, Telecommunications and Computers ( ISEL - Lisbon ) is currently in Crossjoin Solutions working as SQL Server DBA, performing functions on the client PT (Portugal Telecom). He is interested in all areas related to SQL Server and particularly in architecture, implementation, maintenance and performance. He is a member of the Portuguese SQL Server User Group, SQLPort (http://www.sqlport.com) and also one of the founders of TUGA.
LinkedIn: https://uk.linkedin.com/in/robsewellsqldba
Contact: http://sqldbawithabeard.com
Rob was once a production SQL Server DBA, but is now generally found automating the Data Platform and providing training for clients. He has a passion for PowerShell, Data, and DevOps. He is an MVP, an officer for the DevOps PowerShell VG, and has spoken and volunteered at many PowerShell and Data events. He is a member of the committee that organises Data In Devon and the European PowerShell Conference. He is a proud supporter of the Data and PowerShell communities. He relishes sharing and learning and can be found doing both via Twitter @sqldbawithbeard and his blog sqldbawithabeard.com. He spends most of his time looking at a screen and loves to solve problems.
LinkedIn: https://www.linkedin.com/in/raymondsondak/
Raymond Sondak is a technical data architect, analytics consultant, and owner of Analyticsaholic. He is also the 1st BimlHero Certified Expert in the Netherlands. Raymond is an expert in agile end-to-end Business Intelligence and Analytics implementation as well as automation focused on Microsoft and Open Source technologies. His technical expertise extends from Microsoft Data Platform and Hadoop to building cloud solutions with Microsoft Azure and AWS. He also delivers training and workshops on various subjects including Biml and very passionate to share his knowledge. Raymond loves to enjoy life, when he is not behind his laptop you'll find him on adventure trips around the world with his wife and they're both foodies at heart.
LinkedIn: https://de.linkedin.com/in/derfredo
Contact: https://sqlfredo.wordpress.com/
Thomas holds a degree in Business Administration, but has been a data explorer and a developer at heart ever since the days of dBase and Turbo Pascal. He touched his first SQL Server at V6.5 and used covering indexes before they became a feature. Thomas has been developing in Navision/Dynamics/Business Central systems for quite some time (since 2001, one year before MS acquired Navision), joined PASS in 2006 and got his hands on R in 2014 (the year before MS bought Revolution Analytics). He has worked for ISVs as well as end-user companies, as a developer, consultant, accidental DBA and is an author for data-related articles as well as a speaker at SQL events across Europe.
Andrea Martorana Tusa is a Business Intelligence Team Manager at Würth Phoenix, the IT and consulting company of the Würth-Group. He is awarded as MVP in the Data Platform category Former BI Specialist at Widex, a Danish manufacturing company, and BI Developer in the IT department of an Italian banking group. 20+ years of experience working with data. He is focused on the entire BI stack: database development, data warehousing, data analysis, reporting, etc. Andrea is a usual speaker at many events: SQLSaturdays, conferences in Europe and PASS Summit, and for PASS Virtual Groups. Andrea is an author for sqlshack.com, sqlservercentral.com, and UGISS (User Group Italiano SQL Server).
LinkedIn: https://www.linkedin.com/in/olliefrost
I currently work predominantly as a Data Engineer for Consolidata Ltd with side roles in Engagement, Marketing and Sales. My background is in Neuroscience and so I would like to cover cross-over topics in the future (machine learning, deep learning and developing algorithms). My tool set includes: SQL Server, R, Microsoft R Server and Azure. I have a good knowledge of other NoSQL technologies including Hadoop, Spark and Neo4j. I also have an unconventiona background - I am also a current world record holder for solving the 4x4x4 blindfolded Rubik'sCube blindfolded!
LinkedIn: http://uk.linkedin.com/in/dageop
Contact: http://www.yoursqlman.com/index.php/blogs
Dr.SubraMANI Paramasivam is a Microsoft MVP, PhD, Microsoft Certified Trainer and Head of Data AI at DAGEOP Ltd having 21 years of DB, BI, Analytics and AI experience. Mani has also spoken in various events like MS IGNITE, PASS Summit, MS Inspire, SQLBITS, Power Platform Summit, Global AI Bootcamp, Data BI Summit, SQLRelay, Power BI World Tour, Data Platform Summit, INSIDESQL UK, Global AI Nights, SQLServerGeeks Summit, MCT Global Summits, Data Day Events and various user group events. Mani also believes in Empowering Every Person in the planet and spreads the awareness on Global challenges via www.EmpoweringEveryPerson.com. He is also a co-owner of 1 Global AI Meetup and 3 Power BI PUG PASS local / user groups.
LinkedIn: http://nl.linkedin.com/pub/enrico-van-de-laar/33/882/756/
Contact: https://www.enricovandelaar.com
Enrico has been working with data in all kinds of formats and sizes for over 15 years. He is a Data Advanced Analytics Consultant for Dataheroes where he helps organisations take their first steps in the world of Advanced Analytics. Enrico is a Data Platform MVP since 2014 and a frequent speaker on various data related events all over the world. He authored the book “Pro SQL Server Wait Statistics” and blogs about technologies like Microsoft SQL Server and Azure Machine Learning on his blog at www.enricovandelaar.com
LinkedIn: http://LinkedIn.COM/in/AndreMelancia
Contact: http://Andy.PT
Owner / Principal Consultant at LunarCat.PT Microsoft Certified Trainer (MCT) focusing on SQL Server, Azure, IoT and Security. 19+ years' fun developing information and multimedia systems, DBA, project and IT management. Data Community Portugal, IT Pro Portugal and PowerShell Portugal communities organiser. IPv6 Portugal, DNSSec Portugal and Windows Development Portugal online communities moderator. Actively volunteering, organising, speaking or just participating at community meetings and events around the world. Proud uncle and food devouring expert, with dangerous рuϧϧy cat as companion. Go to http://Andy.PT and you'll know the same as the NSA...
LinkedIn: https://dk.linkedin.com/in/bentnissenpedersen
Contact: http://www.biblog.eu
Bent is a Data Platform MVP and working as Senior Business Analytics Architect at Kapacity A/S, where he have been working with enterprise customers on solutions with SSAS Tabular, SQL Server and in general Azure Data Platform services for the last years. During the past years, he concentrated on working with architecturing, devOps, Automation, testing and simplifying development processes for the Azure Data Platform.
Kasper de Jonge is a Program Manager on the SQL Server Analysis Services team, based out of Redmond, WA. With more than 5 years of experience as a developer and consultant on data warehousing solutions using all products from the Microsoft BI stack, Kasper has worked on everything from corporate intranets using SharePoint and Analysis Services to near real-time reporting in the cloud. He has presented at many technical conferences, including PASS Summit and SQLSaturday. You can read Kasper’s blog at http://www.powerpivotblog.nl.
LinkedIn: http://nl.linkedin.com/in/henkvandervalk
Contact: http://www.henkvandervalk.com
Since 2000 Henk has specialized in optimizing large-scale Microsoft application and database solutions. He’s been authoring and contributing numerous whitepapers around SQL Server performance tuning and also produced the famous ETL Data loading World Record with SSIS. About a year ago, Henk left Microsoft and co-founded a new company: SQLMasters BV and dedicates his time since in developing cool SQL Estate data management solutions and full stack performance tuning. During his presentations he usually dives into the real-world performance challenges @customer sites and shares tips that will make you job easier!
LinkedIn: http://si.linkedin.com/in/matijalah/
Contact: http://milambda.blogspot.com
Matija Lah has more than a decade of experience working with Microsoft SQL Server, mostly architecting data-centric solutions in the legal domain. His contributions to the SQL Server community have led to the Microsoft Most Valuable Professional award in 2007 (Data Platform), which he held until 2017. In 2008 Matija joined SolidQ as a Mentor, located in Central and Eastern Europe. He spends most of his time on projects involving advanced information management, and natural language processing.
Contact: https://blogs.sentryone.com/author/AaronBertrand
Aaron, Product Manager at SentryOne, produces performance and optimization tools for the entire Microsoft data platform, as well as Plan Explorer, a free tool for SQL Server execution plan analysis. In his spare time, he is either playing volleyball, curling, blogging at sqlperformance.com and blogs.sentryone.com, or contributing to SQL Server conversations on Twitter and dba.stackexchange.com. He has worked with SQL Server since 6.5 and has been an MVP since 1997.
LinkedIn: https://uk.linkedin.com/in/stephanielocke
Contact: https://itsalocke.com
Steph Locke leads a life of data, coffee, books and board games. During the day, Steph runs her own consultancy helping people start doing data science. Steph enjoys being her own gal as it means she gets to spend plenty of time building communities to provide platforms for people to help each other be better with data.
LinkedIn: https://nl.linkedin.com/in/erwindekreuk
Contact: https://erwindekreuk.com
Erwin de Kreuk is a passionate and very experienced Microsoft Solution Architect. Working as a Principal Consultant/ Lead Data and AI for InSpark, #1 Microsoft Partner of the Year in the Netherlands. Speaking at different national and international data community events. He is working in the world of data on the Microsoft Platform for last 12 years and the last 4 years he has shifted his focus to the Azure Platform. Answering complex customer cases and technical issues are part of his day-to-day work. In addition to this work, he is a member of the Technology Board within InSpark and leads a team of highly experienced Data Expert in the field of Microsoft Data Platform.
LinkedIn: http://nl.linkedin.com/in/peterjonk
Contact: http://dba60k.net
Peter Jonk is a Premier Field Engineer for Microsoft Services and supports customers to get the most out of SQL Server. Areas of expertise include high availability, SQL migrations and performance tuning.
LinkedIn: https://www.linkedin.com/in/slunyakin
Contact: https://slunyakin.wordpress.com
Sergiy Lunyakin (MCSA, MCSE, MCTS) is a Big Data Architect at SoftServe, Inc. He has extensive experience in designing and developing Big Data Analytics, Data Warehouse, and BI solutions, using both on-premises and cloud products. In 2016, Sergiy became Microsoft Data Platform MVP and has since maintained his MVP status. Sharing knowledge is another one of his passions. He regularly speaks at international conferences such as SQLSaturday, SQLDay, SQLNexus, and SQLBits. He also runs the Lviv SQL Server User Group in Ukraine and organizes SQLSaturday Lviv.
LinkedIn: https://www.linkedin.com/in/fransvandergeer
Frans is consultant Microsoft Data Platform for VX Company with 20 years of experience. He has a background as software developer, with a focus on backend systems. He was the first Microsoft Certified Master on SQL Server in the Netherlands. He helps customers implement the Microsoft Data Platform with a focus on Business Intelligence workloads. He builds his own tools for monitoring and data warehouse generation and deployment.
LinkedIn: https://www.linkedin.com/in/guyinacube
Contact: https://guyinacube.com
Adam Saxton is just a guy in a cube doing the work! He is on the Power BI CAT team at Microsoft, working with customers to help adopt Power BI. He is based in Texas and started with Microsoft supporting SQL Server connectivity and Reporting Services in 2005. Adam has worked with Power BI since the beginning, on the support side, and with documentation. In addition, he produces weekly videos for his Guy in a Cube YouTube channel.
LinkedIn: https://www.linkedin.com/in/kevin-chant/
Contact: https://www.KevinRChant.com/
Senior Database Architect originally from the UK and now living in the Netherlands. Over 23 years experience in the IT sector, and has supported databases for companies in the top 10 of the fortune 500 list. In addition to a lot of SQL Server experience also has a fair few Microsoft Certifications. In addition, was probably the last ever person in the world to gain the MCSD Azure Architect certification and has gained the latest Microsoft DevOps Engineer Expert certification. Has real life experience with SQL Server and Azure Devops. Currently SQL Server Product Owner of around 1,900 instances. In addition, done various things over the years for the Data Platform Community.
Contact: http://www.SQLpassion.at/blog
Klaus Aschenbrenner provides independent SQL Server consulting services across the United States and Europe, focusing on the .NET Framework and SQL Server 2005 and 2008. In 2004 and 2005, he received the Microsoft MVP award for his support of the .NET community. Klaus has also written a book, “Pro SQL Server 2008 Service Broker,” published by Apress (2008).
LinkedIn: http://uk.linkedin.com/in/richardpdouglas
Contact: http://SQL.RichardDouglas.co.uk
Lord Richard Douglas is a Principal Solutions Engineer at SentryOne, where he specialises in the SQL Server product range. He has worked with SQL Server since version 7.0 in various developer and DBA roles and holds a number of Microsoft certifications. Richard is a keen member of the SQL Server community, having run a PASS Chapter in the UK and having served on the organizing committee for SQLRelay. Richard is on Twitter as @SQLRich.
LinkedIn: https://be.linkedin.com/in/janvanhumbeek
Jan Van humbeek is a Microsoft BI consultant working for Micropole Belgium. Over the years, he’s gathered experience with all the core BI components the SQL Server stack has to offer. This led him to becoming fully MCSE certified on the SQL Server stack and more recently he achieved an MCSA certification in Office 365. When the working day is done, he loves learning about all the latest and greatest features Microsoft has to offer.
LinkedIn: http://nl.linkedin.com/in/hugokornelis
Contact: http://sqlblog.com/blogs/hugo_kornelis/default.aspx
Hugo Kornelis is an established SQL Server community expert who spends a lot of time at various conferences. He is author of "the Execution Plan Reference" (sqlserverfast.com/epr), blogger, technical editor of Grant Fritchey's "SQL Server Execution Plans, 3rd edition" and some other books, and Pluralsight author. He was awarded SQL Server MVP and Data Platform MVP 12 times (2006 - 2016, and 2019-present). When not working for the community, he is busy at his day job: freelance database developer/consultant. Hugo has over 20 years of SQL Server experience in various roles. He loves to write and tune complex queries, but he also has a strong database design background. When not working for the community, he is busy at his day job: freelance database developer/consultant. Hugo has over 20 years of SQL Server experience in various roles. He loves to write and tune complex queries, but he also has a strong database design background.
LinkedIn: https://www.linkedin.com/in/andrewpruski
Contact: https://dbafromthecold.com/
SQL Server DBA with over 10 year's experience. Originally from Wales but now living in Ireland
LinkedIn: https://www.linkedin.com/in/janmulkens
Contact: http://www.janmulkens.be
Jan Mulkens is a Microsoft MVP in AI, a Solution Expert at Ordina Belgium, and a Microsoft Data Platform BI Consultant. In his spare time, he is a speaker at conferences and user groups in Europe and he organizes two user groups and a conference in Belgium. Power BI Days conference (www.powerbidays.com), Belgium Microsoft Advanced Analytics User Group (bit.ly/msaaug), and Flemish Power BI User Group (meetup.powerbibelgium.com).
Torsten Strauß (MVP, MCSE Data Platform, MCSE Data Management and Analytics, MCT) has been working with Microsoft SQL Server since the turn of the millennium. He currently works as Database Engineer and Microsoft SQL Server Evangelist for BP Europe SE and as Principal Consultant for Sarpedon Quality Lab. Torsten has delivered more than 100 presentations about Microsoft SQL Server at national and international conferences and user groups in recent years.
Contact: http://www.SQLpassion.at/blog
Klaus Aschenbrenner provides independent SQL Server consulting services across the United States and Europe, focusing on the .NET Framework and SQL Server 2005 and 2008. In 2004 and 2005, he received the Microsoft MVP award for his support of the .NET community. Klaus has also written a book, “Pro SQL Server 2008 Service Broker,” published by Apress (2008).
LinkedIn: https://nl.linkedin.com/pub/peter-ter-braake/1/b87/1aa?
Peter ter Braake, SQL Server MVP, co-founder of the Dutch PASS Chapter and organizer of the Dutch SQL Saturday, teaches SQL Server since 2002. Teaching is for Peter the perfect opportunity to share his SQL Server knowledge and his enthusiasm for the product. Since 2008 he works as an independent contractor giving him the opportunity to use his classroom experience in the real world and use his real world experience in the classroom.
Contact: http://robert.hartskeerl.nl
With over 10 years of experience with SQL Server my knowledge goes a long way. But there is always room for more. That's the reason why I love to help people face and solve their everyday or incidental challenges. This helps me to get more out of SQL Server. My main role is as a DBA but I love to hang around in Visual Studio and not afraid to take you up on a discussion about ADO.NET or the Entity Framework.
LinkedIn: http://uk.linkedin.com/in/alexanderyates
Contact: http://www.workingwithdevs.com
Alex Yates has been helping organisations to apply DevOps principles to relational database development and deployment since 2010. He’s most proud of helping Skyscanner develop the ability to deploy 95 times a day. Originally for Redgate, later for DLM Consultants, Alex has worked with clients on every continent except Antarctica – so he’s keen to meet anyone who researches penguins. A keen community member, he co-organises SQL Relay, is the founder of www.SpeakingMentors.com and has been recognised as a Microsoft Data Platform MVP since 2017.
LinkedIn: http://be.linkedin.com/in/sqlwaldorf
Contact: http://blogs.u2u.be/u2u/author/Nico-Jacobs.aspx
Dr. Nico Jacobs is a Microsoft Business Intelligence and SQL Server trainer at the U2U training center since 2004. Before that, he was a machine learning researcher at the university of Leuven, Belgium. At U2U, Nico creates and delivers courses on the SQL Server business intelligence stack (Integration Services, Analysis Services, Reporting Services …), Power BI, the traditional relational database environment (database development and administration) and the Azure AI and Big Data stack (Azure Data Factory, Machine Learning, Synapse Analytics,...)
LinkedIn: https://www.linkedin.com/in/wollatondba
Contact: http://www.chrisadkin.io
Chris is a freelance SQL consultant who has been working with SQL Server since 2000, his passion is for squeezing every last drop of performance out of SQL Server and understanding the database engine at a level which conventional tools cannot provide any insights on.
LinkedIn: https://www.linkedin.com/in/guyinacube
Contact: https://guyinacube.com
Adam Saxton is just a guy in a cube doing the work! He is on the Power BI CAT team at Microsoft, working with customers to help adopt Power BI. He is based in Texas and started with Microsoft supporting SQL Server connectivity and Reporting Services in 2005. Adam has worked with Power BI since the beginning, on the support side, and with documentation. In addition, he produces weekly videos for his Guy in a Cube YouTube channel.
LinkedIn: http://www.linkedin.com/in/wdurkin
Contact: http://williamdurkin.com
William Durkin is a DBA, Data Platform MVP, and Data Platform Architect for Data Masterminds (http://datamasterminds.io). He uses his decade of experience with SQL Server to help multinational corporations achieve their data management goals. Born in the UK and now based in Germany, William has worked as a Database Developer and DBA on projects ranging from single server installations, up to environments spanning 5 continents, using a range of high availability solutions. William is a regular speaker at conferences around the globe, organizes the popular event SQLGrillen (http://sqlgrillen.com).
LinkedIn: https://www.linkedin.com/in/jeroenterheerdt
Contact: http://www.dutchdatadude.com
Dutch Data Dude. Program Manager for Azure SQL Database at Microsoft. He is passionate about "all things data"; whether it is about databases, data warehousing, BI, big data, data visualizations, self-service BI, Hadoop, real-time BI or anything even remotely related to data or insight. He strives to apply these concepts in his life as much as possible. Jeroen combines enthusiasm, vision and hands-on experience into fresh, entertaining and thought-provoking talks. His blog is http://www.dutchdatadude.com can be found on twitter at @jeroenterheerdt.
LinkedIn: http://nl.linkedin.com/in/edgarwalther/
Contact: http://www.itsmart.eu/Blog/tabid/180/Default.aspx
Edgar Walther has been an independent BI consultant for over 12 years and specializes in the Microsoft Data Platform. He runs a BI consulting firm, ITsmart, based in the Netherlands, and he is one of the founders of the Dutch Power BI User Group. With a background of both Business Economics and IT development the area of Business Intelligence is where he can bridge the gap between Business and IT. He is very eager to stay up to date with the latest developments in Microsoft BI products, and he likes to share them with other BI enthusiasts. He has been a speaker at several SQL Server and Power BI events.
The following is a list of sponsors that helped fund the event.


Tips to Manage the JSON Format¶
The purpose of this tutorial is to learn some best practices to use more easily the JSON format. JSON is a string format used very often in Web technologies and particularly with Javascript because it is so easy to manipulate this format in this language (it has been created for that). This format is used in the light client to use native calls (e.g. Android calls) through Javascript on the GUI component.
JSON Format and Javascript¶
"JSON" means "Javascript Object Notation". So, the JSON format is very easy to use in Javascript. As you can see in the JSON website (Page dedicated to Javascript on the JSON website), a lot of objects simplify the use of JSON in Javascript:- JSON.parse(string) to convert a string into a JSON object
- eventually eval('('+string+')') to convert a string into a JSON object, but it is not safe, the previous method is better
- JSON.stringify(json object) to convert a JSON object into a string
JSON Format and Java (and Android)¶
JSON is natively supported by Java. As you can see in the JSON website (Page dedicated to Java on the JSON website), a lot of objects simplify the use of JSON in Java. . Other libraries may help to manipulate this format. We propose to follow best practices bellow:
- Use the native library: to simple manipulation only. E.g. fill a JSON object with values... JSONObject and JSONArray are usually used.
- Use GSON
library: to easily create a JSON object from a Java object
(toJson(java object)), or fill a Java object from a JSON object
(fromJson(json string)).
- GSON user guide
- How to Use GSON (tutorial for beginners)
- Or maybe use the Jackson library: equivalent to GSON, but a little bit more complicated to use for the same performance. Proposition: use GSON.
Maven Dependency to Use GSON¶
<dependency> <groupId>com.google.code.gson</groupId> <artifactId>gson</artifactId> <version>1.7.1</version> </dependency>
Best Practices to Exchange JSON Formatted Data¶
Use JSON
Schemas?
To be completed...


Atmel offers an official 8-bit USB AVR stack, which may be incorporated into user projects and products. As LUFA and the Atmel stack aims to give roughly the same functionality to a design, it is often asked what advantages LUFA carries over the official Atmel USB stack for the 8-bit USB AVRs. Below are just some of the advantages to choosing LUFA over the official Atmel stack.
- Licensing: LUFA is released under a very permissive MIT license (see Source Code License), while the Atmel stack carries several restrictions as to how and where it can be used. LUFA's licensing should be suitable for both Commercial and Non-Commercial entities alike.
- Demos and Projects: Unlike the Atmel stack, LUFA comes with many different Device and Host mode Demos and Projects ready to run out of the box. Atmel favors separate downloads for each of their (small set) of USB AVR demos, which requires more time and offers less to the end-user. LUFA also contains several open source Bootloaders, which can be modified as the user wishes to suit his or her application, instead of being forced to use Atmel's single prebuilt (closed-source) DFU bootloader.
- Central Library Code: LUFA is designed to allow the central library core code to be shared amongst several projects, so long as the compiled object files are cleaned between different projects. This is in direct contrast to the Atmel library, which is strongly coupled to the project it is integrated with. Using LUFA allows for only one copy of the library core to be needed for all applications, and makes updating the library used in all projects a trivial copy-and-paste process.
- Clean API: One of the main design goals of LUFA is to make the API easy to use. While LUFA is a fluid project which has undergone many API improvements, the API is arguably much nicer to use and easier to understand than the equivalent Atmel stack code. LUFA's API is also more complete than the Atmel stack, and contains many features to speed up application development.
- Full Hardware Support: LUFA supports the full range of Atmel's USB AVR microcontrollers (see Device and Hardware Support), with porting between chips being as simple as a single compile switch in many cases. Atmel's stack requires different libraries to be used based on the USB AVR microcontroller series, complicating the process of moving between USB AVR models. In addition, LUFA contains drivers for all the hardware contained on Atmel's USB AVR based boards, so you can get started quickly and easily.
- Better Library Support: As many people are now using LUFA, there is a community being built around it. You can get answers to your LUFA related questions quickly by either emailing the library author (subject to author's schedule) or by posting to the official LUFA support mailing list.
- More Compact Code: LUFA is written from the ground up to compile optimally, using clean code and good design. Two demos giving the same functionality - the LUFA Low Level API Mouse Demo vs. the Atmel AVR270 HID Mouse application note shows LUFA to be the clear size winner *.
- LUFA Low Level Mouse Demo: 3510 bytes, Atmel AVR270 Mouse Application Note: 4222 bytes, using an identical build environment.


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BJP attacks Congress for its members' 'unruly' behaviour
Speaking to reporters, Law Minister Ravi Shankar Prasad hit out at the opposition party soon after Lok Sabha proceedings were adjourned due to vehement opposition by Congress members over the swearing-in of a BJP government in Maharashtra.
by PTIThe BJP on Monday attacked the Congress for "unruly" behaviour of its MPs in Lok Sabha and asked all parties to come together to maintain Parliament's sanctity and great tradition.
Speaking to reporters, Law Minister Ravi Shankar Prasad hit out at the opposition party soon after Lok Sabha proceedings were adjourned due to vehement opposition by Congress members over the swearing-in of a BJP government in Maharashtra.
Prasad also said that it is the Congress, not the BJP, which "murdered" democracy in the state.
The minister asserted that popular mandate was in favour of a BJP-Shiv Sena government under Devendra Fadnavis but the Congress "stole" it by joining hands with the Sena which, he added, is driven by political greed and opportunism.


Leaked Motorola RAZR foldable images show the phone from nearly every angle
Source: Evleaks on Twitter
What you need to know
- Evan Blass has shared a few new images showing Motorola's upcoming foldable RAZR smartphone.
- The images confirm the upcoming foldable smartphone will look very similar to the original RAZR.
- It is set to go official at a global launch event in Los Angeles on November 13.
Yesterday, we got our very first look at Motorola's upcoming foldable RAZR smartphone, courtesy of reliable leaker Evan Blass. Blass has now tweeted out some more photos of the upcoming device, giving us a detailed look at it from various angles.
Source: Evleaks on Twitter
As shown by the image shared by Blass yesterday, Motorola's first foldable smartphone will be almost identical in terms of design to the original RAZR V3 phone that was released in 2004. We also see a 'chin' at the bottom, similar to the original RAZR phone, with a button that likely houses a fingerprint sensor. The phone will have a 6.2-inch flexible OLED display, which users will be able to fold in half thanks to the hinge mechanism.
In addition to the 6.2-inch primary display, the foldable phone will include a 2.7-inch secondary display that will show the time, weather information, as well as notifications. Users will also be able to use the secondary display as a viewfinder when taking selfies.
Unlike the Samsung Galaxy Fold, Motorola's upcoming foldable RAZR will not be a flagship-grade offering in terms of tech specs. The phone is rumored to be powered by a mid-range Snapdragon 710 chipset, paired with up to 6GB RAM and 128GB storage. While the rest of the phone's specs remain a mystery right now, we will not have to wait very long to find out more. Motorola is expected to formally unveil the foldable RAZR smartphone at an event in Los Angeles on November 13.


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title: News
permalink: /news/
pinned: true
no-header: true
description: The latest news and updates on the French Fire
imgur: https://i.imgur.com/Qb7iL3U.png
image: https://i.imgur.com/Qb7iL3Um.png
tags:
- french fire
- news
- alta sierra
- wofford heights
- kern valley
- kern river valley
- fire
---
{% for post in paginator.posts %}
{% include articles/post-preview.html post=post %}
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analyses existing services.
Warning: if 2 or more services have the same name, t-only one will appear in the returned data, as it is an associative array using the service name as key.
| package | Amfphp_Plugins_Discovery |
|---|---|
| author | Ariel Sommeria-Klein |
_getMethodRoles(string $methodName) : array
string
array
discover() : array
array
of AmfphpDiscovery_ServiceInfogetServiceNames(array $serviceFolderPaths, array $serviceNames2ClassFindInfo) : array
array
array
array
of service namesparseMethodComment(string $comment) : \array{'returns'
type is first word after tag name, name of the variable is second word ($ is removed) - return tag
string
\array{'returns'
=> type, 'params' => array{var name => type}}searchFolderForServices(string $rootPath, string $subFolder) : array
If in subfolders add the relative path to the name. recursive, so use with care.
string
string
array
$restrictAccess : boolean
$serviceFolderPaths : array
set by plugin.
$serviceNames2ClassFindInfo : array


|
Copyright 2013-01-06 Werner Randelshofer | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object org.monte.media.quicktime.AbstractQuickTimeStream.Edit
public static class AbstractQuickTimeStream.Edit
An Edit
define the portions of the media that are to be used to
build up a track for a movie. The edits themselves are stored in an edit
list table, which consists of time offset and duration values for each
segment.
In the absence of an edit list, the presentation of the track starts immediately. An empty edit is used to offset the start time of a track.
| Field Summary | |
|---|---|
int |
mediaRate
A 32-bit fixed-point number (16.16) that specifies the relative rate at which to play the media corresponding to this edit segment. |
int |
mediaTime
A 32-bit integer containing the start time within the media of this edit segment (in media time scale units). |
int |
trackDuration
A 32-bit integer that specifies the duration of this edit segment in units of the movie's time scale. |
| Constructor Summary | |
|---|---|
AbstractQuickTimeStream.Edit(int trackDuration,
int mediaTime,
double mediaRate)
Creates an edit. |
|
AbstractQuickTimeStream.Edit(int trackDuration,
int mediaTime,
int mediaRate)
Creates an edit. |
| Method Summary |
|---|
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public int trackDuration
public int mediaTime
public int mediaRate
| Constructor Detail |
|---|
public AbstractQuickTimeStream.Edit(int trackDuration, int mediaTime, double mediaRate)
trackDuration
- Duration of this edit in the movie's time scale.mediaTime
- Start time of this edit in the media's time scale.
Specify -1 for an empty edit. The last edit in a track should never
be an empty edit.mediaRate
- The relative rate at which to play this edit.public AbstractQuickTimeStream.Edit(int trackDuration, int mediaTime, int mediaRate)
Use this constructor only if you want to compute the fixed point media rate by yourself.
trackDuration
- Duration of this edit in the movie's time scale.mediaTime
- Start time of this edit in the media's time scale.
Specify -1 for an empty edit. The last edit in a track should never
be an empty edit.mediaRate
- The relative rate at which to play this edit given
as a 16.16 fixed point value.
|
Copyright 2013-01-06 Werner Randelshofer | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Item Types¶
Item types are the identified entities in your model. They consist of definitions and properties. Properties can be simple types, composite types, or relationships to other item types.
Each item type is defined in a folder located at
baseDirectory/ItemTypes/ItemTypeName
. Within this folder, several
files provide information about the item type.
readme.markdown¶
The baseDirectory/ItemTypes/ItemTypeName/readme.markdown
file contains text
to describe your item type.
See also
See https://daringfireball.net/projects/markdown/basics for a primer on using markdown to format text.
ItemTypeName.csv¶
The baseDirectory/ItemTypes/ItemTypeName/ItemTypeName.csv
file
describes the properties of the item type. Be sure to replace
ItemTypeName with the name of the item type.
The CSV file contains the following columns.
- Name
The name of the property.
Note
An item type can include a property with the same name as a property in another item type. However, the data types of the properties must be the same.
- DataType
The data type of the property. The data type can be one of:
- The name of a simple, primitive type as listed in Primitive Types
- The name of a composite type
- The name of another item type
- MinCardinality
- The minimum number of occurrences of the property that an instance can include. Use 0 to mark the property as optional. Use 1 to mark the property as required.
- MaxCardinality
- The maximum number of occurrences of the property that an instance can include. Use 1 if the property can only appear a single time. Use n to allow an unlimited number of occurrences.
- Description
- The description of the property. This is included in the generated documentation, and as comments or annotations in many of the other publishers.
- MinLength, MaxLength, Enumeration, Pattern, MinInclusive, MinExclusive, MaxInclusive, MaxExclusive
- Used to restrict the allowed values of properties of simple, primitive types as described in Primitive Types.
- DeprecatedNamespace, DeprecatedElementOrAttribute, DeprecatedChoiceGroup
- Deprecated. Used only for DDI 3.x backward compatibility.
Extends.*¶
The baseDirectory/ItemTypes/ItemTypeName/Extends.BaseItemTypeName
file acts as
a marker to indicate that the item type derives from another item type. Derived item
types inherit all properties from their parent item type. Be sure to replace
BaseItemTypeName with the name of another item type.
This file is optional; it is only needed if the item type derives from another item type.
Identification¶
All item types are identified using properties as specified in Identification.
The properties listed in the baseDirectory/Settings/Identification.csv
file are
included as properties in all item types.


A list of available APIs. See also our API design google document.
| Method | Description |
| deviceIdGenerate | Generate a device_id (store in cookie) that can be linked to a voter_id |
| voterAddressRetrieve | Retrieve an address for a voter |
| voterAddressSave | Save or update address for an existing voter |
| voterCount | Count the number of voters in the database. |
| voterCreate | Create an empty voter entry attached to a voter_device_id |
| voterRetrieve/?format=json | Export raw voter data to JSON format |


The Analytic Hierarchy Process is a versatile multi-criteria decision-making tool introduced by Saaty (1987) <doi:10.1016/0270-0255(87)90473-8> that allows decision-makers to weigh attributes and evaluate alternatives presented to them. This package provides a consistent methodology for researchers to reformat data and run analytic hierarchy process in R on data that are formatted using the survey data entry mode. It is optimized for performing the analytic hierarchy process with many decision-makers, and provides tools and options for researchers to aggregate individual preferences and test multiple options. It also allows researchers to quantify, visualize and correct for inconsistency in the decision-maker's comparisons.
| Version: | 0.3.0 |
| Imports: | Rdpack, stats, magrittr, knitr, tidyr, dplyr |
| Suggests: | ggplot2, tidyverse, scales |
| Published: | 2018-12-17 |
| Author: | Frankie Cho [aut, cre] |
| Maintainer: | Frankie Cho <htcho at connect.hku.hk> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | ahpsurvey results |
| Reference manual: | ahpsurvey.pdf |
| Vignettes: |
full |
| Package source: | ahpsurvey_0.3.0.tar.gz |
| Windows binaries: | r-devel: ahpsurvey_0.2.2.zip, r-release: ahpsurvey_0.2.2.zip, r-oldrel: ahpsurvey_0.2.2.zip |
| OS X binaries: | r-release: ahpsurvey_0.2.2.tgz, r-oldrel: ahpsurvey_0.2.2.tgz |
| Old sources: | ahpsurvey archive |
Please use the canonical form https://CRAN.R-project.org/package=ahpsurvey to link to this page.


--- layout: default title: Services permalink: /services/ active: true ---
Dynamic Gravity Systems offers a variety of repair, upgrade, and training services. We can perform
repairs and upgrades on zero-length spring based gravity meters from Micro-g Lacoste (MgL) and ZLS.
We also build new, replacement Control Modules for MgL AirSeaII Systems with upgraded electronics and
performance. These are a direct replacement for old AirSeaII control modules.


Extremely fast HTTP Archive (HAR) validator using JSON Schema.
npm install har-validator
Please refer to har-cli
for more info.
Note: as of v2.0.0
this module defaults to Promise based API. For backward compatibility with v1.x
an async/callback API is also provided
Author: Ahmad Nassri • Github: @ahmadnassri • Twitter: @ahmadnassri


| UCBSTD |
| Patient with at least one initiation for biologic after AS date |
| number |
|---|
| 3906 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with at least one initiation for biologic after AS date and with 183 days observable |
| number |
|---|
| 3515 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with at least one initiation for TNFi after AS date |
| number |
|---|
| 3824 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with at least one initiation for TNFi after AS date and with 183 days observable |
| number |
|---|
| 3436 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with at least one initiation for DMARD after AS date |
| number |
|---|
| 2810 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with at least one initiation for DMARD after AS date and with 183 days observable |
| number |
|---|
| 2465 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with at least one initiation for DMARD after AS date and with 183 days observable and no biologic before DMARD |
| number |
|---|
| 1519 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with at least one initiation for DMARD after AS date and with 183 days observable and no biologic within 6 month before DMARD |
| number |
|---|
| 1703 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with at least one initiation for NSAID after AS date |
| number |
|---|
| 6298 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with at least one initiation for NSAID after AS date and with 183 days observable |
| number |
|---|
| 5848 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with at least one initiation for NSAID after AS date and with 183 days observable and no biologic no DMARD before Nsaid |
| number |
|---|
| 2056 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with at least one initiation for NSAID after AS date and with 183 days observable and no biologic, no DMARD within 6 month before Nsaids |
| number |
|---|
| 2771 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with no biologic, no dmard after index date and no biologic no DMARD before index date |
| number |
|---|
| 8294 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with no Biologic, no Dmard after index date and no biologic no DMARD within 6 month before index date |
| number |
|---|
| 8783 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with no exposure after index date and exposure before index date |
| number |
|---|
| 3426 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with no exposure at index date |
| number |
|---|
| 17500 |
| buildExposureCohorts.sas |
| UCBSTD |
| Patient with no exposure after index date and no exposure within 6 month before index date |
| number |
|---|
| 4504 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with at least one initiation for biologic after AS date |
| number |
|---|
| 846 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with at least one initiation for biologic after AS date and with 183 days observable |
| number |
|---|
| 759 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with at least one initiation for TNFi after AS date |
| number |
|---|
| 841 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with at least one initiation for TNFi after AS date and with 183 days observable |
| number |
|---|
| 755 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with at least one initiation for DMARD after AS date |
| number |
|---|
| 640 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with at least one initiation for DMARD after AS date and with 183 days observable |
| number |
|---|
| 581 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with at least one initiation for DMARD after AS date and with 183 days observable and no biologic before DMARD |
| number |
|---|
| 374 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with at least one initiation for DMARD after AS date and with 183 days observable and no biologic within 6 month before DMARD |
| number |
|---|
| 412 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with at least one initiation for NSAID after AS date |
| number |
|---|
| 1292 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with at least one initiation for NSAID after AS date and with 183 days observable |
| number |
|---|
| 1196 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with at least one initiation for NSAID after AS date and with 183 days observable and no biologic no DMARD before Nsaid |
| number |
|---|
| 435 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with at least one initiation for NSAID after AS date and with 183 days observable and no biologic, no DMARD within 6 month before Nsaids |
| number |
|---|
| 599 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with no biologic, no dmard after index date and no biologic no DMARD before index date |
| number |
|---|
| 2352 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with no Biologic, no Dmard after index date and no biologic no DMARD within 6 month before index date |
| number |
|---|
| 2475 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with no exposure after index date and exposure before index date |
| number |
|---|
| 1262 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with no exposure at index date |
| number |
|---|
| 4423 |
| buildExposureCohorts.sas |
| MPSTD |
| Patient with no exposure after index date and no exposure within 6 month before index date |
| number |
|---|
| 1518 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with at least one initiation for biologic after AS date |
| number |
|---|
| 770 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with at least one initiation for biologic after AS date and with 183 days observable |
| number |
|---|
| 671 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with at least one initiation for TNFi after AS date |
| number |
|---|
| 686 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with at least one initiation for TNFi after AS date and with 183 days observable |
| number |
|---|
| 594 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with at least one initiation for DMARD after AS date |
| number |
|---|
| 2104 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with at least one initiation for DMARD after AS date and with 183 days observable |
| number |
|---|
| 1799 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with at least one initiation for DMARD after AS date and with 183 days observable and no biologic before DMARD |
| number |
|---|
| 1569 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with at least one initiation for DMARD after AS date and with 183 days observable and no biologic within 6 month before DMARD |
| number |
|---|
| 1681 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with at least one initiation for NSAID after AS date |
| number |
|---|
| 6314 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with at least one initiation for NSAID after AS date and with 183 days observable |
| number |
|---|
| 5824 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with at least one initiation for NSAID after AS date and with 183 days observable and no biologic no DMARD before Nsaid |
| number |
|---|
| 3535 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with at least one initiation for NSAID after AS date and with 183 days observable and no biologic, no DMARD within 6 month before Nsaids |
| number |
|---|
| 4436 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with no biologic, no dmard after index date and no biologic no DMARD before index date |
| number |
|---|
| 9333 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with no Biologic, no Dmard after index date and no biologic no DMARD within 6 month before index date |
| number |
|---|
| 9968 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with no exposure after index date and exposure before index date |
| number |
|---|
| 4441 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with no exposure at index date |
| number |
|---|
| 13514 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with no exposure after index date and no exposure within 6 month before index date |
| number |
|---|
| 5999 |
| buildExposureCohorts.sas |
| STD_SABR |
| Patient with no exposure after index date and no exposure within 6 month before index date |
| Directory | |
|---|---|
| Libref | WORK |
| Engine | V9 |
| Physical Name | \\peerfs2.ad.hs.uab.edu\T_saswork$\_TD9304_PEER2_ |
| Filename | \\peerfs2.ad.hs.uab.edu\T_saswork$\_TD9304_PEER2_ |
| Owner Name | AD\benjaminchan |
| File Size | 64KB |
| File Size (bytes) | 65536 |
| # | Name | Member Type | File Size | Last Modified |
|---|---|---|---|---|
| 1 | ALLBIO1STMPSTD | DATA | 2MB | 10/10/2018 14:56:18 |
| 2 | ALLBIO1STSTD_SABR | DATA | 6MB | 10/10/2018 14:56:47 |
| 3 | ALLBIO1STUCBSTD | DATA | 6MB | 10/10/2018 14:54:52 |
| 4 | ALLDMARD1STMPSTD | DATA | 6MB | 10/10/2018 14:56:20 |
| 5 | ALLDMARD1STSTD_SABR | DATA | 26MB | 10/10/2018 14:56:53 |
| 6 | ALLDMARD1STUCBSTD | DATA | 11MB | 10/10/2018 14:54:56 |
| 7 | ALLNSAID1STMPSTD | DATA | 20MB | 10/10/2018 14:56:23 |
| 8 | ALLNSAID1STSTD_SABR | DATA | 60MB | 10/10/2018 14:57:12 |
| 9 | ALLNSAID1STUCBSTD | DATA | 190MB | 10/10/2018 14:55:08 |
| 10 | ASBIO0COHORTMPSTD | DATA | 192KB | 10/10/2018 14:56:25 |
| 11 | ASBIO0COHORTSTD_SABR | DATA | 192KB | 10/10/2018 14:57:26 |
| 12 | ASBIO0COHORTUCBSTD | DATA | 512KB | 10/10/2018 14:55:25 |
| 13 | ASBIOCOHORTMPSTD | DATA | 192KB | 10/10/2018 14:56:29 |
| 14 | ASBIOCOHORTMPSTD_EX1 | DATA | 192KB | 10/10/2018 14:56:41 |
| 15 | ASBIOCOHORTSTD_SABR | DATA | 192KB | 10/10/2018 14:57:41 |
| 16 | ASBIOCOHORTSTD_SABR_EX1 | DATA | 192KB | 10/10/2018 14:58:11 |
| 17 | ASBIOCOHORTUCBSTD | DATA | 512KB | 10/10/2018 14:55:35 |
| 18 | ASBIOCOHORTUCBSTD_EX1 | DATA | 512KB | 10/10/2018 14:56:07 |
| 19 | ASDMARD0COHORTMPSTD | DATA | 192KB | 10/10/2018 14:56:34 |
| 20 | ASDMARD0COHORTSTD_SABR | DATA | 320KB | 10/10/2018 14:57:53 |
| 21 | ASDMARD0COHORTUCBSTD | DATA | 384KB | 10/10/2018 14:55:41 |
| 22 | ASDMARDCOHORTMPSTD | DATA | 192KB | 10/10/2018 14:56:36 |
| 23 | ASDMARDCOHORTMPSTD_EX1 | DATA | 128KB | 10/10/2018 14:56:41 |
| 24 | ASDMARDCOHORTMPSTD_EX2 | DATA | 128KB | 10/10/2018 14:56:45 |
| 25 | ASDMARDCOHORTSTD_SABR | DATA | 320KB | 10/10/2018 14:57:54 |
| 26 | ASDMARDCOHORTSTD_SABR_EX1 | DATA | 320KB | 10/10/2018 14:58:11 |
| 27 | ASDMARDCOHORTSTD_SABR_EX2 | DATA | 320KB | 10/10/2018 14:58:25 |
| 28 | ASDMARDCOHORTUCBSTD | DATA | 384KB | 10/10/2018 14:55:47 |
| 29 | ASDMARDCOHORTUCBSTD_EX1 | DATA | 256KB | 10/10/2018 14:56:07 |
| 30 | ASDMARDCOHORTUCBSTD_EX2 | DATA | 320KB | 10/10/2018 14:56:17 |
| 31 | ASNOBDCOHORTMPSTD_EX1 | DATA | 320KB | 10/10/2018 14:56:41 |
| 32 | ASNOBDCOHORTMPSTD_EX2 | DATA | 320KB | 10/10/2018 14:56:45 |
| 33 | ASNOBDCOHORTSTD_SABR_EX1 | DATA | 960KB | 10/10/2018 14:58:12 |
| 34 | ASNOBDCOHORTSTD_SABR_EX2 | DATA | 896KB | 10/10/2018 14:58:25 |
| 35 | ASNOBDCOHORTUCBSTD_EX1 | DATA | 896KB | 10/10/2018 14:56:08 |
| 36 | ASNOBDCOHORTUCBSTD_EX2 | DATA | 768KB | 10/10/2018 14:56:17 |
| 37 | ASNOEXPCOHORT0MPSTD | DATA | 448KB | 10/10/2018 14:56:37 |
| 38 | ASNOEXPCOHORT0STD_SABR | DATA | 1MB | 10/10/2018 14:57:56 |
| 39 | ASNOEXPCOHORT0UCBSTD | DATA | 1MB | 10/10/2018 14:55:54 |
| 40 | ASNOEXPCOHORTMPSTD_EX1 | DATA | 256KB | 10/10/2018 14:56:42 |
| 41 | ASNOEXPCOHORTMPSTD_EX2 | DATA | 192KB | 10/10/2018 14:56:45 |
| 42 | ASNOEXPCOHORTMPSTD_EX2A | DATA | 448KB | 10/10/2018 14:56:45 |
| 43 | ASNOEXPCOHORTSTD_SABR_EX1 | DATA | 576KB | 10/10/2018 14:58:13 |
| 44 | ASNOEXPCOHORTSTD_SABR_EX2 | DATA | 576KB | 10/10/2018 14:58:25 |
| 45 | ASNOEXPCOHORTSTD_SABR_EX2A | DATA | 1MB | 10/10/2018 14:58:25 |
| 46 | ASNOEXPCOHORTUCBSTD_EX1 | DATA | 448KB | 10/10/2018 14:56:10 |
| 47 | ASNOEXPCOHORTUCBSTD_EX2 | DATA | 448KB | 10/10/2018 14:56:17 |
| 48 | ASNOEXPCOHORTUCBSTD_EX2A | DATA | 1MB | 10/10/2018 14:56:17 |
| 49 | ASNSAID0COHORTMPSTD | DATA | 256KB | 10/10/2018 14:56:36 |
| 50 | ASNSAID0COHORTSTD_SABR | DATA | 960KB | 10/10/2018 14:57:55 |
| 51 | ASNSAID0COHORTUCBSTD | DATA | 832KB | 10/10/2018 14:55:48 |
| 52 | ASNSAIDCOHORTMPSTD | DATA | 256KB | 10/10/2018 14:56:37 |
| 53 | ASNSAIDCOHORTMPSTD_EX1 | DATA | 192KB | 10/10/2018 14:56:41 |
| 54 | ASNSAIDCOHORTMPSTD_EX2 | DATA | 256KB | 10/10/2018 14:56:45 |
| 55 | ASNSAIDCOHORTSTD_SABR | DATA | 960KB | 10/10/2018 14:57:56 |
| 56 | ASNSAIDCOHORTSTD_SABR_EX1 | DATA | 832KB | 10/10/2018 14:58:11 |
| 57 | ASNSAIDCOHORTSTD_SABR_EX2 | DATA | 896KB | 10/10/2018 14:58:25 |
| 58 | ASNSAIDCOHORTUCBSTD | DATA | 832KB | 10/10/2018 14:55:54 |
| 59 | ASNSAIDCOHORTUCBSTD_EX1 | DATA | 704KB | 10/10/2018 14:56:08 |
| 60 | ASNSAIDCOHORTUCBSTD_EX2 | DATA | 832KB | 10/10/2018 14:56:17 |
| 61 | ASTNF0COHORTMPSTD | DATA | 192KB | 10/10/2018 14:56:29 |
| 62 | ASTNF0COHORTSTD_SABR | DATA | 192KB | 10/10/2018 14:57:41 |
| 63 | ASTNF0COHORTUCBSTD | DATA | 512KB | 10/10/2018 14:55:35 |
| 64 | ASTNFCOHORTMPSTD | DATA | 192KB | 10/10/2018 14:56:34 |
| 65 | ASTNFCOHORTMPSTD_EX1 | DATA | 192KB | 10/10/2018 14:56:41 |
| 66 | ASTNFCOHORTSTD_SABR | DATA | 192KB | 10/10/2018 14:57:52 |
| 67 | ASTNFCOHORTSTD_SABR_EX1 | DATA | 192KB | 10/10/2018 14:58:11 |
| 68 | ASTNFCOHORTUCBSTD | DATA | 448KB | 10/10/2018 14:55:41 |
| 69 | ASTNFCOHORTUCBSTD_EX1 | DATA | 512KB | 10/10/2018 14:56:07 |
| 70 | BIO1STMPSTD | DATA | 4MB | 10/10/2018 14:56:19 |
| 71 | BIO1STSTD_SABR | DATA | 15MB | 10/10/2018 14:56:49 |
| 72 | BIO1STUCBSTD | DATA | 15MB | 10/10/2018 14:54:54 |
| 73 | BIOCEN4BIOMPSTD | DATA | 128KB | 10/10/2018 14:56:40 |
| 74 | BIOCEN4BIOSTD_SABR | DATA | 128KB | 10/10/2018 14:58:10 |
| 75 | BIOCEN4BIOUCBSTD | DATA | 128KB | 10/10/2018 14:56:06 |
| 76 | BIOCEN4DMARDMPSTD | DATA | 128KB | 10/10/2018 14:56:41 |
| 77 | BIOCEN4DMARDSTD_SABR | DATA | 128KB | 10/10/2018 14:58:10 |
| 78 | BIOCEN4DMARDUCBSTD | DATA | 192KB | 10/10/2018 14:56:07 |
| 79 | BIOCEN4NOEXPMPSTD | DATA | 192KB | 10/10/2018 14:56:41 |
| 80 | BIOCEN4NOEXPSTD_SABR | DATA | 128KB | 10/10/2018 14:58:10 |
| 81 | BIOCEN4NOEXPUCBSTD | DATA | 512KB | 10/10/2018 14:56:07 |
| 82 | BIOCEN4NSAIDMPSTD | DATA | 448KB | 10/10/2018 14:56:38 |
| 83 | BIOCEN4NSAIDSTD_SABR | DATA | 896KB | 10/10/2018 14:58:00 |
| 84 | BIOCEN4NSAIDUCBSTD | DATA | 2MB | 10/10/2018 14:55:57 |
| 85 | BIOCEN4TNFMPSTD | DATA | 128KB | 10/10/2018 14:56:40 |
| 86 | BIOCEN4TNFSTD_SABR | DATA | 128KB | 10/10/2018 14:58:10 |
| 87 | BIOCEN4TNFUCBSTD | DATA | 128KB | 10/10/2018 14:56:07 |
| 88 | BIOEXC6MDMARDMPSTD | DATA | 128KB | 10/10/2018 14:56:42 |
| 89 | BIOEXC6MDMARDSTD_SABR | DATA | 128KB | 10/10/2018 14:58:14 |
| 90 | BIOEXC6MDMARDUCBSTD | DATA | 320KB | 10/10/2018 14:56:11 |
| 91 | BIOEXC6MNOEXPMPSTD | DATA | 320KB | 10/10/2018 14:56:43 |
| 92 | BIOEXC6MNOEXPSTD_SABR | DATA | 192KB | 10/10/2018 14:58:15 |
| 93 | BIOEXC6MNOEXPUCBSTD | DATA | 1MB | 10/10/2018 14:56:12 |
| 94 | BIOEXC6MNSAIDMPSTD | DATA | 192KB | 10/10/2018 14:56:43 |
| 95 | BIOEXC6MNSAIDSTD_SABR | DATA | 256KB | 10/10/2018 14:58:14 |
| 96 | BIOEXC6MNSAIDUCBSTD | DATA | 768KB | 10/10/2018 14:56:11 |
| 97 | BIOMPSTD | DATA | 53MB | 10/10/2018 14:54:42 |
| 98 | BIOSTD_SABR | DATA | 249MB | 10/10/2018 14:54:42 |
| 99 | BIOUCBSTD | DATA | 158MB | 10/10/2018 14:54:42 |
| 100 | COHORTASTDMPCD | DATA | 448KB | 10/10/2018 14:54:41 |
| 101 | COHORTASTDUCB | DATA | 1MB | 10/10/2018 14:54:41 |
| 102 | COHORTASTD_SABR | DATA | 1MB | 10/10/2018 14:54:41 |
| 103 | DMARD1STMPSTD | DATA | 14MB | 10/10/2018 14:56:22 |
| 104 | DMARD1STSTD_SABR | DATA | 70MB | 10/10/2018 14:57:03 |
| 105 | DMARD1STUCBSTD | DATA | 27MB | 10/10/2018 14:54:57 |
| 106 | DMARDCEN4NOEXPMPSTD | DATA | 192KB | 10/10/2018 14:56:41 |
| 107 | DMARDCEN4NOEXPSTD_SABR | DATA | 320KB | 10/10/2018 14:58:10 |
| 108 | DMARDCEN4NOEXPUCBSTD | DATA | 384KB | 10/10/2018 14:56:07 |
| 109 | DMARDCEN4NSAIDMPSTD | DATA | 128KB | 10/10/2018 14:56:41 |
| 110 | DMARDCEN4NSAIDSTD_SABR | DATA | 256KB | 10/10/2018 14:58:10 |
| 111 | DMARDCEN4NSAIDUCBSTD | DATA | 256KB | 10/10/2018 14:56:07 |
| 112 | DMARDEXC6MNOEXPMPSTD | DATA | 256KB | 10/10/2018 14:56:44 |
| 113 | DMARDEXC6MNOEXPSTD_SABR | DATA | 640KB | 10/10/2018 14:58:21 |
| 114 | DMARDEXC6MNOEXPUCBSTD | DATA | 832KB | 10/10/2018 14:56:13 |
| 115 | DMARDEXC6MNSAIDMPSTD | DATA | 192KB | 10/10/2018 14:56:43 |
| 116 | DMARDEXC6MNSAIDSTD_SABR | DATA | 512KB | 10/10/2018 14:58:18 |
| 117 | DMARDEXC6MNSAIDUCBSTD | DATA | 512KB | 10/10/2018 14:56:13 |
| 118 | DMARDMPSTD | DATA | 172MB | 10/10/2018 14:54:43 |
| 119 | DMARDSTD_SABR | DATA | 1GB | 10/10/2018 14:54:45 |
| 120 | DMARDUCBSTD | DATA | 251MB | 10/10/2018 14:54:43 |
| 121 | NSAID1STMPSTD | DATA | 60MB | 10/10/2018 14:56:25 |
| 122 | NSAID1STSTD_SABR | DATA | 204MB | 10/10/2018 14:57:26 |
| 123 | NSAID1STUCBSTD | DATA | 514MB | 10/10/2018 14:55:25 |
| 124 | NSAIDCEN4NOEXPMPSTD | DATA | 192KB | 10/10/2018 14:56:41 |
| 125 | NSAIDCEN4NOEXPSTD_SABR | DATA | 448KB | 10/10/2018 14:58:10 |
| 126 | NSAIDCEN4NOEXPUCBSTD | DATA | 512KB | 10/10/2018 14:56:07 |
| 127 | NSAIDEXC6MNOEXPMPSTD | DATA | 320KB | 10/10/2018 14:56:45 |
| 128 | NSAIDEXC6MNOEXPSTD_SABR | DATA | 704KB | 10/10/2018 14:58:25 |
| 129 | NSAIDEXC6MNOEXPUCBSTD | DATA | 1MB | 10/10/2018 14:56:17 |
| 130 | NSAIDMPSTD | DATA | 209MB | 10/10/2018 14:54:48 |
| 131 | NSAIDSTD_SABR | DATA | 1021MB | 10/10/2018 14:54:50 |
| 132 | NSAIDUCBSTD | DATA | 1GB | 10/10/2018 14:54:48 |
| 133 | SASMAC3 | CATALOG | 13KB | 10/10/2018 14:54:40 |
| 134 | SASMACR | CATALOG | 53KB | 10/10/2018 14:54:40 |
| 135 | TNF1STMPSTD | DATA | 4MB | 10/10/2018 14:56:20 |
| 136 | TNF1STSTD_SABR | DATA | 13MB | 10/10/2018 14:56:51 |
| 137 | TNF1STUCBSTD | DATA | 13MB | 10/10/2018 14:54:55 |
| 138 | TNFMPSTD | DATA | 52MB | 10/10/2018 14:54:51 |
| 139 | TNFSTD_SABR | DATA | 234MB | 10/10/2018 14:54:52 |
| 140 | TNFUCBSTD | DATA | 150MB | 10/10/2018 14:54:51 |
| buildExposureCohorts.sas |


OrderManualEvent
class OrderManualEvent extends OrderEvent
Methods
__construct(
Order $order,
Currency $currency,
Lang $lang,
Cart $cart,
Customer $customer)
No description
getCurrency()
No description
$this
getCustomer()
No description
$this
boolean
getUseOrderDefinedAddresses()
No description
$this
setUseOrderDefinedAddresses(
boolean $useOrderDefinedAddresses)
If true, the order will be created using the delivery and invoice addresses defined in $this->order instead of creating new OrderAdresses using the Order::getChoosenXXXAddress().
Details
in
ActionEvent at line 30
__set($name, $value)
in
ActionEvent at line 35
__get($name)
in
ActionEvent at line 44
bindForm(
Form $form)
at line 36
__construct(
Order $order,
Currency $currency,
Lang $lang,
Cart $cart,
Customer $customer)
in
OrderEvent at line 81
$this
setOrder(
Order $order)
in
OrderEvent at line 92
$this
setCartItemId(
int $cartItemId)
in
OrderEvent at line 102
int
getCartItemId()
in
OrderEvent at line 111
$this
setPlacedOrder(
Order $order)
in
OrderEvent at line 122
$this
setInvoiceAddress(
int $address)
in
OrderEvent at line 133
$this
setDeliveryAddress(
int $address)
in
OrderEvent at line 144
$this
setDeliveryModule(
int $module)
in
OrderEvent at line 155
$this
setPaymentModule(
int $module)
in
OrderEvent at line 166
$this
setPostage(
double $postage)
in
OrderEvent at line 177
$this
setRef(
string $ref)
in
OrderEvent at line 188
$this
setStatus(
int $status)
in
OrderEvent at line 199
$this
setDeliveryRef(
string $deliveryRef)
in
OrderEvent at line 209
Order
getOrder()
in
OrderEvent at line 218
Order
getPlacedOrder()
in
OrderEvent at line 230
null|int
getInvoiceAddress()
in
OrderEvent at line 238
null|int
getDeliveryAddress()
in
OrderEvent at line 246
null|int
getDeliveryModule()
in
OrderEvent at line 254
null|int
getPaymentModule()
in
OrderEvent at line 262
null|double
getPostage()
in
OrderEvent at line 270
null|string
getRef()
in
OrderEvent at line 278
null|int
getStatus()
in
OrderEvent at line 286
null|string
getDeliveryRef()
in
OrderEvent at line 295
$this
setResponse(
Response $response)
in
OrderEvent at line 305
Response
getResponse()
in
OrderEvent at line 313
bool
hasResponse()
in
OrderEvent at line 321
null
getPostageTax()
in
OrderEvent at line 330
$this
setPostageTax(
null $postageTax)
in
OrderEvent at line 340
null
getPostageTaxRuleTitle()
in
OrderEvent at line 349
$this
setPostageTaxRuleTitle(
null $postageTaxRuleTitle)
at line 48
getCurrency()
at line 57
$this
setCurrency(
Currency $currency)
at line 67
Lang
getLang()
at line 76
$this
setLang(
Lang $lang)
at line 86
Cart
getCart()
at line 95
$this
setCart(
Cart $cart)
at line 105
Customer
getCustomer()
at line 114
$this
setCustomer(
Customer $customer)
at line 124
boolean
getUseOrderDefinedAddresses()
at line 136
$this
setUseOrderDefinedAddresses(
boolean $useOrderDefinedAddresses)
If true, the order will be created using the delivery and invoice addresses defined in $this->order instead of creating new OrderAdresses using the Order::getChoosenXXXAddress().


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Bhaskalc
Type of equation to be calculated:
First degree
Second degree
Work in Progress!
Fill in the coeficients from the quadratic equation:
Coeficient A
Coeficient B
Coeficient C
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