NIS To Begin E-registration Of Immigrants
by Donatus AnichukwuezeThe Comptroller, Imo Command of Nigeria Immigration Service (NIS), David Ringshum, has advised irregular migrants to register during the oncoming Migrant e-Registration scheduled to start in the state on Nov. 26.
Ringshum gave the advice when the staff of the News Agency of Nigeria (NAN) in Owerri Office paid him a courtesy visit in his office in Owerri.
He said that strict measures would be taken against defaulters after the deadline of the registration on Jan. 11, 2020.
Ringshum said that the equipment for the programme had just been installed in Imo, while the exercise had been ongoing in other parts of the country.
The comptroller said that all avenues had been explored to ensure that those who were affected were duly informed of the exercise to ensure its success.
He said that leaders of ECOWAS communities like Togo, Niger, Chad and Benin Republic had been properly educated on the need to encourage their citizens in Nigeria to comply with the exercise.
Mrs Uju Ezenyi, the Deputy Comptroller of NIS in charge of Passport Office, in her contribution, said the e-registration for migrants was to promote togetherness and business establishments in Nigeria.
She said the move would make it easier to track activities of foreigners in the country for the duration of their stay.
Ezenyi said hat the exercise was also in line with the service compliance in the promotion of businesses in the country.
Earlier, the NAN State Correspondent, Chidi Opara, who led the staff to NIS, described NAN as “mother of all media houses” in the country providing them with objective and informed news.
She said they were in NIS to reinforce partnership with the service and asked for their cooperation.


Library: Net
Package: HTTPServer
Header: Poco/Net/HTTPRequestHandlerFactory.h
Description
A factory for HTTPRequestHandler objects. Subclasses must override the createRequestHandler() method.
Member Summary
Member Functions: createRequestHandler
Types
Ptr
typedef Poco::SharedPtr < HTTPRequestHandlerFactory > Ptr;
Constructors
HTTPRequestHandlerFactory
Creates the HTTPRequestHandlerFactory.
Destructor
~HTTPRequestHandlerFactory
virtual ~HTTPRequestHandlerFactory();
Destroys the HTTPRequestHandlerFactory.
Member Functions
createRequestHandler
virtual HTTPRequestHandler * createRequestHandler(
const HTTPServerRequest & request
) = 0;
Must be overridden by subclasses.
Creates a new request handler for the given HTTP request.
The method should inspect the given HTTPServerRequest object (e.g., method and URI) and create an appropriate HTTPRequestHandler object to handle the request.
Variables
serverStopped
Poco::BasicEvent < const bool > serverStopped;


Toggle navigation
Menu
ENTENDU!
Home
Lesson One
Lesson Two
QUIZ ONE
The Sounds of French: the international phonetic alphabet
and French "phonemes" (sounds).
Indicate the correct choice for the following sets of sounds
Question 1
Back Vowels
Front Vowels
Question 2
Half-Closed Vowels
Half-Open Vowels
Question 3
Nasal Vowels
Non-nasal Vowels
Question 4
Rounded Vowels
Un-rounded vowels
Question 5
Front
Less Front
Question 6
Back
Less Back


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This one, too.


How Much Did Tourism Contribute to French Economy in 2016?
An example goes here.
Tourism always come witn economic contribution. As the most popular travel destination for years, France gained a substantial amount from tourism alone. About 10% of French GDP are tourism related either direct or indirect.
A pie chart gives a clear breakdown on how much tourism contributes to French GDP.
Source: www.wttc.org


{% extends "_includes/page.html" %} {% block body %}
Well Ganttable is simply a website where you can plan any project you like and have it displayed as a Gantt chart. Gantt charts are a key tool for project planning and our website makes sure you stay on track!
Choose how many hours you can work each day, so that you’re not overwhelmed with tasks. Even add some holidays!

Assign resources, members and dependencies. Even limit how many resources you can use at once!
Download your chart to put into your important reports.
Access your charts on the go, and make sure you get this project done!
We’re just two university students who didn’t like the options out there for making Gantt charts. We are using this website for our own projects very successfully and we’ll be continuing to maintain the website throughout the year — and for however long it is popular.
If you spot any bugs or problems, bear with us, we’ll aim to fix them within the week. Giving us an email at<EMAIL_ADDRESS>is a good way of asking us any questions. Don’t worry if we don’t reply immediately, we’ll aim to get back to you in a few days at most.
What are you waiting for?


Seriatim Privacy Policy
This Privacy Policy describes how your personal information is collected, used, and shared when you visit or make a purchase from seriatim.io (the “Site”).
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{% extends "_base.html" %} {% block meta_title %}PyCon CZ - Call for Papers{% endblock %} {% block content %}
PyCon CZ (Czech Republic) 2016 is pleased to announce that its Call for Proposals is now open! The conference this year will be held from Friday 28th October to Sunday 30th October. The main conference is held on Friday 28th and Saturday 29th. Sunday 30th are developer sprints and workshops. DjangoGirls workshop will take place again before the event on Thursday 27th October, along with an unofficial pre-conference local Python meet-up hosted by Pyvec!
PyCon CZ attracts professional developers, test engineers and localization and documentation specialists from all walks of life, including industry, government, and science, as well as enthusiast and students. We’re looking for proposals for presentations and workshops on any aspect of Python programming, at all skill levels from novice to advanced. For your inspiration, our local Python Community has suggested they’d love to see talks on the following topics, for example:
Testing, AsyncIO, REST, Interoperability, Python Vs JS, IoT, Android.PY, Community
Presentation subjects may range from reports on open source, academic or commercial projects; or even tutorials and case studies. If a presentation is interesting and useful to the Python community, it will be considered for inclusion in the program. We're especially interested in short presentations that will teach conference-goers something new and useful. Can you show attendees how to use a module? Explore a Python language feature? Package an application? Do you have inspiring lesson’s learned to share?
Two special events will be held on Thursday 27th of October, as a prelude to the main conference. These special events are:
If you’re a woman or know one that wants to learn what it's like to code Python, apply for Django Girls! (mentors too!).
If you’d like to meetup for an early preview of some talks and grab a beer, join us at the Pyvo meetup!
We welcome first-time speakers; we are a community conference and we are eager to hear about your experience. If you have friends or colleagues who have something valuable to contribute, twist their arms to tell us about it! Please also forward this Call for Proposals to anyone that you feel may be interested.
All speakers will receive ONE free conference ticket automatically. As a speaker, if you or your organisation can afford to register and purchase a ticket anyway at full price, please do! All such payments will go directly towards our financial assistance pool to help people who could not otherwise afford to attend the conference.
You will not be automatically registered as an attendee for the conference; you will need to register yourself and failure to do so may result in loss of your talk slot(s)!
PyCon CZ offers a generous financial assistance programme, so that some attendees and speakers have some -- or in rare cases, all -- of their expenses such as flight, hotel and admission provided to them from the conference budget.
PyCon CZ strongly encourages those people in need to apply for financial assistance -- even if we can’t cover all of your expenses, we will try our best to at least give you discounted admission! The application process is simple, and straightforward. It’s also very liberal.
Suitable topics for PyCon CZ presentations include, but are not limited to:
The preferred length for talks is 30 minutes. We also accept extended proposals for tutorials and workshops of 90 minutes or three hours. However, if you need something special, don’t hesitate to ask! We’ll do what we can to accommodate.
Session lengths include time for audience questions. You should budget at least five minutes for questions; for example, a 30-minute talk could comprise 25 minutes of presentation and 5 minutes of questions.
Every session at PyCon CZ will have professionally-produced video recordings for release to our media channels. If you do not wish to have your talk recorded then please note this in your submission. Doing so will not influence the selection process in any way. You have the option to change your mind at any time.
Use the Proposal Submission form to send us your proposals. You are welcome to submit multiple proposals.
Submit your talk Submit your workshop


Prolog Data Structures
Terms
In Prolog, all data are represented by Prolog terms.
| Video: |
|
Each term is either a variable, an atomic term or
a compound term:
- variables start with an uppercase letter or with an
underscore (_). A single underscore denotes
an anonymous variable and can be read as
"any term". For example, X, Y, _爱 and Prolog are variables.
- atomic terms are:
- atoms, such as x, test
and 'quotes and space'
- integers, such as 42
- floating point numbers
- depending on the Prolog system, there are also
other kinds of atomic terms, such as complex numbers
and rational numbers.
- compound terms are defined inductively as
follows: If T1, T2,
..., TN are
terms, then F(T1, T2,
..., TN) is also a term, where F is
called a functor name and adheres to the same
syntax rules as atoms. F/N is called
the principal functor of the compound term,
and N is called the arity.
Examples: f(a), g(f(X)) and +(a,
f(X)).
All data can be represented in this way. Terms come into existence
by simply writing them down.
A term is called ground if it contains no variables. A
compound term is called partially instantiated if
one of its subterms is a variable.
Prolog is dynamically typed and allows us great freedom for
representing data. For example, we could represent natural
numbers as follows:
- we could use the atom zero to represent 0
- we could use the compound term s(X) to
represent the successor of X.
In this representation, the term s(s(s(zero))) represents
the number 3. Such a representation is of course highly
impractical due to its enormous size for large numbers. For
this reason, Prolog provides integer
arithmetic with a much more efficient number representation.
Prolog terms naturally correspond
to trees. There is a
standard order on terms.
You can define custom prefix-, infix- and postfix operators
that let you write terms in different ways.
| Video: |
|
Several standard
operators are predefined. For example, you can write a+b
and X=Y instead of +(a,b) and =(X,Y),
respectively.
Lists
Prolog lists are a special case of terms.
| Video: |
|
Lists are defined inductively:
- the atom [] is a list, denoting the empty list
- if Ls is a list, then the term '.'(L,
Ls) is also a list.
There is a special syntax for denoting lists conveniently in Prolog:
- The list '.'(a, '.'(b, '.'(c, []))) can also be
written as [a,b,c].
- The term '.'(L, Ls) can also be written as [L|Ls].
These notations can be combined in any way. For example, the term
[a,b|Ls] is a list iff Ls is a list.
Lists naturally represent collections of elements and arise
in almost all Prolog programs. When choosing between lists
and any other way to represent collections of elements, ask
yourself the following questions:
- Can there be arbitrarily many elements?
- Can there be zero elements?
- Is the order of elements significant?
- Are the elements of the same kind?
If your answer is "yes" to most of these questions, lists are
often a good fit.
In contrast, consider for example the case where each collection
comprises exactly three elements. You can use lists of the
form [A,B,C] to represent such collections, and thus
benefit from meta-predicates that
reason over lists. On the other hand, using compound terms of
the form f(A,B,C) is more memory efficient: This is
because the list [A,B,C] is the compound
term '.'(A,'.'(B,'.'(C,[]))) and thus takes
roughly twice the space of the term f(A,B,C)
in memory.
DCG notation is frequently used for
describing lists.
Pairs
Pairs are terms with principal functor (-)/2. For
example, the term -(A, B) denotes the pair of
elements A and B. In Prolog, (-)/2 is
defined as an infix operator. Therefore, the term can be
written equivalently as A-B.
| Video: |
|
The standard
predicate keysort/2 is an
example of a predicate that reasons about pairs. They are
called Key-Value pairs because the first argument of each
pair is used as the key for sorting. Many Prolog systems
provide additional predicates for reasoning about pairs, and they
often adopt this terminology from keysort/2. Common
examples of such predicates are
pairs_keys_values/3 and pairs_keys/2 to relate a
list of pairs to their respective keys and values.
Association lists
In many Prolog systems, association lists are available to
allow faster than linear access to a collection of elements. These
association lists are typically based on balanced trees
like AVL trees.
There is a public domain library called library(assoc)
that ships with many Prolog systems and provides operations for
inserting, fetching and changing elements of a collection.
Here is one way to represent an AVL tree in Prolog:
- the empty tree is represented by the
atom t.
- an inner node is represented as
a compound term of the form t(Key, Value,
Balance, Left, Right) where:
- Key and Value represent
the association of Key
with Value.
- Balance is an atom that denotes a
balance criterion, such as a relation between the number of
children of the left and right subtrees. For example, we can
use the atoms <, = and > to
denote different states between the subtrees.
- Left and Right are AVL trees.
Every operation on AVL trees, such as adding or changing an
association, can be described as a relation between
different trees, one tree before the operation and
one after it. See Thinking
in States for more information.
AVL trees let us perform many important operations
in O(log(N)) time, where N is the number
of associations. This is acceptably efficient in many cases.
Strings?
Almost everything that needs to be said about strings has
been brilliantly said by Richard O'Keefe in
his Prolog
Library Proposal. In particular, strings
are wrong, and:
For almost any use case that involves some kind of processing,
the only sensible thing to do with string data is to turn it
into some kind of tree early. Prolog is brilliant with
trees.
In Prolog, a convenient and natural representation of strings is
to use lists of characters, which are
one-character atoms. If you set the Prolog
flag double_quotes to chars, then
double-quoted strings are automatically interpreted as lists of
characters. To enable this setting, add the following directive at
the start of your Prolog programs, or to your Prolog system's
configuration file:
:- set_prolog_flag(double_quotes, chars).
With this setting, we obtain for example:
?- "abc" = [a,b,c].
true.
Thus, working with strings is reduced to working
with lists, which can be easily handled
in Prolog and are amenable to built-in mechanisms such
as DCGs.
Treating strings as lists of characters has a long tradition in
Prolog systems, starting with the very first Prolog system,
Marseille Prolog. Some Prolog systems opted to represent
strings as lists of character codes, i.e., as lists
of integers instead of lists of atoms, and these
integers represent code points that depend on the used
encoding. This behaviour is available if you set the Prolog
flag double_quotes to codes. This is not
recommended though, because it renders answers much less readable
than using chars. Alternatively, you can also set the
flag to atom, which makes Prolog treat double-quoted
strings as
atoms. The main drawback of using the value atom
is that atoms preclude the use of DCGs and other predicates that
reason about lists and partial lists. In addition, since
atoms cannot be partially instantiated, your code will
become moded: This means that it can only be used in a
proper subset of all conceivable directions.
For these reasons, chars is the recommended setting, and
newer Prolog systems such as Tau Prolog and Scryer Prolog
already use this value by default.
With a suitable implementation technique, Prolog systems can
represent lists of characters very efficiently, making them both
convenient and fast to work with.
| Video: |
|
There is thus hope that more Prolog systems will
use chars as the default value in the future.
Arrays?
What about arrays? Is there any data structure in Prolog to
represent a collection of terms, allowing
for O(1) access to individual elements?
First of all, the concept of destructive modifications is
alien to logic programming. In Prolog, we
describe relations between entities, not destructive
effects. To express a change in a Prolog data structure, we define
a predicate that relates the state of the structure before
the change to a different structure after the change. For
this reason, pure modifications
often entail some copying of data and typically lead to at
least logarithmic overhead (for example, to copy a subtree of a
balanced tree).
That being said, we can access the arguments of a term
in O(1) with the built-in predicate arg/3. There
even is an impure predicate called setarg/3 which allows
destructive modifications to a term. Use it at your own peril: If
you use setarg/3, you can no longer reason about your
code in the way you expect from pure relations. If you really need
efficient destructive modifications of terms, it is better to
use attributed variables.
Type tests
There are predicates to test for specific types
of terms.
Unfortunately, the standard predicates for type testing
(atom/1, integer/1, compound/1 etc.)
are logically flawed because they are not monotonic: If you
use these predicates, then generalizing a query may lead
to fewer solutions,
preventing declarative debugging
based on logical properties. For example, atom/1 fails
for the most general query, even though it succeeds for
more specific queries:
?- atom(X).
false.
?- X = a, atom(X).
X = a.
Therefore, new families of predicates for type testing are now
becoming available. They implement type tests with desirable
logical properties:
- the ..._si/1 family of predicates which are like
the standard predicates if the arguments
are sufficiently instantiated
- must_be/2, used for input arguments
- can_be/2, used for output arguments and general arguments.
| Video: |
|
The predicates must_be/2 and can_be/2 are
especially useful for Prolog library
authors. The ..._si/1 family of type tests are useful in
normal Prolog programs, if you want to explicitly test for
specific types without raising type errors.
Importantly, all these predicates
raise instantiation errors
if the terms that are tested are not sufficiently instantianted to
allow a sound decision.
Clean vs. defaulty representations
When representing data with Prolog terms, ask yourself the following question:
Can I distinguish the kind of each component from
its principal functor?
If this holds, your representation is called clean. If
you cannot distinguish the elements by their principal
functor, your representation is called defaulty, a
wordplay combining "default" and "faulty". This is
because reasoning about your data will need a
"default case", which is applied if everything else
fails. In addition, such a representation prevents argument
indexing, and is considered
faulty due to this shortcoming. Always aim to avoid
defaulty representations! Aim for cleaner representations instead.
| Video: |
|
For example, suppose you represent a full binary tree in
Prolog. There are two kinds of elements in a full binary
tree:
- leaves, which are concrete elements of the tree
- nodes, which have two children that are again full
binary trees.
We can represent such trees with Prolog terms as follows:
- leaf(L) represents the leaf L
- node(Left, Right) represents a node and its
two children.
The above is a clean representation: It lets us distinguish
the kinds of elements by their principal functor. We can reason
about such trees in a way that keeps the code very general and
usable in all directions. Such a representation is also amenable
to argument indexing.
You can also recognize a clean representation if it lets you
describe the general structure of your data while retaining
enough flexibility to keep the concrete elements unspecified. For
example, in the case of full binary trees, we can represent the
general outline of all trees with exactly two leaves
as node(leaf(_),leaf(_)). If you had chosen
to omit the leaf/1 wrapper for representing
leaves, this would become node(_,_): a defaulty
representation that no longer represents precisely such
manifestations.
Sometimes, it will appear to you that there is no clean way out.
For instance, you may be faced with a representation
that requires you to distinguish different cases in an
impure and non-monotonic way, such as by testing
the instantiation of certain arguments
via var/1. In such cases, it is good practice to
restrict the impure parts of your program to small fragments, and
to convert any defaulty representation to a clean one
by introducing suitable wrappers that let you distinguish the
cases by pattern matching. This helps to ensure that you can
use your core predicates in multiple directions.
Term inspection
In Prolog, the most natural way to reason about terms is to
rely on unification. In addition, there are several
predicates that let you decompose and analyze terms. The most
important of these term inspection predicates
are functor/3, arg/3 and (=..)/2.
Here are examples that illustrate their usage:
?- functor(f(a,g(X)), Functor, Arity).
Functor = f, Arity = 2.
?- functor(Term, f, 2).
Term = f(_A,_B).
?- arg(2, f(a,g(X)), Arg).
Arg = g(X).
?- f(a,g(X)) =.. [Functor|Args].
Functor = f, Args = [a,g(X)].
?- Term =.. [f,a,g(X)].
Term = f(a,g(X)).
These predicates cannot be defined by a finite set of clauses, and
can therefore be considered higher-order
predicates. Do not get carried away with these predicates!
Everything that can be expressed by
pattern matching should be expressed by
pattern matching. For example, instead of functor(Term,
f, 2), you can simply write Term = f(_,_).
Every Prolog term has a canonical representation. If you
are ever unsure about the structure of a term,
use write_canonical/1 to obtain the canonical
representation. For example:
?- write_canonical(a+b=[x,y,z]).
=(+(a,b),'.'(x,'.'(y,'.'(z,[]))))
The canonical representation shows the structure of the term
in such a way that it can also be easily parsed by
external programs.
More about Prolog
Main page


This is the first webpage I've ever designed within terminal and gosh am I proud.
Here is a random list of my top 5 favorite movies to celebrate this achievement!
Also, if you're a fan of terrible B-movies like I am, then I highly recommend 'Hell Comes to Frogtown'. Its an obscure Roddy Piper gem that literally has mutant frog people in a dystopian wasteland.


Lopez has 4 TD passes, N Alabama rallies from 20-point hole
BOILING SPRINGS, N.C. (AP) - Christian Lopez threw for 446 yards and four touchdowns and North Alabama rallied from a 20-point deficit to defeat Gardner-Webb 34-30 on Saturday.
Lopez’s 16-yard touchdown pass to Dexter Boykin with 1:57 remaining gave the Lions (4-7) just their second lead of the game.
Gardner-Webb (3-8, 1-4 Big South) took a 20-0 lead on three first-half touchdown passes by Kalen Whitlow. Two of the throws went to Izaiah Gathings who had 12 receptions for 143 yards.
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Franklin Graham calls on nation to pray for Trump as impeachment effort gains speed
Lopez quickly got the Lions back in the game, throwing a 13-yard touchdown pass to Boykin. The two connected three times for 42 yards on the 8-play, 59-yard drive. North Alabama forced a three-and-out and the Lions quickly scored, Jaxton Carson finishing a 38-yard drive with an 8-yard touchdown run. Gardner-Webb led 20-14 at halftime.
Lopez threw second-half touchdown passes of 61 yards to Andre Little, 14 yards to Jakobi Byrd and the game-winning 16-yarder to Boykin. Little had nine receptions for 183 yards and Boykin caught nine for 125 yards.
Whitlow completed 29 of 45 passes for 288 yards and four touchdowns with one pick.
North Alabama is transitioning to Division I and although the Lions play a full Big South schedule, their games do not count in the league standings.
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Released on Tuesday, Oct 4, 2016
The existing Active Directory and custom HL7 authentication has been replaced with a generic authentication method (OAuth 2.0). OAuth is an open standard for authorization, commonly used as a way for Internet users to log in to third party websites using their Google, Facebook, Microsoft, GitHub, etc. accounts without exposing their password.
As part of this migration, all existing Trifolia user accounts have been added/imported to the new OAuth Trifolia directory. Existing Trifolia users will receive a Password Reset link via e-mail that will allow them to use their Trifolia credentials when logging in using the Auth0. Clicking on the link in the email will direct the user to auth0.com and prompt the user to create a new password for your Trifolia account.
When users click the "Login" option in Trifolia version 4.2.0, they will now see the Auth0 login form. Existing users can use their Trifolia credentials to login, and pre-existing permissions to implementation guides, templates, value sets, code systems, etc., will persist. Each authentication method (Facebook, Microsoft Account, Username/Password, etc.) represents a separate user in Trifolia, with separate permissions. In the future, Trifolia may support linking these multiple authentication methods to a single account.
Three options are now available for support requests, that are configurable
be opened in a separate window to allow users to log a ticket in GitHub.
Lantana's installation of Trifolia (https://trifolia.lantanagroup.com) re-directs all users to the Github Trifolia repository's issues page.
Users and groups are not organization-specific; they are now managed individually. Each user can have their own groups that they can assign members to. Any user can request to join any group.
When editing an implementation guide, the user no longer needs to select an organization prior to selecting a user/group; searching is performed globally and returns any matching user or group.
Group Disclaimers can now be added in a new option called My Groups when the logged-in user clicks on their name. Users can see disclaimers for groups they are associated with by clicking "Disclaimers" from the Help menu.
In FHIR implementation guides, it is important to have a base identifier for the implementation guide that each profile follows. A new field has been added to the implementation guide editor which will require identifying a base identifier for all existing implementation guides.
The MS Word and web-based Implementation Guide exports will now include a column for the "Data Type" of the constraint in the "Constraints Overview" tables.
Trifolia has a new option under Export XML/JSON to export a "FHIR Build" package. The "FHIR Build" package option is only available for FHIR-based implementation guides. The export produces a ZIP file that includes all files pre-configured to run the FHIR IG Publisher.
Type |
Description |
New Feature |
Add field to IG for "Identifier / Base URL" |
New Feature |
Use OAuth2 to authenticate users |
New Feature |
Show Group Disclaimers |
New Feature |
Create a migration path for user account |
New Feature |
Removed association between User and Organization |
New Feature |
Generate FHIR build/export |
Improvement |
Include the FHIR data-type in IG exports |
Improvement |
Default new FHIR profiles to use IG's base URL |
Task |
Redirect Trifolia Support Requests to Trifolia Open Source GitHub |
Defect |
Cannot download Web-IG for C-CDA on FHIR |


Questly |
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A
acceptFriend(int, int)
- Method in class applicationCache.
ApplicationCacheClient
acceptFriend(int, int)
- Method in class basicDB.
TestClient
AcceptFriend
- Class in
client1_Partition
AcceptFriend()
- Constructor for class client1_Partition.
AcceptFriend
acceptFriend(int, int)
- Method in class client1_Partition.
VoltdbClient
AcceptFriend
- Class in
client1_Replication
AcceptFriend()
- Constructor for class client1_Replication.
AcceptFriend
acceptFriend(int, int)
- Method in class client1_Replication.
VoltdbClient
acceptFriend(int, int)
- Method in class client2_Partition.
voltdbclient
acceptFriend(int, int)
- Method in class client2_Replication.
voltdbclient
acceptFriend(int, int)
- Method in class edu.usc.bg.base.
DB
This function is called in the benchmarking phase which is executed with the -t argument.
acceptFriend(int, int)
- Method in class edu.usc.bg.base.
DBWrapper
acceptFriend(int, int)
- Method in class Ehcache.
EhcacheBoostedClient
acceptFriend(int, int)
- Method in class Ehcache.
EhcacheClient
acceptFriend(int, int)
- Method in class Ehcache.
EhcacheFinalOracleClient
acceptFriend(int, int)
- Method in class finalRelational.
Boosted2R1TClient
acceptFriend(int, int)
- Method in class finalRelational.
Boosted2Table1RecordOracleClient
acceptFriend(int, int)
- Method in class finalRelational.
EnhancedFinalOracleClient
acceptFriend(int, int)
- Method in class finalRelational.
FinalOracleClient
acceptFriend(int, int)
- Method in class KosarSolo.
KosarSoloClient
acceptFriend(int, int)
- Method in class memcached.
ClientForLoadDataOnly
acceptFriend(int, int)
- Method in class memcached.
ClientForRefreshTechnique
acceptFriend(int, int)
- Method in class memcached.
ClientForRefreshTechniqueOptimization
acceptFriend(int, int)
- Method in class memcached.
ClientForRefreshTechniqueWriteLeaseNoBackoff
acceptFriend(int, int)
- Method in class memcached.
ClientForSXQLeases
acceptFriend(int, int)
- Method in class memcached.
ClientForSXQLeasesTrigger
acceptFriend(int, int)
- Method in class memcached.
RefreshTechniqueClientWithLeaseBeforeTransaction
acceptFriend(int, int)
- Method in class memcached.
SpyMemcachedBGClient
acceptFriend(int, int)
- Method in class memcached.
TCNWhalinMemcached2R1TBoostedClientQR
acceptFriend(int, int)
- Method in class memcached.
WhalinMemcached1R1TBGClient
acceptFriend(int, int)
- Method in class memcached.
WhalinMemcached2R1TBoostedClient
acceptFriend(int, int)
- Method in class memcached.
WhalinMemcached2R1TBoostedClientQR
acceptFriend(int, int)
- Method in class memcached.
WhalinMemcachedBGClient
acceptFriend(int, int)
- Method in class memcached.
WhalinMemcachedBoostedClient
acceptFriend(int, int)
- Method in class memcached.
WhalinMemcachedFinalOracleBGClient
acceptFriend(int, int)
- Method in class memcached.
XMemcachedBGClient
acceptFriend(int, int)
- Method in class mongoDB.
MongoDbClient
acceptFriend(int, int)
- Method in class mongoDB.
MongoDbClientAggregatesAsAttrs
acceptFriend(int, int)
- Method in class mongoDB.
MongoDbClientAllImagesAsByteArrays
acceptFriend(int, int)
- Method in class mongoDB.
MongoDbClientEnhanced
acceptFriend(int, int)
- Method in class mongoDB.
MongoDbClientFS
acceptFriend(int, int)
- Method in class mongoDB.
MongoDbClientResArray
acceptFriend(int, int)
- Method in class mongoDB.
MongoDbClientThumbsWMembers
acceptFriend(int, int)
- Method in class mongoDB.
MongoDbWhalinMemcachedClientThumbsWMembers
acceptFriend(int, int)
- Method in class MySQL.
AggrAsAttr1R1TMySQLClient
acceptFriend(int, int)
- Method in class MySQL.
AggrAsAttr1R2TMySQLClient
acceptFriend(int, int)
- Method in class MySQL.
AggrAsAttr2R1TMySQLClient
acceptFriend(int, int)
- Method in class MySQL.
AggrAsAttr2R2TMySQLClient
acceptFriend(int, int)
- Method in class MySQL.
Basic1R1TMySQLClient
acceptFriend(int, int)
- Method in class MySQL.
Basic1R2TMySQLClient
acceptFriend(int, int)
- Method in class MySQL.
Basic2R1TMySQLClient
acceptFriend(int, int)
- Method in class MySQL.
Basic2R2TMySQLClient
acceptFriend(int, int)
- Method in class MySQL.
Ehcache1T1RMySQLClient
acceptFriend(int, int)
- Method in class MySQL.
Memcached1T1RMySQLClient
acceptFriend(int, int)
- Method in class MySQL.
MVMySQLClient
acceptFriend(int, int)
- Method in class oracleTriggerCOSAR.
TriggerCacheClient
acceptFriend(int, int)
- Method in class oracleTriggerCOSAR.
WhalinTriggerMemcached2R1TBoostedClient
acceptFriend(int, int)
- Method in class oracleTriggerCOSAR.
WhalinTriggerMemcached2R1TBoostedClientQR
acceptFriend(int, int)
- Method in class oracleTriggerCOSAR.
WhalinTriggerMemcachedBGClient
acceptFriend(int, int)
- Method in class oracleTriggerCOSAR.
WhalinTriggerMemcachedBoostedClient
acceptFriend(int, int)
- Method in class postgredb.
PostgreDBClient
acceptFriend(int, int)
- Method in class postgredb.
PostgreDBClient_Count
acceptFriend(int, int)
- Method in class relational.
AggrAsAttr1Rec2TableJdbcDBClient
acceptFriend(int, int)
- Method in class relational.
AggregateJdbcDBClient
acceptFriend(int, int)
- Method in class relational.
JdbcDBClient
acceptFriend(int, int)
- Method in class relational.
JdbcProfileFSThumbBlobDBClient
acceptFriend(int, int)
- Method in class relational.
MaterializedViewJdbcDBClient
acceptFriend(int, int)
- Method in class relational.
MultiFriendshipTableJdbcDBClient
acceptFriend(int, int)
- Method in class relational.
NotInlineJdbcDBClient
acceptFriend(int, int)
- Method in class relational.
SqlServerDBClient
acceptFriend(int, int)
- Method in class relational.
TestDSClient
acceptFriend(int, int)
- Method in class simpleRelational.
SimpleJdbcDBClient
acceptFriend(int, int)
- Method in class simpleRelationalStoredProc.
AggrAsAttr2Rec1TableJdbcDBClient
acceptFriend(int, int)
- Method in class simpleRelationalStoredProc.
AggrAsAttr2Rec2TableJdbcDBClient
acceptFriend(int, int)
- Method in class simpleRelationalStoredProc.
SimpleMultiTableStoredProcJdbcDBClient
acceptFriend(int, int)
- Method in class simpleRelationalStoredProc.
SimpleStoredProcJdbcDBClient
acceptFriend(int, int)
- Method in class sqlServerTrig.
SqlServerTriggerCacheClient
acceptFriend(int, int)
- Method in class SQLTrigQR.
SQLTrigQRBoostedClient
acceptFriend(int, int)
- Method in class SQLTrigQR.
SQLTrigQRClient
acceptFriend(int, int)
- Method in class SQLTrigQR.
WhalinTriggerMemcached2R1TBoostedClientQR
acceptFriend(int, int)
- Method in class sqlTrigSemiData.
SqlTrigSemiDataBoostedClient
acceptFriend(int, int)
- Method in class sqlTrigSemiData.
SqlTrigSemiDataClient
AcceptFriend
- Class in
voltdb
AcceptFriend()
- Constructor for class voltdb.
AcceptFriend
acceptFriend(int, int)
- Method in class voltdb.
VoltdbClient
acceptFriend(int, int)
- Method in class voltdb.
VoltdbClientV2
acceptFriend(int, int)
- Method in class voltdb.
VoltdbClientV3
acceptFriend(int, int)
- Method in class voltdb.
VoltdbClientV4
acceptFriend(int, int)
- Method in class voltdbbg.
CopyOfvoltdbclient
acceptFriend(int, int)
- Method in class voltdbbg.
voltdbclient
AcceptFriend2
- Class in
voltdb
AcceptFriend2()
- Constructor for class voltdb.
AcceptFriend2
ACCEPTFRIENDSACTION_PROPORTION_PROPERTY
- Static variable in class edu.usc.bg.workloads.
CoreWorkload
Percentage users that do the acceptfriends action
ACCEPTFRIENDSACTION_PROPORTION_PROPERTY_DEFAULT
- Static variable in class edu.usc.bg.workloads.
CoreWorkload
The default proportion of acceptfriends action
AcceptFriendship
- Class in
client2_Partition
AcceptFriendship()
- Constructor for class client2_Partition.
AcceptFriendship
AcceptFriendship
- Class in
client2_Replication
AcceptFriendship()
- Constructor for class client2_Replication.
AcceptFriendship
AcceptFriendship
- Class in
voltdbbg
AcceptFriendship()
- Constructor for class voltdbbg.
AcceptFriendship
ACCEPTFRIENDSHIP_PROPORTION_PROPERTY
- Static variable in class edu.usc.bg.workloads.
CoreWorkload
Percentage users that do the accept friendship sequence
ACCEPTFRIENDSHIP_PROPORTION_PROPERTY_DEFAULT
- Static variable in class edu.usc.bg.workloads.
CoreWorkload
The default proportion of functionalities that are acceptFriendship
AcceptFriendV2
- Class in
voltdb
AcceptFriendV2()
- Constructor for class voltdb.
AcceptFriendV2
AcceptFriendV3
- Class in
voltdb
AcceptFriendV3()
- Constructor for class voltdb.
AcceptFriendV3
AcceptFriendV4
- Class in
voltdb
AcceptFriendV4()
- Constructor for class voltdb.
AcceptFriendV4
activateUser(int)
- Method in class edu.usc.bg.workloads.
CoreWorkload
ActsTillFirstDeath
- Static variable in class edu.usc.bg.base.
Client
addInterval(Interval<Type>)
- Method in class edu.usc.bg.validator.
IntervalTree
Add an interval object to the interval tree's list.
addInterval(long, long, Type)
- Method in class edu.usc.bg.validator.
IntervalTree
Add an interval object to the interval tree's list.
addInterval(long, long, long)
- Method in class edu.usc.bg.validator.
resourceUpdateStat
addValue(double, String)
- Method in class edu.usc.bg.base.generator.
DiscreteGenerator
AggrAsAttr1R1TMySQLClient
- Class in
MySQL
AggrAsAttr1R1TMySQLClient()
- Constructor for class MySQL.
AggrAsAttr1R1TMySQLClient
AggrAsAttr1R2TMySQLClient
- Class in
MySQL
AggrAsAttr1R2TMySQLClient()
- Constructor for class MySQL.
AggrAsAttr1R2TMySQLClient
AggrAsAttr1Rec2TableJdbcDBClient
- Class in
relational
A class that wraps a JDBC compliant database to allow it to be interfaced with BG.
AggrAsAttr1Rec2TableJdbcDBClient()
- Constructor for class relational.
AggrAsAttr1Rec2TableJdbcDBClient
AggrAsAttr2R1TMySQLClient
- Class in
MySQL
AggrAsAttr2R1TMySQLClient()
- Constructor for class MySQL.
AggrAsAttr2R1TMySQLClient
AggrAsAttr2R2TMySQLClient
- Class in
MySQL
AggrAsAttr2R2TMySQLClient()
- Constructor for class MySQL.
AggrAsAttr2R2TMySQLClient
AggrAsAttr2Rec1TableJdbcDBClient
- Class in
simpleRelationalStoredProc
A class that wraps a JDBC compliant database to allow it to be interfaced with BG.
AggrAsAttr2Rec1TableJdbcDBClient()
- Constructor for class simpleRelationalStoredProc.
AggrAsAttr2Rec1TableJdbcDBClient
AggrAsAttr2Rec2TableJdbcDBClient
- Class in
simpleRelationalStoredProc
A class that wraps a JDBC compliant database to allow it to be interfaced with BG.
AggrAsAttr2Rec2TableJdbcDBClient()
- Constructor for class simpleRelationalStoredProc.
AggrAsAttr2Rec2TableJdbcDBClient
AggregateJdbcDBClient
- Class in
relational
A class that wraps a JDBC compliant database to allow it to be interfaced with BG.
AggregateJdbcDBClient()
- Constructor for class relational.
AggregateJdbcDBClient
applicationCache
- package applicationCache
ApplicationCacheClient
- Class in
applicationCache
ApplicationCacheClient()
- Constructor for class applicationCache.
ApplicationCacheClient
ASCIIString(int)
- Static method in class edu.usc.bg.base.
Utils
Generate a random ASCII string of a given length.
avgFriendCount
- Static variable in class edu.usc.bg.workloads.
FriendshipWorkload
avgResourceCount
- Static variable in class edu.usc.bg.workloads.
ResourceWorkload
Overview
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--- layout: post title: Soulflower Mud A lot Soap tags: - Soulflower status: publish type: post published: true meta: _edit_last: '2' wp_plus_one_redirect: '' wp_plus_one_disabled: '1' _aioseop_title: Soulflower Mud A lot Soap _aioseop_keywords: Soulflower Mud A lot Soap, Soulflower Mud A lot Soap review _yoast_wpseo_linkdex: '0' _thumbnail_id: '3564' _yoast_wpseo_focuskw: '' _yoast_wpseo_title: '' _yoast_wpseo_metadesc: '' _yoast_wpseo_metakeywords: '' _yoast_wpseo_meta-robots-noindex: '0' _yoast_wpseo_meta-robots-nofollow: '0' _yoast_wpseo_meta-robots-adv: none _yoast_wpseo_sitemap-include: ! '-' _yoast_wpseo_sitemap-prio: ! '-' _yoast_wpseo_canonical: '' _yoast_wpseo_redirect: '' --- I have been using soulflower soaps, for quite sometime now and have discovered some really good pieces and some ok ones. Mud A Lot named sound quite interesting and different and I had to pick this one up, Price: Rs.250 for 150gm
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Mud Soap is suitable for all skin types, specially for use in hot summer. Ingredients: Vanilla absolute, crushed vanilla pods, white thai mud, Vitamin E, glycerin, aqua, saponified oils of olive, coconut, castor, palm & palm kernel.


Though we are a Startup , we have strong foundation of vast and enriched experience in this field.
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“STELLA” Flat No. C2 305 Gat No. 216/2
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Mon-Sat (8.30-18.30)
Sun (Closed)


{: id=“4.17.1”}
The 4.17.1 patch release includes one bug fix:
res.status()
API has been reverted due to
causing regressions in existing Express 4 applications.
For a complete list of changes in this release, see History.md.
{: id=“4.17.0”}
The 4.17.0 minor release includes bug fixes and some new features, including:
express.raw()
and
express.text()
middleware have been added to provide
request body parsing for more raw request payloads. This uses the
expressjs/body-parser module
module underneath, so apps that are currently requiring the module
separately can switch to the built-in parsers.
res.cookie()
API now supports the
"none"
value for the sameSite
option.
"trust proxy"
setting is enabled, the
req.hostname
now supports multiple
X-Forwarded-For
headers in a request.
res.sendFile()
API now provides and more immediate and
easier to understand error when a non-string is passed as the
path
argument.
res.status()
API now provides and more immediate and
easier to understand error when null
or
undefined
is passed as the argument.
For a complete list of changes in this release, see History.md.
{: id=“4.16.4”}
The 4.16.4 patch release includes various bug fixes:
"Request aborted"
may be logged in
res.sendfile
.
For a complete list of changes in this release, see History.md.
{: id=“4.16.3”}
The 4.16.3 patch release includes various bug fixes:
%
at the end of the url in the
res.location
method or the res.redirect
method
would not get encoded as %25
.
req.url
value can result in a
thrown error within the default 404 handling.
express.static
redirect
responses to properly include </html>
.
For a complete list of changes in this release, see History.md.
{: id=“4.16.2”}
The 4.16.2 patch release includes a regression bug fix:
TypeError
that can occur in the
res.send
method when a Buffer
is passed to
res.send
and the ETag
header is already set on
the response.
For a complete list of changes in this release, see History.md.
{: id=“4.16.1”}
The 4.16.1 patch release includes a regression bug fix:
express.static
.
For a complete list of changes in this release, see History.md.
{: id=“4.16.0”}
The 4.16.0 minor release includes security updates, bug fixes, performance enhancements, and some new features, including:
req.host
, req.hostname
, req.ip
,
req.ips
, req.protocol
.
mime
dependency. This may affect your application if
untrusted string input is passed to the following APIs:
res.type()
.
express.static
, res.sendfile
, and
res.sendFile
.
"json escape"
can be enabled to escape
characters in res.json()
, res.jsonp()
and
res.send()
responses that can trigger clients to sniff the
response as HTML instead of honoring the Content-Type
. This
can help protect an Express app from a class of persistent XSS-based
attacks.
res.download()
method
now accepts an optional options
object.
express.json()
and
express.urlencoded()
middleware have been added to provide
request body parsing support out-of-the-box. This uses the
expressjs/body-parser module
module underneath, so apps that are currently requiring the module
separately can switch to the built-in parsers.
express.static()
middleware
and
res.sendFile()
method
now support setting the immutable
directive on the
Cache-Control
header. Setting this header with an
appropriate maxAge
will prevent supporting web browsers
from sending any request to the server when the file is still in their
cache.
Content-Type
of more files. There are 70 new types for file
extensions.
For a complete list of changes in this release, see History.md.
{: id=“4.15.5”}
The 4.15.5 patch release includes security updates, some minor performance enhancements, and a bug fix:
express.static
, req.fresh
,
res.json
, res.jsonp
, res.send
,
res.sendfile
res.sendFile
,
res.sendStatus
.
If-None-Match
) faster.
For a complete list of changes in this release, see History.md.
{: id=“4.15.4”}
The 4.15.4 patch release includes some minor bug fixes:
"trust proxy"
value being
manipulated in certain conditions.
For a complete list of changes in this release, see History.md.
{: id=“4.15.3”}
The 4.15.3 patch release includes a security update and some minor bug fixes:
maxAge
option in the following APIs:
express.static
, res.sendfile
, and
res.sendFile
.
res.set
cannot add charset to
Content-Type
.
</html>
in HTML document.
For a complete list of changes in this release, see History.md.
{: id=“4.15.2”}
The 4.15.2 patch release includes a minor bug fix:
[
in the extended
(default) query parser.
For a complete list of changes in this release, see History.md.
{: id=“4.15.1”}
The 4.15.1 patch release includes a minor bug fix:
express.static()
middleware
and
res.sendFile()
method
would incorrectly respond with 412 Precondition Failed.
For a complete list of changes in this release, see History.md.
{: id=“4.15.0”}
The 4.15.0 minor release includes bug fixes, performance improvements, and other minor feature additions, including:
express.static()
middleware
and
res.sendFile()
method
now support the If-Match
and
If-Unmodified-Since
request headers.
For a complete list of changes in this release, see History.md.
{: id=“4.14.1”}
The 4.14.1 patch release includes bug fixes and performance improvements, including:
Error
object
which has a headers
property that is not an object.
For a complete list of changes in this release, see History.md.
{: id=“4.14.0”}
The 4.14.0 minor release includes bug fixes, security update, performance improvements, and other minor feature additions, including:
The
res.sendFile()
method
now accepts two new options: acceptRanges
and
cacheControl
.
acceptRanges
(defaut is true
), enables
or disables accepting ranged requests. When disabled, the response
does not send the Accept-Ranges
header and ignores
the contents of the Range
request header.
cacheControl
, (default is true
),
enables or disables the Cache-Control
response
header. Disabling it will ignore the
maxAge
option.
res.sendFile
has also been updated to handle
Range
header and redirections better.
res.location()
method
and
res.redirect()
method
will now URL-encode the URL string, if it is not already encoded.
res.json()
method
and
res.jsonp()
method
have been improved in the common cases.
The
jshttp/cookie module
(in addition to a number of other improvements) has been updated
and now the
res.cookie()
method
supports the sameSite
option to let you specify the
SameSite cookie attribute. NOTE: This attribute has not yet been fully standardized, may
change in the future, and many clients may ignore it.
The possible value for the sameSite
option are:
true
, which sets the
SameSite
attribute to Strict
for
strict same site enforcement.
false
, which does not set the
SameSite
attribute.
'lax'
, which sets the
SameSite
attribute to Lax
for lax same
site enforcement.
'strict'
, which sets the
SameSite
attribute to Strict
for
strict same site enforcement.
req.range()
method
options object now supports a combine
option (false
by default), which when true
, combines overlapping and
adjacent ranges and returns them as if they were specified that way
in the header.
For a complete list of changes in this release, see History.md.


Name: Bill Gates
CEO: Microsoft
About Bill
Married to: Melinda French Gates Children: Three Career highlights: Microsoft, Bill & Melinda Gates Foundation 1955— Born Seattle, WA.1973 Entered Harvard University. 1967 Enrolled at Lakeside School. First used computer. 1968 Began programming with Paul Allen in the computer center. Bill Gates is a technologist, business leader, and philanthropist. He grew up in Seattle, Washington, with an amazing and supportive family who encouraged his interest in computers at an early age. He dropped out of college to start Microsoft with his childhood friend Paul Allen. He married Melinda French in 1994 and they have three children. Today, Bill and Melinda Gates co-chair the charitable foundation bearing their names and are working together to give their wealth back to society. Bill grew up in Seattle with his two sisters. His dad, William H. Gates II, is a Seattle attorney and one of the co-chairs of the Bill & Melinda Gates Foundation. His late mother, Mary Gates, was a schoolteacher, University of Washington regent, and chairwoman of United Way International.


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The general form of the connection URL is
jdbc:sqlserver://[serverName[\instanceName][:portNumber]][;property=value[;property=value]]
where:
-
jdbc:sqlserver:// (Required) is known as the sub-protocol and is constant.
-
serverName (Optional) is the address of the server to connect to. This could be a DNS or IP address, or it could be localhost or <IP_ADDRESS> for the local computer. If not specified in the connection URL, the server name must be specified in the properties collection.
-
instanceName (Optional) is the instance to connect to on serverName. If not specified, a connection to the default instance is made.
-
portNumber (Optional) is the port to connect to on serverName. The default is 1433. If you are using the default, you do not have to specify the port, nor its preceding ':', in the URL.
Note: For optimal connection performance, you should set the portNumber when you connect to a named instance. This will avoid a round trip to the server to determine the port number. If both a portNumber and instanceName are used, the portNumber will take precedence and the instanceName will be ignored. -
property (Optional) is one or more option connection properties. For more information, see Setting the Connection Properties. Any property from the list can be specified. Properties can only be delimited by using the semicolon (';'), and they cannot be duplicated.
Connection Examples
Connect to the default database on the local computer by using a user name and password:
jdbc:sqlserver://localhost;user=MyUserName;password=*****;
Connect to the default database on the local computer by using integrated authentication:
jdbc:sqlserver://localhost;integratedSecurity=true;
Connect to a named database on a remote server:
jdbc:sqlserver://localhost;databaseName=AdventureWorks;integratedSecurity=true;
Connect on the default port to the remote server:
jdbc:sqlserver://localhost:1433;databaseName=AdventureWorks;integratedSecurity=true;
Connect by specifying a customized application name:
jdbc:sqlserver://localhost;databaseName=AdventureWorks;integratedSecurity=true;applicationName=MyApp;
Named and Multiple SQL Server Instances
SQL Server allows for the installation of multiple database instances per server. Each instance is identified by a specific name. To connect to a named instance of SQL Server, you can either specify the port number of the named instance (preferred), or you can specify the instance name as a JDBC URL property or a datasource property. If no instance name or port number property is specified, a connection to the default instance is created. See the following examples:
To use a port number, do the following:
jdbc:sqlserver://localhost:1433;integratedSecurity=true;<more properties as required>;
To use a JDBC URL property, do the following:
jdbc:sqlserver://localhost;instanceName=instance1;integratedSecurity=true;<more properties as required>;
Escaping Values in the Connection URL
You might have to escape certain parts of the connection URL values because of the inclusion of special characters such as spaces, semicolons, and quotation marks. The JDBC driver supports escaping these characters if they are enclosed in braces. For example, {;} escapes a semicolon.
Escaped values can contain special characters (especially '=', ';', '[]', and space) but cannot contain braces. Values that must be escaped and contain braces should be added to a properties collection.
Connecting with Integrated Authentication
The JDBC driver supports the use of Type 2 integrated authentication on Windows operating systems through the integratedSecurity connection string property. To use integrated authentication, copy the sqljdbc_auth.dll file to a directory on the Windows system path on the computer where the JDBC driver is installed.
The sqljdbc_auth.dll files are installed in the following location:
<installation directory>\sqljdbc_<version>\<language>\auth\
Alternatively you can set the java.libary.path system property to specify the directory of the sqljdbc_auth.dll. For example, if the JDBC driver is installed in the default directory, you can specify the location of the DLL by using the following virtual machine (VM) argument when the Java application is started:
-Djava.library.path=C:\Microsoft SQL Server JDBC Driver\sqljdbc_<version>\enu\auth\x86
Connecting with IPv6 Addresses
The JDBC driver supports the use of IPv6 addresses with the connection properties collection, and with the serverName connection string property. The initial serverName value, such as jdbc:sqlserver://serverName, is not supported for IPv6 addresses in connection strings. Using a name for serverName instead of a raw IPv6 address will work in every case in the connection. The following examples provide more information.
To use the serverName property
jdbc:sqlserver://;serverName=3ffe:8311:eeee:f70f:0:5eae:<IP_ADDRESS>\\instance1;integratedSecurity=true;
To use the properties collection
Properties pro = new Properties();
pro.setProperty("serverName", "serverName=3ffe:8311:eeee:f70f:0:5eae:<IP_ADDRESS>\\instance1");
Connection con = DriverManager.getConnection("jdbc:sqlserver://;integratedSecurity=true;", pro);


Set up forms that will be used by your virtual receptionist to automatically collect customer information, surveys, billing information etc.


Hello, good monday, tried again to build and a new error shows:
``error[E0308]: mismatched types
--> mm2src/peers/peers.rs:1171:17
|
1171 | saltbuf.as_mut_ptr(), saltbuf.len() as i32,
| ^^^^^^^^^^^^^^^^^^^^ expected i8, found u8
|
= note: expected type mut i8 found type mut u8error: aborting due to previous error
For more information about this error, try rustc --explain E0308`.
error: Could n...


{%- autoescape false %} {#- i.e. don't turn < into < #} {#- See https://github.com/blueimp/jQuery-File-Upload/wiki/Basic-plugin #}


This section presents additional options regarding the loading of columns.
This feature allows particular columns of a table be loaded only
upon direct access, instead of when the entity is queried using
Query
. This feature is useful when one wants to avoid
loading a large text or binary field into memory when it’s not needed.
Individual columns can be lazy loaded by themselves or placed into groups that
lazy-load together, using the orm.deferred()
function to
mark them as “deferred”. In the example below, we define a mapping that will load each of
.excerpt
and .photo
in separate, individual-row SELECT statements when each
attribute is first referenced on the individual object instance:
from sqlalchemy.orm import deferred
from sqlalchemy import Integer, String, Text, Binary, Column
class Book(Base):
__tablename__ = 'book'
book_id = Column(Integer, primary_key=True)
title = Column(String(200), nullable=False)
summary = Column(String(2000))
excerpt = deferred(Column(Text))
photo = deferred(Column(Binary))
Classical mappings as always place the usage of orm.deferred()
in the
properties
dictionary against the table-bound Column
:
mapper(Book, book_table, properties={
'photo':deferred(book_table.c.photo)
})
Deferred columns can be associated with a “group” name, so that they load
together when any of them are first accessed. The example below defines a
mapping with a photos
deferred group. When one .photo
is accessed, all three
photos will be loaded in one SELECT statement. The .excerpt
will be loaded
separately when it is accessed:
class Book(Base):
__tablename__ = 'book'
book_id = Column(Integer, primary_key=True)
title = Column(String(200), nullable=False)
summary = Column(String(2000))
excerpt = deferred(Column(Text))
photo1 = deferred(Column(Binary), group='photos')
photo2 = deferred(Column(Binary), group='photos')
photo3 = deferred(Column(Binary), group='photos')
You can defer or undefer columns at the Query
level using options, including orm.defer()
and orm.undefer()
:
from sqlalchemy.orm import defer, undefer
query = session.query(Book)
query = query.options(defer('summary'))
query = query.options(undefer('excerpt'))
query.all()
orm.deferred()
attributes which are marked with a “group” can be undeferred
using orm.undefer_group()
, sending in the group name:
from sqlalchemy.orm import undefer_group
query = session.query(Book)
query.options(undefer_group('photos')).all()
An arbitrary set of columns can be selected as “load only” columns, which will
be loaded while deferring all other columns on a given entity, using orm.load_only()
:
from sqlalchemy.orm import load_only
session.query(Book).options(load_only("summary", "excerpt"))
New in version 0.9.0.
To specify column deferral options within a Query
that loads multiple types
of entity, the Load
object can specify which parent entity to start with:
from sqlalchemy.orm import Load
query = session.query(Book, Author).join(Book.author)
query = query.options(
Load(Book).load_only("summary", "excerpt"),
Load(Author).defer("bio")
)
To specify column deferral options along the path of various relationships,
the options support chaining, where the loading style of each relationship
is specified first, then is chained to the deferral options. Such as, to load
Book
instances, then joined-eager-load the Author
, then apply deferral
options to the Author
entity:
from sqlalchemy.orm import joinedload
query = session.query(Book)
query = query.options(
joinedload(Book.author).load_only("summary", "excerpt"),
)
In the case where the loading style of parent relationships should be left
unchanged, use orm.defaultload()
:
from sqlalchemy.orm import defaultload
query = session.query(Book)
query = query.options(
defaultload(Book.author).load_only("summary", "excerpt"),
)
New in version 0.9.0: support for Load
and other options which
allow for better targeting of deferral options.
sqlalchemy.orm.
deferred
(*columns, **kw)¶Indicate a column-based mapped attribute that by default will not load unless accessed.
| Parameters: |
|
|---|
See also
sqlalchemy.orm.
defer
(key, *addl_attrs)¶Indicate that the given column-oriented attribute should be deferred, e.g. not loaded until accessed.
This function is part of the Load
interface and supports
both method-chained and standalone operation.
e.g.:
from sqlalchemy.orm import defer
session.query(MyClass).options(
defer("attribute_one"),
defer("attribute_two"))
session.query(MyClass).options(
defer(MyClass.attribute_one),
defer(MyClass.attribute_two))
To specify a deferred load of an attribute on a related class,
the path can be specified one token at a time, specifying the loading
style for each link along the chain. To leave the loading style
for a link unchanged, use orm.defaultload()
:
session.query(MyClass).options(defaultload("someattr").defer("some_column"))
A Load
object that is present on a certain path can have
Load.defer()
called multiple times, each will operate on the same
parent entity:
session.query(MyClass).options(
defaultload("someattr").
defer("some_column").
defer("some_other_column").
defer("another_column")
)
| Parameters: |
|---|
sqlalchemy.orm.
load_only
(*attrs)¶Indicate that for a particular entity, only the given list of column-based attribute names should be loaded; all others will be deferred.
This function is part of the Load
interface and supports
both method-chained and standalone operation.
Example - given a class User
, load only the name
and fullname
attributes:
session.query(User).options(load_only("name", "fullname"))
Example - given a relationship User.addresses -> Address
, specify
subquery loading for the User.addresses
collection, but on each
Address
object load only the email_address
attribute:
session.query(User).options(
subqueryload("addresses").load_only("email_address")
)
For a Query
that has multiple entities, the lead entity can be
specifically referred to using the Load
constructor:
session.query(User, Address).join(User.addresses).options(
Load(User).load_only("name", "fullname"),
Load(Address).load_only("email_addres")
)
New in version 0.9.0.
sqlalchemy.orm.
undefer
(key, *addl_attrs)¶Indicate that the given column-oriented attribute should be undeferred, e.g. specified within the SELECT statement of the entity as a whole.
The column being undeferred is typically set up on the mapping as a
deferred()
attribute.
This function is part of the Load
interface and supports
both method-chained and standalone operation.
Examples:
# undefer two columns
session.query(MyClass).options(undefer("col1"), undefer("col2"))
# undefer all columns specific to a single class using Load + *
session.query(MyClass, MyOtherClass).options(
Load(MyClass).undefer("*"))
| Parameters: |
|---|
sqlalchemy.orm.
undefer_group
(name)¶Indicate that columns within the given deferred group name should be undeferred.
The columns being undeferred are set up on the mapping as
deferred()
attributes and include a “group” name.
E.g:
session.query(MyClass).options(undefer_group("large_attrs"))
To undefer a group of attributes on a related entity, the path can be
spelled out using relationship loader options, such as
orm.defaultload()
:
session.query(MyClass).options(
defaultload("someattr").undefer_group("large_attrs"))
Changed in version 0.9.0: orm.undefer_group()
is now specific to a
particiular entity load path.
The Bundle
may be used to query for groups of columns under one
namespace.
New in version 0.9.0.
The bundle allows columns to be grouped together:
from sqlalchemy.orm import Bundle
bn = Bundle('mybundle', MyClass.data1, MyClass.data2)
for row in session.query(bn).filter(bn.c.data1 == 'd1'):
print(row.mybundle.data1, row.mybundle.data2)
The bundle can be subclassed to provide custom behaviors when results
are fetched. The method Bundle.create_row_processor()
is given
the Query
and a set of “row processor” functions at query execution
time; these processor functions when given a result row will return the
individual attribute value, which can then be adapted into any kind of
return data structure. Below illustrates replacing the usual KeyedTuple
return structure with a straight Python dictionary:
from sqlalchemy.orm import Bundle
class DictBundle(Bundle):
def create_row_processor(self, query, procs, labels):
"""Override create_row_processor to return values as dictionaries"""
def proc(row):
return dict(
zip(labels, (proc(row) for proc in procs))
)
return proc
Changed in version 1.0: The proc()
callable passed to the create_row_processor()
method of custom Bundle
classes now accepts only a single
“row” argument.
A result from the above bundle will return dictionary values:
bn = DictBundle('mybundle', MyClass.data1, MyClass.data2)
for row in session.query(bn).filter(bn.c.data1 == 'd1'):
print(row.mybundle['data1'], row.mybundle['data2'])
The Bundle
construct is also integrated into the behavior
of composite()
, where it is used to return composite attributes as objects
when queried as individual attributes.


Techuz Solutions. 9th floor, Dev Path Complex, Behiend Lal Banglow, C G Road, Gujrat Ahmedabad, 380059
Techuz Solutions. 9th floor, Dev Path Complex, Behiend Lal Banglow, C G Road, Gujrat Ahmedabad, 380059
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Reads Planetary Data Systems v3 (PDS3 - <https://pds.jpl.nasa.gov/>) Object Description Language (ODL) formatted files, typically used in NASA missions for storing metadata about observations.
| Version: | 0.5.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | rly |
| Suggests: | testthat, covr, lintr, knitr, curl, rmarkdown |
| Published: | 2018-06-06 |
| Author: | Micah Waldstein [aut, cre] |
| Maintainer: | Micah Waldstein <micah at waldste.in> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | pds3 results |
| Reference manual: | pds3.pdf |
| Vignettes: |
Getting Started With PDS3 |
| Package source: | pds3_0.5.0.tar.gz |
| Windows binaries: | r-devel: pds3_0.5.0.zip, r-release: pds3_0.5.0.zip, r-oldrel: not available |
| OS X binaries: | r-release: pds3_0.5.0.tgz, r-oldrel: not available |
Please use the canonical form https://CRAN.R-project.org/package=pds3 to link to this page.


Home
About
Contact
Welcome to Software Engineering XYZ
Here you will find tutorials about new and exciting technologies!


Introducing: GitFlow
What Is GitFlow?
GitFlow is a branching model for
Git, created by Vincent Driessen. It has attracted a lot of attention because it is very well suited to
collaboration and scaling the development team.
- At DataSift, we’ve standardised on:
- - Using Git for our source control,
- - Using GitHub to host our public and private Git repositories,
- - Using GitFlow as our common workflow
- - This website explains how we’ve adapted GitFlow and the GitFlow tools git extension for working
with GitHub.


Get up and running immediately. No need to register, use your GitHub login.
Completely open and transparent, community driven. You drive the features you want. View the code in our repo - we welcome all contributions!
See your environment pipeline and releases on a single screen.
We eat our own dogfood.
Followers - build a community and stay up-to-date.
Simple Monitoring for your Environment.
Schedule releases / deployments to notify your community.
Get notified when releasing / deploying and when completed.
All we need is your domain/sub-domain! We cater for Build Only, Deploy Only and Build & Deploy environments
On the overview page of the environment you have just created, you can create monitors (which send pings to your domains/sub-domains to ensure they're still up and running). You'll be notified at once if pings are unsuccessful
Once you've finished a build or a deploy, in order to send (i.e. "beacon") that data to DashboardHub, simply fill out a short form, or use a curl command. Voila!
Pretty graphs galore at DashboardHub to track everything from information about your releases, versions and ping history!
Founder
Consultant
Marketing Intern
Software Engineer
Consultant
Consultant
"DashboardHub has adopted a startup culture that promotes inclusivity and flexibility for those involved. A key aspect in making this a successful culture is the adoption of Open Source development in harmony with Remote Working. All of the team at DashboardHub are treated as equals and trusted to perform their tasks autonomously knowing they have the full support of the company's founders."
"Transparency, trust and inclusion are all vital to our team’s success."
"If you want a truly great team and company culture give people direction and support but ultimately let them get on with it in their own way; DashboardHub gets this balance just right."


Install sandesha2 module into <axis2 deploy folder>/modules folder. Add the RMPhase information into axis2.xml as following. Search for 'RMPhase' in the following xml block to identify the RM specific entries.
<!-- ================================================= --> <!-- Phases --> <!-- ================================================= --> <phaseOrder type="inflow"> <!-- System pre defined phases --> <phase name="Transport"/> <phase name="PreDispatch"/> <phase name="Dispatch"/> <phase name="PostDispatch"/> <!-- End system pre defined phases --> <!-- After PostDispatch phase, module or service author can add any phase as required --> <!-- User defined phases could be added here --> <!--phase name="userphase1"/--> <phase name="RMPhase"/> </phaseOrder> <phaseOrder type="outflow"> <!-- User defined phases could be added here --> <phase name="RMPhase"/> <!--phase name="RMPhase"/--> <!--phase name="userphase1"/--> <!--system predefined phase--> <phase name="MessageOut"/> </phaseOrder> <phaseOrder type="INfaultflow"> <!-- User defined phases could be added here --> <!--phase name="userphase1"/--> <phase name="RMPhase"/> </phaseOrder> <phaseOrder type="Outfaultflow"> <!-- User defined phases could be added here --> <phase name="RMPhase"/> <!--phase name="userphase1"/--> <phase name="MessageOut"/> </phaseOrder>
In the services xml file for the service which require RM enabled add the entry <module ref="sandesha2"/> If you need all services in the engine RM enabled add the above entry into axis2.xml.
You can use samples in the samples folder for testing.


This page displays measures for the network Semantic Network [Behavioral2 x Assets].
Network images have nodes colored by group and sized by betweenness centrality.
Behavioral2-Level Measures
These measures were run on the specified network, and produced a vector of values, one value for each Behavioral2 node.
Input network: Semantic Network [Behavioral2 x Assets]
Measure Min Mean Max Std.Dev Centrality, Hub 0.444 0.722 1 0.278 Centrality, Hub [Unscaled] 0.406 0.660 0.914 0.254 Centrality, Out-Degree 0.043 0.068 0.093 0.025 Centrality, Out-Degree [Unscaled] 106 169 232 63 Assets-Level Measures
These measures were run on the specified network, and produced a vector of values, one value for each Assets node.
Input network: Semantic Network [Behavioral2 x Assets]
Measure Min Mean Max Std.Dev Centrality, Authority 0 0.026 0.280 0.066 Centrality, Authority [Unscaled] 0 0.018 0.198 0.046 Centrality, In-Degree 0 0.068 0.739 0.172 Centrality, In-Degree [Unscaled] 0 12.519 136 31.558 Overall Top-Ranked Behavioral2
This chart shows the Behavioral2 nodes that are top-ranked overall using the node-level measures listed below. The value shown for a node is the average of its measure values, each of which is first converted into a z-score. The final scores are then normalized to be between zero and one.
There are 2 nodes in the nodeset.
Overall Score Measure List
Centrality, Hub [Semantic Network [Behavioral2 x Assets]]
Centrality, Out-Degree [Semantic Network [Behavioral2 x Assets]]
Centrality, Hub
A node is hub-central to the extent that its out-links are to nodes that have many in-links. Individuals or organizations that act as hubs are sending information to a wide range of others each of whom has many others reporting to them. Technically, an agent is hub-central if its out-links are to agents that have many other agents sending links to them. The scientific name of this measure is hub centrality and it is calculated on agent by agent matrices.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Behavioral2 x Assets]
Rank Behavioral2 Value Unscaled 1 simplifying 1 0.914 2 judging 0.444 0.406
Value statistics Min: 0.444 Mean: 0.722 Lower quartile: 0.444 Max: 1 Std.dev: 0.278 Median: 0.722 Upper quartile: 1
Unscaled value statistics Min: 0.406 Mean: 0.660 Lower quartile: 0.406 Max: 0.914 Std.dev: 0.254 Median: 0.660 Upper quartile: 0.914 Centrality, Out-Degree
For any node, e.g. an individual or a resource, the out-links are the connections that the node of interest has to other nodes. For example, imagine an agent by knowledge network where the number of out-links an agent would have is the number of pieces of knowledge it is connected to. The scientific name of this measure is out-degree and it can be calculated on any network.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Behavioral2 x Assets]
Rank Behavioral2 Value Unscaled 1 simplifying 0.093 232 2 judging 0.043 106
Value statistics Min: 0.043 Mean: 0.068 Lower quartile: 0.043 Max: 0.093 Std.dev: 0.025 Median: 0.068 Upper quartile: 0.093
Unscaled value statistics Min: 106 Mean: 169 Lower quartile: 106 Max: 232 Std.dev: 63 Median: 169 Upper quartile: 232 Overall Top-Ranked Assets
This chart shows the Assets nodes that are top-ranked overall using the node-level measures listed below. The value shown for a node is the average of its measure values, each of which is first converted into a z-score. The final scores are then normalized to be between zero and one.
There are 27 nodes in the nodeset.
Overall Score Measure List
Centrality, Authority [Semantic Network [Behavioral2 x Assets]]
Centrality, In-Degree [Semantic Network [Behavioral2 x Assets]]
Centrality, Authority
A node is authority-central to the extent that its in-links are from nodes that have many out-links. Individuals or organizations that act as authorities are receiving information from a wide range of others each of whom sends information to a large number of others.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Behavioral2 x Assets]
Rank Assets Value Unscaled 1 gain 0.280 0.198 2 loss 0.194 0.137 3 policy 0.105 0.074 4 source 0.081 0.057 5 resource 0.028 0.020 6 rewards 0.010 0.007 7 hypervisor 0.003 0.002 8 air 0 0 9 airline 0 0 10 airport 0 0 11 baggage 0 0 12 communication_link 0 0 13 domain_generation_algorithms 0 0 14 facilities 0 0 15 firewalls 0 0 16 house 0 0 17 label 0 0 18 labels 0 0 19 list 0 0 20 news 0 0 21 product 0 0 22 remote_access_tools 0 0 23 report 0 0 24 rundll32 0 0 25 space 0 0 26 time 0 0 27 toc 0 0
Value statistics Min: 0 Mean: 0.026 Lower quartile: 0 Max: 0.280 Std.dev: 0.066 Median: 0 Upper quartile: 0.003
Unscaled value statistics Min: 0 Mean: 0.018 Lower quartile: 0 Max: 0.198 Std.dev: 0.046 Median: 0 Upper quartile: 0.002 Centrality, In-Degree
The number of links directed into a node normalized by the maximum number of such links. This measure is also called Column Degree Centrality because it is computed by taking the sum of the column values in the input network.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Behavioral2 x Assets]
Rank Assets Value Unscaled 1 gain 0.739 136 2 loss 0.484 89 3 policy 0.266 49 4 source 0.250 46 5 resource 0.071 13 6 rewards 0.022 4 7 hypervisor 0.005 1 8 air 0 0 9 airline 0 0 10 airport 0 0 11 baggage 0 0 12 communication_link 0 0 13 domain_generation_algorithms 0 0 14 facilities 0 0 15 firewalls 0 0 16 house 0 0 17 label 0 0 18 labels 0 0 19 list 0 0 20 news 0 0 21 product 0 0 22 remote_access_tools 0 0 23 report 0 0 24 rundll32 0 0 25 space 0 0 26 time 0 0 27 toc 0 0
Value statistics Min: 0 Mean: 0.068 Lower quartile: 0 Max: 0.739 Std.dev: 0.172 Median: 0 Upper quartile: 0.005
Unscaled value statistics Min: 0 Mean: 12.519 Lower quartile: 0 Max: 136 Std.dev: 31.558 Median: 0 Upper quartile: 1


The Ethiopian wolf (Canis simensis) is a canid native to the Ethiopian Highlands. It is similar to the coyote in size and build, and is distinguished by its long and narrow skull, and its red and white fur. Unlike most large canids, which are widespread, generalist feeders, the Ethiopian wolf is a highly specialised feeder of Afroalpine rodents with very specific habitat requirements. It is one of the world's rarest canids, and Africa's most endangered carnivore.
The species' current range is limited to seven isolated mountain ranges at altitudes of 3,000–4,500 m, with the overall adult population estimated at 360-440 individuals in 2011, more than half of them in the Bale Mountains.
The Ethiopian wolf is listed as endangered by the IUCN, on account of its small numbers and fragmented range. Threats include increasing pressure from expanding human populations, resulting in habitat degradation through overgrazing, and disease transference from free-ranging dogs. Its conservation is headed by Oxford University's Ethiopian Wolf Conservation Programme, which seeks to protect wolves through vaccination and community outreach programs.


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This report summarizes the process and results of creating one year of department-wise sales forecasts for 45 Walmart stores (over 3,000 individual departments). Because of the large number of time series, forecasts were created using inter-store department averages and transforming results to estimate store-specific department sales.
Analysis: Autoregressive Integrated Moving Average (ARIMA) modeling was used to create one year of weekly forecasts using 2.75 years of sales data, with features for store, department, date, weekly sales, and if the week contains a major holiday. The latter was used as an exogenous variable. Performance was measured using normalized root-mean-square error (NRMSE)
Results and Conclusions: The 75th percentile NRMSE for department-average forecasts was 0.092, and the 75th percentile NRMSE for individual department forecasts was 0.155. In other words, 75 percent of the department-wise forecasts had an average error that was less than 15.5% of the mean sales during that time. While the method used worked well for most individual departments, some had NRMSE > 0.5.
While it requires more computational resources, it would likely be more effective to model every department within each store individually, which would require 3,331 models for the departments in this set. That said, there are other possibilities for improving results, such as modeling a dataset that contains at least four or five years of sales data.
Limitations:The primary limitation is this projects was the size of the dataset: the entire set consists of only 143 weeks (2.75 years) of data, which prohibited modeling any seasonal component that might exist. There was also missing sales data for some departments.
Sales forecasting is an important component of modern business, as it helps decision makers anticipate fluctuations in sales that could be costly if missed. Changes in sales can arise from long-term trends, seasonal and non-seasonal effects, external factors like holidays or weather (exogenous variables), and there are also random fluctuations. ARIMA is a popular method of modeling time series, because of it’s flexibility and generalizability.
The primary goal of this project was to examine a method to use fewer than 100 non-seasonal ARIMA models to generate weekly forecasts for over 3000 individual series, over the span of one year. The forecasts were generated by simulating the passage of time and updating the models each week from left out data. That is not be confused with producing iterative projections by updating using previous predictions.
Two questions were posed at the start of this project:
The dataset used in this project contained five features:
| Store | Dept | Date | Weekly_Sales | IsHoliday | |
|---|---|---|---|---|---|
| 0 | 1 | 1 | 2010-02-05 | 24924.50 | False |
| 1 | 1 | 1 | 2010-02-12 | 46039.49 | True |
| 2 | 1 | 1 | 2010-02-19 | 41595.55 | False |
IsHoliday was placed in a separate data frame, with the unique Date values as the index. The strings representing dates were converted to actual date-time values. The resulting frame contained 143 rows, one for each week in the set. This data frame will be referred to as is_holiday.
The data frame containing the remaining features was restructured into a more useful form. Specifically, the data frame was grouped by Date, Store and Dept, using mean as the aggregate function. In this case, using an aggregate function did not affect the values, because that grouping left only one value per unique triple. As with is_holida, the Date strings were converted to date-time values. Unstacking the Store and Dept levels left the final sales data:
| Store | 1 | ... | 45 | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dept | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ... | 87 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 |
| Date | |||||||||||||||||||||
| 2010-02-05 00:00:00 | 24924.50 | 50605.27 | 13740.12 | 39954.04 | 32229.38 | 5749.03 | 21084.08 | 40129.01 | 16930.99 | 30721.50 | ... | 8818.12 | 32016.42 | 22708.11 | 68203.08 | 4002.34 | NaN | 63833.63 | NaN | 8393.22 | 347.23 |
| 2010-02-12 00:00:00 | 46039.49 | 44682.74 | 10887.84 | 35351.21 | 29620.81 | 9135.00 | 18310.31 | 37334.83 | 16562.49 | 31494.77 | ... | 5895.61 | 19724.40 | 13860.64 | 44898.91 | 2665.98 | 2.94 | 41131.42 | NaN | 5011.36 | 553.25 |
To help ensure successful modeling, columns with more than 10% of their values missing were dropped, and those with less than this cutoff had missing values forward filled, then backward filled to fill all missing values.
The sales data was used to create two data frames central to the modeling: store_avg and dept_avg. These two frames contained sales averaged within individual stores, and cross-store averages by department number, respectively. The former thus had columns indicating only unique store numbers, and the latter only unique department numbers.
A final step of preprocessing was to necessarily held off until the first portion of exploratory analysis could be conducted, and so it will be mentioned in the next section.
Because the method of modeling depends on estimating department-specific sales from averaged sales, it was important to minimize scale differences between individual series and these averages: For an individual series with values substantially smaller than the averaged forecast from which it is estimated, relatively small errors in the forecast will be exaggerated for the individual. Thus, the store_avg and dept_avg sets were analyzed to determine which had the smallest overall scale differences when compared to the individual series. This was done by computing the ratios of original and averaged series, and comparing their distributions.
The box plots show that dept_avg was the ideal set for the method tested, and so was chosen over store_avg going forward. This leads to the final portion of preprocssing that was skipped in the last section: A set called mean_diffs was created using
\begin{equation} \Delta_d = S_d - D_d, \end{equation}where Δ, S, and D are the matrix representations of mean_diffs, sales, and dept_avg, respectively. The subscript d represents performing operations on matching department numbers through broadcasting, rather than column-wise operations. It is this new set that will be used to compute estimates from forecasts of averages.
Because time series modeling depends on stationarity, initial steps to ARIMA modeling typically include plotting the autocorrelation function (ACF) and partial-autocorrelation function (PACF) of the series to be modeled, seasonal decomposition, and tests for stationarity. The ACF and PACF help to determine how stationary a series is and what terms to use in fitting the model. Seasonal decomposition separates the series into three components: trend, seasonal, and what remains (residual), for the purpose of understanding the structure of the series. The test of stationarity in this project was the Dickey-Fuller test.
Because there were so many models to be fitted in this project (and because most of the series have similar structures), the above steps were taken on one representative series. The results of this step were not used to determine the terms to use in every model, rather they were used to gain some insight into the overall structures.
The Dickey-Fuller tests for stationarity were performed on the original series, as well as after taking first and second differences. The resulting p-values are tabulated below.
| Difference: | None | First | Second |
|---|---|---|---|
| p-val | 0.1731 | 0.0047 | 0.0000 |
ARIMA models require order values (p, d, q) to be set, which correspond to autoregressive terms, nonseasonal differences, and lagged forecast errors. These values can be determined using the methods mentioned at the end of the last section, but because this project involves so many models, selecting and evaluating the order for each by hand would be a lengthy process. To that end, it was necessary to automate the selection process for each model, as described in the following paragraph.
Automated selection was achieved by iterating over each series, and fitting each to all order combinations, with values from zero to two. The models were evaluated, and orders selected, using Akaike Information Criterion (AIC): the order that produced the smallest value for a given model was selected. Error handling was necessary for this process, particularly for the event of maximum likelihood failing to converge with a particular order.
To check that values were being selected with some success, one of the original series was overlaid with in-sample predictions:
As can be seen, at least with this series, the selection process was successful.
In order to achieve the goal of this project, the final part of the modeling process was to simulate the passing of time and update the models (i.e. re-fit with new orders). With each new set of updates, sales forecasts for all series were made and recorded, along with standard error and the lower/upper bounds of 95% confidence intervals.
Because holidays tend to fall on either different days of the week each year (e.g. Christmas), or different dates (e.g. Thanksgiving), the week of a holiday month where the peak sales fall might not fall in the same week as the holiday. Because of this, using is_holiday as an exogenous variable can cause some problems with the forecasts. With this in mind, a one week shift in the forecasts for this particular date range was found to produce the best results. A more complex method of shifting sales around to match individual holidays could be devised, but was unnecessary for this project.
The department-wise forecasts (store specific) were computed using
\begin{equation} \hat{S}_d = \hat{D}_d + \Delta_d, \end{equation}where the "hat" accents indicate forecast values, and the subscripts have the same meaning as in Eq. 1. This resulted in a data frame with the same structure as sales.
To evaluate the performance of the forecasts, the NRMSE was computed for both the initial department-average forecasts and the final forecasts. NRMSE was computed on the forecast range as follows:
\begin{equation} NRMSE = \frac{RMSE_d}{|\bar{D}_d|}, \end{equation}where RMSE is the common root mean square error, and the denominator is the absolute values of series means from dept_avg.
The NRMSE values were plotted as the heat maps in the figures below, and descriptive statistics tabulated:
| mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|
| Dept_Avg | 0.083 | 0.069 | 0.015 | 0.040 | 0.055 | 0.092 | 0.352 |
| Dept_Ind | 0.618 | 6.345 | 0.007 | 0.039 | 0.070 | 0.156 | 208.302 |
Sales forecasts for department-average sales were overall quite accurate, with the median NRMSE being 0.055, which can be interpreted as forecast errors being within 5.5% of the sales means. The estimation of store-specific department sales resulted in a median NRMSE of approximately 0.070. Even with relatively low overall error, there are still some individual forecasts that have unacceptably high error. A pair of plots below show a selection of good and bad forecasts overlaid with the actual sales:
The method outlined in this project, used to significantly reduce the number of models required to make individual forecasts, was able to produce results with reasonably low error, though it is clear that some departments within specific stores should be modeled separately.
The primary limitation in this project was the size of the dataset: the entire set consists of only 143 weeks (2.75 years) of data, which prohibited modeling any seasonal component that might exist. Additionally, there was missing sales data for some departments. Only one exogenous variable was used in modeling, which could limit forecasting potential; weather and unemployment data (available in a complementary set), might have improved the accuracy of forecasts.


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