Antoine Logean's bookmarks

Monday, November 25, 2019

mySQL: the basic that I always forget

Connect to the DB:
mysql -h hote -u utilisateur -p

SELECT VERSION(),CURRENT_DATE;

Then you can make some calculations:
SELECT SIN(PI()/4), (4+1)*5;

Creation of a database:
CREATE DATABASE menagerie;

Use it:
USE menagerie;

Connect you to the DB-server and specify the DB you are interested in:
mysql -h hote -u utilisateur -p menagerie
SHOW TABLES;
CREATE TABLE animal (nom VARCHAR(20), maitre VARCHAR(20),
espece VARCHAR(20), sexe CHAR(1), naissance DATE, mort DATE);
DESCRIBE animal;
LOAD DATA LOCAL INFILE "animal.txt" INTO TABLE animal;
INSERT INTO animal VALUES ('Puffball','Diane','hamster','f','1999-03-30',NULL);

Some
* Query 1: Find products for a given set of generic features
SELECT DISTINCT ?product ?label
WHERE {
?product rdfs:label ?label .
?product rdf:type %ProductType% .
?product bsbm:productFeature %ProductFeature1% .
?product bsbm:productFeature %ProductFeature2% .
?product bsbm:productPropertyNumeric1 ?value1 .
FILTER (?value1 > %x%)}
ORDER BY ?label
LIMIT 10

* Query 2: Retrieve basic information about a specific product for display purposes
SELECT ?label ?comment ?producer ?productFeature ?propertyTextual1
?propertyTextual2 ?propertyTextual3 ?propertyNumeric1
?propertyNumeric2 ?propertyTextual4 ?propertyTextual5
?propertyNumeric4
WHERE {
%ProductXYZ% rdfs:label ?label .
%ProductXYZ% rdfs:comment ?comment .
%ProductXYZ% bsbm:producer ?p .
?p rdfs:label ?producer .
%ProductXYZ% dc:publisher ?p .
%ProductXYZ% bsbm:productFeature ?f .
?f rdfs:label ?productFeature .
%ProductXYZ% bsbm:productPropertyTextual1 ?propertyTextual1 .
%ProductXYZ% bsbm:productPropertyTextual2 ?propertyTextual2 .
%ProductXYZ% bsbm:productPropertyTextual3 ?propertyTextual3 .
%ProductXYZ% bsbm:productPropertyNumeric1 ?propertyNumeric1 .
%ProductXYZ% bsbm:productPropertyNumeric2 ?propertyNumeric2 .
OPTIONAL { %ProductXYZ% bsbm:productPropertyTextual4 ?propertyTextual4 }
OPTIONAL { %ProductXYZ% bsbm:productPropertyTextual5 ?propertyTextual5 }
OPTIONAL { %ProductXYZ% bsbm:productPropertyNumeric4 ?propertyNumeric4 }}

* Query 3: Find products having some specific features and not having one feature
SELECT ?product ?label
WHERE {
?product rdfs:label ?label .
?product rdf:type %ProductType% .
?product bsbm:productFeature %ProductFeature1% .
?product bsbm:productPropertyNumeric1 ?p1 .
FILTER ( ?p1 > %x% )
?product bsbm:productPropertyNumeric3 ?p3 .
FILTER (?p3 < %y% ) OPTIONAL { ?product bsbm:productFeature %ProductFeature2% . ?product rdfs:label ?testVar } FILTER (!bound(?testVar)) } ORDER BY ?label LIMIT 10 Query 4: Find products matching two different sets of features SELECT ?product ?label WHERE { { ?product rdfs:label ?label . ?product rdf:type %ProductType% . ?product bsbm:productFeature %ProductFeature1% . ?product bsbm:productFeature %ProductFeature2% . ?product bsbm:productPropertyNumeric1 ?p1 . FILTER ( ?p1 > %x% )
} UNION {
?product rdfs:label ?label .
?product rdf:type %ProductType% .
?product bsbm:productFeature %ProductFeature1% .
?product bsbm:productFeature %ProductFeature3% .
?product bsbm:productPropertyNumeric2 ?p2 .
FILTER ( ?p2> %y% ) }}
ORDER BY ?label
LIMIT 10 OFFSET 10

* Query 5: Find products that are similar to a given product
SELECT DISTINCT ?product ?productLabel
WHERE {
?product rdfs:label ?productLabel .
FILTER (%ProductXYZ% != ?product)
%ProductXYZ% bsbm:productFeature ?prodFeature .
?product bsbm:productFeature ?prodFeature .
%ProductXYZ% bsbm:productPropertyNumeric1 ?origProperty1 .
?product bsbm:productPropertyNumeric1 ?simProperty1 .
FILTER (?simProperty1 < (?origProperty1 + 120) && ?simProperty1 >
(?origProperty1 - 120))
%ProductXYZ% bsbm:productPropertyNumeric2 ?origProperty2 .
?product bsbm:productPropertyNumeric2 ?simProperty2 .
FILTER (?simProperty2 < (?origProperty2 + 170) && ?simProperty2 >
(?origProperty2 - 170)) }
ORDER BY ?productLabel
LIMIT 5

* Query 6: Find products having a label that contains a specific string
SELECT ?product ?label
WHERE {
?product rdfs:label ?label .
?product rdf:type bsbm:Product .
FILTER regex(?label, "%word1%")}
Query 7: Retrieve in-depth information about a product including offers and reviews
SELECT ?productLabel ?offer ?price ?vendor ?vendorTitle ?review
?revTitle ?reviewer ?revName ?rating1 ?rating2
WHERE {
%ProductXYZ% rdfs:label ?productLabel .
OPTIONAL {
?offer bsbm:product %ProductXYZ% .
?offer bsbm:price ?price .
?offer bsbm:vendor ?vendor .
?vendor rdfs:label ?vendorTitle .
?vendor bsbm:country .
?offer dc:publisher ?vendor .
?offer bsbm:validTo ?date .
FILTER (?date > %currentDate% ) }
OPTIONAL {
?review bsbm:reviewFor %ProductXYZ% .
?review rev:reviewer ?reviewer .
?reviewer foaf:name ?revName .
?review dc:title ?revTitle . OPTIONAL { ?review bsbm:rating1 ?rating1 . }
OPTIONAL { ?review bsbm:rating2 ?rating2 . } } }
Query 8: Give me recent English language reviews for a specific product
SELECT ?title ?text ?reviewDate ?reviewer ?reviewerName ?rating1
?rating2 ?rating3 ?rating4
WHERE {
?review bsbm:reviewFor %ProductXYZ% .
?review dc:title ?title .
?review rev:text ?text .
FILTER langMatches( lang(?text), "EN" )
?review bsbm:reviewDate ?reviewDate .
?review rev:reviewer ?reviewer .
?reviewer foaf:name ?reviewerName .
OPTIONAL { ?review bsbm:rating1 ?rating1 . }
OPTIONAL { ?review bsbm:rating2 ?rating2 . }
OPTIONAL { ?review bsbm:rating3 ?rating3 . }
OPTIONAL { ?review bsbm:rating4 ?rating4 . } }
ORDER BY DESC(?reviewDate) LIMIT 20
Query 9: Get information about a reviewer.
DESCRIBE ?x
WHERE {
%ReviewXYZ% rev:reviewer ?x }
Query 10: Get cheap offers which fulfill the consumer’s delivery requirements.
SELECT DISTINCT ?offer ?price
WHERE {
?offer bsbm:product %ProductXYZ% .
?offer bsbm:vendor ?vendor .
?offer dc:publisher ?vendor .
?vendor bsbm:country %CountryXYZ% .
?offer bsbm:deliveryDays ?deliveryDays .
FILTER (?deliveryDays <= 3) ?offer bsbm:price ?price . ?offer bsbm:validTo ?date . FILTER (?date > %currentDate% ) }
ORDER BY xsd:double(str(?price))
LIMIT 10
Query 11: Get all information about an offer.
SELECT ?property ?hasValue ?isValueOf
WHERE {
{ %OfferXYZ% ?property ?hasValue }
UNION
{ ?isValueOf ?property %OfferXYZ% } }
Query 12: Export information about an offer into another schema.
CONSTRUCT {
%OfferXYZ% bsbm-export:product ?productURI .
%OfferXYZ% bsbm-export:productlabel ?productlabel .
%OfferXYZ% bsbm-export:vendor ?vendorname .
%OfferXYZ% bsbm-export:vendorhomepage ?vendorhomepage .
%OfferXYZ% bsbm-export:offerURL ?offerURL .
%OfferXYZ% bsbm-export:price ?price .
%OfferXYZ% bsbm-export:deliveryDays ?deliveryDays .
%OfferXYZ% bsbm-export:validuntil ?validTo }
WHERE {
%OfferXYZ% bsbm:product ?productURI .
?productURI rdfs:label ?productlabel .
%OfferXYZ% bsbm:vendor ?vendorURI .
?vendorURI rdfs:label ?vendorname .
?vendorURI foaf:homepage ?vendorhomepage .
%OfferXYZ% bsbm:offerWebpage ?offerURL .
%OfferXYZ% bsbm:price ?price .
%OfferXYZ% bsbm:deliveryDays ?deliveryDays . %OfferXYZ% bsbm:validTo ?validTo

vi commands

Here some basic vi commands that I always forget:
:set number switch on/off line number

Quelques remarques sur la conscience

Quand nous parlons de conscience, nous faisons pour la plupart du temps preuve d’

- anthropocentrisme. Nous considérons la conscience comme une qualité exclusivement humaine. De plus nous la percevons souvent comme une qualité binaire: soit je suis conscience soit je ne le suis pas. Cependant, n’existe-il pas un grand nombre de types de consciences variant aussi bien par leur nature que par leur « intensité »? Prenons quelques exemples :

Un très grand nombre de mammifères supérieurs possèdent des sens beaucoup plus développés que ceux de l’homme. Cela leur confère une prise de conscience de leur environnement supérieure/différente de la notre et bien souvent d’une autre nature. Un chien par exemple sent 57 fois mieux que nous. Il vit dans un monde d’odeurs. Là où nous ne voyons qu’une forme colorée, le chien voit une forme colorée doublée d’une odeur. C’est comme une nouvelle dimension. Cela lui permet entre autre de sentir les phéromones produites par les êtres l’entourant. Il est donc à même de « sentir » l’état psychologique d’une personne.
Plus fascinant encore est le monde des insectes. Nous voyons émerger là le concept d’intelligence/conscience collective. Une fourmi seul ne peut effectuer qu’un nombre limité de tâches lui demandant peu d’ »intelligence ». Quand est-il de 1000000 de fourmis ? La fourmilière est à même de résoudre des problèmes bien plus complexes. Que pouvons nous dire de la conscience d’une fourmilière ? Elle certainement d’un autre ordre. Peut-être ne sommes nous pas à même de véritablement la comprendre.

La nature regorge de ce genre d’exemples. Elle nous apprend l’humilité et nous montre que la conscience humaine n’est qu’un type de consciences ayant sous bien des égards de nombreuses limitations.

Dans le développement d’une conscience artificielle il serait donc faut de se borner à imiter la conscience humaine.

Thursday, September 17, 2015

Consuming Linked Data in the browser

http://codepen.io/alogean/full/epZddr/

Monday, March 18, 2013

Some notes about the 10 opendata principles

Integral

Tuesday, June 12, 2012

Some facts about XPath

What is XPath, where did it come from ?

XPath is a language for finding information in an XML document
XPath uses path expressions to select nodes or node-sets in an XML document.

taken from here

XPath is part of XSLT and XQuerry. If you want to use XSLT or XQuerry you will have to learn XPath
In XPath, there are seven kinds of nodes:

element,
attribute,
text,
namespace,
processing-instruction,
comment, and
document nodes.

Some terms have to be known

XML documents are treated as trees of nodes. The topmost element of the tree is called the root element.
Atomic values are nodes with no children or parent.
Items are atomic values or nodes.
Each element and attribute has one parent.
Element nodes may have zero, one or more children.
Nodes that have the same parent are called siblings.
Ancestores of a node are node's parent, parent's parent, etc.
Descendants of a node are node's children, children's children, etc.

Selection nodes

XPath uses path expressions to select nodes in an XML document.

nodename	Selects all nodes with the name "nodename"
/	Selects from the root node
//	Selects nodes in the document from the current node that match the selection no matter where they are
.	Selects the current node
..	Selects the parent of the current node
@	Selects attributes

Wednesday, December 28, 2011

Introduction to RDFLib

>>> import rdflib
>>> from rdflib import ConjunctiveGraph
>>> graph = ConjunctiveGraph()
>>> graph.parse("http://semantictweet.com/ecolix")
)>
>>> for triple in graph:
... print triple

Tuesday, December 6, 2011

Git is your friend !

> make dir myproject
> cd myproject
> git clone git@github.com:myproject.git .
> git branch --track abranch origin/abranch

Some tutorials:

Sunday, October 30, 2011

Notes about "JavaScript: The Definition Guide" of David Flanagan

Chapter 1: Introduction to JavaScript
there are :

the core JavaScript language = minimal API without I/O functions
client-side JavaScript = hosting environment (browser)

Development environment : Firebug
to debug : console.log() or alert()

Chapter 2: Lexical Structure

Character Set

support Unicode
Case Sensitive
ignores Whitespace

Comments

 // This is a single-line comments.

 /* This is also a comment */  // and here is another comment.

/*

  * This is yet another comment.

  * It has multiple lines

Literal

12 // The number twelve

 1.2            // The number one point two

 "hello world"  // A string of text

 'Hi'           // Another string

 true           // A Boolean value

 false          // The other Boolean value

 /javascript/gi // A "regular expression" literal (for pattern  

 matching)

null // Absence of an object

{ x:1, y:2 }    // An object initializer

[ 1, 2, 3, 4 ] // An array initializer

Monday, September 12, 2011

Some unsorted facts about RDF

Here some facts taken from the book "practical RDF" of Shelley Powers

about RDF

RDF provides a standard way of expressing graphs of data and sharing them with other people and with machines.
RDF is a language for expressing data models using statements expressed as triples. Each statements is composed of a subject, a predicate, and an object.
RDF conceptualizes anything (and everything) in the universe as a resource. A resource is simply anything that can be identified with a Universal Resource Identifier (URI). And by design, anything we can talk about can be assigned a URI.
URLs are a subset of URIs that identify where digital information can be retrieved.
Because URIs uniquely identify resources (things in the world), we consider them strong identifiers. There is no ambiguity about what they represent, and they always represent the same thing, regardless of the context we find them in.

about RDF graph model

the RDF data model is best represented by a directed labeled graph
the RDF directed graph consists of a set of nodes connected by arcs, forming a pattern of node-arc-node. Additionally, the nodes come in three varieties: uriref, blank nodes, and literals.
there are RDF data models that can be represented in RDF graphs, but not in RDF/XML. The addition of rdf:nodeIDs provided some of the necessary syntactic elements that allow RDF/XML to record all RDF graphs. However, RDF/XML still can't encode graphs whose properties (predicates) cannot be recorded as namespace-qualified XML names, or QNames.
the components of the RDF graph - the uriref, bnode, literal, and arc - are the only components used to document a specific instance of an RDF data model.
there is no rule or regulation within the RDF graph that insists that all nodes be somehow connected with one another.
an RDF graph is considered grounded if there are no blank nodes.
an instance of an RDF graph is a graph in which each blank node has been replaced by an identifier, becoming a named node.

about RDF tiple

each RDF triple is a complete and unique fact
each RDF triple can be joined with other RDF triples, but it still retains its own unique meaning, regardless of the complexity of the model in which it is included
regardless of how complex an RDF graph, it still consists of only a grouping of unique, simple RDF triples, and each is made upof a subject, predicate and object.

about urirefs

uriref within a RDF model has not to be resolvable (point to something that is accessible on the web). RDF is designed to be a generic means of recording data, it can't restrict urirefs to being "real" data sources.
URIs provide a common syntax for naming a resource regardless of the protocol used to access the resource.
A URI is only an identifier. A specific protocol does not need to be specified, nor must the object identified physically exist on the Web
you could use as URI a UUID (Universally Unique Identifier) referencing a COM or other technology components.
URL is a location of an object, while a URI can function as a name or a location.

about blank nodes

blank nodes are also called bnodes or anonymous nodes
blank nodes are nodes that did not have a URI
most RDF parsers generate an unique identifier (genid:xxxxx) for each blank nodes. This is needed to distinguish blank nodes from each others within the single instance of the graph.
blank nodes are never merged in a graph because there is no way of determining whether two nodes are the same.

about literals

a literal consist of three parts: a character string and an optional language tag and data type.
literals represent RDF objects only, never subjects or predicates

about predicates

every arc (predicate), without exception, must be labeled within the graph.

Saturday, August 13, 2011

Molecular substructure & similarity search

Surveys

Fingerprint

Substructure Search

Structure Editorts

http://merian.pch.univie.ac.at/~nhaider/cheminf/moldb.html

Markush Suche (R-Gruppe)

Chemistry Databases

Chemoinformatics Libs

Chemistry Developement Kit (CDK)

JoeLib

http://sourceforge.net/projects/joelib/

OpenBabel

http://openbabel.org/wiki/Main_Page

LOD Visualisation using JavaScript

Graphical Representation:

jQuery Sparkline
jQuery plugin generates sparklines (small inline charts) directly in the browser using data supplied either inline in the HTML, or via javascript.

Protovis

D3.js

gRaphaël

smoothiecharts

Processing.js

Geo Information :

http://openlayers.org/
Javascript mapping abstraction library mapstraction

Javascript 3D Engine
three.js

SVG

Polymaps

Tuesday, July 19, 2011

Word Bank is giving public access to 7000 data sets

The Data Catalog of Word Bank provides now download access to over 7,000 indicators from World Bank data sets.

Schema.org: Spoonfeeding Library Data to Search Engines

Not sure what Schema.org is about ? Read the nice post of Eric Hellman.

The internet of thinks

Nice explanation of what the internet of thinks could be : http://blogs.cisco.com/news/the-internet-of-things-infographic/

Definition of an Open Government Data Ontology (OGDO)

Let say we aim to build an open government data catalog with the following properties:

the solution should be based on open source software
with minimal self-development. One should be able to configure an existing framework
the content of the catalog should be readable for computer (as LOD) and for human (HTML)
everyone should be able to edit and add content.

The first step will be to develop a formal context of the catalog, to define an OGD-ontology. An OGD-ontology can be subdivided in 3 parts that are orthogonal. They can be define separately:

a Data-ontology (D-ontology)
an Open-ontology (O-ontology)
a Government-ontology (G-ontology)

1) eine Data-Ontologie (og[D]) : diese Ontologie (ein SKOS Taxonomie könnte erstmal reichen) definiert die Semantik der Daten. Die hätte Konzepten wir "Healthcare", "Army", "Defence", "Religion", "Education", ... einfach alle die nötigen Schubladen die wir brauchen um OGD einzuordnen. Bevor eine richtige OWL Ontologie zu definieren kann man hier erstmal mit einer SKOS Taxonomie anfangen. Das sollte auch einfacher das in semantic MediaWiki zu integrieren. Diese Ontologie ist auch ganz generisch. Sie gilt in Prinzip für alle Landen. Ein gut Anfang wäre die Katalog von open.gov anzuschauen. Wahrscheinlich hat man das auch schon gemacht. Ich habe bis jetzt noch nichts gefunden. Bis jetzt das beste das ich habe ist [ ]

2) eine Open-Ontologie ([O]gd) : diese Ontologie beschreibt die Art wir die Daten veröffentlicht sind, den so genannte Dienst Vertrag, die nicht funktionalen Aspekten der Schnittstelle: wo sind die Daten zu finden (URI)? in welche Format (in LOD sollte das eher mit Content-Negociation machen), gibt es ein Gebühr ? Wenn ja wieviel. Welche Copyright ist mit der Daten gebunden, wie grosse sind die Daten?, Wann wurden sie das letzte Mal aktualiesiert? gibt es ein Kontakt Personn ? ... Genau wie bei der Daten-Ontologie ist diese Ontologie ganz generisch und gar nicht CH-spezifisch. Im SOA Umfeld hat man bestimmt etwas ähnlich schon definiert.

3) eine Government-Ontologie (o[G]d): mit dieser Ontologie kann man die politische Organisation/Strukturen des Landes spezifizieren. Wir haben hier Konzepten wie "Bund", "Kantonen", "Gemeide", "Departement",... In Prinzip wird so eine Ontologie einmal für die Schweiz definiert und sollte sich nicht so viel ändern (es hat sich diese letzte 100 Jahren kaum geändert ...). Hier auch sollte erstmal ein Taxonomie reichen.

Diese 3 Ontologien definieren den formalen Rahme des Verzeichnis. Dann sollte man semantic MediaWiki so konfiguriert das es nur möglich ist, diese OGD-Ontologie/Taxonomie zu instanzieren. Da weiss ich nicht genau ob die semantic extension von MediaWiki so etwas ermöglicht. Grundsätzlich kann man 2 Sichten auf die Daten definieren: eine Daten Sicht und eine Government Sicht. Die Daten Sicht listet (es wird eher ein Baum-hierachie) einfach die verschieden Arten von Daten. Für eine bestimmte OGD-Daten Kategorie (zum Beispiel "Kultur" ) sehe

Tuesday, July 12, 2011

Semantic Wiki with Referata

Referata offers hosting of semantic wikis (MediaWiki + semantic MediaWiki extension ) : http://tinyurl.com/67sm9u3

Data.gov catalogs

An interactive dataset containing the metadata for the Data.gov raw datasets and tools catalogs : http://explore.data.gov/Other/Data-gov-Catalog/pyv4-fkgv

Friday, May 20, 2011

DNS entries caching by Windows

Do you want to know which websites your child has visited ? In windows simply enter in a command line ipconfig /displaydns
This command shows all the dns domain name of the web servers web sites that a user has been visited.

ipconfig /flushdns:
removes that lists.