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Contents contributed and discussions participated by Erwin Karbasi

Erwin Karbasi

The Semantic Web Client Library - Consuming Linked Data in Your Applications - 0 views

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    Semantic Web Client Library - SWCL Slide
Erwin Karbasi

XML.com: Introducing SPARQL: Querying the Semantic Web - 0 views

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    SPARQL Tutorial
Erwin Karbasi

Visual Understanding Environment (VUE) | OpenCalais - 0 views

  • The Visual Understanding Environment (VUE) is an Open Source project based at Tufts University. The VUE project is focused on creating flexible tools for managing and integrating digital resources in support of teaching, learning and research. VUE provides a flexible visual environment for structuring, presenting, and sharing digital information.
Erwin Karbasi

Meandre » Meandre Workbench - 0 views

  • Meandre Workbench is a visual programming environment that allows users to easily connect software components together in a unique data flow environment. This application relies on the Google Web Toolkit (GWT) and is accessed via your Internet browser. You can use this interface to develop diagrams of data operations relevant to your research. Each operation is represented by an icon, and the icons are linked together in a flow representing the movement of data through each operation. Each of these icons represents a component. These software components are reusable components that facilitate collaboration among developers. These components can be written in Java, Python, or Lisp. A set of components can be loaded into your Workbench for creating a flow (application). Flows are essentially applications composed of components connected together. Flow complexity is limited only by the needs of your project. Pre-built flows can be loaded into the Workbench and modified as needed. A number of flows for use in a variety of data mining problems and domains have been developed and can be added to your Workbench. Components and flows have tags and additional metadata associated with them that can be used to assist in searching and sorting.
Erwin Karbasi

Contextual configuration - Semantic Web development for visually minded webmasters - be... - 0 views

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    Semantic Content Management
Erwin Karbasi

Linked Data for Dummies « Web of Data - 0 views

  • Every now and then I ask myself: how would you explain the Linked Data stuff I’m doing to our children or to my parents, FWIW. So, here is an attempt to explain the Linked Data Web, and I promise that I wont use any lingo in the following: Imagine you’re in a huge building with several storeys, each with an incredible large amount of rooms. Each room has tons of things in it. It’s utterly dark in that building, all you can do is walk down a hallway till you bang into a door or a wall. All the rooms in the buildings are somehow connected but you don’t know how. Now, I tell you that in some rooms there is a treasure hidden and you’ve got one hour to find it.
Erwin Karbasi

D2R Server - Publishing Relational Databases on the Semantic Web - 0 views

  • D2R Server is a tool for publishing relational databases on the Semantic Web. It enables RDF and HTML browsers to navigate the content of the database, and allows applications to query the database using the SPARQL query language.
Erwin Karbasi

Joseki - A SPARQL Server for Jena - 0 views

  • Joseki is an HTTP engine that supports the SPARQL Protocol and the SPARQL RDF Query language. SPARQL is developed by the W3C RDF Data Access Working Group. Joseki Features: RDF Data from files and databases HTTP (GET and POST) implementation of the SPARQL protocol
Erwin Karbasi

RelFinder - Interactive Relationship Discovery in RDF Datasets - 0 views

  • Are you interested in how things are related with each other? The RelFinder helps to get an overview: It extracts and visualizes relationships between given objects in datasets and makes these relationships interactively explorable. Highlighting and filtering features support analysis both on a global and detailed level. The RelFinder is based on the open source framework Adobe Flex, easy-to-use and works on any RDF dataset that provides standardized SPARQL access.
Erwin Karbasi

Lars Kirchhoff [Web Journal] - - 0 views

  • For more then half a year we are running a research project about social networks within the blogosphere. The social network analysis is only one step to get information about the topic flow within a certain blog networks. We used Technorati as a source for the detection of the blog networks using a snow ball approach and than crawled the found blog nodes to identify the network edges. So far we have five sample networks analyzed ranging from 300 to 14'000 nodes with more than 200'000 edges. One task within the project is the visualization of these networks with appropriate tools that enable the easy access to the gathered information. Various levels of detail are needed to extract and highlight different network parameter and make them easily understandable. Therefore I did a research on current tools available.
Erwin Karbasi

MySQL vs. Neo4j on a Large-Scale Graph Traversal - 0 views

  • Traversing the Graph The traversal that was evaluated on each database started from some root vertex and emanated n-steps out. There was no sorting, no distinct-ing, etc. The only two variables for the experiments are the length of the traversal and the root vertex to start the traversal from. In MySQL, the following 5 queries denote traversals of length 1 through 5. Note that the "?" is a variable parameter of the query that denotes the root vertex.     SELECT a.inV FROM graph as a WHERE a.outV=?     SELECT b.inV FROM graph as a, graph as b WHERE a.inV=b.outV AND a.outV=?     SELECT c.inV FROM graph as a, graph as b, graph as c WHERE a.inV=b.outV AND b.inV=c.outV AND a.outV=?     SELECT d.inV FROM graph as a, graph as b, graph as c, graph as d WHERE a.inV=b.outV AND b.inV=c.outV AND c.inV=d.outV AND a.outV=?     SELECT e.inV FROM graph as a, graph as b, graph as c, graph as d, graph as e WHERE a.inV=b.outV AND b.inV=c.outV AND c.inV=d.outV AND d.inV=e.outV AND a.outV=? For Neo4j, the Blueprints Pipes framework was used. A pipe of length n was constructed using the following static method.     public static Pipeline createPipeline(final Integer steps) {         final ArrayList<Pipe> pipes = new ArrayList<Pipe>();         for (int i = 0; i < steps; i++) {             Pipe pipe1 = new VertexEdgePipe(VertexEdgePipe.Step.OUT_EDGES);             Pipe pipe2 = new EdgeVertexPipe(EdgeVertexPipe.Step.IN_VERTEX);             pipes.add(pipe1);             pipes.add(pipe2);         }         return new Pipeline(pipes);     } For both MySQL and Neo4j, the results of the query (SQL and Pipes) were iterated through. Thus, all results were retrieved for each query. In MySQL, this was done as follows.     while (resultSet.next()) {         resultSet.getInt(finalColumn);     } In Neo4j, this is done as follows.     while (pipeline.hasNext()) {         pipeline.next();     }
Erwin Karbasi

InfoQ: Graph Databases, NOSQL and Neo4j - 0 views

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    "Example - the MATRIX The Graph As mentioned before, Social Networks represent just a tiny fraction of the applications of graph databases, but they are easy to understand for this example. To demonstrate the basic functionality of Neo4j, below is a small graph from the Matrix movie, visualized with the Eclipse RCP based Neoclipse for Neo4j: The graph is connected to a known reference node (id=0) for convenience in order to find the way into the network from a known starting point. This is not necessary, but has proven very usable in practice. The Java implementation looks something like this: Create a new graph database in folder "target/neo" EmbeddedGraphDatabase graphdb = new EmbeddedGraphDatabase("target/neo"); Relationship types can be created on-the-fly: RelationshipType KNOWS = DynamicRelationshipType.withName("KNOWS"); or via typesafe Java Enum: enum Relationships implements RelationshipType { KNOWS, INLOVE, HAS_CODED, MATRIX } Now, create two nodes and attach a "name" property to each of them. Then, connect these nodes witha KNOWS relationship: Node neo = graphdb.createNode(); node.setProperty("name", "Neo"); Node morpheus = graphdb.createNode(); morpheus.setProperty("name", "Morpheus"); neo.createRelationshipTo(morpheus, KNOWS); Any operation modifying the graph or needing isolation levels for data is wrapped in a transaction, so rollback and recovery work out of the box: Transaction tx = graphdb.beginTx(); try { Node neo = graphdb.createNode(); ... tx.success(); } catch (Exception e) { tx.failure(); } finally { tx.finish(); } The full code to create the Matrix graph the looks something like this: graphdb = new EmbeddedGraphDatabase("target/neo4j"); index = new LuceneIndexService(graphdb); Transaction tx = graphdb.beginTx(); try { Node root = graphdb.getReferenceNode(); // we connect Neo with the root node, to gain an entry point to the graph // not neccessary but practical. neo = createAndConnectNode("Neo", root, MATRIX); Node mo
Erwin Karbasi

NoSQL Graph Database Comparison | Javalobby - 0 views

  • A few days ago I published a short overview of the most trendy graph databases. Today I'm bringing you a review of the most important features of them. As you can see the current ecosystem is quite bit, without general uniformity, although this is normal when analyzing an ongoing technology movement.  As you can see in the previous table,  there are substantial differences that can help our projects. Next we are going to analyze these main differences.
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