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What is Node.js?

Views: 547 Average Rating:
Added by Martin on 2011-07-08 10:26:39
What is Node.js? Learning Node might take a little effort, but it's going to pay off. Why? Because you're afforded solutions to your web application problems that require only JavaScript to solve. Brett McLaughlin
SimPack

Views: 543 Average Rating:
Added by gromgull on 2011-04-20 11:01:21
SimPack SimPack is intended primarily for the research of similarity between concepts in ontologies or ontologies as a whole. Possible other application areas of SimPack include
LingPipe: Tutorials

Views: 469 Average Rating:
Added by benbanbun on 2011-02-22 16:34:55
LingPipe: Tutorials Excellent tutorials about text-analytical algorithms. The application program interface (API) turorials are intended to help developers get started with the LingPipe API. Each tutorial is designed to stand alone. alias-i
GeoNetwork opensource

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Added by gromgull on 2011-01-12 10:28:07
GeoNetwork opensource "GeoNetwork is a catalog application to manage spatially referenced resources. It provides powerful metadata editing and search functions as well as an embedded interactive web map viewer. It is currently used in numerous Spatial Data Infrastructure initiatives across the world." Implements INSPIRE - integrates AGROVOC?
Mobile Web Application Best Practices

Views: 625 Average Rating:
Added by Martin on 2010-12-23 11:01:56
Mobile Web Application Best Practices
A Survey on Link Prediction Models for Social Network Data

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Added by benbanbun on 2010-11-17 09:53:32
A Survey on Link Prediction Models for Social Network Data Link prediction for social network data is a fundamental data mining task in various application domains, including social network analysis, information retrieval, recommendation systems, record linkage, marketing and bioinformatics. There are a variety of techniques for the link prediction problem, ranging from graph theory, metric learning, statistical relational learning to matrix factorization and probabilistic graphical models. In this survey, we organize the sparse related literature into a structured presentation and summarize the recent research works on the link prediction task. We categorize the current link prediction methods into three classes: the node-wise similarity based methods try to seek an appropriate distance measurement for two objects; the topological pattern based methods focus on exploiting either local or global patterns that could well describing the network; probabilistic model based methods try to learn a compact model that could abstracting the social network best. We will first review these methods, from detailed approaches to the evolution of the ideas, and then comment on their relative strengths and weaknesses. Finally, we give a brief summary on them and discuss some possible research issues. Evan Wei Xiang
Algorithms - Apache Mahout

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Added by benbanbun on 2010-11-15 14:21:33
Algorithms - Apache Mahout This section contains links to information, examples, use cases, etc. for the various algorithms we intend to implement. Click the individual links to learn more. The initial algorithms descriptions have been copied here from the original project proposal. The algorithms are grouped by the application setting, they can be used for. In case of multiple applications, the version presented in the paper was chosen, versions as implemented in our project will be added as soon as we are working on them. Apache Software Foundation
Analysing Dependency Dynamics in Web Data

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Added by benbanbun on 2010-11-09 11:20:35
Analysing Dependency Dynamics in Web Data Modern web sites provide easy access to large amounts of data via open application programming interfaces. Users interacting with these sites constantly change the underlying data sets, which can be represented in graph-structured form. Nodes in these dynamic graph structures exhibit dependencies over time (for example, one node changes before other nodes change in the same way). Analysing these dependencies is crucial for understanding and predicting the dynamics inherent to temporally changing graph structures on the web. When the graphs become large however, it is not feasible to take into account all properties of the graph and in general it is unclear how to choose the appropriate features. Moreover, comparing two nodes becomes difficult, if the nodes do not share exactly the same features. In this work we propose an algorithm that automatically learns the features that govern temporal dependencies between nodes in large dynamic graph structures. We present preliminary results of applying the algorithm to data collected from the web, discuss potential extensions of the framework and anticipate how a major problem in machine learning, sparse data, could be tackled by leveraging Linked Data. Felix Bießmann, Andreas Harth
m2eclipse-book.pdf (application/pdf-Objekt)

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Added by benbanbun on 2010-07-20 09:24:05
m2eclipse-book.pdf (application/pdf-Objekt) The m2eclipse plugin (http://m2eclipse.sonatype.org/) provides Maven integration for Eclipse. m2eclipse also has hooks into the features of both the Subclipse plugin (http://subclipse.tigris.org/) and the Mylyn plugin (http://www.eclipse.org/mylyn/). The Subclipse plugin provides the m2eclipse plugin with the ability to interact with Subversion repositories, and the Mylyn plugin provides the m2eclipse plugin with the ability to interact with a task-focused interface that can keep track of development context. Sonatype, http://www.sonatype.com Attribution-NonCommercial-ShareAlike License (CC BY-NC-SA)
The Mobile Augmented Reality Competitive Landscape

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Added by Martin on 2010-06-10 12:23:27
The Mobile Augmented Reality Competitive Landscape Analysis of augmented reality applications in the iPhone application store. Who’s building what, what price, what categories. Augmented Planet