Linked Enterprise Data PatternsThe goal of this workshop is threefold:
Describe a set of standard patterns, design choices and best practices that will give application writers much stronger guidance and reduce the number of design decisions they have to make when adopting Linked Data as an application architecture.
Identify a handful of gaps in the current Linked Data specs that are uncovered by common application scenarios.
Propose a set of simple solutions to those gaps that could be rapidly specified and approved by W3C
SUBDUE - Graph Based Knowledge DiscoverySUBDUE is a graph-based knowledge discovery system that finds structural, relational patterns in data representing entities and relationships. SUBDUE represents data using a labeled, directed graph in which entities are represented by labeled vertices or subgraphs, and relationships are represented by labeled edges between the entities. SUBDUE uses the minimum description length (MDL) principle to identify patterns that minimize the number of bits needed to describe the input graph after being compressed by the pattern. SUBDUE can perform several learning tasks, including unsupervised learning, supervised learning, clustering and graph grammar learning.
Adventures in Data Land, Speeding up Latent Dirichlet AllocationSpeeding up Latent Dirichlet Allocation The code to our LDA implementation on Hadoop is released on Github under the Mozilla Public License. It's seriously fast and scales very well to 1000 machines...
Accepted Papers -- W3C Workshop on Web Tracking and User PrivacyThis workshop serves to establish a common view on possible Recommendation-track work in the Web privacy and tracking protection space at W3C, and on the coordination needs for such work.
The workshop is expected to attract a broad set of stakeholders, including implementers from the mobile and desktop space, large and small content delivery providers, advertisement networks, search engines, policy and privacy experts, experts in consumer protection, and other parties with an interest in Web tracking technologies, including the developers and operators of Services on the Web that make use of tracking technologies for purposes other than to behavioral advertising.
Mining of Massive DatasetsAt the highest level of description, this book is about data mining. However,
it focuses on data mining of very large amounts of data, that is, data so large
it does not fit in main memory. Because of the emphasis on size, many of our
examples are about the Web or data derived from the Web. Further, the book
takes an algorithmic point of view: data mining is about applying algorithms
to data, rather than using data to â€œtrainâ€ a machine-learning engine of some
A Standards-based, Open and Privacy-aware Social WebThe Social Web is a set of relationships that link together people over the Web. The Web is an universal and open space of information where every item of interest can be identified with a URI. While the best known current social networking sites on the Web limit themselves to relationships between people with accounts on a single site, the Social Web should extend across the entire Web. Just as people can call each other no matter which telephone provider they belong to, just as email allows people to send messages to each other irrespective of their e-mail provider, and just as the Web allows links to any website, so the Social Web should allow people to create networks of relationships across the entire Web, while giving people the ability to control their own privacy and data. The standards that enable this should be open and royalty-free. We present a framework for understanding the Social Web and the relevant standards (from both within and outside the W3C) in this report, and conclude by proposing a strategy for making the Social Web a "first-class citizen" of the Web.
Structured machine learning: the next ten yearsThe field of inductive logic programming (ILP) has made steady progress, since the first ILP workshop in 1991, based on a balance of developments in theory, implementations and applications. More recently there has been an increased emphasis on Probabilistic ILP and the related fields of Statistical Relational Learning (SRL) and Structured Prediction. The goal of the current paper is to consider these emerging trends and chart out the strategic directions and open problems for the broader area of structured machine learning for the next 10 yearsThomas G. Dietterich, Pedro Domingos, Lise Getoor, Stephen Muggleton, Prasad Tadepalli