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An effective data placement strategy for XML documents
As XML is increasingly being used in Web applications, new
technologies need to be investigated for processing XML documents with high
performance. Parallelism is a promising solution for structured document
processing and data placement is a major factor for system performance
improvement in parallel processing. This paper describes an effective XML
document data placement strategy. The new strategy is based on a multilevel
graph partitioning algorithm with the consideration of the unique features of
XML documents and query distributions. A new algorithm, which is based on
XML query schemas to derive the weighted graph from the labelled directed
graph presentation of XML documents, is also proposed. Performance analysis
on the algorithm presented in the paper shows that the new data placement
strategy exhibits low workload skew and a high degree of parallelism
A Distributed Method for Trust-Aware Recommendation in Social Networks
This paper contains the details of a distributed trust-aware recommendation
system. Trust-base recommenders have received a lot of attention recently. The
main aim of trust-based recommendation is to deal the problems in traditional
Collaborative Filtering recommenders. These problems include cold start users,
vulnerability to attacks, etc.. Our proposed method is a distributed approach
and can be easily deployed on social networks or real life networks such as
sensor networks or peer to peer networks
Graph Summarization
The continuous and rapid growth of highly interconnected datasets, which are
both voluminous and complex, calls for the development of adequate processing
and analytical techniques. One method for condensing and simplifying such
datasets is graph summarization. It denotes a series of application-specific
algorithms designed to transform graphs into more compact representations while
preserving structural patterns, query answers, or specific property
distributions. As this problem is common to several areas studying graph
topologies, different approaches, such as clustering, compression, sampling, or
influence detection, have been proposed, primarily based on statistical and
optimization methods. The focus of our chapter is to pinpoint the main graph
summarization methods, but especially to focus on the most recent approaches
and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie
Optimal web-scale tiering as a flow problem
We present a fast online solver for large scale parametric max-flow problems as they occur in portfolio optimization, inventory management, computer vision, and logistics. Our algorithm solves an integer linear program in an online fashion. It exploits total unimodularity of the constraint matrix and a Lagrangian relaxation to solve the problem as a convex online game. The algorithm generates approximate solutions of max-flow problems by performing stochastic gradient descent on a set of flows. We apply the algorithm to optimize tier arrangement of over 84 million web pages on a layered set of caches to serve an incoming query stream optimally
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