Distributed data aggregation is an important task, allowing the decentralized determination
of meaningful global properties, which can then be used to direct the execution of other …

Today's scientific simulations require a significant reduction of the data size because of
extremely large volumes of data they produce and the limitation of storage bandwidth and …

Using microwaves to treat metal-based materials is rapidly emerging as an energy-efficient
tool to interact with metals for a number of processes such as sintering, melting, brazing …

Estimating characteristics of large graphs via sampling is a vital part of the study of complex
networks. Current sampling methods such as (independent) random vertex and random …

Graph sampling via crawling has been actively considered as a generic and important tool
for collecting uniform node samples so as to consistently estimate and uncover various …

The completely decoupled interaction model offered by the publish/subscribe
communication paradigm perfectly suits the interoperability needs of todays large-scale …

We present Brahms, an algorithm for sampling random nodes in a large dynamic system
prone to malicious behavior. Brahms stores small membership views at each node, and yet …

Balanced graph partitioning is a well known NP-complete problem with a wide range of
applications. These applications include many large-scale distributed problems including …

Data-intensive applications dominated by random accesses to large working sets fail to
utilize the computing power of modern processors. Graph random walk, an indispensable …

Self-organizing structure and availability of almost unlimited resource capacities make the
peer-to-peer architecture very attractive for large-scale sharing of annotated data in Web 2.0 …