The chatty web: emergent semantics through gossiping
WWW '03 Proceedings of the 12th international conference on World Wide Web
Peer-to-peer information retrieval using self-organizing semantic overlay networks
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
QC-trees: an efficient summary structure for semantic OLAP
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Hierarchical dwarfs for the rollup cube
DOLAP '03 Proceedings of the 6th ACM international workshop on Data warehousing and OLAP
Efficient query reformulation in peer data management systems
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Scalability analysis of three monitoring and information systems: MDS2, R-GMA, and Hawkeye
Journal of Parallel and Distributed Computing
Querying the internet with PIER
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
GrouPeer: Dynamic clustering of P2P databases
Information Systems
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Concept hierarchies greatly help in the organization and reuse of information and are widely used in a variety of information systems applications. In this paper, we describe a method for efficiently storing and querying data organized into concept hierarchies and dispersed over a DHT. In our method, peers individually decide on the level of indexing according to the granularity of the incoming queries. Roll-up and drill-down operations are performed on a per-node basis in order to minimize the required bandwidth for answering queries on variable aggregation levels. We motivate our approach by applying it on a large-scale Grid system: Specifically, we plan to apply our fully decentralized scheme that creates, queries and updates large volumes of hierarchical data on-line and replace the traditional centralized and strictly indexed information systems. Our extensive experimental results support this argument on many diverse configurations: Our system proves very efficient in skewed workloads, both over single and multiple hierarchy levels at the same time. It adapts to sudden changes in popularity and effectively stores and updates large amounts of data at very low cost.