Online querying of d-dimensional hierarchies

In this paper we describe a distributed system designed to efficiently store, query and update multidimensional data organized into concept hierarchies and dispersed over a network. Our system employs an adaptive scheme that automatically adjusts the level of indexing according to the granularity of...

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Veröffentlicht in:Journal of parallel and distributed computing Jg. 71; H. 3; S. 424 - 437
Hauptverfasser: Doka, Katerina, Tsoumakos, Dimitrios, Koziris, Nectarios
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier Inc 01.03.2011
Elsevier
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Computer networks
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Concept hierarchies
Data warehousing
Distributed hash tables
Exact sciences and technology
Hierarchies
Indexing
Information systems. Data bases
Memory organisation. Data processing
Networks
On-line systems
Peer-to-Peer
Preserves
Query processing
Software
Theoretical computing
Workload
Distributed hash tables
Concept hierarchies
Peer-to-Peer
Data warehousing
Grain size
Peer to peer
On line
Database query
Adaptive system
Updating
Distributed system
Workload
Distributed memory
Overload
Hashing
Data warehouse
Indexing
ISSN:0743-7315, 1096-0848
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Zusammenfassung:In this paper we describe a distributed system designed to efficiently store, query and update multidimensional data organized into concept hierarchies and dispersed over a network. Our system employs an adaptive scheme that automatically adjusts the level of indexing according to the granularity of the incoming queries, without assuming any prior knowledge of the workload. Efficient roll-up and drill-down operations take place in order to maximize the performance by minimizing query flooding. Updates are performed on-line, with minimal communication overhead, depending on the level of consistency needed. Extensive experimental evaluation shows that, on top of the advantages that a distributed storage offers, our method answers the vast majority of incoming queries, both point and aggregate ones, without flooding the network and without causing significant storage or load imbalance. Our scheme proves to be especially efficient in cases of skewed workloads, even when these change dynamically with time. At the same time, it manages to preserve the hierarchical nature of data. To the best of our knowledge, this is the first attempt towards the support of concept hierarchies in DHTs. ► First work to address the problem of hierarchical data search in DHT systems. ► HiPPIS allows the reorganization of the indexing structure in favor of resolving queries for the most popular data. ► It manages to preserve useful hierarchy-specific information, that hashing destroys. ► It allows for fast, on-line updates, with an overhead depending on the level of consistency needed by the application. ► HiPPIS proves particularly efficient with highly skewed data distributions, without inducing significant load or storage imbalance.
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ISSN:0743-7315
1096-0848
DOI:10.1016/j.jpdc.2010.10.005