Paper Info
Title
Expressive Query Support for Multidimensional Data in Distributed Hash Tables
Abstract
The quantity and precision of geospatial and time series observational data being collected has increased in tandem with the steady expansion of processing and storage capabilities in modern computing hardware. The storage requirements for this information are vastly greater than the capabilities of a single computer, and are primarily met in a distributed manner. However, distributed solutions often impose strict constraints on retrieval semantics. In this paper, we investigate the factors that influence storage and retrieval operations on large datasets in a cloud setting, and propose a lightweight data partitioning and indexing scheme to facilitate these operations. Our solution provides expressive retrieval support through range-based and exact-match queries and can be applied over massive quantities of multidimensional data. We provide benchmarks to illustrate the relative advantage of using our solution over an established cloud storage engine in a distributed network of heterogeneous computing resources.
Year
DOI
Venue
2012
10.1109/UCC.2012.41
UCC
Keywords
Field
DocType
distributed network,distributed hash tables,expressive retrieval support,range-based queries,cloud infrastructure,established cloud storage engine,lightweight data,retrieval semantics,storage capability,retrieval operation,multidimensional data,distributed file systems,lightweight data partitioning,time series observational data,hash tables,modern computing hardware,exact-match queries,pattern matching,resource allocation,storage operations,cloud storage engine,processing steady expansion,storage capabilities,heterogeneous computing resources,query evaluation,storage requirements,geospatial quantity,file organisation,data partitioning,expressive query support,distributed manner,geospatial precision,cloud computing,distributed solutions,cloud setting,storage requirement,distributed databases,influence storage,query processing,indexing scheme
Geospatial analysis,Data mining,Computer science,Search engine indexing,Symmetric multiprocessor system,Distributed algorithm,Distributed database,Cloud storage,Hash table,Cloud computing
Conference
ISSN
ISBN
Citations 
2373-6860
978-1-4673-4432-6
11
PageRank 
References 
Authors
0.65
15
3
Name
Order
Citations
PageRank
Matthew Malensek19310.44
Sangmi Lee Pallickara217024.46
Shrideep Pallickara383792.72