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Issue No. 09 - Sept. (2014 vol. 26)
ISSN: 1041-4347
pp: 2222-2236
ABSTRACT
Given a spatio-temporal network (STN) and a set of STN operations, the goal of the Storing Spatio-Temporal Networks (SSTN) problem is to produce an efficient method of storing STN data that minimizes disk I/O costs for given STN operations. The SSTN problem is important for many societal applications, such as surface and air transportation management systems. The problem is NP hard, and is challenging due to an inherently large data volume and novel semantics (e.g., Lagrangian reference frame). Related works rely on orthogonal partitioning approaches (e.g., snapshot and longitudinal) and incur excessive I/O costs when performing common STN queries. Our preliminary work proposed a non-orthogonal partitioning approach in which we optimized the LGetOneSuccessor() operation that retrieves a single successor for a given node on STN. In this paper, we provide a method to optimize the LGetAllSuccessors() operation, which retrieves all successors for a given node on a STN. This new approach uses the concept of a Lagrangian Family Set (LFS) to model data access patterns for STN queries. Experimental results using real-world road and flight traffic datasets demonstrate that the proposed approach outperforms prior work for LGetAllSuccessors() computation workloads.
INDEX TERMS
visual databases, query processing, storage management, temporal databases,real-world road traffic datasets, Lagrangian approaches, spatio-temporal network dataset storage, SSTN problem, disk I/O cost minimization, air transportation management systems, NP hard problem, large data volume, orthogonal partitioning approaches, STN queries, nonorthogonal partitioning approach, LGetOneSuccessor() operation, Lagrangian family set, LFS, data access patterns, flight traffic datasets,Roads, Indexes, Data structures, Sensor phenomena and characterization, Delays,Data Storage Representations, Data, Data Structures, Graphs and networks, Storage/repositories, Information Storage, Information Storage and Retrieval, Mathematics of Computing, Discrete Mathematics, Combinatorics, Combinatorial algorithms, Information Technology and Systems, Database Management, Languages, Query languages, Systems, Spatial databases, Temporal databases, Spatial databases and GIS,geographic information systems, Storage and access methods, spatio-temporal network databases, graph partitioning, lagrangian reference frame
CITATION
"Lagrangian Approaches to Storage of Spatio-Temporal Network Datasets", IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. , pp. 2222-2236, Sept. 2014, doi:10.1109/TKDE.2013.92
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