Spatio-temporal composition and indexing for large multimedia applications

Vazirgiannis, M.; Theodoridis, Yannis; Sellis, Timos

doi:10.1007/s005300050094

Cited by 106 publications

(67 citation statements)

References 36 publications

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“…The 3D R-tree clustering periodically builds a small in-memory 3D R-tree [18] with incoming data stream. Data elements pointed to by the same leaf-node of the inmemory R-tree are grouped as one cluster.…”

Section: Methodsmentioning

confidence: 99%

DCF: An Efficient Data Stream Clustering Framework for Streaming Applications

Cho

Jang

et al. 2006

Lecture Notes in Computer Science

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Abstract. Streaming applications, such as environment monitoring and vehicle location tracking require handling high volumes of continuously arriving data and sudden fluctuations in these volumes while efficiently supporting multidimensional historical queries. The use of the traditional database management systems is inappropriate because they require excessive number of disk I/O in continuously updating massive data streams. In this paper, we propose DCF (Data Stream Clustering Framework), a novel framework that supports efficient data stream archiving for streaming applications. DCF can reduce a great amount of disk I/O in the storage system by grouping incoming data into clusters and storing them instead of raw data elements. In addition, even when there is a temporary fluctuation in the amount of incoming data, it can stably support storing all incoming raw data by controlling the cluster size. Our experimental results show that our approach significantly reduces the number of disk accesses in terms of both inserting and retrieving data.

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Section: Methodsmentioning

confidence: 99%

DCF: An Efficient Data Stream Clustering Framework for Streaming Applications

Cho

Jang

et al. 2006

Lecture Notes in Computer Science

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“…The underlying query language is nonstandard, and is based on the Future Temporal Logic (FTL). Similar issues are addressed in [34]. A trajectory model similar to ours is given in [35] where the authors extend range queries with new operators for special cases of spatio-temporal range queries.…”

Section: Conclusion Related Work and Future Directionsmentioning

confidence: 98%

“…For the purpose of query processing, we assume an available 3D indexing scheme in the underlying DBMS, similar to the ones proposed in [17,28,34]. The insertion of a trajectory is done by enclosing, for each trajectory, each trajectory volume between t i and t i+1 in a Minimum Bounding Box (MBB).…”

Section: Processing the Range Operatorsmentioning

confidence: 99%

The Geometry of Uncertainty in Moving Objects Databases

Trajcevski

Wolfson

Zhang

et al. 2002

Advances in Database Technology — EDBT 2002

100

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Abstract. This work addresses the problem of querying moving objects databases. which capture the inherent uncertainty associated with the location of moving point objects. We address the issue of modeling, constructing, and querying a trajectories database. We propose to model a trajectory as a 3D cylindrical body. The model incorporates uncertainty in a manner that enables efficient querying. Thus our model strikes a balance between modeling power, and computational efficiency. To demonstrate efficiency, we report on experimental results that relate the length of a trajectory to its size in bytes. The experiments were conducted using a real map of the Chicago Metropolitan area. We introduce a set of novel but natural spatio-temporal operators which capture uncertainty, and are used to express spatio-temporal range queries. We also devise and analyze algorithms to process the operators. The operators have been implemented as a part of our DOMINO project.

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“…1 assuming that at time t 1 , objects d, and e issue location updates, which result in new versions d 0 and e 0 , leading to another leaf node N 0 2 . Variations of the above idea have been exploited for indexing multimedia objects [48], trajectory [32], and historical information retrieval [38] for real-world data. Furthermore, the 3D R-tree has been used as a part of a multitree structure for aggregate processing in the context of spatio-temporal data warehouses [35].…”

Section: As Followsmentioning

confidence: 99%

Performance analysis of R*-trees with arbitrary node extents

Tao

Papadias

2004

IEEE Trans. Knowl. Data Eng.

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Abstract-Existing analysis for R-trees is inadequate for several traditional and emerging applications including, for example, temporal, spatio-temporal, and multimedia databases because it is based on the assumption that the extents of a node are identical on all dimensions, which is not satisfied in these domains. In this paper, we propose analytical models that can accurately predict R*-tree performance without this assumption. Our derivation is based on the novel concept of extent regression function, which computes the node extents as a function of the number of node splits. Detailed experimental evaluation reveals that the proposed models are accurate, even in cases where previous methods fail completely.

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Spatio-temporal composition and indexing for large multimedia applications

Cited by 106 publications

References 36 publications

DCF: An Efficient Data Stream Clustering Framework for Streaming Applications

DCF: An Efficient Data Stream Clustering Framework for Streaming Applications

The Geometry of Uncertainty in Moving Objects Databases

Performance analysis of R*-trees with arbitrary node extents

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