The MP-tree: a data structure for spatio-temporal data

Teraoka, Teruhiko; Maruyama, Mitsuru; Nakamura, Yūichi; Nishida, Shogo

doi:10.1109/pccc.1995.472473

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…The problem of SDH computation over multiple consecutive frames is related to persistent data structures [17], [18], which allow for various versions of the computation results to be maintained and updated over time for quick query processing. Building persistent index schemes on complex spatio-temporal data allows for a time efficient retrieval [19].…”

Section: Comparison To Related Workmentioning

confidence: 99%

Computing Spatial Distance Histograms for Large Scientific Data Sets On-the-Fly

Kumar

Grupcev

Yuan

et al. 2014

IEEE Trans. Knowl. Data Eng.

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This paper focuses on an important query in scientific simulation data analysis: the Spatial Distance Histogram (SDH). The computation time of an SDH query using brute force method is quadratic. Often, such queries are executed continuously over certain time periods, increasing the computation time. We propose highly efficient approximate algorithm to compute SDH over consecutive time periods with provable error bounds. The key idea of our algorithm is to derive statistical distribution of distances from the spatial and temporal characteristics of particles. Upon organizing the data into a Quad-tree based structure, the spatiotemporal characteristics of particles in each node of the tree are acquired to determine the particles’ spatial distribution as well as their temporal locality in consecutive time periods. We report our efforts in implementing and optimizing the above algorithm in Graphics Processing Units (GPUs) as means to further improve the efficiency. The accuracy and efficiency of the proposed algorithm is backed by mathematical analysis and results of extensive experiments using data generated from real simulation studies.

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Section: Comparison To Related Workmentioning

confidence: 99%

Computing Spatial Distance Histograms for Large Scientific Data Sets On-the-Fly

Kumar

Grupcev

Yuan

et al. 2014

IEEE Trans. Knowl. Data Eng.

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“…Spatio-temporal data structures [4,5] have been developed to provide efficient spatial searches with time intervals. We have also developed a spatio-temporal data structure called the PMD-free (Persistent Multi-Dimensional…”

Section: Introductionmentioning

confidence: 99%

An efficient management method of moving spatial objects

Nakamura

Dekihara²

1999 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM 1999). Conference Proceedings (Cat.

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t=O T1 T2 NowA spatio-temporal data structure, called the PMD-tree (Persistent Multi-Dimensional tree), has been proposed for managing the live intervals and locations of spatial objects. In the paper, novel concepts of time space bounding box (TSBB) and motion Iist are introduced to the PMD-tree to manage moving spatial objects efficiently. TSBB is an extended bounding box for a moving object that covers the trajectory ofthe object. As an object moves, a E B B corresponding to the object is enlarged to enclose the trajectory of the object. A TSBB is divided, when it becomes greater than a limit. An object and corresponding EBBS are managed by a doubly connected linked list, called motion list. TSBBs are also managed by the PMD-tree. Introducing the concept of TSBB and motion list to the PMD-tree, moving objects can be efficiently managed and be quickly found for spatiotemporal queries. By the series of simulation tests, the storage requirements and search performances are evaluated for several types of moving objects.

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