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Optimization issues for indexing mov...

Lin, Bin.

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Optimization issues for indexing moving objects.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Optimization issues for indexing moving objects./
作者:	Lin, Bin.
面頁冊數:	214 p.
附註:	Adviser: Jianwen Su.
Contained By:	Dissertation Abstracts International67-05B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3218849
ISBN:	9780542682261

Optimization issues for indexing moving objects.
Lin, Bin.

Optimization issues for indexing moving objects. - 214 p.

Adviser: Jianwen Su.

Thesis (Ph.D.)--University of California, Santa Barbara, 2006.

Continuous movement of objects in MOD introduces the frequent update problem for index maintaining. We study frequent update problem in both disk based and main memory environment and present solutions for both of them.

ISBN: 9780542682261Subjects--Topical Terms:

626642
Computer Science.

Optimization issues for indexing moving objects.
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520 $a Continuous movement of objects in MOD introduces the frequent update problem for index maintaining. We study frequent update problem in both disk based and main memory environment and present solutions for both of them.
520 $a With the rapid development of new technologies, moving object applications (e.g. mobile computing, traffic monitoring, digital battle fields etc.) become more and more realistic and attractive. Managing the information of continuous movements by objects becomes a new challenge in database research. One important issue in moving object databases (MOD) is indexing. Indices are important for MOD to provide efficient query performance. But in MOD objects are moving continuously, which introduces challenges for indices of moving objects. In this dissertation we focus on optimization issues on indexing moving objects: bulk loading, update, and similarity search.
520 $a Bulk loading is an effective method to achieve good space utilization, efficient query performance and fast tree building time. We focus on algorithms fitting different data distributions. We develop a histogram-based bottom up (HBU) bulk loading algorithm for TPR-tree. This algorithm can easily accommodate datasets with different distributions. Actually HBU outperform existing algorithms not only for non-uniform distribution, but for uniform distribution as well.
520 $a Lazy Group Update (LGU) algorithm is introduced for disk based index structures. LGU utilizes memory wisely by employing disk based I-Buffers and a main memory D-Table to group insertions and deletions. Group insertion is processed in a top down way, while group deletion is handled in a bottom up manner. LGU can achieve much higher update throughput than existing methods and reasonable query performance. But if incoming update frequency is extremely high, LGU is still not good enough due to the bottleneck of disk access. In this case we have to resort to main memory solutions.
520 $a HG-tree is a main memory index structure developed for frequent update handling. It uses a hashing function to fast locate object for update and build the tree based on grids, which avoids the necessity of storing bounding rectangles and simplifies query process. Bulk update strategies are also developed to improve update throughput further. Empirical study shows that HG-tree outperforms a main memory R-tree significantly.
520 $a Similarity search in MOD can be divided into two groups: time sensitive ones and time independent ones. We focus on the latter group in our study. We develop a shape based similarity function---One Way Distance (OWD), as well as algorithms based on it. An index structure for OWD in grid representation, which guarantees no false dismissals, is also given to improve the efficiency of similarity search. By utilizing local min points, our algorithm can achieve asymptotically better performance and better precision than existing algorithms (e.g. DTW).
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