Indexing Large Moving Objects from Past to Future with PCFI+-Index
نویسندگان
چکیده
Ideally, moving object databases should handle the past, current and future positions of moving objects efficiently. However, existing indexes such as SEB-Tree, SETI-Tree, 2+3R-Tree, 23RT-Tree and etc. can only provide the capability for past and current query, and the others such as TPR-Tree, and TPR*-Tree can only provide the capability for current and future query. None of them can provide a strategy for indexing the past, current and also the future information of moving objects. In this paper, we present the Past-Current-Future-Index (PCFI-Index) which indexes the past, current & future information of the moving objects. The PCFI-Index builds upon the PCFI-Index which was based on SETI-tree and TPR*-tree. The PCFI-Index consists of two parts, in memory part with the name frontline, and disk based part. The whole region is partitioned into none-overlapping cells, and a spatial access method is used to index these cells. A set of main-memory TPR*-tree is used to index the moving objects that belong to the cells (one cell, one TPR*-tree). Considering the large update operation triggered by moving objects, the current data file which contains the moving objects’ current information is organized as a hash index file. By keeping the restriction in SETI-Index, one page only contains the segments from one cell. Another sparse R*-tree is used to index the lifetimes of the pages. The performance analysis proves that the PCFI-Index can handle most of the queries efficiently and provides a uniform solution for the trajectory, time-slice, internal and moving queries, and has a better performance than the SETI-Index, TPR*-Index, and PCFI-
منابع مشابه
On past - time indexing of moving objects q
Tracking of mobile objects trajectories is one of many modern applications supported by Spatiotemporal databases. Within the context of this application, queries about the present, future or past positions of the objects need to be answered. Several indexing methods have been proposed to efficiently handle such spatiotemporal queries. In the current paper, we propose a method for indexing the h...
متن کاملA Pattern-Based Predictive Indexing Method for Distributed Trajectory Databases
Recently, it has become possible to collect large amounts of trajectory data of moving objects by using sensor networks. To manage such trajectory data, we have developed a distributed trajectory database composed of a server and many sensor nodes deployed over wide areas. The server manages the trajectory data of each moving object by using indices. However, since each sensor node cannot send ...
متن کاملIndexing, Query and Velocity-Constrained
Moving object environments are characterized by large 10 numbers of moving objects and numerous concurrent con11 tinuous queries over these objects. Efficient evaluation of 12 these queries in response to the movement of the objects is 13 critical for supporting acceptable response times. In such 14 environments the traditional approach of building an index 15 on the objects (data) suffers from...
متن کاملTowards Increasingly Update Efficient Moving-Object Indexing
Current moving-object indexing concentrates on point-objects capable of continuous movement in one-, two-, and three-dimensional Euclidean spaces, and most approaches are based on well-known, conventional spatial indices. Approaches that aim at indexing the current and anticipated future positions of moving objects generally must contend with very large update loads because of the agility of th...
متن کاملBoosting Moving Object Indexing through Velocity Partitioning
There have been intense research interests in moving object indexing in the past decade. However, existing work did not exploit the important property of skewed velocity distributions. In many real world scenarios, objects travel predominantly along only a few directions. Examples include vehicles on road networks, flights, people walking on the streets, etc. The search space for a query is hea...
متن کامل