19-02-2012, 01:14 PM
i want a full detail for continuous monitoring of spatial queries in wireless broadcast environment ppt and full details
19-02-2012, 01:14 PM
i want a full detail for continuous monitoring of spatial queries in wireless broadcast environment ppt and full details
19-02-2012, 05:07 PM
20-02-2012, 12:27 PM
to get information about the topic Continuous Monitoring of Spatial Queries in Wireless Broadcast Environments full report ppt and related topic refer the link bellow
https://seminarproject.net/Thread-contin...vironments
07-09-2012, 11:44 AM
Continuous Monitoring of Spatial Queries in Wireless Broadcast Environments
Continuous Monitoring.doc (Size: 31.5 KB / Downloads: 27) ABSTRACT Wireless data broadcast is a promising technique for information dissemination that leverages the computational capabilities of the mobile devices in order to enhance the scalability of the system. Under this environment, the data are continuously broadcast by the server, interleaved with some indexing information for query processing. Clients may then tune in the broadcast channel and process their queries locally without contacting the server. Previous work on spatial query processing for wireless broadcast systems has only considered snapshot queries over static data. In this paper, we propose an air indexing framework that 1) outperforms the existing (i.e., snapshot) techniques in terms of energy consumption while achieving low access latency and 2) constitutes the first method supporting efficient processing of continuous spatial queries over moving objects. ALGORITHM / TECHNIQUE USED We focus solely on kNN, since range queries do not benefit from a monitoring algorithm in a system with a high rate of updates. This is because range queries need to access only a small number of cells (i.e., the ones intersecting the query range) and the savings of the dirty grid are counterbalanced by its overhead. Thus, the diagrams for the monitoring and recomputation approaches are almost identical. Algorithm Description: Consider, for instance, a user (mobile client) in an unfamiliar city, who would like to know the 10 closest restaurants. This is an instance of a k nearest neighbor (kNN) query, where the query point is the current location of the client and the set of data objects contains the city restaurants. Alternatively, the user may ask for all restaurants located within a certain distance, i.e., within 200 meters. This is an instance of a range query. Most relevant to our work are the techniques related to kNN search on the air. Zheng propose an approximate kNN query processing algorithm that is not guaranteed to always return k objects. The idea is to use an estimate r of the radius that is expected to contain at least k points. Using this estimate, the search space can be pruned efficiently at the beginning of the search process. The authors also introduce a learning algorithm that adaptively reconfigures the estimation algorithm to reflect the distribution of the data. Regarding the query processing phase, two different approaches are proposed: 1) The standard R_- tree index enhanced with the aforementioned pruning criterion and 2) A new sorted list index that maintains a sorted list of the objects on each spatial dimension. The sorted list method is shown to be superior to the R_-tree only for small values of k. Gedik describe several algorithms to improve kNN query processing in sequential-access R-trees. They investigate the effect of different broadcast organizations on the tuning time and also propose the use of histograms to enhance the pruning capabilities of the search algorithms. Park focus on reducing the access latency of kNN search by accessing the data segment of the broadcast cycle. In particular, they propose a method where the data objects are sorted according to one spatial coordinate. In this way, the client does not need to wait for the next index segment to arrive, but can start query processing immediately by retrieving the actual data objects. EXISTING SYSTEM Previous work on location-dependent spatial query processing for wireless broadcast systems has only considered snapshot queries over static data. On the other hand, existing spatial monitoring techniques do not apply to the broadcast environment because they assume that the server is aware of the client locations and processes their queries centrally. In this paper, we propose the Broadcast Grid Index (BGI) method, which is suitable for both snapshot and continuous queries. Furthermore, BGI extends to the case that the data are also dynamic. PROPOSED SYSTEM Recent research considers continuous monitoring of multiple queries over arbitrarily moving objects. In this setting, there is a central server that monitors the locations of both objects and queries. The task of the server is to report and continuously update the query results as the clients and the objects move. In the aforementioned methods, the processing load at the server side increases with the number of queries. In applications involving numerous clients, the server may be overwhelmed by their queries or take prohibitively long time to answer them. To avoid this problem, Imielinski propose wireless data broadcast, a promising technique that leverages the computational capabilities of the clients’ mobile devices and pushes the query processing task entirely to the client side. In this environment, the server only monitors the locations of the data objects, but is unaware of the clients and their queries. The data objects are continuously broadcast by the server, interleaved with some indexing information. The clients utilize the broadcast index, called air index, to tune in the channel only during the transmission of the relevant data and process their queries locally. Thus, the server load is independent of the number of clients. |
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