A peer to peer approach to large scale information monitoring

Issued as final reportNational Science Foundation (U.S.

Predictive Query Indexing for Ambiguous Moving Objects in Uncertain Data Mining

Indexing and query processing is a developing examination field in spatio-temporal data. The majority of the continuous applications, for example, area based administrations, armada administration, movement expectation and radio recurrence recognizable proof and sensor systems depend on spatiotemporal indexing and query preparing. All the indexing and query processing applications is any of the structures, for example, spatio file get to and supporting inquiries or spatio-transient indexing technique and bolster query or temporal measurement, while in spatial data it is considered as the second need. The majority of the current overview takes a shot at spatio-fleeting depend on indexing techniques and query preparing, yet exhibited independently. Probabilistic range query is an essential kind of query in the region of dubious data administration. A probabilistic range query restores every one of the articles inside a particular range from the query question with a likelihood no not as much as a given edge. A query protest is either a specific question or an indeterminate question demonstrated by a Gaussian appropriation. We propose a few sifting systems and a U-tree-based list to effectively bolster probabilistic range questions over Gaussian items. Broad tests on genuine data exhibit the proficiency of our proposed approach

Managing continuous k-nearest neighbor queries in mobile peer-to-peer networks

A continuous k nearest neighbor (CKNN) query retrieves the set of k mobile nodes that are nearest to a query point, and provides real-time updates whenever this set of nodes changes. A CKNN query can be either stationary or mobile, depending on the mobility of its query point. Efficient processing of CKNN queries is essential to many applications, yet most existing techniques assume a centralized system, where one or more central servers are used for query management. In this thesis, we assume a fully distributed mobile peer-to-peer system, where mobile nodes are the only computing devices, and present a unified platform for efficient processing of both stationary and mobile CKNN queries. For each query, our technique computes a set of safe boundaries and lets mobile nodes monitor their movement with respect to these boundaries. We show that the result of a query does not change unless a node crosses over a safe boundary. As such, our technique requires a query to be re-evaluated only when there is a crossing event, thus minimizing the cost of query evaluation. For performance study, we model the communication cost incurred in query processing with a detailed mathematical analysis and verify its accuracy using simulation. Our extensive study shows that the proposed technique is able to provide real-time and accurate query results with a reasonable cost

Processing Moving Queries over Moving Objects Using Motion Adaptive Indexes

This paper describes a motion adaptive indexing scheme for efficient evaluation of moving queries (MQs) over moving objects. It uses the concept of motion-sensitive bounding boxes to model the dynamic behavior of both moving objects and moving queries. Instead of indexing frequently changing object positions, we index less frequently changing motion sensitive bounding boxes together with the motion functions of the objects. This significantly decreases the number of update operations performed on the indexes. We use predictive query results to optimistically precalculate query results, thus decreasing the number of search operations performed on the indexes. More importantly, we propose a motion adaptive indexing method. Instead of using fixed parameters for motion sensitive bounding boxes, we automatically adapt the sizes of the motion sensitive bounding boxes to the dynamic motion behaviors of the corresponding individual objects. As a result, the moving queries can be evaluated faster by performing fewer IOs. Furthermore, we introduce the concept of guaranteed safe radius and optimistic safe radius to extend our motion adaptive indexing scheme to evaluating moving continual k-nearest neighbor (kNN) queries. Our experiments show that the proposed motion adaptive indexing scheme is efficient for evaluation of both moving continual range queries and moving continual kNN queries

Processing Moving Queries over Moving Objects Using Motion Adaptive Indexes

This paper describes a motion adaptive indexing scheme for efficient evaluation of moving queries (MQs) over moving objects. It uses the concept of motion-sensitive bounding boxes to model the dynamic behavior of both moving objects and moving queries. Instead of indexing frequently changing object positions, we index less frequently changing motion sensitive bounding boxes together with the motion functions of the objects. This significantly decreases the number of update operations performed on the indexes. We use predictive query results to optimistically precalculate query results, thus decreasing the number of search operations performed on the indexes. More importantly, we propose a motion adaptive indexing method. Instead of using fixed parameters for motion sensitive bounding boxes, we automatically adapt the sizes of the motion sensitive bounding boxes to the dynamic motion behaviors of the corresponding individual objects. As a result, the moving queries can be evaluated faster by performing fewer IOs. Furthermore, we introduce the concept of guaranteed safe radius and optimistic safe radius to extend our motion adaptive indexing scheme to evaluating moving continual k-nearest neighbor (kNN) queries. Our experiments show that the proposed motion adaptive indexing scheme is efficient for evaluation of both moving continual range queries and moving continual kNN queries.