Abstract (Summary)
We work with temporal data stored in distributed databases that are spread over a region. We have considered a sensor network where a lot of sensor nodes are spread in a grid like manner. These sensor nodes are capable of storing data and thus act as a separate dataset. The entire network of these sensors act as a set of distributed datasets. An algorithm is introduced that mines global temporal patterns from these datasets and results in the discovery of linear trajectories of moving objects under supervision. Each of these datasets has its local temporal dataset along with spatial data and the geographical coordinates of a given object or target. The main objective here is to perform in-network aggregation between the data contained in the various datasets to discover global spatio-temporal patterns; the main constraint is that there should be minimal communication among the participating nodes. We present the algorithm and analyze it in terms of the communication costs. The cost of our algorithm is much smaller than that of the alternative in which the data must be transferred to a single site and then mined. In addition to this, we vary the requirements of our algorithm slightly and present a variant of it that enhances its performance in terms of the overall complexity of computations. We go on to show that the while the efficiency of the algorithm increases in terms of the number of messages exchanged between nodes, the amount of information available to all the nodes in the system decrease. The advantages and drawbacks of this variant of our algorithm is also presented.
Bibliographical Information:


School:University of Cincinnati

School Location:USA - Ohio

Source Type:Master's Thesis

Keywords:distributed data sets aggregation in network temporal databases sensor


Date of Publication:01/01/2003

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