Abstracto

A Survey on Activity Detection using Data Mining

Santosh S.Gurav, Prof. S. R. Todmal

Today, various applications require the ability to monitor a continuous stream of fine-grained data for the occurrence of certain high-level activities. A number of computerized systems—including ATM networks, web servers, and intrusion detection systems—systematically track every atomic action we perform, thus generating massive streams of times tamped observation data, possibly from multiple concurrent activities. In this paper, we address the problem of efficiently detecting occurrences of high-level activities from such interleaved data streams. A solution to this important problem would greatly benefit a broad range of applications, including fraud detection, video surveillance, and cyber security. We define algorithms for insertion and bulk insertion into the tMAGIC index and show that this can be efficiently accomplished. We also define algorithms to solve two problems: the “evidence” problem that tries to find all occurrences of an activity (with probability over a threshold) within a given sequence of observations, and the “identification” problem that tries to find the activity that best matches a sequence of observations. We introduce complexity reducing restrictions and pruning strategies to make the problem—which is intrinsically exponential—linear to the number of observations. Our experiments confirm that tMAGIC has time and space complexity linear to the size of the input, and can efficiently retrieve instances of the monitored activities.