Fast activity detection indexing for temporal stochastic automaton based activity models

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FAST ACTIVITY DETECTION INDEXING FOR TEMPORAL STOCHASTIC
AUTOMATON-BASED ACTIVITY MODELS
ABSTRACT:
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.
There has been extensive work in the last few years on modeling activities using probabilistic
models. In this paper, we propose a temporal probabilistic graph so that the elapsed time between
observations also plays a role in defining whether a sequence of observations constitutes an
activity.
We first propose a data structure called “temporal multiactivity graph” to store multiple activities
that need to be concurrently monitored. We then define an index called Temporal Multiactivity
Graph Index Creation (tMAGIC) that, based on this data structure, examines and links
observations as they occur. We define algorithms for insertion and bulk insertion into the
tMAGIC index and show that this can be efficiently accomplished.
We 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 tMAGI- has time and space complexity linear to the size of the input,
and can efficiently retrieve instances of the monitored activities.
ECWAY TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE
CELL: +91 98949 17187, +91 875487 2111 / 3111 / 4111 / 5111 / 6111
VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com

An integrated event summarization approach for complex system management Event mining is a useful way to understand computer system behaviors. The focus of recent works on event mining has been shifted to event summarization from discovering frequent patterns. Event summarization seeks to provide a comprehensible explanation of the event sequence on certain aspects. Previous methods have several limitations such as high time complexity, a low precision especially with the presence of noise and phase shifts, and providing a summary which is difficult for a human to understand. In this paper, we propose an integrated event summarization approach towards the understanding of the chaotic temporal data. Our approach focuses on two kinds of temporal relationships, the periodic pattern and the correlation pattern, hidden in event sequences of at most two event types. For the periodic patterns, we propose an event periodicity detection algorithm to discover them directly. For the correlation patterns, we make a simple statistical test based on low order statistics to check temporal dependency of events of two types and to eliminate event correlation candidate space dramatically, and then apply inter-arrival histograms to summarize an event sequence and capture the correlation patterns. In order to balance between accuracy and brevity, the minimum description length principle is used to guide the summarization process. Further, the event relationship network is built to describe discovered patterns. We conduct several groups of experiments on synthetic and real data. Experimental results show our approach is capable of producing usable event summarization, robust to noises, and scalable. The average compression ratio of event sequences reaches 99.7%.

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Fast activity detection indexing for temporal stochastic automaton based activity models

1. FAST ACTIVITY DETECTION INDEXING FOR TEMPORAL STOCHASTIC AUTOMATON-BASED ACTIVITY MODELS ABSTRACT: 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. There has been extensive work in the last few years on modeling activities using probabilistic models. In this paper, we propose a temporal probabilistic graph so that the elapsed time between observations also plays a role in defining whether a sequence of observations constitutes an activity. We first propose a data structure called “temporal multiactivity graph” to store multiple activities that need to be concurrently monitored. We then define an index called Temporal Multiactivity Graph Index Creation (tMAGIC) that, based on this data structure, examines and links observations as they occur. We define algorithms for insertion and bulk insertion into the tMAGIC index and show that this can be efficiently accomplished. We 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 tMAGI- has time and space complexity linear to the size of the input, and can efficiently retrieve instances of the monitored activities. ECWAY TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE CELL: +91 98949 17187, +91 875487 2111 / 3111 / 4111 / 5111 / 6111 VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com

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