This document proposes a new method called temporal multiactivity graph indexing (tMAGIC) to efficiently detect occurrences of high-level activities from data streams. tMAGIC uses a temporal probabilistic graph structure to model activities and link observations over time. Algorithms are introduced for indexing observations into the tMAGIC structure as they occur in constant time. Two problems are defined - finding all activity occurrences above a probability threshold within a sequence, and identifying the activity that best matches a given sequence. Restrictions and pruning are used to make the inherently exponential problems linear in complexity to the number of observations. Experiments confirm tMAGIC has linear time and space complexity and can efficiently retrieve activity instances.

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