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teddy

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Table of Contents

Temporal Dependency Discovery In Data Stream

With Marian Scuturici (Database Team, LIRIS), Céline Robardet (DM2L Team, LIRIS) and Antoine Fraboulet (HiKoB).

TEDDY Algorithm

Pattern mining over data streams is critical to a variety of applications such as prediction and evolution of weather phenomena or anomaly detection in security applications. Most of the current techniques attempt to discover associations between events appearing on the same data stream but are not able to discover associations over multiple heterogeneous data streams. In this work, we aim to identify temporal dependencies between data streams. We represent event streams by state streams that are induced by the streams' events themselves. Each state has a duration, represented as a set of disjoint time intervals with respect to the events that occurred in the stream. Temporal relations between these interval sets infers dependencies between the corresponding datasources. Our

interval-based approach is robust to the temporal variability of
events that characterizes the time intervals during which the events
are related. It links two types of events if the occurrence of one
is often followed by the appearance of the other in a certain time
interval. The proposed approach determines the most appropriate time
intervals of a temporal dependency whose validity is assessed by a
chi2 test. As several intervals may redundantly describe the
same dependency, the approach retrieves only the few most specific
intervals with respect to a dominance relationship over temporal
dependencies, and thus avoids the classical problem of pattern
flooding in data mining. TEDDY algorithm, TEmporal Dependency
DiscoverY, prunes the search space while certifying the discovery of
all valid and significant temporal dependencies. We present
empirical results on simulated data to show the scalability and the
robustness of our approach. We also report on case studies from
smart real-world environments equipped with a number of cameras and
motion sensors. These experiments demonstrate the efficiency and the
effectiveness of our approach.

Applications

teddy.1380870114.txt.gz · Last modified: 2013/10/04 09:01 by mplantev

CNRS INSA de Lyon Université Lyon 1 Université Lyon 2 École centrale de Lyon