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--- teddy [2013/10/04 09:01]
mplantev
+++ teddy [2013/10/04 09:12]
mplantev
@@ Line 1: / Line 1: @@
-====== Temporal Dependency Discovery In Data Stream ======
+====== Temporal Dependency Discovery In Data Streams ======
-With Marian Scuturici (Database Team, LIRIS),  Céline Robardet (DM2L Team, LIRIS) and Antoine Fraboulet (HiKoB).
+<note>With Marian Scuturici (Database Team, LIRIS),  Céline Robardet (DM2L Team, LIRIS) and Antoine Fraboulet (HiKoB).
+This work was partially funded by the  LIRIS Project Stream Mining.
+</note>
 ===== 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
+ 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
-  interval-based approach is robust to the temporal variability of
+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.
-  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.
@@ Line 31: / Line 17: @@
 ===== Applications =====
+==== Triggering Road De-icing Operations ====
+We present how these dependencies can be used within the GrizzLY project to tackle an environmental and technical issue: the deicing of the roads. This project aims to wisely organize the deicing operations of an urban area, based on several sensor network measures of local atmospheric phenomena. A spatial and temporal dependency-based model is built from these data to predict freezing alerts.
+<note important>This work was published as a demo at SIGKDD 2013 </note>
+{{:poster_demo_kdd13_a2.pdf| Poster@KDD'2013}}

Marc Plantevit

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