Comparing Motif Discovery Techniques with Sequence Mining in the Context of Space-Time Series

Title: Comparing Motif Discovery Techniques with Sequence Mining in the Context of Space-Time Series

Venue: INRIA / LIRMM / University of Montpellier

Date: November / 2018

Location: Montpellier, France

Abstract:

A relevant area that is being explored in time series analysis community is finding patterns. Patterns are sub-sequences of time series that are related to some special properties or behaviors. A particular pattern that occurs a significant number of times in time series is denominated motif. Discovering motifs in time series data has been widely explored. Many time series techniques were developed to tackle this problem. However, various important time-series phenomena present different behaviors when observed at points of space (for example, series collected by sensors and IoT) and are better modeled as spatial-time series, in which each time series is associated to a position in space. When it comes to spatial-time series, it is possible to observe an open gap according to the literature review. Under such scenarios, motifs might not be discovered when we analyze each time series individually. They may be frequent if we consider different spatial-time series at some time interval. Finding patterns that are frequent in a constrained space and time, i.e., find spatial-time motifs, may enable us to comprehend how a phenomenon occurs concerning space and time. Meanwhile, database/data mining community studies the problem of discovering spatiotemporal sequential patterns, which appears in a broad range of applications. Many initiatives find sequences constrained by space and time, which can shed light to tackle spatial-time motif discovery. We are going to present these different techniques and potential challenges and solutions arising from these two communities in the context of spatial-time series motif discovery.

Presentation

About Eduardo Ogasawara
I am a Professor of the Computer Science Department of the Federal Center for Technological Education of Rio de Janeiro (CEFET / RJ) since 2010. I hold a PhD in Systems Engineering and Computer Science at COPPE / UFRJ. Between 2000 and 2007 I worked in the Information Technology (IT) field where I acquired extensive experience in workflows and project management. I have solid background in the Databases and my primary interest is Data Science. He currently studies space-time series, parallel and distributed processing, and data preprocessing methods. I am a member of the IEEE, ACM, INNS, and SBC. Throughout my career I have been presenting consistent number of published articles and projects approved by the funding agencies, such as CNPq and FAPERJ. I am also reviewer of several international journals, such as VLDB Journal, IEEE Transactions on Service Computing and The Journal of Systems and Software. Currently, I am heading the Post-Graduate Program in Computer Science (PPCIC) of CEFET / RJ.

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