Zenith seminar: 26/11/18, 14h – BAT5-02.249
Comparing Motif Discovery Techniques with Sequence Mining in the Context of Space-Time Series
CEFET / Rio de Janeiro)
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.
Short bio: Eduardo Ogasawara is a Professor of the Computer Science Department of the Federal Center for Technological Education of Rio de Janeiro (CEFET / RJ) since 2010. He holds a D.Sc. in Systems Engineering and Computer Science at COPPE / UFRJ. His background is in Databases, and his primary interest is Data Science. He is currently interested in data preprocessing, prediction, and pattern discovery regarding spatial-time series and also data-driven parallel and distributed processing. He is a member of the IEEE, ACM, INNS, and SBC. He led the creation of the Post-Graduate Program in Computer Science (PPCIC) of CEFET/RJ approved by CAPES in 2016. Currently, he is heading PPCIC.