Enabling Exploratory Analysis on Very Large Scientific Data
There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to index and mine very large collections of data series. Examples of such applications come from biology, astronomy, the web, and other domains. It is not unusual for these applications to involve numbers of data series in the order of hundreds of millions to billions.
In this talk, we describe iSAX 2.0 and its improvements, iSAX 2.0 Clustered and iSAX2+, three methods designed for indexing and mining truly massive collections of data series. We show that the main bottleneck in mining such massive datasets is the time taken to build the index, and we thus introduce a novel bulk loading mechanism, the first of this kind specifically tailored to a data series index. Furthermore, we observe that in several cases scientists, and data analysts in general, need to issue a set of queries as soon as possible, as a first exploratory step of the datasets. We discuss extensions of our previous techniques that adaptively create data series indexes, and at the same time are able to correctly answer user queries.
We show how our methods allows mining on datasets that would otherwise be completely untenable, including the first published experiments to index one billion data series, and experiments in mining massive data from domains as diverse as genome sequences, entomology, and web-scale image collections.
Themis Palpanas is a professor of computer science at the Paris Descartes University, France. He received the BS degree from the National Technical University of Athens, Greece, and the MSc and PhD degrees from the University of Toronto, Canada. He has previously held positions at the IBM T.J. Watson Research Center and the University of Trento. He has also been a Visiting Professor at the National University of Singapore, worked for the University of California, Riverside, and visited Microsoft Research and the IBM Almaden Research Center. His research solutions have been implemented in world-leading commercial data management products and he is the author of eight US patents. He is the recipient of three Best Paper awards (including ICDE and PERCOM), and the IBM Shared University Research (SUR) Award in 2012, which represents a recognition of research excellence at worldwide level. He has been a member of the IBM Academy of Technology Study on Event Processing, and is a founding member of the Event Processing Technical Society. He has served as General Chair for VLDB 2013.
