Backer Profile: Themis Palpanas – Director of diNO Group, Paris Descartes University

Themis is aThemis Palpanas professor in LIPADE, the computer science department of the Paris Descartes University and a director of the Data Intensive and Knowledge Oriented Systems (diNo) group. He is also very active in the data management and engineering community as Research Track Associate Editor for VLDB, Editor in Chief for the Big Data Research (BDR) journal, and Associate Editor for the Transactions on Knowledge and Data Engineering (TKDE) journal.

Professor Palpanas’ research is on problems related to online and offline data analytics (focusing on fast streams and massive collections). His group has world-leading expertise on time series management, indexing, and mining. Together they have developed the current state-of-the-art data series indexes, iSAX2+ (bulk loading) and ADS+ (adaptive indexing), the only data series query workload benchmark, as well as DSStat, a toolset for data series preprocessing and visualization.

They have applied their techniques on streaming and uncertain data series, and have worked with data from diverse domains, such as home networks, road tunnels, and manufacturing. The data will significantly help their efforts in building efficient tools for the management and analysis of such data, as well as in delivering effective applications to end users for capitalizing on the data they produce.

Learn More:
Professor Themis Palpanas
The diNO group

Featured Sponsor: Database, Data Mining, and Bioinformatics Lab – UC Santa Barbara

The Database, Data Mining, and Bioinformatics lab’s (DBL)
research focuses on network science, scalable querying, and mining of graphs, and bioinformatics.

Network science is a new and emerging scientific discipline that examines the interconnections among diverse physical or engineered networks, information networks, biological networks, cognitive and semantic networks, and social networks. This field of science seeks to discover common principles, algorithms and tools that govern network behavior.

DBL is developing methodologies, algorithms, and implementations needed for scalable, dynamic, and resilient networks. Specific problems include querying composite networks, modeling dynamic networks, sentiment analysis, analysis of content and user behavior, discovering unusual patterns, and sampling in composite networks.

DBL PeopleDBL will use the data for academic research, studying the dataset from a network-based modeling perspective.

Learn More: