Speaker: Mohammad Sadoghi, UC Davis
A. Erdem Sarıyüce, University at Buffalo
Abstract: Finding dense substructures in a network is a fundamental graph mining operation, with applications in bioinformatics, social networks, and visualization to name a few. Yet most standard formulations of this problem (like clique, quasi-clique, densest at-least-k subgraph) are NP-hard. Furthermore, the goal is rarely to find the “true optimum” but to identify many (if not all) dense substructures, understand their distribution in the graph, and ideally determine relationships among them. In this talk, I will talk about a framework that we designed to find dense regions of the graph with hierarchical relations.
Speaker: Stratos Idreos, Harvard University
Benny Kimelfeld, Technion
Panos K. Chrysanthis, University of Pittsburgh
Abstract: Online analytics, in most advanced scientific, business, and defense applications, rely heavily on the efficient execution of large numbers of Aggregate Continuous Queries (ACQs). ACQs continuously aggregate streaming data and periodically produce results such as max or average over a given window of the latest data. It was shown that in processing ACQs it is beneficial to use incremental evaluation, which involves storing and reusing calculations performed over the unchanged parts of the window, rather than performing the re-evaluation of the entire window after each update.
Paolo Atzeni, Database Professor and Head of the Department of Engineering
Università Roma Tre
NoSQL systems have gained their popularity for many reasons, including the flexibility they provide with modeling, which tries to relax the rigidity provided by the relational model and by the other structured models.
Torben Bach Pedersen, Professor of Computer Science
Aalborg University, Denmark
Data collected from new sources such as sensors and smart devices is large, fast, and often complex. There is a universal wish to perform multidimensional OLAP-style analytics on such data, i.e., to turn it into “Big Multidimensional Data”. Supporting this is a multi-stage journey, requiring new tools and systems, and forming a new, extended data cycle with models as a key concept.
Panos Ipeirotis, Professor and George A. Kellner Faculty Fellow
Department of Information, Operations, and Management Sciences
New York University
Daniel Lemire
Université Télug
Maximizing performance in data engineering is a daunting challenge. We present some of our work on designing faster indexes, with a particular emphasis on compressed indexes. Some of our prior work includes (1) Roaring indexes which are part of multiple big-data systems such as Spark, Hive, Druid, Atlas, Pinot, Kylin, (2) EWAH indexes are part of Git (GitHub) and included in major Linux distributions.
Rachel Pottinger, Department of Computer Science
University of British Columbia
Users are faced with an increasing onslaught of data, whether it's in their choices of movies to watch, assimilating data from multiple sources, or finding information relevant to their lives on open data registries.