Events

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.

Babar Naveed Memon, Master’s candidate
David R. Cheriton School of Computer Science

Remote Direct Memory Access (RDMA) can be used to implement a shared storage abstraction or a shared nothing abstraction for distributed applications. We argue that the shared storage abstraction is an overkill for loosely coupled applications and that the shared nothing abstraction does not leverage all the benefits of RDMA.

Dallas Fraser, Master’s candidate
David R. Cheriton School of Computer Science

Combining text and mathematics when searching in a corpus with extensive mathematical notation remains an open problem. Recent results for math information retrieval systems on the math and text retrieval task at NTCIR-12, for example, show room for improvement, even though formula retrieval appears to be fairly successful.

Panos Ipeirotis, Professor and George A. Kellner Faculty Fellow
Department of Information, Operations, and Management Sciences
New York University

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.

Anil Pacaci, PhD candidate
David R. Cheriton School of Computer Science

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.

Speaker: Stratos Idreos, Harvard University

Amine Mhedhbi, PhD candidate
David R. Cheriton School of Computer Science

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.