Xi He, PhD candidate
Computer Science Department, Duke University
Jennifer Widom
Frederick Emmons Terman Dean, School of Engineering
Fletcher Jones Professor, Computer Science and Electrical Engineering
Stanford University
Lei Zou, Institute of Computer Science and Technology
Peking University
In this talk, I focus on accelerating a widely employed computing pattern — set intersection, to boost a group of relevant graph algorithms. Graph’s adjacency-lists can be naturally considered as node sets, thus set intersection is a primitive operation in many graph algorithms. We propose QFilter, a set intersection algorithm using SIMD instructions. QFilter adopts a merge-based framework and compares two blocks of elements iteratively by SIMD instructions.
Barzan Mozafari, Department of Computer Science and Engineering
University of Michigan
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.
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.