Walid G. Aref

About

Walid G. Aref is a professor of computer science at Purdue University. His research interests are in extending the functionality of database systems to support emerging applications, including spatial, spatio-temporal, graph, biological, and sensor databases. He is also interested in query processing, indexing, data streaming, and geographic information systems (GIS). Walid's research has been supported by various organizations such as the National Science Foundation, the National Institute of Health, Purdue Research Foundation, CERIAS, Panasonic, and Microsoft Corp. He received his BSc and MSc in Computer Science from Alexandria University, Egypt, in 1983 and 1986 respectively, and his PhD from the University of Maryland at College Park in 1993. Walid has been a faculty member at Purdue since Fall 1999, and he has received several awards including the NSF CAREER Award in 2001 and a Purdue University Faculty Scholar award in 2004. He is a member of Purdue's CERIAS, serves as the Editor-in-Chief of the ACM Transactions of Spatial Algorithms and Systems, and has held editorial positions in other prominent journals. Walid is a Fellow of the IEEE and a member of the ACM, and he has served as the chair of the ACM Special Interest Group on Spatial Information (SIGSPATIAL) from 2011 to 2014.

Research topics

• Computer Science
• Data Mining
• Theoretical computer science
• Data science

Selected publications

• The future is big graphs

  Communications of the ACM · 2021 · 152 citations

  Ensuring the success of big graph processing for the next decade and beyond.

  PublisherOA PDFDOI

• Horizon

  Proceedings of the VLDB Endowment · 2021-07-01 · 36 citations

  article

  A large class of data repair algorithms rely on integrity constraints to detect and repair errors. A well-studied class of constraints is Functional Dependencies (FDs, for short). Although there has been an increased interest in developing general data cleaning systems for a myriad of data errors, scalability has been left behind. This is because current systems assume data cleaning is performed offline and in one iteration. However, developing data science pipelines is highly iterative and requ…

  PublisherDOI

• LocationSpark: In-memory Distributed Spatial Query Processing and Optimization

  Frontiers in Big Data · 2020-10-16 · 24 citations

  articleOpen accessSenior author

  Due to the ubiquity of spatial data applications and the large amounts of spatial data that these applications generate and process, there is a pressing need for scalable spatial query processing. In this paper, we present new techniques for spatial query processing and optimization in an in-memory and distributed setup to address scalability. More specifically, we introduce new techniques for handling query skew that commonly happens in practice, and minimizes communication costs accordingly. W…

  PublisherDOI

• Mobility Data Science: Perspectives and Challenges

  ACM Transactions on Spatial Algorithms and Systems · 2024-05-07 · 22 citations

  articleOpen access

  Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of Global Positioning System (GPS)–equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated a significant impact in various domains, including traffic management, urban planning, and health sciences. In this article, we present the domain of mobility data science. To…

  PublisherOA PDFDOI

• Cache Coherence Over Disaggregated Memory

  Proceedings of the VLDB Endowment · 2025-05-01 · 5 citations

  articleSenior author

  Disaggregating memory from compute offers the opportunity to better utilize stranded memory in cloud data centers. It is important to cache data in the compute nodes and maintain cache coherence across multiple compute nodes. However, the limited computing power on disaggregated memory servers makes traditional cache coherence protocols suboptimal, particularly in the case of stranded memory. This paper introduces SELCC; a Shared-Exclusive Latch Cache Coherence protocol that maintains cache cohe…

  PublisherDOI

Recent grants

• III-COR-Small: Collaborative Research: Preference- And Context-Aware Query Processing for Location-based Database Servers

  NSF · $193k · 2008–2012

• III:Small: Commugrate -- A Community-based Data Integration System

  NSF · $498k · 2009–2014

• CAREER: Research and Development of Database Technologies for Modern Applications

  NSF · $300k · 2001–2007

Frequent coauthors

• Mohamed F. Mokbel

  University of Minnesota System

  80 shared

• Ahmed K. Elmagarmid

  77 shared

• Mourad Ouzzani

  Hamad bin Khalifa University

  46 shared

• Ahmed R. Mahmood

  Google (United States)

  43 shared

• Moustafa A. Hammad

  39 shared

• Ahmed M. Aly

  Future University in Egypt

  27 shared

• Mingjie Tang

  Sichuan University

  23 shared

• Arif Ghafoor

  22 shared

Education

• Ph.D., Computer Science

  The University of Maryland

  1993

Awards & honors

• CAREER Award from the National Science Foundation (2001)
• Purdue University Faculty Scholar award (2004)
• VLDB ten-year best paper award (2016)

Similar researchers at Purdue University

• Edward J. Delph-69
• Bharat Bhargavah-52
• Tamal Krishna Deyh-47