International Conference on Very Large Data Bases
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| International Conference on Very Large Data Bases | |
|---|---|
| Name | International Conference on Very Large Data Bases |
| Abbreviation | VLDB |
| Established | 1975 |
| Discipline | Computer science |
| Frequency | Annual |
International Conference on Very Large Data Bases The International Conference on Very Large Data Bases is an annual academic conference focusing on Database management systems and data science research, attracting contributions from institutions such as Massachusetts Institute of Technology, Stanford University, University of California, Berkeley, Carnegie Mellon University and University of Washington. Papers presented at the conference often intersect with research from ACM SIGMOD, IEEE, Google Research, Microsoft Research, and IBM Research, and the proceedings are cited alongside work published at SIGMOD Conference, ICDE, KDD, NeurIPS, and ICML. The conference has become a central venue for collaborations linking scholars from University of Toronto, ETH Zurich, University of Cambridge, University of Oxford, and Tsinghua University.
History
The conference originated in the 1970s amid parallel efforts at IBM Research, Bell Labs, University of California, Los Angeles, University of Southern California, and Princeton University to address challenges in relational database implementations and large-scale storage. Early meetings included researchers affiliated with Ingres, System R, Codasyl, Oracle Corporation, and Ingres Corporation, and drew participants from National Science Foundation, DARPA, European Research Council, and national laboratories. Through the 1980s and 1990s the conference documented advances from teams at Bellcore, AT&T Laboratories, Xerox PARC, Hewlett-Packard Laboratories, and Sun Microsystems, and later incorporated work from Yahoo! Research, Facebook AI Research, Amazon Web Services, and Alibaba Group. Notable organizers and contributors have been associated with Michael Stonebraker, Jeffrey Ullman, Hector Garcia-Molina, Raghu Ramakrishnan, and David DeWitt.
Scope and Topics
The conference covers topics including query optimization research from groups at Princeton University, Cornell University, and University of Wisconsin–Madison; transaction processing studies connected to Tanenbaum-related research and ACID theory from laboratories such as IBM Research and Microsoft Research; and data warehousing reports from Teradata and SAP. Other areas include distributed systems work tied to Google, Amazon, Netflix, and LinkedIn; stream processing contributions from Apache Flink and Twitter teams; graph databases research associated with Neo4j and Facebook; and machine learning integration efforts involving DeepMind, OpenAI, and university labs. Cross-disciplinary submissions often reference projects at CERN, NASA, NOAA, and European Space Agency.
Organization and Governance
The conference is managed by an international council composed of representatives from academic institutions such as University of California, San Diego, National University of Singapore, Peking University, McGill University, and professional organizations including VLDB Endowment, ACM, and IEEE Computer Society. Program committees routinely include members from MIT CSAIL, Berkeley AI Research, EPFL, RIKEN, and Max Planck Institute for Informatics, with peer review standards aligned with practices at SIGMOD, ICDE, and KDD. Steering committees have featured leaders from University of Illinois Urbana-Champaign, Columbia University, University of Maryland, and industry liaisons from Oracle Corporation and SAP.
Conference Format and Activities
Typical formats include peer-reviewed technical paper sessions, poster sessions populated by researchers from University of Michigan, University of British Columbia, and Monash University, tutorial tracks led by faculty from ETH Zurich and University of Texas at Austin, demonstration tracks showcasing systems from Google Cloud, Microsoft Azure, and IBM Cloud, and industry keynotes from executives at Amazon, Facebook, Alibaba, and Apple Inc.. Workshops co-located with the conference often involve topics driven by researchers at Carnegie Mellon University, University of Pennsylvania, University College London, and Imperial College London, and panels include representatives from European Commission, Japanese Science and Technology Agency, and National Institute of Standards and Technology.
Notable Papers and Contributions
Landmark contributions presented at the conference connect to seminal works by researchers including Michael Jordan, Jennifer Widom, Andrew Ng, Jeff Dean, and Umeshwar Dayal. Influential systems and algorithms introduced or advanced in proceedings have ties to MapReduce, Hadoop, Spark, Bloom filter research stemming from University of California, Santa Barbara collaborations, and indexing techniques originating with R-tree and B-tree innovators at Bell Labs and IBM Research. Papers bridging databases and machine learning cite cross-institutional projects from Stanford AI Lab, Berkeley AI Research, Toyota Technological Institute at Chicago, and Yahoo! Labs.
Locations and Attendance
The conference rotates geographically, with past venues in cities such as San Francisco, New York City, London, Berlin, Tokyo, Beijing, Sydney, Toronto, Paris, and Barcelona, drawing attendees from institutions including Seoul National University, Korea Advanced Institute of Science and Technology, Indian Institute of Science, and University of São Paulo. Attendance has included thousands of participants from research groups at University of Melbourne, University of Amsterdam, Osaka University, Weizmann Institute of Science, and Technion – Israel Institute of Technology, alongside engineers from SAP Labs, Intel, NVIDIA, and Qualcomm.
Awards and Recognition
The conference presents awards such as best paper and best demo honors, recognizing work by teams from University of California, Irvine, Arizona State University, University of Illinois Chicago, and Brown University, and has conferred career awards to figures associated with SIGMOD, ACM Fellows, IEEE Fellows, AAAI Fellows, and recipients of national honors like Turing Award laureates. These awards are often announced in cooperation with organizations like VLDB Endowment, Association for Computing Machinery, IEEE Computer Society, and academic departments at Princeton University and Harvard University.