=Paper=
{{Paper
|id=Vol-3741/keynote03
|storemode=property
|title=Scalable Vector Analytics: A Story of Twists and Turns
|pdfUrl=https://ceur-ws.org/Vol-3741/keynote03.pdf
|volume=Vol-3741
|authors=Themis Palpanas
|dblpUrl=https://dblp.org/rec/conf/sebd/Palpanas24
}}
==Scalable Vector Analytics: A Story of Twists and Turns==
<pdf width="1500px">https://ceur-ws.org/Vol-3741/keynote03.pdf</pdf>
<pre>
                                Scalable Vector Analytics: A Story of Twists and Turns
                                Themis Palpanas1
                                1
                                    University Paris Cite, France


                                               Abstract
                                               On 24th June 2024, Themis Palpanas delivered a keynote talk at the 32nd Symposium on Advanced
                                               Database Systems in Villasimius (Sardinia, Italy). The following is the abstract of his talk and a short
                                               biography




                                Abstract of the Keynote
                                Similarity search in high-dimensional data spaces was a relevant and challenging data man-
                                agement problem in the early 1970s, when the first solutions to this problem were proposed.
                                Today, fifty years later, we can safely say that the exact same problem is more relevant (from
                                Time Series Management Systems to Vector Databases) and challenging than ever. Very large
                                amounts of high-dimensional data are now omnipresent (ranging from traditional multidi-
                                mensional data to time series and deep embeddings), and the performance requirements (i.e.,
                                response-time and accuracy) of a variety of applications that need to process and analyze these
                                data have become very stringent and demanding. In these past fifty years, high-dimensional
                                similarity search has been studied in its many flavors. Similarity search algorithms for exact
                                and approximate, one-off and progressive query answering. Approximate algorithms with and
                                without (deterministic or probabilistic) quality guarantees. Solutions for on-disk and in-memory
                                data, static and streaming data. Approaches based on multidimensional space-partitioning and
                                metric trees, random projections and locality-sensitive hashing (LSH), product quantization
                                (PQ) and inverted files, k-nearest neighbor graphs and optimized linear scans. Surprisingly,
                                the work on data-series (or time-series) similarity search has recently been shown to achieve
                                the state-of-the-art performance for several variations of the problem, on both time-series and
                                general high-dimensional vector data. In this talk, we will touch upon the different aspects of
                                this interesting story, present some of the state-of-the-art solutions, and discuss open research
                                directions.


                                Short Biography
                                Themis Palpanas is an elected Senior Member of the French University Institute (IUF), a dis-
                                tinction that recognizes excellence across all academic disciplines, and Distinguished Professor
                                of computer science at the University Paris Cite (France), where he is director of the Data

                                SEBD 2024: 32nd Symposium on Advanced Database Systems, June 23-26, 2024, Villasimius, Sardinia, Italy
                                Envelope-Open themis@mi.parisdescartes.fr (T. Palpanas)
                                Orcid 0000-0002-8031-0265 (T. Palpanas)
                                             © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).




CEUR
                  ceur-ws.org
Workshop      ISSN 1613-0073
Proceedings
Intelligence Institute of Paris (diiP), and director of the data management group, diNo. He
received the BS degree from the National Technical University of Athens, Greece, and the MSc
and PhD degrees from the University of Toronto, Canada. He has previously held positions at
the University of California at Riverside, University of Trento, and at IBM T.J. Watson Research
Center, and visited Microsoft Research, and the IBM Almaden Research Center. His interests
include problems related to data science (big data analytics and machine learning applications).
He is the author of 14 patents. He is the recipient of 3 Best Paper awards, and the IBM Shared
University Research (SUR) Award. His service includes the VLDB Endowment Board of Trustees
(2018-2023), Editor-in-Chief for PVLDB Journal (2024-2025) and BDR Journal (2016- 2021), PC
Chair for IEEE BigData 2023 and ICDE 2023 Industry and Applications Track, General Chair for
VLDB 2013, Associate Editor for the TKDE Journal (2014-2020), and Research PC Vice Chair for
ICDE 2020.

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