Beyond Analytics: The Evolution of Stream Processing Systems
2020 (English)In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Association for Computing Machinery , 2020, p. 2651-2658Conference paper, Published paper (Refereed)
Abstract [en]
Stream processing has been an active research field for more than 20 years, but it is now witnessing its prime time due to recent successful efforts by the research community and numerous worldwide open-source communities. The goal of this tutorial is threefold. First, we aim to review and highlight noteworthy past research findings, which were largely ignored until very recently. Second, we intend to underline the differences between early ('00-'10) and modern ('11-'18) streaming systems, and how those systems have evolved through the years. Most importantly, we wish to turn the attention of the database community to recent trends: streaming systems are no longer used only for classic stream processing workloads, namely window aggregates and joins. Instead, modern streaming systems are being increasingly used to deploy general event-driven applications in a scalable fashion, challenging the design decisions, architecture and intended use of existing stream processing systems.
Place, publisher, year, edition, pages
Association for Computing Machinery , 2020. p. 2651-2658
Keywords [en]
cloud computing, distributed computing, MapReduce, shared-nothing architectures, stream processing, Database community, Design decisions, Event driven applications, Open source communities, Research communities, Stream processing systems, Streaming systems, Database systems
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-45153DOI: 10.1145/3318464.3383131Scopus ID: 2-s2.0-85086259004ISBN: 9781450367356 (print)OAI: oai:DiVA.org:ri-45153DiVA, id: diva2:1453890
Conference
2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020, 14 June 2020 through 19 June 2020
Note
Funding details: 870228; Funding details: Stiftelsen för Strategisk Forskning, SSF, BD15-0006; Funding text 1: Asterios Katsifodimos is an Assistant Professor at TU Delft. His research focuses on scalable data management and stream processing, and received the ACM SIGMOD Research Highlights award in 2016 and the Best Paper Award in EDBT 2019. Before TU Delft, Asterios worked at SAP and TU Berlin, and received his PhD from INRIA in Paris. 6 ACKNOWLEDGEMENTS This work is partly funded by the Swedish Foundation for Strategic Research under Grant No.: BD15-0006, the EU H2020 OpertusMundi project No.870228 and Delft Data Science.
2020-07-132020-07-132023-06-02Bibliographically approved