Beyond Analytics: The Evolution of Stream Processing Systems

被引:27
|
作者
Carbone, Paris [1 ]
Fragkoulis, Marios [2 ]
Kalavri, Vasiliki [3 ]
Katsifodimos, Asterios [2 ]
机构
[1] RISE, Gothenburg, Sweden
[2] Delft Univ Technol, Delft, Netherlands
[3] Boston Univ, Boston, MA 02215 USA
基金
欧盟地平线“2020”;
关键词
MODEL; ARCHITECTURE; SEMANTICS; LATENCY;
D O I
10.1145/3318464.3383131
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:2651 / 2658
页数:8
相关论文
共 50 条
  • [1] Enabling Deep Analytics in Stream Processing Systems
    Nikolic, Milos
    Chandramouli, Badrish
    Goldstein, Jonathan
    DATA ANALYTICS, 2017, 10365 : 94 - 98
  • [2] New Challenges and Opportunities in Stream Processing: Transactions, Predictive Analytics, and Beyond
    Tatbul, Nesime
    DEBS'18: PROCEEDINGS OF THE 12TH ACM INTERNATIONAL CONFERENCE ON DISTRIBUTED AND EVENT-BASED SYSTEMS, 2018, : 14 - 15
  • [3] A survey on the evolution of stream processing systems
    Fragkoulis, Marios
    Carbone, Paris
    Kalavri, Vasiliki
    Katsifodimos, Asterios
    VLDB JOURNAL, 2024, 33 (02): : 507 - 541
  • [4] A Survey of Distributed Stream Processing Systems for Smart City Data Analytics
    Nasiri, Hamid
    Nasehi, Saeed
    Goudarzi, Maziar
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SMART CITIES AND INTERNET OF THINGS (SCIOT'18), 2018,
  • [5] IoT Stream Processing and Analytics in the Fog
    Yang, Shusen
    IEEE COMMUNICATIONS MAGAZINE, 2017, 55 (08) : 21 - 27
  • [6] Predictive Analytics for Event Stream Processing
    Roudjane, Massiva
    Rebaine, Djamal
    Khoury, Raphael
    Halle, Sylvain
    2019 IEEE 23RD INTERNATIONAL ENTERPRISE DISTRIBUTED OBJECT COMPUTING CONFERENCE (EDOC), 2019, : 171 - 182
  • [7] SmartNIC-accelerated Stream Processing Analytics
    Lettieri, Giuseppe
    Fais, Alessandra
    Antichi, Gianni
    Procissi, Gregorio
    2023 IEEE CONFERENCE ON NETWORK FUNCTION VIRTUALIZATION AND SOFTWARE DEFINED NETWORKS, NFV-SDN, 2023, : 135 - 140
  • [8] Data Stream Processing for Packet-Level Analytics
    Fais, Alessandra
    Lettieri, Giuseppe
    Procissi, Gregorio
    Giordano, Stefano
    Oppedisano, Francesco
    SENSORS, 2021, 21 (05) : 1 - 22
  • [9] A Software Chain Approach to Big Data Stream Processing and Analytics
    Xhafa, Fatos
    Naranjo, Victor
    Caballe, Santi
    Barolli, Leonard
    2015 9TH INTERNATIONAL CONFERENCE ON COMPLEX, INTELLIGENT, AND SOFTWARE INTENSIVE SYSTEMS CISIS 2015, 2015, : 179 - 186
  • [10] Parallel Stream Processing with MPI for Video Analytics and Data Visualization
    Vogel, Adriano
    Rista, Cassiano
    Justo, Gabriel
    Ewald, Endrius
    Griebler, Dalvan
    Mencagli, Gabriele
    Fernandes, Luiz Gustavo
    HIGH PERFORMANCE COMPUTING SYSTEMS, WSCAD 2018, 2020, 1171 : 102 - 116