Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3410566.3410607acmotherconferencesArticle/Chapter ViewAbstractPublication PagesideasConference Proceedingsconference-collections
research-article

Implementation of dynamic page generation for stream data by SuperSQL

Published: 25 August 2020 Publication History

Abstract

SuperSQL is an extension of SQL that allows you to structure the output of relational databases by writing your own queries and to express various layouts. However, this method is not suitable for data with high update frequency, such as stream data, because the information in the database refers to the data at the time of SuperSQL execution. In this study, we propose an implementation of a web page generation function that asynchronously updates a web page with the latest information for frequently updated data, using PipelineDB and SuperSQL, both of which are DBMSs capable of processing streams. You can specify the dynamic part of the stream by specifying the stream in the "decorator" which is a feature of SuperSQL. At the same time, you can specify "pull" and "push" in the stream decorator to select how the dynamic part is updated. This makes it possible to create a web page that displays the latest stock prices at any time in a page that displays a list of stock prices.

References

[1]
SuperSQL: http://ssql.db.ics.keio.ac.jp/
[2]
M. Toyama: "SuperSQL: An Extended SQL for Database Publishing and Presentation", Proceedings of ACM SIGMOD'98 International Conference on Management of Data, pp.584--586, 1998.
[3]
Toshiyuki Seto, Takuhiro Nagafuji and Motomichi Toyama. Generating HTML Sources with TFE Enhanced SQL, In ACM Symposium on Applied Computing, pp.96--100, 1997.
[4]
K. Goto and M. Toyama, "Mobile Web Application Generation Features For SuperSQL", in Proceedings of the 20th International Database Engineering & Applications Symposium, IDEAS 2016, pp. 308--315, 2016.
[5]
PipelineDB: https://www.pipelinedb.com/
[6]
Jay Kreps, Neha Narkhede, and Jun Rao. Kafka: a distributed messaging system for log processing. ACM SIGMOD Workshop on Networking Meets Databases. page 6. 2011.
[7]
Spark Streaming: https://spark.apache.org/streaming/
[8]
Amazon Kinesis: http://aws.amazon.com/kinesis/

Index Terms

  1. Implementation of dynamic page generation for stream data by SuperSQL

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    IDEAS '20: Proceedings of the 24th Symposium on International Database Engineering & Applications
    August 2020
    252 pages
    ISBN:9781450375030
    DOI:10.1145/3410566
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 25 August 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. IDEAS
    2. international database engineering & applications symposium

    Qualifiers

    • Research-article

    Conference

    IDEAS 2020

    Acceptance Rates

    IDEAS '20 Paper Acceptance Rate 27 of 57 submissions, 47%;
    Overall Acceptance Rate 74 of 210 submissions, 35%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 28
      Total Downloads
    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 09 Nov 2024

    Other Metrics

    Citations

    View Options

    Get Access

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media