demonstration

Crossing an OCEAN of queries: analyzing SQL query logs with OCEANLog

Authors:

Andreas M. Wahl,

Peter K. Schwab,

Sebastian Herbst,

Richard LenzAuthors Info & Claims

SSDBM '18: Proceedings of the 30th International Conference on Scientific and Statistical Database Management

Article No.: 30, Pages 1 - 4

https://doi.org/10.1145/3221269.3223025

Published: 09 July 2018 Publication History

Abstract

SQL queries encapsulate the knowledge of their authors about the usage of the queried data sources. This knowledge also contains aspects that cannot be inferred by analyzing the contents of the queried data sources alone. Due to the complexity of analytical SQL queries, specialized mechanisms are necessary to enable the user-friendly formulation of meta-queries over an existing query log. Currently existing approaches do not sufficiently consider syntactic and semantic aspects of queries along with contextual information.

During our demonstration, conference participants learn how to use the latest release of OCEANLog, a framework for analyzing SQL query logs. Our demonstration encompasses several scenarios. Participants can explore an existing query log using domain-specific graph traversal expressions, set up continuous subscriptions for changes in the graph, create time-based visualizations of query results, configure an OCEANLog instance and learn how to choose a decide which specific graph database to use. We also provide them with access to the native meta-query mechanisms of a DBMS to further emphasize the benefits of our graph-based approach.

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Danaparamita and Gatterbauer. 2011. QueryViz: helping users understand SQL queries and their patterns. In EDBT'11.

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Van den Bussche et al. 2005. Towards Practical Meta-querying. Inf. Syst. 30, 4 (2005).

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Divesh and Velegrakis. 2007. Intensional Associations Between Data and Metadata. In SIGMOD'07.

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Deng et al. 2017. The Data Civilizer System. In CIDR.

[5]

Halevy et al. 2016. Goods: Organizing Google's Datasets. In SIGMOD '16. 12.

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[6]

Khoussainova et al. 2009. A Case for A Collaborative Query Management System. In CIDR'09.

[7]

Koutrika et al. 2010. Explaining structured queries in natural language. In ICDE'10.

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Khoussainova et al. 2010. SnipSuggest: Context-Aware Autocompletion for SQL. PVLDB 4, 1 (2010).

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Khoussainova et al. 2011. Session-Based Browsing for More Effective Query Reuse. In SSDBM'11.

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Pirahesh et al. 1992. Extensible/Rule Based Query Rewrite Optimization in Starburst. In SIGMOD'92.

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Papastefanatos et al. 2005. Hecataeus: A framework for representing SQL constructs as graphs. In CEUR Workshop Proceedings, Vol. 363.

[12]

Stonebraker et al. 2013. Data Curation at Scale: The Data Tamer System. In CIDR 2013.

[13]

Wahl et al. 2017. Query-Driven Knowledge-Sharing for Data Integration and Collaborative Data Science. In ADBIS'17.

[14]

Wahl et al. 2018. A Graph-Based Framework for Analyzing SQL Query Logs. In GRADESNDA'18.

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[15]

Haas. 2017. Leveraging Data and People to Accelerate Data Science. In ICDE'17.

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Heudecker and White. 2014. The Data Lake Fallacy: All Water and Little Substance. Gartner Inc. (2014).

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Rodriguez. 2015. The Gremlin graph traversal machine and language. In DBPL'15.

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Cited By

Nair LBecher AMeyer-Wegener KPorobic DNeumann T(2020)The ReProVide query-sequence optimization in a hardware-accelerated DBMSProceedings of the 16th International Workshop on Data Management on New Hardware10.1145/3399666.3399926(1-3)Online publication date: 15-Jun-2020
https://dl.acm.org/doi/10.1145/3399666.3399926
Sigl M(2019)Don't Fear the REAPER: A Framework for Materializing and Reusing Deep-Learning Models2019 IEEE 35th International Conference on Data Engineering (ICDE)10.1109/ICDE.2019.00246(2091-2095)Online publication date: Apr-2019
https://doi.org/10.1109/ICDE.2019.00246

Index Terms

Crossing an OCEAN of queries: analyzing SQL query logs with OCEANLog
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Graph-based database models
  2. Information systems applications
    1. Collaborative and social computing systems and tools

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Information & Contributors

Information

Published In

cover image ACM Other conferences

SSDBM '18: Proceedings of the 30th International Conference on Scientific and Statistical Database Management

July 2018

314 pages

ISBN:9781450365055

DOI:10.1145/3221269

Conference Chair:
Dimitris Sacharidis
TU Vienna
,
General Chair:
Johann Gamper
Free University of Bozen-Bolzano
,
Program Chair:
Michael Böhlen
University of Zurich

Copyright © 2018 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 July 2018

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SSDBM '18

SSDBM '18: 30th International Conference on Scientific and Statistical Database Management

July 9 - 11, 2018

Bozen-Bolzano, Italy

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SSDBM '18 Paper Acceptance Rate 30 of 75 submissions, 40%;

Overall Acceptance Rate 56 of 146 submissions, 38%

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Cited By

Nair LBecher AMeyer-Wegener KPorobic DNeumann T(2020)The ReProVide query-sequence optimization in a hardware-accelerated DBMSProceedings of the 16th International Workshop on Data Management on New Hardware10.1145/3399666.3399926(1-3)Online publication date: 15-Jun-2020
https://dl.acm.org/doi/10.1145/3399666.3399926
Sigl M(2019)Don't Fear the REAPER: A Framework for Materializing and Reusing Deep-Learning Models2019 IEEE 35th International Conference on Data Engineering (ICDE)10.1109/ICDE.2019.00246(2091-2095)Online publication date: Apr-2019
https://doi.org/10.1109/ICDE.2019.00246

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