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A Review on OLAP Technologies Applied to Information Networks

Authors:

Paulo Orlando Queiroz-Sousa,

Ana Carolina SalgadoAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 1

Article No.: 8, Pages 1 - 25

https://doi.org/10.1145/3370912

Published: 13 December 2019 Publication History

Abstract

Many real systems produce network data or highly interconnected data, which can be called information networks. These information networks form a critical component in modern information infrastructure, constituting a large graph data volume. The analysis of information network data covers several technological areas, among them OLAP technologies. OLAP is a technology that enables multi-dimensional and multi-level analysis on a large volume of data, providing aggregated data visualizations with different perspectives. This article presents a literature review on the main applications of OLAP technology in the analysis of information network data. To achieve such goal, it shows a systematic review to list the works that apply OLAP technologies in graph data. It defines seven comparison criteria (Materialization, Network, Selection, Aggregation, Model, OLAP Operations, Analytics) to qualify the works found based on their functionalities. The works are analyzed according to each criterion and discussed to identify trends and challenges in the application of OLAP in the information network.

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Index Terms

A Review on OLAP Technologies Applied to Information Networks
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Graph-based database models
  2. Information systems applications
    1. Data mining
    2. Decision support systems
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 14, Issue 1

February 2020

325 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3375789

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

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Copyright © 2019 ACM.

© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 December 2019

Accepted: 01 October 2019

Revised: 01 October 2019

Received: 01 September 2018

Published in TKDD Volume 14, Issue 1

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Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte
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