More Web Proxy on the site http://driver.im/

panel

Data warehousing and OLAP over big data: current challenges and future research directions

Authors:

Alfredo Cuzzocrea,

Ladjel Bellatreche,

Il-Yeol SongAuthors Info & Claims

DOLAP '13: Proceedings of the sixteenth international workshop on Data warehousing and OLAP

Pages 67 - 70

https://doi.org/10.1145/2513190.2517828

Published: 28 October 2013 Publication History

Abstract

In this paper, we highlight open problems and actual research trends in the field of Data Warehousing and OLAP over Big Data, an emerging term in Data Warehousing and OLAP research. We also derive several novel research directions arising in this field, and put emphasis on possible contributions to be achieved by future research efforts.

References

[1]

Cuzzocrea, A., Song, I.-Y., and Davis, K. C. Analytics over Large-Scale Multidimensional Data: The Big Data Revolution! Proc. of ACM DOLAP, 2011.

Digital Library

[2]

Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., and Pirahesh, H. Data Cube: A Relational Aggregation Operator Generalizing Group-by, Cross-Tab, and Sub Totals. Data Mining and Knowledge Discovery 1(1), 1997.

Digital Library

[3]

Harinarayan, V., Rajaraman, A., and Ullman, J. D. Implementing Data Cubes Efficiently. Proc. of SIGMOD Conference, 1996.

Digital Library

[4]

Chen, C., Yan, X., Zhu, F., Han, J., and Yu, P. S. Graph OLAP: A Multi-Dimensional Framework for Graph Data Analysis. Knowledge and Information Systems 21(1), 2009.

Digital Library

[5]

Jensen, M. R., Møller, T. H., and Pedersen, T. B. Specifying OLAP Cubes on XML Data. Proc. of SSDBM, 2001.

Digital Library

[6]

Zhao, P., Li, X., Xin, D., and Han, J. Graph Cube: On Warehousing And OLAP Multidimensional Networks. Proc. of ACM SIGMOD, 2011.

Digital Library

[7]

Yuan, Y., Lin, X., Liu, Q., Wang, W., Yu, J. X., and Zhang, Q. Efficient Computation of the Skyline Cube. Proc. of VLDB, 2005.

Digital Library

[8]

Dehne, F. K. H. A., Eavis,T., and Rau-Chaplin, A. The cgmCUBE Project: Optimizing Parallel Data Cube Generation for ROLAP. Distributed and Parallel Databases 19(1), 2006.

Digital Library

[9]

Sitaridi, E. A., and Ross, K. A. Ameliorating Memory Contention of OLAP Operators on GPU Processors. Proc. of ACM DaMoN, 2012.

Digital Library

[10]

Sarawagi, S., Agrawal, R., and Megiddo, N. Discovery-Driven Exploration of OLAP Data Cubes. Proc. of EDBT, 1998.

Digital Library

[11]

Abouzeid, A., Bajda-Pawlikowski, K., Abadi, D. J., Rasin, A., and Silberschatz, A. HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads. PVLDB 2(1), 2009.

Digital Library

[12]

Agrawal, D., Das, D., and El Abbadi, A. Big Data and Cloud Computing: Current State and Future Opportunities. Proc. of EDBT, 2011.

Digital Library

[13]

Cuzzocrea, A., and Bertino, E. Privacy Preserving OLAP over Distributed XML Data: A Theoretically-Sound Secure-Multiparty-Computation Approach. Journal of Computer and System Sciences 77(6), 2011.

Digital Library

[14]

Cattell, R. Scalable SQL and NoSQL Data Stores. SIGMOD Record 39(4), 2010.

Digital Library

[15]

Cuzzocrea, A., and Saccà, D. Balancing Accuracy and Privacy of OLAP Aggregations on Data Cubes. Proc. of DOLAP, 2010.

Digital Library

[16]

Bellatreche, L., Cuzzocrea, A., and Benkrid, S. Effectively and Efficiently Designing and Querying Parallel Relational Data Warehouses on Heterogeneous Database Clusters: The F&A Approach. Journal of Database Management 23(4), 2012.

Digital Library

[17]

Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., and Welton, C. MAD Skills: New Analysis Practices for Big Data. PVLDB 2(2), 2009.

Digital Library

[18]

Dean, J., and Ghemawat, S. MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM 51(1), 2008.

Digital Library

[19]

Khouri, S., Bellatreche, L., and Berkani, N. MODETL: A Complete MODeling and ETL Method for Designing Data Warehouses from Semantic Databases. Proc. of COMAD, 2012.

Digital Library

[20]

Khouri, S., and Bellatreche, L. DWOBS: Data Warehouse Design from Ontology-Based Sources. Proc. of DASFAA, 2011.

Digital Library

[21]

Herodotou, H., Lim, H., Luo, G., Borisov, N., Dong, L., Cetin, F. B., and Babu, S. Starfish: A Self-Tuning System for Big Data Analytics. Proc. of CIDIR, 2011.

[22]

Jiang, D., Ooi, B.C., Shi, L., and Wu, S. The Performance of MapReduce: An In-depth Study. PVLDB 3(1), 2010.

Digital Library

[23]

Thusoo, A. Sarma, J.S., Jain, N., Shao, Z., Chakka, P. Zhang, N., Antony, S., Liu, H., and Murthy, R. Hive - A Petabyte Scale Data Warehouse Using Hadoop. Proc. of ICDE, 2010.

[24]

Bizer, C., Boncz, P. A., Brodie, M. L., and Erling, O. The Meaningful Use of Big Data: Four Perspectives - Four Challenges. SIGMOD Record 40(4), 2011.

Digital Library

[25]

Chen, Y., Alspaugh, S., and Katz, R. H. Interactive Analytical Processing in Big Data Systems: A Cross-Industry Study of MapReduce Workloads. PVLDB 5(12), 2012.

Digital Library

[26]

Cuzzocrea, A., Saccà, D., and Serafino, P. Semantics-Aware Advanced OLAP Visualization of Multidimensional Data Cubes. International Journal of Data Warehousing and Mining 3(4), 2007.

[27]

Cuzzocrea, A., Saccà, D., and Serafino, P. A Hierarchy-Driven Compression Technique for Advanced OLAP Visualization of Multidimensional Data Cubes. Proc. of DaWaK, 2006.

Digital Library

[28]

Cuzzocrea, A. Retrieving Accurate Estimates to OLAP Queries over Uncertain and Imprecise Multidimensional Data Streams. Proc. of SSDBM, 2011.

Digital Library

[29]

Cuzzocrea, A., and Chakravarthy, S. Event-based Lossy Compression for Effective and Efficient OLAP over Data Streams. Data and Knowledge Engineering 69(7), 2010.

Digital Library

[30]

Cuzzocrea, A. Providing Probabilistically-Bounded Approximate Answers to Non-Holistic Aggregate Range Queries in OLAP. Proc. of ACM DOLAP, 2005.

Digital Library

Cited By

Bouaziz SBoukettaya SNabli AGargouri F(2025)A formal algebra for document-oriented NoSQL data warehouses: formalisation and evaluationCluster Computing10.1007/s10586-024-04776-x28:3Online publication date: 21-Jan-2025
https://doi.org/10.1007/s10586-024-04776-x
Jokhakar V(2024)An Approach Based on Metadata for Big Data InfrastructureICT for Intelligent Systems10.1007/978-981-97-6684-0_45(557-567)Online publication date: 27-Dec-2024
https://doi.org/10.1007/978-981-97-6684-0_45
Trigiante LBeneventano DBergamaschi S(2023)[Vision Paper] Privacy-Preserving Data Integration2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386703(5614-5618)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386703
Show More Cited By

Index Terms

Data warehousing and OLAP over big data: current challenges and future research directions
1. Information systems
  1. Data management systems
    1. Information integration
      1. Data warehouses
  2. Information systems applications
    1. Decision support systems
      1. Data warehouses

Recommendations

Analytics over large-scale multidimensional data: the big data revolution!
DOLAP '11: Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP

In this paper, we provide an overview of state-of-the-art research issues and achievements in the field of analytics over big data, and we extend the discussion to analytics over big multidimensional data as well, by highlighting open problems and ...
Warehousing and Protecting Big Data: State-Of-The-Art-Analysis, Methodologies, Future Challenges
ICC '16: Proceedings of the International Conference on Internet of things and Cloud Computing

This paper proposes a comprehensive critical survey on the issues of warehousing and protecting big data, which are recognized as critical challenges of emerging big data research. Indeed, both are critical aspects to be considered in order to build ...
Data Warehousing in Big Data: From Multidimensional to Tabular Data Models
C3S2E '16: Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering

Data warehouses are central pieces in business intelligence and analytics as these repositories ensure proper data storage and querying, being supported by data models that allow the analysis of data by different perspectives. Those perspectives support ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

DOLAP '13: Proceedings of the sixteenth international workshop on Data warehousing and OLAP

October 2013

110 pages

ISBN:9781450324120

DOI:10.1145/2513190

General Chair:
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Ladjel Bellatreche
ISAE-ENSMA, France
,
Alfredo Cuzzocrea
ICAR-CNR and University of Calabria, Italy

Copyright © 2013 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.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 October 2013

Check for updates

Author Tags

Qualifiers

Panel

Conference

CIKM'13

Sponsor:

CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 28, 2013

California, San Francisco, USA

Acceptance Rates

DOLAP '13 Paper Acceptance Rate 13 of 26 submissions, 50%;

Overall Acceptance Rate 29 of 79 submissions, 37%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

88
Total Citations
View Citations
2,197
Total Downloads

Downloads (Last 12 months)28
Downloads (Last 6 weeks)3

Reflects downloads up to 20 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Bouaziz SBoukettaya SNabli AGargouri F(2025)A formal algebra for document-oriented NoSQL data warehouses: formalisation and evaluationCluster Computing10.1007/s10586-024-04776-x28:3Online publication date: 21-Jan-2025
https://doi.org/10.1007/s10586-024-04776-x
Jokhakar V(2024)An Approach Based on Metadata for Big Data InfrastructureICT for Intelligent Systems10.1007/978-981-97-6684-0_45(557-567)Online publication date: 27-Dec-2024
https://doi.org/10.1007/978-981-97-6684-0_45
Trigiante LBeneventano DBergamaschi S(2023)[Vision Paper] Privacy-Preserving Data Integration2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386703(5614-5618)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386703
Taschner ACharifzadeh MTaschner ACharifzadeh M(2023)Digitalization and Supply Chain AccountingManagement Accounting in Supply Chains10.1007/978-3-658-41300-2_9(281-324)Online publication date: 30-Nov-2023
https://doi.org/10.1007/978-3-658-41300-2_9
Nambiar AMundra D(2022)An Overview of Data Warehouse and Data Lake in Modern Enterprise Data ManagementBig Data and Cognitive Computing10.3390/bdcc60401326:4(132)Online publication date: 7-Nov-2022
https://doi.org/10.3390/bdcc6040132
Davardoost FBabazadeh Sangar AMajidzadeh K(2022)An Innovative Model for Extracting OLAP Cubes from NOSQL Database Based on Scalable Naïve Bayes ClassifierMathematical Problems in Engineering10.1155/2022/28607352022(1-11)Online publication date: 11-Apr-2022
https://doi.org/10.1155/2022/2860735
Tanisha MGupta M(2022)An Intensive review on implementation of Big Data in different applications and its associated issues and Challenges2022 5th International Conference on Contemporary Computing and Informatics (IC3I)10.1109/IC3I56241.2022.10072480(670-673)Online publication date: 14-Dec-2022
https://doi.org/10.1109/IC3I56241.2022.10072480
Cuzzocrea AKarras PVlachou A(2022)Effective and efficient skyline query processing over attribute-order-preserving-free encrypted data in cloud-enabled databasesFuture Generation Computer Systems10.1016/j.future.2021.08.008126(237-251)Online publication date: Jan-2022
https://doi.org/10.1016/j.future.2021.08.008
Medina-Moreira JLagos-Ortiz KSinche-Guzmán A(2022)Digital Transformation of Health Care Services: Médikal Case StudyTechnologies and Innovation10.1007/978-3-031-19961-5_6(75-89)Online publication date: 23-Oct-2022
https://doi.org/10.1007/978-3-031-19961-5_6
Rizzi S(2022)OLAP and NoSQL: Happily Ever AfterAdvances in Databases and Information Systems10.1007/978-3-031-15740-0_4(35-44)Online publication date: 29-Aug-2022
https://doi.org/10.1007/978-3-031-15740-0_4
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents