research-article

Open business intelligence: on the importance of data quality awareness in user-friendly data mining

Authors:

Jose-Norberto Mazón,

Jose Jacobo Zubcoff,

Irene Garrigós,

Roberto Espinosa, and

Rolando RodríguezAuthors Info & Claims

EDBT-ICDT '12: Proceedings of the 2012 Joint EDBT/ICDT Workshops

March 2012

Pages 144 - 147

https://doi.org/10.1145/2320765.2320812

Published: 30 March 2012 Publication History

Abstract

Citizens demand more and more data for making decisions in their daily life. Therefore, mechanisms that allow citizens to understand and analyze linked open data (LOD) in a user-friendly manner are highly required. To this aim, the concept of Open Business Intelligence (OpenBI) is introduced in this position paper. OpenBI facilitates non-expert users to (i) analyze and visualize LOD, thus generating actionable information by means of reporting, OLAP analysis, dashboards or data mining; and to (ii) share the new acquired information as LOD to be reused by anyone. One of the most challenging issues of OpenBI is related to data mining, since non-experts (as citizens) need guidance during preprocessing and application of mining algorithms due to the complexity of the mining process and the low quality of the data sources. This is even worst when dealing with LOD, not only because of the different kind of links among data, but also because of its high dimensionality. As a consequence, in this position paper we advocate that data mining for OpenBI requires data quality-aware mechanisms for guiding non-expert users in obtaining and sharing the most reliable knowledge from the available LOD.

References

[1]

R. Ananthakrishna, S. Chaudhuri, and V. Ganti. Eliminating fuzzy duplicates in data warehouses. In VLDB, pages 586--597. Morgan Kaufmann, 2002.

Digital Library

[2]

L. Berti-Equille. Measuring and modelling data quality for quality-awareness in data mining. In F. Guillet and H. J. Hamilton, editors, Quality Measures in Data Mining, volume 43 of Studies in Computational Intelligence, pages 101--126. Springer, 2007.

[3]

C. Bizer, T. Heath, and T. Berners-Lee. Linked data - the story so far. Int. J. Semantic Web Inf. Syst., 5(3):1--22, 2009.

[4]

R. H. L. Chiang, T. M. Barron, and V. C. Storey. Extracting domain semantics for knowledge discovery in relational databases. In KDD Workshop, pages 299--310, 1994.

[5]

A. K. Elmagarmid, P. G. Ipeirotis, and V. S. Verykios. Duplicate record detection: A survey. IEEE Trans. Knowl. Data Eng., 19(1):1--16, 2007.

Digital Library

[6]

R. Espinosa, J. J. Zubcoff, and J.-N. Mazón. A set of experiments to consider data quality criteria in classification techniques for data mining. In B. Murgante, O. Gervasi, A. Iglesias, D. Taniar, and B. O. Apduhan, editors, ICCSA (2), volume 6783 of Lecture Notes in Computer Science, pages 680--694. Springer, 2011.

Digital Library

[7]

U. M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. Knowledge discovery and data mining: Towards a unifying framework. In KDD, pages 82--88, 1996.

[8]

I. K. Fodor. A survey of dimension reduction techniques. LLNL technical report, (June):1--24, 2002.

[9]

J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000.

Digital Library

[10]

M. Jarke and Y. Vassiliou. Data warehouse quality: A review of the dwq project. In D. M. Strong and B. K. Kahn, editors, IQ, pages 299--313. MIT, 1997.

[11]

H.-P. Kriegel, K. M. Borgwardt, P. Kröger, A. Pryakhin, M. Schubert, and A. Zimek. Future trends in data mining. Data Min. Knowl. Discov., 15(1):87--97, 2007.

Digital Library

[12]

Object Management Group. Common Warehouse Metamodel Specification 1.1. http://www.omg.org/cgi-bin/doc?formal/03-03-02.

[13]

E. Rahm and H. H. Do. Data cleaning: Problems and current approaches. IEEE Data Eng. Bull., 23(4):3--13, 2000.

[14]

D. M. Strong, Y. W. Lee, and R. Y. Wang. 10 potholes in the road to information quality. IEEE Computer, 30(8):38--46, 1997.

Digital Library

[15]

D. M. Strong, Y. W. Lee, and R. Y. Wang. Data quality in context. Commun. ACM, 40(5):103--110, 1997.

Digital Library

[16]

O. G. Troyanskaya, M. Cantor, G. Sherlock, P. O. Brown, T. Hastie, R. Tibshirani, D. Botstein, and R. B. Altman. Missing value estimation methods for dna microarrays. Bioinformatics, 17(6):520--525, 2001.

[17]

X. Zhu, T. M. Khoshgoftaar, I. Davidson, and S. Zhang. Editorial: Special issue on mining low-quality data. Knowl. Inf. Syst., 11:131--136, February 2007.

Digital Library

Cited By

Garcia-Varona AAlvarez-Meaza IRio-Belver RBorregan-Alvarado J(2024)Open Data Strategy in Competitive Intelligence: Analyzing the Scientific TrendsProceedings of the 17th International Conference on Industrial Engineering and Industrial Management (ICIEIM) – XXVII Congreso de Ingeniería de Organización (CIO2023)10.1007/978-3-031-57996-7_36(207-213)Online publication date: 26-Apr-2024
https://doi.org/10.1007/978-3-031-57996-7_36
Mohana Priya D. (2022)Multi-Object Tracking Using Gradient-Based Learning Model in Video SurveillanceInternational Journal of Software Innovation10.4018/IJSI.2891689:4(1-17)Online publication date: 29-Jun-2022
https://doi.org/10.4018/IJSI.289168
Chen YLi CWang H(2022)Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)Forecasting10.3390/forecast40400424:4(767-786)Online publication date: 23-Sep-2022
https://doi.org/10.3390/forecast4040042
Show More Cited By

Index Terms

Open business intelligence: on the importance of data quality awareness in user-friendly data mining
1. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Data integration flows for business intelligence
EDBT '09: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology

Business Intelligence (BI) refers to technologies, tools, and practices for collecting, integrating, analyzing, and presenting large volumes of information to enable better decision making. Today's BI architecture typically consists of a data warehouse (...
Read More
Exploiting Open Data to Improve the Business Intelligence & Business Discovery Experience
BDCA'17: Proceedings of the 2nd international Conference on Big Data, Cloud and Applications

The extent to which data mining tools are able to make efficient use of an open data oriented strategy in a smart city is limited. In a sense that it is not fully automated, incompatible or has to be supervised. These sets of tools may offer the ...
Read More
Business models for open government data
ICEGOV '14: Proceedings of the 8th International Conference on Theory and Practice of Electronic Governance

The commercial re-use of open government data is broadly expected to generate economic value. However, the practice and study of this trend is still in its infancy. In particular, the issue of value creation in the commercial re-use open government data ...
Read More

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

EDBT-ICDT '12: Proceedings of the 2012 Joint EDBT/ICDT Workshops

March 2012

265 pages

ISBN:9781450311434

DOI:10.1145/2320765

Editors:
Divesh Srivastava
AT&T Labs-Research
,
Ismail Ari
Ozyegin University, Turkey

Copyright © 2012 ACM.

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: 30 March 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Conference

ICDT '12

ICDT '12: 15th International Conference on Database Theory

March 30, 2012

Berlin, Germany

Acceptance Rates

Overall Acceptance Rate 7 of 10 submissions, 70%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
576
Total Downloads

Downloads (Last 12 months)17
Downloads (Last 6 weeks)1

Other Metrics

View Author Metrics

Citations

Cited By

Garcia-Varona AAlvarez-Meaza IRio-Belver RBorregan-Alvarado J(2024)Open Data Strategy in Competitive Intelligence: Analyzing the Scientific TrendsProceedings of the 17th International Conference on Industrial Engineering and Industrial Management (ICIEIM) – XXVII Congreso de Ingeniería de Organización (CIO2023)10.1007/978-3-031-57996-7_36(207-213)Online publication date: 26-Apr-2024
https://doi.org/10.1007/978-3-031-57996-7_36
Mohana Priya D. (2022)Multi-Object Tracking Using Gradient-Based Learning Model in Video SurveillanceInternational Journal of Software Innovation10.4018/IJSI.2891689:4(1-17)Online publication date: 29-Jun-2022
https://doi.org/10.4018/IJSI.289168
Chen YLi CWang H(2022)Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)Forecasting10.3390/forecast40400424:4(767-786)Online publication date: 23-Sep-2022
https://doi.org/10.3390/forecast4040042
Corrales-Garay DOrtiz-de-Urbina-Criado MMora-Valentín E(2022)Understanding open data business models from innovation and knowledge management perspectivesBusiness Process Management Journal10.1108/BPMJ-06-2021-037328:2(532-554)Online publication date: 14-Mar-2022
https://doi.org/10.1108/BPMJ-06-2021-0373
Sundararaman A(2021)Big Data Quality for Data Mining in Business Intelligence ApplicationsIntegration Challenges for Analytics, Business Intelligence, and Data Mining10.4018/978-1-7998-5781-5.ch004(64-91)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-5781-5.ch004
Ekie JGueye BEkie TNiang I(2021)An Evidence-Based Approach on Public Health Decisions in Low-Middle Income Countries: Use Case of Senegal at the Verge of COVID-192021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA)10.1109/ICDATA52997.2021.00046(193-200)Online publication date: Jun-2021
https://doi.org/10.1109/ICDATA52997.2021.00046
Boulahia CBehja HLouhdi M(2020)Towards Semantic ETL for integration of textual scientific documents in a Big Data environment: a theoretical approach2020 6th IEEE Congress on Information Science and Technology (CiSt)10.1109/CiSt49399.2021.9357280(133-138)Online publication date: 5-Jun-2020
https://doi.org/10.1109/CiSt49399.2021.9357280
Castellanos MDaniel FGarrigós IMazón J(2018)Business Intelligence and the WebInformation Systems Frontiers10.1007/s10796-013-9435-815:3(307-309)Online publication date: 24-Dec-2018
https://dl.acm.org/doi/10.1007/s10796-013-9435-8
Mejía JÍñiguez FMuñoz M(2017)Data Analysis for Software Process Improvement: A Systematic Literature ReviewRecent Advances in Information Systems and Technologies10.1007/978-3-319-56535-4_5(48-59)Online publication date: 28-Mar-2017
https://doi.org/10.1007/978-3-319-56535-4_5
Abello ARomero OPedersen TBerlanga RNebot VAramburu MSimitsis A(2015)Using Semantic Web Technologies for Exploratory OLAP: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2014.233082227:2(571-588)Online publication date: 1-Feb-2015
https://doi.org/10.1109/TKDE.2014.2330822
Show More Cited By

View Options

Get Access

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