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;Rolando Rodríguez
Affiliations:
University of Alicante, Alicante, Spain;University of Alicante, Alicante, Spain;University of Alicante, Alicante, Spain;University of Matanzas, Matanzas, Cuba;University of Matanzas, Matanzas, Cuba
Venue:
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Year:
2012

Citing 8
Cited 1

Data quality in context

Communications of the ACM
Data mining: concepts and techniques

Data mining: concepts and techniques
10 Potholes in the Road to Information Quality

Computer
Duplicate Record Detection: A Survey

IEEE Transactions on Knowledge and Data Engineering
Editorial: Special issue on mining low-quality data

Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Future trends in data mining

Data Mining and Knowledge Discovery
Eliminating fuzzy duplicates in data warehouses

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A set of experiments to consider data quality criteria in classification techniques for data mining

ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part II

Business Intelligence and the Web

Information Systems Frontiers

Quantified Score

Hi-index	0.00

Visualization

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.