Title :
Modeling Information Quality Risk in Data Mining
Author :
Su, Ying ; Li, Donghong ; Peng, Jie
Author_Institution :
Inst. of Sci. & Tech. Inf. of China, Beijing
Abstract :
Information quality (IQ) is a critical factor in the success of the data mining (DM). Therefore, it is essential to measure the risk of IQ in a data warehouse to ensure success in implementing DM. This paper presents a methodology to determine two IQ characteristics-accuracy and comprehensiveness-that are of critical importance to decision makers. This methodology can examine how the quality risks of source information affect the quality for information outputs produced using the relational algebra operations selection, projection, and Cubic product. It can be used to determine how quality risks associated with diverse data sources affect the quality of the derived data. The study resulted in the development of a model of a data cube and an algebra to support IQ risk operations on this cube. The model we present is simple and intuitive, and the algebra provides a means to concisely express complex DM queries.
Keywords :
data mining; data warehouses; decision making; relational algebra; risk analysis; cubic product; data mining; data warehouse; decision making; information quality risk; relational algebra; source information; Algebra; Companies; Costs; Data mining; Data warehouses; Databases; Delta modulation; Information analysis; Quality management; Risk management;
Conference_Titel :
Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4244-2107-7
Electronic_ISBN :
978-1-4244-2108-4
DOI :
10.1109/WiCom.2008.2424
