WSEMQT: a novel approach for quality-based evaluation of web data sources for a data warehouse

作     者：Bhutani, Priyanka Saha, Anju Gosain, Anjana 

作者机构：GGS Indraprastha Univ USIC&T Sec 16C New Delhi India 

出 版 物：《IET SOFTWARE》 (IET软件)

年 卷 期：2020年第14卷第7期

页      面：806-815页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 

主　　题：Internet decision making Web sites data warehouses data warehouse World Wide Web quality-aware selection Web data sources mixed quality factors evaluation process expert-based evaluation multicriteria decision-making methods evaluation scores quality-based evaluation external data multilevel approach Web source evaluation real-world academic Web data case study Web source based evaluation quality model Web quality model Web quality testing tools WSEMQT automated Web source quality evaluation 

摘      要：The incorporation of suitable external data from the World Wide Web offers an effective solution for enriching the data in the data warehouse (DW). However, the main challenge is the quality-aware selection of web data sources to maintain the quality of the DW. In the previous works, the quality evaluation of web sources is through expert evaluation only, which makes it a very lengthy process. Also, since the quality model consists of mixed quality factors from diverse domains of Web, DW and underlying business, finding an expert possessing an expertise of all these domains is a huge bottleneck in the evaluation process. In order to overcome these existing issues, this study proposes a novel multi-level approach web source evaluation with multi-criteria decision-making and web quality testing tools (WSEMQT) and underlying quality model web quality model for evaluating web sources for the DW. The authors introduce automated web source quality evaluation in the first level of web source based evaluation and multiple dimensions of quality evaluation at the second level of expert-based evaluation. At both the levels, multi-criteria decision-making methods are applied to the evaluation scores obtained to ascertain the ranked list of Web sources. The authors present a real-world academic web data case study which shows that the proposed approach can be executed successfully for real-world problems.