Search Approach for External Data Sources for Data Warehouse Enrichment in Business Intelligence Context

被引:0
|
作者
Djiroun, Rahma [1 ]
Lachachi, Lilia Yasmine [1 ]
Azzouni, Noufel Fares Eddine [1 ]
Guessoum, Meriem Amel [1 ]
Boukhalfa, Kamel [1 ]
Benkhelifa, El Hadj [2 ]
机构
[1] USTHB, LSI, Algiers, Algeria
[2] Staffordshire Univ, CCA, Stoke On Trent, Staffs, England
来源
2023 20TH ACS/IEEE INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS, AICCSA | 2023年
关键词
Business Intelligence; Data Warehouses; External sources; Open Data;
D O I
10.1109/AICCSA59173.2023.10479350
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In Business Intelligence (BI) systems, decision-making is based on data warehouses (DW), alimented, generally, from internal sources to the organization. Decision-making based solely on this data, sometimes, gives a partial and limited view of certain activities. Consequently, the decision maker finds himself constrained to search additional information on the Web in order to understand the external environment. This information can be found in external data sources as the Open Data (OD), to complete a decisional analysis. Therefore, it is valuable to enrich DWs with external data sources. In this context, we propose, in this paper, an approach that aims to explore the Web in order to search and select appropriate OD sources for DW enrichment. This approach is carried out on the basis of Natural Language Processing (NLP) techniques as well as a scrapping process related to the Google Dataset Search engine, in order to provide an efficient solution for making decisions to validate this approach, a tool called "Open Data Search" is developed and the obtained results are presented.
引用
收藏
页数:8
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