Innovative Approaches for efficiently Warehousing Complex Data from the Web

TL;DR

This paper discusses innovative methods for building and managing web data warehouses, including XML modeling, integrating data mining with OLAP, and schema evolution for personalized analysis, enhancing decision support systems.

Contribution

It introduces three novel approaches—XML-based modeling, combined data mining and OLAP, and schema evolution—for complex web data warehousing.

Findings

XML as a model for complex data warehouses

Integration of data mining with OLAP for advanced analysis

Schema evolution techniques for personalized data analysis

Abstract

Research in data warehousing and OLAP has produced important technologies for the design, management and use of information systems for decision support. With the development of Internet, the availability of various types of data has increased. Thus, users require applications to help them obtaining knowledge from the Web. One possible solution to facilitate this task is to extract information from the Web, transform and load it to a Web Warehouse, which provides uniform access methods for automatic processing of the data. In this chapter, we present three innovative researches recently introduced to extend the capabilities of decision support systems, namely (1) the use of XML as a logical and physical model for complex data warehouses, (2) associating data mining to OLAP to allow elaborated analysis tasks for complex data and (3) schema evolution in complex data warehouses for personalized analyses. Our contributions cover the main phases of the data warehouse design process: data integration and modeling and user driven-OLAP analysis.