Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Business Intelligence and the Web. Guest editors' introduction

The special issue of Information Systems Frontiers on Business Intelligence and the Web discusses the increasing use of Business Intelligence (BI) solutions that allow enterprise to query, understand, and analyze business data to make better decisions. The special issue targets the issues of Web data feeding BI and engineering Web-enabled BI in the discussion. It states that the amount and complexity of data available on the Web has been growing rapidly over a period of time. Designers of BI applications making use of data from the Web have to deal with several challenges due to these developments. The importance of Web opinion feeds as information sources for companies is highlighted in an article entitled 'Storing and Analyzing Voice of the Market Data in the Corporate Data Warehouse'. The authors propose a technique to integrate opinion data in BI models. Specifically, they present a multidimensional data model that integrates sentiment data extracted from customer opinion forums into the corporate data warehouse

XML content warehousing: Improving sociological studies of mailing lists and web data

In this paper, we present the guidelines for an XML-based approach for the sociological study of Web data such as the analysis of mailing lists or databases available online. The use of an XML warehouse is a flexible solution for storing and processing this kind of data. We propose an implemented solution and show possible applications with our case study of profiles of experts involved in W3C standard-setting activity. We illustrate the sociological use of semi-structured databases by presenting our XML Schema for mailing-list warehousing. An XML Schema allows many adjunctions or crossings of data sources, without modifying existing data sets, while allowing possible structural evolution. We also show that the existence of hidden data implies increased complexity for traditional SQL users. XML content warehousing allows altogether exhaustive warehousing and recursive queries through contents, with far less dependence on the initial storage. We finally present the possibility of exporting the data stored in the warehouse to commonly-used advanced software devoted to sociological analysis

Multi-Agent System for Decision Support in Enterprises

Business decisions must rely not only on organisation’s internal data but also on external data from competitors or relevant events. This information can be obtained from the Web but must be integrated with the data in an organisation’s Data Warehouse (DW). In this paper we discuss the agent-based integration approach using ontologies. To enable common understanding of a domain between people and application systems we introduce business rules approach towards ontology management. Because knowledge in organisation’s ontologies is acquired from business users without technical knowledge simple user interface based on ontology restrictions and predefined templates are used. After data from internal DW, Web and business rules are acquired; agent can deduce new knowledge and therefore facilitate decision making process. Tasks like information retrieval from competitors, creating and reviewing OLAP reports are autonomously performed by agents, while business users have control over their execution through knowledge base in ontology. The approach presented in the paper was verified on the case study from the domain of mobile communications with the emphasis on supply and demand of mobile phones and its accessories