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

Implementing a Three-Tier Data Warehouse

This paper aims at developing a 3-tier inter-universities data warehouse prototype for the Egyptian universities. The implementation scope is restricted to the student enrollment process. The bottom-up approach and the multi-tier (3-tier) client/server architecture were used to implement the proposed prototype. The implementation required a number of steps to be undertaken. First, a star schema data warehouse was built based on an operational database of a public university. Second, another star schema data warehouse was built based on an operational database of a private university. Third, data from the two warehouses were abstracted to formulate the inter-universities data warehouse (tier 3). Hence, a data cube based on the resulting interuniversities data warehouse was developed using an OLAP server (tier 2). The data cube is then accessed by a client tool (tier 1) for the purpose of query and analysis. Although two universities were used to implement the interuniversities data warehouse, this scenario could be applied with any number of universities in a quite similar way. This inter-universities prototype is scalable and flexible to retain more than two universities regardless of the size of the operational databases. The interuniversities prototype fosters the coordination between participating universities and supports the decision making process. The proposed system could be used to generate a variety of strategic reports

Conceptual design of an XML FACT repository for dispersed XML document warehouses and XML marts

Since the introduction of eXtensible Markup Language (XML), XML repositories have gained a foothold in many global (and government) organizations, where, e-Commerce and e-business models have maturated in handling daily transactional data among heterogeneous information systems in multi-data formats. Due to this, the amount of data available for enterprise decision-making process is increasing exponentially and are being stored and/or communicated in XML. This presents an interesting challenge to investigate models, frameworks and techniques for organizing and analyzing such voluminous, yet distributed XML documents for business intelligence in the form of XML warehouse repositories and XML marts. In this paper, we address such an issue, where we propose a view-driven approach for modelling and designing of a Global XML FACT (GxFACT) repository under the MDA initiatives. Here we propose the GxFACT using logically grouped, geographically dispersed, XML document warehouses and Document Marts in a global enterprise setting. To deal with organizations? evolving decision-making needs, we also provide three design strategies for building and managing of such GxFACT in the context of modelling of further hierarchical dimensions and/or global document warehouses

Aggregating sentiment in Europe: the relationship with volatility and returns

This paper presents several proposals for creating an aggregate sentiment index for the European stock market. We achieve this objective by using the OWA and WOWA operators, which have been successful in finance and have a strong financial interpretation. We compute ten different aggregate sentiment indices for the 2007-2021 period and evaluate their ability to provide information about current and future market volatility and returns. We find several results of interest for both investors and policymakers. Sentiment indices have a strong negative relationship with market volatility. Extreme values of sentiment can predict future market returns, with low values indicating positive returns and high values suggesting negative returns. Finally, using stock market capitalisation as an input of the WOWA operator enhances explanatory power of the indices on future market returns compared to the OWA operator