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

A Multiple Criteria Framework to Evaluate Bank Branch Potential Attractiveness

Remarkable progress has occurred over the years in the performance evaluation of bank branches. Even though financial measures are usually considered the most important in assessing branch viability, we posit that insufficient attention has been given to other factors that affect the branches’ potential profitability and attractiveness. Based on the integrated used of cognitive maps and MCDA techniques, we propose a framework that adds value to the way that potential attractiveness criteria to assess bank branches are selected and to the way that the trade-offs between those criteria are obtained. This framework is the result of a process involving several directors from the five largest banks operating in Portugal, and follows a constructivist approach. Our findings suggest that the use of cognitive maps systematically identifies previously omitted criteria that may assess potential attractiveness. The use of MCDA techniques may clarify and add transparency to the way trade-offs are dealt with. Advantages and disadvantages of the proposed framework are also discussed.

Soft Seeded SSL Graphs for Unsupervised Semantic Similarity-based Retrieval

Semantic similarity based retrieval is playing an increasingly important role in many IR systems such as modern web search, question-answering, similar document retrieval etc. Improvements in retrieval of semantically similar content are very significant to applications like Quora, Stack Overflow, Siri etc. We propose a novel unsupervised model for semantic similarity based content retrieval, where we construct semantic flow graphs for each query, and introduce the concept of "soft seeding" in graph based semi-supervised learning (SSL) to convert this into an unsupervised model. We demonstrate the effectiveness of our model on an equivalent question retrieval problem on the Stack Exchange QA dataset, where our unsupervised approach significantly outperforms the state-of-the-art unsupervised models, and produces comparable results to the best supervised models. Our research provides a method to tackle semantic similarity based retrieval without any training data, and allows seamless extension to different domain QA communities, as well as to other semantic equivalence tasks.Comment: Published in Proceedings of the 2017 ACM Conference on Information and Knowledge Management (CIKM '17

A theory-grounded framework of Open Source Software adoption in SMEs

This is a post-peer-review, pre-copyedit version of an article published in European Journal of Information Systems. The definitive publisher-authenticated version Macredie, RD and Mijinyawa, K (2011), "A theory-grounded framework of Open Source Software adoption in SMEs", European Journal of Informations Systems, 20(2), 237-250 is available online at: http://www.palgrave-journals.com/ejis/journal/v20/n2/abs/ejis201060a.html.The increasing popularity and use of Open Source Software (OSS) has led to significant interest from research communities and enterprise practitioners, notably in the small business sector where this type of software offers particular benefits given the financial and human capital constraints faced. However, there has been little focus on developing valid frameworks that enable critical evaluation and common understanding of factors influencing OSS adoption. This paper seeks to address this shortcoming by presenting a theory-grounded framework for exploring these factors and explaining their influence on OSS adoption, with the context of study being small- to medium-sized Information Technology (IT) businesses in the U.K. The framework has implications for this type of business – and, we will suggest, more widely – as a frame of reference for understanding, and as tool for evaluating benefits and challenges in, OSS adoption. It also offers researchers a structured way of investigating adoption issues and a base from which to develop models of OSS adoption. The study reported in this paper used the Decomposed Theory of Planned Behaviour (DTPB) as a basis for the research propositions, with the aim of: (i) developing a framework of empirical factors that influence OSS adoption; and (ii) appraising it through case study evaluation with 10 U.K. Small- to medium-sized enterprises in the IT sector. The demonstration of the capabilities of the framework suggests that it is able to provide a reliable explanation of the complex and subjective factors that influence attitudes, subjective norms and control over the use of OSS. The paper further argues that the DTPB proved useful in this research area and that it can provide a variety of situation-specific insights related to factors that influence the adoption of OSS

Creating knowledge maps in Virtual Learning Environments

The intention of this paper is to show a reflexive study about knowledge representation through maps in virtual learning environments (VLE). The aim of this research is to investigate how maps can be used to build information networks, contribute to the collective building of knowledge, and facilitate research and pedagogical mediation in VLEs. For this purpose, the specialization post-graduation online course “Software Use in Qualitative Research” was analyzed. During this analysis, I discuss meaningful learning through maps built using the mapping software Nestor Web Cartographer, CMap tools and Compendium. I then present some important aspects about how maps can contribute to online content design, tutoring diagnostic and assessment applications

Playing the wrong game: An experimental analysis of relational complexity and strategic misrepresentation

It has been suggested that players often produce simplified and/or misspecified mental representations of interactive decision problems (Kreps, 1990). We submit that the relational structure of players’ preferences in a game induces cognitive complexity, and may be an important driver of such simplifications. We provide a formal classification of order structures in two-person normal form games based on the two properties of monotonicity and projectivity, and present experiments in which subjects must first construct a representation of games of different relational complexity, and subsequently play the games according to their own representation. Experimental results support the hypothesis that relational complexity matters. More complex games are harder to represent, and this difficulty is correlated with measures of short term memory capacity. Furthermore, most erroneous representations are less complex than the correct ones. In addition, subjects who misrepresent the games behave consistently with such representations according to simple but rational decision criteria. This suggests that in many strategic settings individuals may act optimally on the ground of simplified and mistaken premises.pure motive, mixed motive, preferences, bi-orders, language, cognition, projectivity, monotonicity, short term memory, experiments