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

Conceptual modeling of knowledge based systems for digital ecosystems

The agents or entities frequently require intelligence in the form of knowledge based systems(KBS) to support many of their functions. In this Paper we discuss how these KBSs are conceptual are conceptually modeled as a first step towards their development. In particular, we show to effectively model all the different knowledge constructs using an extended definition of an object. The notation used to express this is UML [Booch 2005]

Use and modeling of multi-agent systems in medicine

Multi-Agent System (MAS), and more specifically, ontology-based MAS, are increasingly being proposed and used within the medical domain. In this paper we represent an ontology-based multi-agent system specifically designed to intelligently retrieve information about human diseases. Thehuman disease ontology is organized according to the four dimensions: disease types, symptoms, causes and treatments. The multi-agent system consists of four different types of agent: Interface, Manger, Information and Smart agent. We use of UML 2.1 to model social and goal-driven nature of agents. We believe that UML 2.1 has not only provided a way for standardized notation of MAS, but also for effective representation of the dynamic processes associated with these MAS

Using the symmetrical Tau criterion for feature selection decision tree and neural network learning

The data collected for various domain purposes usually contains some features irrelevant tothe concept being learned. The presence of these features interferes with the learning mechanism and as a result the predicted models tend to be more complex and less accurate. It is important to employ an effective feature selection strategy so that only the necessary and significant features will be used to learn the concept at hand. The Symmetrical Tau (t) [13] is a statistical-heuristic measure for the capability of an attribute in predicting the class of another attribute, and it has successfully been used as a feature selection criterion during decision tree construction. In this paper we aim to demonstrate some other ways of effectively using the t criterion to filter out the irrelevant features prior to learning (pre-pruning) and after the learning process (post-pruning). For the pre-pruning approach we perform two experiments, one where the irrelevant features are filtered out according to their t value, and one where we calculate the t criterion for Boolean combinations of features and use the highest t-valued combination. In the post-pruning approach we use the t criterion to prune a trained neural network and thereby obtain a more accurate and simple rule set. The experiments are performed on data characterized by continuous and categorical attributes and the effectiveness of the proposed techniques is demonstrated by comparing the derived knowledge models in terms of complexity and accuracy

Modelling volatility with mixture density networks

Volatility is an important variable in financial forecasting. Forecasting volatility requires a development of a suitable model for it. In this paper, we examine different time series models for volatility modelling. Specifically, we will study the use of recurrent mixture density networks, GARCH and EGARCH models to model volatility. In addition, we demonstrate the impact of different factors on the accuracy and completeness of each of these models

Ascertaining the financial loss from non-dependable events in business interactions by using the Monte Carlo method

Risk Assessment in business interactions is carried out to determine beforehand the occurrence ofundesirable events and their associated consequences. In the literature, various approaches have beenproposed by which an interaction initiating agent can ascertain the occurrence of undesirable event/s and determine their consequences in an interaction. But all of those approaches just consider those events that are related on the performance of the other agent, with whom the interaction initiating agent is forming an interaction with. It is possible that there are also such events that are not dependant on the other agent?s performance, but will directly or in-directly have an impact on the successful completion of the interaction. In this paper we will highlight the importance of considering such event/s during the process of risk assessment, and propose a methodology by which the interaction initiating agent can determine and quantify their effect on the successful completion of its business interaction

DYNASTAT: A Methodology for Dynamic and Static Modeling of Multi-agent Systems

Multi-agent systems are increasingly being used within various knowledge domains. The need for modeling of the multi-agent systems in a systematic and effective way is becoming more evident. In this chapter, we present the DYNASTAT methodology. This methodology involves a conceptual overview of multi-agent systems, a selection of specific agent characteristics to model, and a discussion of what has to be modeled for each of these agent characteristics. DYNASTAT is independent of any particular modeling language but provides a framework that can be used to realize a particular language in the context of a real-world example. UML 2.2 was chosen as the modeling language to implement the DYNASTAT methodology and this was illustrated using examples from the medical domain. Several UML 2.2 diagrams were selected including a use case, composite structure, sequence and activity diagram to model a multi-agent system able to assist botha medical researcher and a primary care physician. UML 2.2 provides a framework for effective modeling of agent-based systems in a standardized way which this chapter endeavors to demonstrate

Application of digital ecosystems in health domain

Digital Ecosystems (DES) have recently been introduced into the computer and information societies. A Digital Ecosystem is the dynamic and synergetic complex of Digital Communities consisting of interconnected, interrelated and interdependent Digital Species situated in a Digital Environment, that intereact as a functional unit and are linked together through actions, information and transaction flows. Digital Ecosystems integrate various cutting-edge technologies including ontologies, agent-based and self-organizing systems, swarm intelligence, ambient intelligence, data mining etc. The synergetic effects of these methodologies results in a more efficient, effective, reliable and secure system.The application of DES within the health domain would transform the way in which health information is created, stored, accessed, used, managed, analyzed and shared, and would bring an innovative breakthrough within bealth domain. In this paper, we illustrate how the DES Design Methodology can be implemented within the health domain. We focus on the key factors associated with the DES design. The design methodology framework allows better control over the design process and serves as a navigating tool during the Digital Health Ecosystems design

Modeling the dynamics of web-based service and resource-oriented digital ecosystems

The notion of digital species is broadened to include services and resources, special issues arise in modeling the dynamics and workflows with representations associated with these services and resources. To address these issues, this paper explores two different yet related approaches: the traditional BPEL-based workflow modeling approach and the Mashupbased Web approach. In this paper, we first demonstrate two examples of service-oriented and resource-oriented digital ecosystems on the Web. We then identify key issues pertinent to both types of DES. We discuss formal definition, specifications and issues of BPEL-based approach and Mashup-based modeling techniques with computational formalisms. Finally, we propose a hybrid approach to deal with modeling the dynamicsin processes associated with such Digital Ecosystems

Semantic web support for open-source software development

Open-source software is unique in that the development of the product is performed in public over the Internet by developers who elect to contribute to the project and rarely if ever meet face-to-face. Software development is a knowledge intensive process and the information generated in open-source software development projects is typically housed in a central Internet repository. Open-source repositories typically contains vast amounts of information, much of it unstructured, meaning that even if a question has previously been discussed and dealt with it is not a trivial task to locate it, leading to rework, confusion amongst developers and possibly deterring new developers from getting involved.This paper develops an ontology based software development architecture for open-source software development. Such an architecture would enable better categorisation of information, communication, co-ordination and the development of sophisticated search agents