An important feature of some conceptual modelling grammars is the features they provide to allow database designers to show real-world thingsmay ormay not possess a particular attribute or relation...
Retrieving information from heterogeneous database systems involves a complex process and remains a challenging research area. We propose a cognitively guided approach for developing an information...
An agency framework is used to model the behavior of software developers as they weigh concerns about product quality against concerns about missing individual task deadlines. Developers who care a...
Retrieving information from heterogeneous database systems involves a complex process and remains a challenging research area. We propose a cognitively guided approach for developing an information-retrieval agent that takes the user's information request, identifies relevant information sources, and generates a multidatabase access plan. Our work is distinctive in that the agent design is based on an empirical study of how human experts retrieve information from multiple, heterogeneous database systems. To improve on empirically observed information-retrieval capabilities, the design incorporates mathematical models and algorithmic components. These components optimize the set of information sources that need to be considered to respond to a user query and are used to develop efficient multidatabase-access plans. This agent design, which integrates cognitive and mathematical models, has been implemented using Soar, a knowledge-based architecture.
In this paper the author describes a formal language for communication based on linguistics—more specifically, a theory of natural language communication and models of natural language conversations. The language has a small number of general message types that are formally defined by their intended effects on the recipient. For each message type he defines a standard automated method of responding that depends only on the message type and is independent of the message's content. For more complex conversations he provides methods for responding that do depend on the content. In this system, a message's sender—automated or human—constructs and sends a message knowing that he cannot know, but can only predict, how it will be interpreted. The agent receiving the message interprets it and then uses it as a basis for inferring how he should respond. The message interpretation mechanism for this language is reusable, modular, and shared by all applications. The benefit of this communication system is that it makes the communication infrastructure more flexible, easier to modify, easier to expand, and more capable.
The facilitation and analytical support of argumentation-based collaborative decision making is the focus of this research. We model collaborative decision making as an argumentation process. We develop a connectionist modeling framework, a network representation formalism for argument structures, connectionist network mechanisms, and their models of computations to extract the behavior of argument structures. We use two examples from the case study literature to illustrate the concepts. Several interesting properties of the connectionist network models are observed from our computational results. We find that although the length of the computation is affected by parametric values, the final activation levels of the units are largely unaffected. We observe that the initial activation levels of the defeasible units seem to have no effect on their final activation levels. The proposed modeling approach generates valuable insights into the characteristics of specific argumentative discussions. While the intention of this work is not to introduce the connectionist paradigm as a means to bring arguments to a closure (resolution), we show that certain resolution mechanisms can be easily implemented under the connectionist framework.
Data warehousing and On-Line Analytical Processing (OLAP) are two of the most significant new technologies in the business data processing arena. A data warehouse, or decision support database, can be defined as a “very large” repository of historical data pertaining to an organization. OLAP refers to the technique of performing complex analysis over the information stored in a data warehouse. The complexity of queries required to support OLAP applications makes it difficult to implement using standard relational database technology. Moreover, currently there is no standard conceptual model for OLAP. There clearly is a need for such a model and an algebra as evidenced by the numerous SQL extensions offered by many vendors of OLAP products. In this paper we address this issue by proposing a model of a data cube and an algebra to support OLAP operations on this cube. The model we present is simple and intuitive, and the algebra provides a means to concisely express complex OLAP queries.
As demand for online network services continues to grow, service providers are looking to meet this need and avail themselves of business opportunities. However, despite strong growth in demand, providers continue to have difficulty achieving profitability, customer churn remains high, and network performance continues to draw complaints. We suggest that strategic business planning for network services would benefit from a systems thinking approach that analyzes the feedback effects present in the underlying business process. These feedback loops can be complex and have significant impact on business performance. For instance, while the size of a provider's customer base depends on price and network performance, network performance is itself dependent on the size of the customer base. In this paper, we develop a planning model that represents these feedback effects using the finite difference equations methodology of systems dynamics. The model is validated by showing its fit with essential characteristics of the underlying problem domain, and by showing its ability to replicate observed reference mode behaviors. Simulations are then carried out under a variety of scenarios to examine issues important to service providers. Among other findings, the simulations suggest that (a) under flat-rate pricing, lowering price to increase customer base can hurt profitability as well as network performance; (b) under usage-based pricing, lowering price need not necessarily lead to a larger customer base; and (c) in addition to price, the customers' threshold of tolerance for performance degradation plays a significant role in balancing market share with profitability. We briefly present a prototype decision support system based on the systems thinking approach, and suggest ways in which it could be used to help business planning for network services.
Kohonen's self-organizing map (SOM) network is one of the most important network architectures developed during the 1980s. The main function of SOM networks is to map the input data from an n-dimensional space to a lower dimensional (usually one- or two-dimensional) plot while maintaining the original topological relations. Therefore, it can be viewed as an analog of factor analysis. In this research, we evaluate the feasibility of using SOM networks as a robust alternative to factor analysis and clustering for data mining applications. Specifically, we compare SOM network solutions to factor analytic and K-Means clustering solutions on simulated data sets with known underlying factor and cluster structures. The comparisons indicate that the SOM networks provide solutions superior to unrotated factor solutions in general and provide more accurate recovery of underlying cluster structures when the input data are skewed. Our findings suggest that SOM networks can provide robust alternatives to traditional factor analysis and clustering techniques in data mining applications.