L. Miller’s research while affiliated with Iowa State University and other places

Data warehouses have become an instant phenomenon in many large organizations that deal with massive amounts of information. Drawing on the experiences from the systems development field, we surmise that an effective design tool will enhance the success of warehouse implementations. Thus, we present a CASE tool designed to generate the SQL queries necessary to build a warehouse from a set of operational relational databases. The warehouse designer simply specifies a list of attribute names that will appear in the warehouse, conditions if any are desired, and a description of the operational databases. The tool returns the queries needed to populate the warehouse table.

Tools for selective proactive as well as reactive information retrieval, information extraction, information organization and assimilation, knowledge discovery using heterogeneous, distributed knowledge and data sources constitute some of the key enabling technologies for managing the data overload and translating recent advances in automated data acquisition, digital storage, computers and communications into advances in decision support, scientific discovery and related applications. Such distributed knowledge networks (DKN) have to be able to effectively utilize multiple autonomous, often independently owned and operated information systems. Given the complexity of the such systems and the need for autonomy of the components, multi agent systems, because of their modularity, offer an attractive framework for the design of DKN. In such multi agent systems, satisfactory completion of the tasks at hand depend critically on effective communication and coordination among the agents. The paper describes an approach to coordination and control of DKN through inter agent negotiation using the contract net protocol (CNP). The feasibility of the proposed design is demonstrated on tasks involving information retrieval and knowledge discovery using distributed data and knowledge sources

Tools for selective proactive as well as reactive information retrieval and knowledge discovery constitute some of the key enabling technologies for managing the data overload and translating recent advances in automated data acquisition, digital storage, computers and communications into fundamental advances in decision support, scientific discovery and related applications. The paper describes an implementation of intelligent, customizable mobile software agents for information retrieval and knowledge discovery from distributed data sources. These tools are part of the distributed knowledge network (DKN) toolbox that is being developed at the Iowa State University's Artificial Intelligence Laboratory. Experiments with retrieval of journal paper abstracts demonstrate the feasibility of using machine learning to design mobile intelligent agents for customized information retrieval. A similar approach has been successfully employed for knowledge discovery (using machine learning) from distributed data collections

Distributed knowledge networks (DKN) provide some of the key enabling technologies for translating recent advances in automated data acquisition, digital storage, computers and communications into fundamental advances in organizational decision support, data analysis, and related applications. DKN include computational tools for accessing, organizing, transforming, and analyzing the contents of heterogeneous, distributed data and knowledge sources and for distributed problem solving and decision making under tight time, resource, and performance constraints. The paper presents an overview of the DKN project in the Iowa State University Artificial Intelligence Laboratory

The paper focuses on intrusion detection and countermeasures with respect to widely-used operating systems and networks. The design and architecture of an intrusion detection system built from distributed agents is proposed to implement an intelligent system on which data mining can be performed to provide global, temporal views of an entire networked system. A starting point for agent intelligence in the system is the research into the use of machine learning over system call traces from the privileged sendmail program on UNIX. The authors use a rule learning algorithm to classify the system call traces for intrusion detection purposes and show the results

Data warehouses have become an instant phenomenon in many large organizations that deal with a massive amount of information. Drawing on the experiences from the systems development field, we surmise that an effective CASE tool will enhance the success of warehouse implementations. Thus, we present a CASE tool designed to generate the SQL queries necessary to build a warehouse from a set of operational relational databases. The warehouse designer simply specifies a list of attribute names that are to appear in the warehouse, conditions if any are desired, and a description of the operational databases. The tool returns the queries needed to populate the warehouse table

Organizations, be they global, multinational, or local, are finding that information technology is a powerful enabler of their business processes. IT management decisions therefore have profound impact on organizational performance. Such decisions then have qualities that set them apart from other types of decisions (e.g., individual, group, etc.) and special considerations must be accorded to support these decisions. Recent studies of organizational decision support have brought forth the need to focus on organizational memory, its retention, retrieval, and use, as an effective support and coordination mechanism. Organizations retain memory among its individuals, culture, transformations, structure, and ecology, but it is the memory retained by the individuals that we find most conducive to systematizing. In our proposal, we describe a model for the retention and retrieval of organizational memory among the individuals. We propose to build a prototype of the system built around readily available off-the-shelf software whenever possible and field test the use of the system