Profit Maximization with Data Management Systems

Data, a core component of information systems, has long been recognized as a critical resource to firms. Data is the backbone of business processes; it enables efficient operations, supports managerial decision-making, and generates revenues as a commodity. This study identifies a significant gap between the technical and the business perspectives of data management. While functionality and technical efficiency are well addressed, the consideration of economic perspectives, such as value-contribution and profitability, is not evident. This study suggests that introducing economic perspectives can better inform the design and the administration of data management systems by accounting for the interplay between business benefits and implementation costs. To address the identified gap, the paper proposes a quantitative microeconomic framework for data management that links value and cost to the impartial/technological characteristics of data and the related information system. Such a mapping allows cost/benefit assessment and determination of optimal configuration of system and data characteristics to maximize value and profits. The framework is demonstrated through development of a model for tabular datasets, and the optimal design of dataset characteristics (such as time- span, desired quality-level, and the set of attributes to be included). The application of the model is illustrated using numerical examples

Dynamic schema evolution in a heterogeneous database environment: A graph theoretic approach

The objective of this dissertation is to create a theoretical framework and mechanisms for automating dynamic schema evolution in a heterogeneous database environment. The structure or schema of databases changes over time. Accommodating changes to the schema without loss of existing data and without significantly affecting the day to day operation of the database is the management of dynamic schema evolution. To address the problem of schema evolution in a heterogeneous database environment, we first propose a comprehensive taxonomy of schema changes and examine their implications. We then propose a formal methodology for managing schema evolution using graph theory with a well-defined set of operators and graph-based algorithms for tracking and propagating schema changes. We show that these operators and algorithms preserve the consistency and correctness of the schema following the changes. The complete framework is embedded in prototype software system called SEMAD (Schema Evolution Management ADvisor). We evaluate the system for its usefulness by conducting exploratory case studies using two different heterogeneous database domains, viz., a University database environment and a scientific database environment that is used by atmospheric scientists and hydrologists. The results of the exploratory case studies supported the hypothesis that SEMAD does help database administrators in their tasks. The results indicate that SEMAD helps the administrators identify and incorporate changes better than performing these tasks manually. An important overhead cost in SEMAD is the creation of the semantic data model, capturing the meta data associated with the model, and defining the mapping information that relates the model and the set of underlying databases. This task is a one-time effort that is performed at the beginning. The subsequent changes are incrementally captured by SEMAD. However, the benefits of using SEMAD in dynamically managing schema evolution appear to offset this overhead cost

A Machine Learning Approach to Inductive Query by Examples: An Experiment Using Relevance Feedback, ID3, Genetic Algorithms, and Simulated Annealing

Artificial Intelligence Lab, Department of MIS, University of ArizonaInformation retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to â â intelligentâ â information retrieval and indexing. More recently, information science researchers have turned to other newer inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information systems. In this article, we first provide an overview of these newer techniques and their use in information retrieval research. In order to familiarize readers with the techniques, we present three promising methods: The symbolic ID3 algorithm, evolution-based genetic algorithms, and simulated annealing. We discuss their knowledge representations and algorithms in the unique context of information retrieval. An experiment using a 8000-record COMPEN database was performed to examine the performances of these inductive query-by-example techniques in comparison with the performance of the conventional relevance feedback method. The machine learning techniques were shown to be able to help identify new documents which are similar to documents initially suggested by users, and documents which contain similar concepts to each other. Genetic algorithms, in particular, were found to out-perform relevance feedback in both document recall and precision. We believe these inductive machine learning techniques hold promise for the ability to analyze usersâ preferred documents (or records), identify usersâ underlying information needs, and also suggest alternatives for search for database management systems and Internet applications