Stuart E. Madnick

Context interchange: new features and formalisms for the intelligent integration of information

The Context Interchange strategy presents a novel perspective for mediated data access in which semantic conflicts among heterogeneous systems are not identified a priori, but are detected and reconciled by a context mediator through comparison of contexts axioms corresponding to the systems engaged in data exchange. In this article, we show that queries formulated on shared views, export schema, and shared “ontologies” can be mediated in the same way using the Context Interchange framework. The proposed framework provides a logic-based object-oriented formalsim for representing and reasoning about data semantics in disparate systems, and has been validated in a prototype implementation providing mediated data access to both traditional and web-based information sources.

Overview and Framework for Data and Information Quality Research

Awareness of data and information quality issues has grown rapidly in light of the critical role played by the quality of information in our data-intensive, knowledge-based economy. Research in the past two decades has produced a large body of data quality knowledge and has expanded our ability to solve many data and information quality problems. In this article, we present an overview of the evolution and current landscape of data and information quality research. We introduce a framework to characterize the research along two dimensions: topics and methods. Representative papers are cited for purposes of illustrating the issues addressed and the methods used. We also identify and discuss challenges to be addressed in future research.

Data quality requirements analysis and modeling

A set or premises, terms, and definitions for data quality management are established, and a step-by-step methodology for defining and documenting data quality parameters important to users is developed. These quality parameters are used to determine quality indicators about the data manufacturing process, such as data source creation time, and collection method, that are tagged to data items. Given such tags, and the ability to query over them, users can filter out data having undesirable characteristics. The methodology provides a concrete approach to data quality requirements collection and documentation. It demonstrates that data quality can be an integral part of the database design process. A perspective on the migration towards quality management of data in a database environment is given.<<ETX>>

Context interchange: overcoming the challenges of large-scale interoperable database systems in a dynamic environment

Research in database interoperability has primarily focused on circumventing schematic and semantic incompatibility arising from autonomy of the underlying databases. We argue that, while existing integration strategies might provide satisfactory support for small or static systems, their inadequacies rapidly become evident in large-scale interoperable database systems operating in a dynamic environment. This paper highlights the problem of receiver heterogeneity, scalability, and evolution which have received little attention in the literature, provides an overview of the Context Interchange approach to interoperability, illustrates why this is able to better circumvent the problems identified, and forges the connections to other works by suggesting how the context interchange framework differs from other integration approaches in the literature.

Lessons learned from modeling the dynamics of software development

Software systems development has been plagued by cost overruns, late deliveries, poor reliability, and user dissatisfaction. This article presents a paradigm for the study of software project management that is grounded in the feedback systems principles of system dynamics.

The inter-database instance identification problem in integrating autonomous systems

The issue of joining information about the same instance across disparate databases in a composite information system (CIS) environment is discussed. A technique called interdatabase instance identification is presented that is a combination of database management systems and artificial intelligence techniques. Common attributes in the disparate databases are applied first to reduce the number of potential candidates for the same instance. Other attributes in these databases, auxiliary databases, and inferencing rules are utilized next to identify the same instance. A detailed example of the interdatabase instance identification technique is presented using an operational research prototype.<<ETX>>

Application and analysis of the virtual machine approach to information system security and isolation

Security is an important factor if the programs of independent and possibly malicious users are to coexist on the same computer system. In this paper we show that a combined virtual machine monitor/operating system (VMM/OS) approach to information system isolation provides substantially better software security than a conventional multiprogramming operating system approach. This added protection is derived from redundant security using independent mechanisms that are inherent in the design of most VMM/OS systems.

Context Knowledge Representation and Reasoning in the Context Interchange System

The Context Interchange Project presents a unique approach to the problem of semantic conflict resolution among multiple heterogeneous data sources. The system presents a semantically meaningful view of the data to the receivers (e.g. user applications) for all the available data sources. The semantic conflicts are automatically detected and reconciled by a Context Mediator using the context knowledge associated with both the data sources and the data receivers. The results are collated and presented in the receiver context. The current implementation of the system provides access to flat files, classical relational databases, on-line databases, and web services. An example application, using actual financial information sources, is described along with a detailed description of the operation of the system for an example query.

Discovering and reconciling value conflicts for numerical data integration

The built-up in Information Technology capital fueled by the Internet and cost-effectiveness of new telecommunications technologies has led to a proliferation of information systems that are in dire need to exchange information but incapable of doing so due to the lack of semantic interoperability. It is now evident that physical connectivity (the ability to exchange bits and bytes) is no longer adequate: the integration of data from autonomous and heterogeneous systems calls for the prior identification and resolution of semantic conflicts that may be present. Unfortunately, this requires the system integrator to sift through the data from disparate systems in a painstaking manner. We suggest that this process can be partially automated by presenting a methodology and technique for the discovery of potential semantic conflicts as well as the underlying data transformation needed to resolve the conflicts. Our methodology begins by classifying data value conflicts into two categories: context independent and context dependent. While context independent conflicts are usually caused by unexpected errors, the context dependent conflicts are primarily a result of the heterogeneity of underlying data sources. To facilitate data integration, data value conversion rules are proposed to describe the quantitative relationships among data values involving context dependent conflicts. A general approach is proposed to discover data value conversion rules from the data. The approach consists of the five major steps: relevant attribute analysis, candidate model selection, conversion function generation, conversion function selection and conversion rule formation. It is being implemented in a prototype system, DIRECT, for business data using statistics based techniques. Preliminary study using both synthetic and real world data indicated that the proposed approach is promising. r 2001 Elsevier Science Ltd. All rights reserved.

The Context Interchange mediator prototype

The <italic>Context Interchange</italic> strategy presents a novel approach for mediated data access in which semantic conflicts among heterogeneous systems are not identified a priori, but are detected and reconciled by a <italic>context mediator</italic> through comparison of <italic>contexts</italic>. This paper reports on the implementation of a Context Interchange Prototype which provides a concrete demonstration of the features and benefits of this integration strategy.