Complex adaptive systems based data integration : theory and applications

Data Definition Languages (DDLs) have been created and used to represent data in programming languages and in database dictionaries. This representation includes descriptions in the form of data fields and relations in the form of a hierarchy, with the common exception of relational databases where relations are flat. Network computing created an environment that enables relatively easy and inexpensive exchange of data. What followed was the creation of new DDLs claiming better support for automatic data integration. It is uncertain from the literature if any real progress has been made toward achieving an ideal state or limit condition of automatic data integration. This research asserts that difficulties in accomplishing integration are indicative of socio-cultural systems in general and are caused by some measurable attributes common in DDLs. This research’s main contributions are: (1) a theory of data integration requirements to fully support automatic data integration from autonomous heterogeneous data sources; (2) the identification of measurable related abstract attributes (Variety, Tension, and Entropy); (3) the development of tools to measure them. The research uses a multi-theoretic lens to define and articulate these attributes and their measurements. The proposed theory is founded on the Law of Requisite Variety, Information Theory, Complex Adaptive Systems (CAS) theory, Sowa’s Meaning Preservation framework and Zipf distributions of words and meanings. Using the theory, the attributes, and their measures, this research proposes a framework for objectively evaluating the suitability of any data definition language with respect to degrees of automatic data integration. This research uses thirteen data structures constructed with various DDLs from the 1960\u27s to date. No DDL examined (and therefore no DDL similar to those examined) is designed to satisfy the law of requisite variety. No DDL examined is designed to support CAS evolutionary processes that could result in fully automated integration of heterogeneous data sources. There is no significant difference in measures of Variety, Tension, and Entropy among DDLs investigated in this research. A direction to overcome the common limitations discovered in this research is suggested and tested by proposing GlossoMote, a theoretical mathematically sound description language that satisfies the data integration theory requirements. The DDL, named GlossoMote, is not merely a new syntax, it is a drastic departure from existing DDL constructs. The feasibility of the approach is demonstrated with a small scale experiment and evaluated using the proposed assessment framework and other means. The promising results require additional research to evaluate GlossoMote’s approach commercial use potential

A Transformational Reengineering System That Supports Software Maintenance Using a Graph Representation for the Identification of an Object-Oriented Software Architecture.

The process of maintenance and enhancement of legacy software systems is a laborious and unavoidable task. Often these systems lack structure or modularity, as they were developed using programming languages and paradigms that do not incorporate object-oriented features and sound design principles. The software engineer\u27s task can be simplified if tools are available to identify object like features in the code. These tools can help transform the non-object-oriented code to object oriented code. This research describes a comprehensive and systematic process for transformational reengineering of legacy systems. Research in reengineering is mainly focused on clustering techniques that group procedures present in the legacy system into candidate objects. These clustering approaches are limited to systems with well-defined data structures and procedures. Several of these approaches are either not comprehensive, limited to certain types of systems, or depend extensively on engineer knowledge of the system. Unlike these approaches that analyze legacy systems at the procedural level, the reengineering process we present analyzes systems at the statement level. This process results in the identification of object operations. These operations, along with the state variables and the user defined data structures, are arranged in a hierarchy that represents the object structure of the reengineered variant of the legacy system. From this system hierarchy, objects are identified and encapsulated by streamlining the interfaces. The reengineering process is incorporated in a tool, ReArchitect. Programs are statically analyzed and represented as a statement dependence graph (StDG) for further processing. The StDG is a fine-grained representation with modular representation for functions and program slices. It can adapt to program changes, unlike other representations. The StDG is restructured by merging cohesive components in the graph. The restructured graph is used to build the object structure, which is used to identify the objects. The StDG is a theoretically sound framework that provides support for many problems found in the reengineering domain. We show the value of the StDG in two such domains: program slicing and maintenance. The StDG is restructured differently for different requirements (space/time), and for different types of applications