Tauqeer Hussain

Eliminating process of normalization in relational database design

The relational database design approach requires the process of normalization in order to minimize data redundancy and update anomalies in the relational schema. Algorithms defined in normalization theory depend upon various dependencies namely functional, multivalued, join and inclusion dependencies that should be carefully defined for a database application. Identification of these dependencies and a minimal cover is a complex and time consuming task for almost all practical problems. This work discusses how the normalization process can be eliminated from the required steps of database design. It explores various constructs of entity relationship diagram (ERD) and their transformation to relational schema. This work elaborates how un-normalized relations are created during the entity relationship (ER) model to relational schema transformation. A set of rules is presented which if followed at the stage of conceptual modeling would always generate a relational schema that satisfies normal forms up to Boyce-Codd normal form (BCNF), thus eliminating the need for normalization. The motivation behind this paper is to save the database designers valuable time and effort otherwise required in defining dependencies, in finding a minimal cover and in normalizing a given relational schema.

Comparative Study of Various Artificial Intelligence Techniques to Predict Software Quality

Software quality prediction models are used to identify software modules that may cause potential quality problems. These models are based on various metrics available during the early stages of software development life cycle like product size, software complexity, coupling and cohesion. In this survey paper, we have compared and discussed some software quality prediction approaches based on Bayesian belief network, neural networks, fuzzy logic, support vector machine, expectation maximum likelihood algorithm and case-based reasoning. This study gives better comparative insight about these approaches, and helps to select an approach based on available resources and desired level of quality.

Autonomic Success in Database Management Systems

One of the primary uses of computer is to reduce cost and manage complexity with increase in efficiency and performance. Now system complexity is reaching a level that is beyond human ability. With the development of technology, people want to manage complex systems in an efficient and reliable manner. Development of raw computing power and proliferation of computer devices and usage of internet has grown up to exponential rates. This growth and unprecedented levels of complexity is leading towards new direction - Autonomic Computing. Autonomic features in system increase speed, efficiency, reliability and accuracy with less or no human interaction, ultimately providing error free environment. These autonomic capabilities are important in Database Management Systems (DBMSs). The DBMSs which have the capability to manage and maintain themselves are called Autonomic Database Management Systems (ADBMS). The ADBMSs are evolving from last many years. At present most of the activities in DBMS are performed autonomically and have achieved certain level of autonomicity. The paper identified some autonomic shortcomings in commercial DBMSs up to 2002. We made a survey on achievements of autonomic computing against these shortcomings in current DBMSs. For this purpose, we have studied and analyzed IBM DB2, Oracle and Microsoft SQL Server.

Applying fuzzy logic to measure completeness of a conceptual model

In a computing environment, the success of an information system depends upon the quality of its conceptual model. The importance of measuring quality of a conceptual model in quantitative terms has been emphasized in the research but still the quantitative measures are very scarce in the literature. A new Fuzzy Completeness Index (FCI) is introduced in this paper as a quantitative measure for the quality of a conceptual model. It takes into account completeness of a conceptual model based upon the concept of functional dependencies. For a given conceptual model the incorporation of functional dependencies is mapped onto a TAS Graph and is then measured using the fuzzy membership values and fuzzy hedges. The FCI has been calculated for different conceptual models. It has been illustrated that the quality in terms of completeness can effectively be measured through the FCI based approach. The higher the value of FCI the closer is the conceptual model to the real world in representing functional constraints.

A fuzzy based approach to measure completeness of an entity-relationship model

Completeness is one of the important measures for semantic quality of a conceptual model, an ER model in our case. In this paper, a complete methodology is presented to measure completeness quantitatively. This methodology identifies existence of functional dependencies in the given conceptual model and transforms it into a multi-graph using the transformation rules proposed in this paper. This conversion can be helpful in implementing and automating computation of quality metrics for a given conceptual model. The new Fuzzy Completeness Index (FCI) introduced in this paper adopts an improved approach over Completeness Index proposed by authors in the previous research. FCI takes into account the extent a functional dependency has its representation in the conceptual model even when it is not fully represented. This partial representation of a functional dependency is measured using the fuzzy membership values and fuzzy hedges. The value of FCI varies between 0 and 1, where 1 represents a model that incorporates all the functional dependencies associated with it. Computation of FCI is demonstrated for a number of conceptual models. It is illustrated that the quality in terms of completeness can effectively be measured and compared through the FCI based approach.

Autonomicity in Universal Database DB2

Functionality, complexity, heterogeneity and dynamism in computing environment are increasing day by day. This enhanced utility of computers has a profound impact on the system’s brittleness, manageability and security. Self-management is important in systems, networks, communication as well as in Database Management Systems (DBMSs). Autonomic computing reduces the problems and increases accuracy and efficiency of the DBMSs. Recent years have seen an upsurge in research related to incorporation of autonomic computing within the computing systems. IBM is working on autonomic computing in systems and DBMSs from last many years. The paper [1] discussed the autonomic features and tools in DB2 from the literature available up to 2002. Our paper presents an extension of that paper which covers available autonomic features, components, utilities and tools up till now. A comparison of DB2 with Oracle is presented w.r.t autonomic computing. We have also identified the optional and essential human intervention (HI) in autonomic components, utilities and tools of IBM Universal Database DB2 that reveals the degree of autonomic computing in these.

Analytical Hierarchy Process Approach to Rank Measures for Structural Complexity of Conceptual Models

This paper presents the result of a controlled experiment conducted to determine the relative importance of some measures, identified in research, for the structural complexity of entity-relationship (ER) models. The relative importance amongst these measures is calculated by applying the analytical hierarchy process approach. The results reveal that the number of relations in an ER diagram are of the highest importance in measuring the structural complexity in terms of understandability, analyzability and modifiability; whereas, the number of attributes do not play an important role. The study presented here can lead to developing quantitative metrics for comparing the quality of alternative conceptual models of the same problem

Schema transformation - a quality perspective

In this paper we give new definitions of functional dependency, a key, a key attribute, and a non-key attribute at conceptual level and provide rules to implement these at conceptual level. We also propose two quality metrics, completeness index and normalization index, to measure the quality of the conceptual model that results after applying the proposed rules. We apply these rules on a hypothetical case and measure the quality of the transformed schema. It is observed that by applying our definitions and rules the quality of transformed schema fractionally improves completeness index up to 50% and normalization index up to 81%.