Dinesh Batra

Comparing representations with relational and EER models

The diffusion of technology to end users who can now develop their own information systems raises issues concerning the cost, quality, efficiency, and accuracy of such systems.

Conceptual Data Modeling Patterns: Representation and Validation

The tremendous demand for software productivity has led to the idea of reuse of solutions that have worked successfully in the past. The notion of a design pattern is now well accepted in software design, and research in the area of data modeling has also begun. Although two books have explicitly attempted to cover this area, the representations provided in the books seem to be focused on specific applications and do not provide a generic and comprehensive set of templates. Another book attempts to address the problem but provides patterns at a level of granularity too small to be useful. This paper teases out underlying structures that tend to occur frequently in these books and provides patterns at an abstract and more useful level of granularity. It describes 11 data modeling patterns commonly found in business scenarios. The patterns are then validated by checking the frequency of occurrence of each pattern in the data representations included in three comprehensive texts of reference models. Two of these sources are targeted mainly at practitioners, and the third is academic oriented and targeted at students learning data modeling. Results indicate that although certain patterns are used more frequently than others, most of the 11 structures occur with adequate frequency to qualify as patterns. A comparison reveals that the frequency distribution of patterns is different among these sources. Further, the academic-oriented source distinctly focuses on different patterns as compared to the other two sources. The paper discusses the differences and provides specific recommendations on improving pedagogy in conceptual data modeling.

A framework for studying human error behavior in conceptual database modeling

Abstract A framework is developed to explain human error behavior in modeling conceptual databases. The framework is based on the notion of directness distance or ‘gulf’ suggested in recent literature. It specifies four aspects of ‘gulf’ in the context of conceptual database design - syntax, mapping, rules, and consistency. Based on the model, six types of errors are suggested - syntactic, abstraction, simplification, overload, convergence, and divergence. These are then matched to errors found in four empirical studies on database representation. Four types of errors - convergence, abstraction, simplification and overload - were typically found in these studies. The paper provides design guidelines to prevent these errors.

CODASYS: a consulting tool for novice database designers

The paper describes the main features of a prototype tool CODASYS (COnceptual modeling tool for DAtabase SYStems), which purports to help novice designers engaged in conceptual data modeling. It is well known that conceptual data modeling is an error-prone process for novice database designers. The tool assists a designer in developing an entity-relationship diagram that can be translated to a normalized relational representation, free of derived dependencies. We first discuss a set of requirements for the tool that is based on achieving normal forms, on preventing data modeling errors, and on a theoretical foundation. We then discuss how the tool achieves the four normal forms and prevents common database errors such as incorrect degree, incorrect connectivity, and derived relationships. Further, we provide a cognitive framework for understanding novice error behavior and elaborate on how CODASYS addresses cognitive strain in database design. Next, we present the features of the tool and, with the help of flowcharts, discuss how these features have been implemented using a rule-based approach. It may be noted that empirical studies have been conducted to test the efficacy of the tool and have indicated significant improvement in novice designer performance.

Cognitive complexity in data modeling: causes and recommendations

Data modeling is a complex task for novice designers. This paper conducts a systematic study of cognitive complexity to reveal important factors pertaining to data modeling. Four major sources of complexity principles are identified: problem solving principles, design principles, information overload, and systems theory. The factors that lead to complexity are listed in each category. Each factor is then applied to the context of data modeling to evaluate if it affects data modeling complexity. Redundant factors from different sources are ignored, and closely linked factors are merged. The factors are then integrated to come up with a comprehensive list of factors. The factors that cannot largely be controlled are dropped from further analysis. The remaining factors are employed to develop a semantic differential scale for assessing cognitive complexity. The paper concludes with implications and recommendations on how to address cognitive complexity caused by data modeling.

Comparing a rule-based approach with a pattern-based approach at different levels of complexity of conceptual data modelling tasks

It is well known that conceptual database design is an unusually difficult and error-prone task for novice designers. To address the problem, at least two training approaches--rule-based and pattern-based--have been suggested. A rule-based approach prescribes a sequence in modelling the conceptual modelling constructs, and the action to be taken at each stage. A pattern-based approach presents data modelling structures that occur frequently in practice, and prescribes guidelines on how to recognize these structures. This paper describes the conceptual framework, experimental design, and results of a laboratory study that employed novice designers to compare the effectiveness of the two training approaches (between-subjects) at three levels of task complexity (within subjects). Results indicate an interaction effect between treatment and task complexity. The rule-based approach was significantly better in the low-complexity and the high-complexity cases; there was no statistical difference in the medium-complexity case. Designer performance fell significantly as complexity increased. Overall, although the rule-based approach was not significantly superior to the pattern-based approach, the study still recommends the rule-based approach for novice designers given the significantly better performance at two out of three complexity levels.

The use of a knowledge-based system in conceptual data modeling

Based on a study of the data modeling process of novice designers, and the errors they commit, a knowledge-based system (KBS) was designed and developed. It was found that the performance of novice designers was significantly better when they utilized the KBS instead of a system with no knowledge base. Two versions of the KBS--one with a guidance interface that advised the designer on appropriate design choices and another with a restrictive interface that restricted the design choices available to the designer--were developed. The restrictive interface was rated as being significantly easier to use than the guidance interface.

Effects of data model and task characteristics on designer performance: a laboratory study

Abstract A laboratory experiment was conducted to compare designer performance in modelling user views using the relational and the entity relationship models. A user view is a form or a report used in an information system and is one of the sources of user requirements. Previous studies have not considered the effect of user view characteristics on designer performance. This study considered nine user views, which varied in two task characteristics: degree of nesting and derivation span. Degree of nesting is the number of nests in a user view, where a nest pertains to a group of attributes that is multivalued with respect to another group of attributes. Derivation span refers to the presence of attributes from different objects in the same view. Three levels each of degree of nesting and derivation span were considered. Subjects enrolled in a database class were trained in one of the two modelling approaches and were asked to conduct conceptual database design of a specified problem. Each view was graded using a predefined scheme. Results indicated that subjects using the entity relationship model took longer to complete the task but outscored subjects using the relational model. The degree of nesting emerged as a significant predictor of scores. The derivation span also seemed to account for the variation in scores although the effect was not statistically significant. The findings suggest that for novice designers the entity relationship is the appropriate choice for modelling user views. Further, the degree of nesting is an important indicator of the complexity of the user view.

A review and analysis of the usability of data management environments

Abstract Our objective in this paper is to provide a thorough understanding of the usability of data management environments with an end to conducting research in this area. We do this by synthesizing the existing literature that pertains to (i) data modelling as a representation medium and (ii) query interface evaluation in the context of data management. We were motivated by several trends that are prevalent in the current computing context. First, while there seems to be a proliferation of new modelling ideas that have been proposed in the literature, commensurate experimental evaluation of these ideas is lacking. Second, there appears to exist a significant user population that is quite adept at working in certain computing environments (e.g. spreadsheets) with a limited amount of computing skills. Finally, the choices in terms of technological platforms that are now available to implement new software designs allow us to deal with the implementation issue more effectively. The outcomes of this paper include a delineation of what constitutes an appropriate conceptualization of this area and a specification of research issues that tend to dominate the design of a research agenda.

Extending Agile Principles to Larger, Dynamic Software Projects: A Theoretical Assessment

The article evaluates the feasibility of extending agile principles to larger, dynamic, and possibly distributed software development projects by uncovering the theoretical basis for agile values and principles for achieving agility. The extant literature focuses mainly on one theory-complex adaptive systems-to support agile methods, although recent research indicates that the control theory and the adaptive structuration theory are also applicable. This article proposes that at least three other theories exist that are highly relevant: transaction cost economics, social exchange theory, and expectancy theory. By employing these theories, a rigorous analysis of the Agile Manifesto is conducted. Certain agile values and principles find theoretical support and can be applied to enhance agility dynamic projects regardless of size; some agile principles find no theoretical support while others find limited support. Based on the analysis and the ensuing discussion, the authors propose a framework with five dimensions of agility: process, design, people, outcomes, and adaptation.