Hierarchical ER Diagrams (HERD) – The Method and Experimental Evaluation

Shoval, Peretz; Danoch, Revital; Balaban, Mira

doi:10.1007/978-3-540-45275-1_24

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…As in Bodart et al . (2001), Shoval et al . (2002) and Parsons (2003), comprehension time was chosen as a measure of the efficiency of user comprehension.…”

Section: Definition Planning and Operation Of The Experimentsmentioning

confidence: 99%

“…a REA or non‐REA diagram), and answering the questions by writing down the solutions. The effectiveness of user comprehension was measured by the accuracy of comprehension, which is defined as the ratio of the number of correctly answered questions to the total number of questions that make up the comprehension task (Kim & March, 1995; Siau et al ., 1997; Bodart et al ., 2001; Shoval et al ., 2002; Burton‐Jones & Weber, 2003; Parsons, 2003). Finally, the efficacy of user comprehension was measured with the normalized accuracy measure that was defined by Bodart et al .…”

Section: Definition Planning and Operation Of The Experimentsmentioning

confidence: 99%

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The pragmatic quality of Resources- Events-Agents diagrams: an experimental evaluation

Poels¹,

Maes²,

Gailly³

et al. 2010

Information Systems Journal

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The Resources-Events-Agents (REA) model is a semantic data model for the development of enterprise information systems. Although this model has been proposed as a benchmark for enterprise information modelling, only few studies have attempted to empirically validate the claimed benefits of REA modelling. Moreover, these studies focused on the evaluation of REA-based system implementations rather than directly assessing the REA-modelled conceptual schemas that these systems are based on. This paper presents a laboratory experiment that measured the user understanding of diagrammatic conceptual schemas developed using the REA model. The theoretical foundation for the hypotheses are cognitive theories that explain pattern recognition phenomena and the resulting reduction in cognitive effort for understanding conceptual schemas. The results of the experiment indicate a more accurate understanding of the business processes and policies modelled when users recognize the REA model's core pattern of enterprise information in the diagram. The implication for modelling practice is that the use of the REA model improves the requirements engineering process by facilitating the user validation of conceptual schemas produced by analysts, and thus helps ensuring the quality of the enterprise information system that is developed or implemented

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“…As in Bodart et al . (2001), Shoval et al . (2002) and Parsons (2003), comprehension time was chosen as a measure of the efficiency of user comprehension.…”

Section: Definition Planning and Operation Of The Experimentsmentioning

confidence: 99%

Section: Definition Planning and Operation Of The Experimentsmentioning

confidence: 99%

The pragmatic quality of Resources- Events-Agents diagrams: an experimental evaluation

Poels¹,

Maes²,

Gailly³

et al. 2010

Information Systems Journal

View full text Add to dashboard Cite

show abstract

“…Duplication of entities in different subject areas is not allowed. Shoval et al [104,105] HERD: Hierarchical Entity-Relationship Diagrams. n-level hierarchichal clustering of entities of ER diagrams.…”

Section: Contribution Characteristicsmentioning

confidence: 99%

“…The method of Teorey et al may be adapted to other modeling languages such as UML. Shoval, Danoch and Balabam in [104,105] proposed a revision of the approach of Teorey et al, previously defined. They call their new solution HERD (Hierarchical Entity-Relationship Diagrams) and basically includes minimum changes in grouping operations and their application.…”

mentioning

confidence: 99%

A filtering engine for large conceptual schemas

Villegas Niño

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Nowadays, the need for representation and conceptualization of real world information has dramatically increased. Organizations evolution and diversification require the management and maintenance of large amounts of knowledge from their domains of interest. That growth also has an impact in the size of conceptual schemas of information systems, making them larger. The sheer size of those schemas transforms them into very useful artifacts for the communities and organizations for which they are developed. However, the size of the schemas and their overall structure and organization make it difficult to manually extract knowledge from them, to understand their characteristics, and to change them.There are many information system development activities in which people needs to get a piece of the knowledge contained in the conceptual schema. For example, a conceptual modeler needs to check with a domain expert that the knowledge is correct, a database designer needs to implement that knowledge into a relational database, a software tester needs to write tests checking that the knowledge has been correctly implemented in the system components, or a member of the maintenance team needs to change that knowledge.Dealing with large conceptual schemas is one of the most challenging and long-standing goals in conceptual modeling. The purpose of this thesis is to formally define a new information filtering methodology to help users of very large conceptual schemas to understand the characteristics and knowledge these schemas contain. This thesis analyzes and describes the different phases of an information filtering engine, identifies and studies several properties of relevance for elements of large conceptual schemas, provides a catalog of specific filtering requests to explore a schema in several filtering scenarios, and implements and evaluates the efficiency and effectiveness of a filtering engine prototype with several real case studies.The filtering methodology studies the characteristics of the knowledge contained within a large conceptual schema, and proposes ways to select and represent the user interest in order to specialize the results of a filtering engine. Consequently, a user focus on a fragment of the large schema of interest to her and our methodology automatically obtains a reduced conceptual schema extracted from the large schema and focused on the knowledge that has a closer relation with the focus of the user. Such filtered conceptual schema is a subset of the original one, and because of its reduced size it is more comprehensible to the user.The filtering approach provides knowledge extraction techniques aligned with the user interest representation, and presents such knowledge in an appropriate way to simplify its understanding. Furthermore, using this filtering approach the large conceptual schemas are navigated more quickly, increasing their usability and reducing the user effort. LIST OF FIGURES xiiAnyone who has never made a mistake has never tried something new.

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Automatic Issue Extraction from a Focused Dialogue

Hindriks

Hoppenbrouwers

Jonker

et al.

Natural Language Processing and Information Systems

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Abstract.Various methodologies for structuring the process of domain modeling have been proposed, but there are few software tools that provide automatic support for the process of constructing a domain model. The problem is that it is hard to extract the relevant concepts from natural language texts since these typically include many irrelevant details that are hard to discern from relevant concepts. In this paper, we propose an alternative approach to extract domain models from natural language input. The idea is that more effective, automatic extraction is possible from a natural language text that is produced in a focused dialogue game. We present an application of this idea in the area of pre-negotiation, in combination with sophisticated parsing and transduction techniques for natural language and fairly simple pattern matching rules. Furthermore, a prototype is presented of a conversation-oriented experimentation environment for cooperative conceptualization. Several experiments have been performed to evaluate the approach and environment, and a technique for measuring the quality of extraction has been defined. The experiments indicate that even with a simple implementation of the proposed approach reasonably acceptable results can be obtained.

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Hierarchical ER Diagrams (HERD) – The Method and Experimental Evaluation

Cited by 6 publications

References 9 publications

The pragmatic quality of Resources- Events-Agents diagrams: an experimental evaluation

The pragmatic quality of Resources- Events-Agents diagrams: an experimental evaluation

A filtering engine for large conceptual schemas

Automatic Issue Extraction from a Focused Dialogue

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