Semi-automatic conceptual data modeling using entity and relationship instance repositories

Conceptual modeling is the foundation of analysis and design methodologies for the development of information systems. It is challenging because it requires a clear understanding of an application domain and an ability to translate the requirement specifications into a data model. However, novice designers frequently lack experience and have incomplete knowledge about the application being designed. We propose new types of reusable artifacts called Entity Instance Repository (EIR) and Relationship Instance Repository (RIR), which contain ER (Entity-Relationship) modeling patterns from prior designs and serve as knowledge-based repositories for conceptual modeling. We also select six data modeling rules to check whether they are comprehensive enough in creating quality conceptual models. This research aims to develop effective knowledge-based systems (KBSs) with EIR and RIR. Our proposed artifacts are likely to be useful for conceptual designs in the following aspects: (1) they contain knowledge about a domain; (2) automatic generation of EIR and RIR overcomes a major problem ofinefficient manual approaches that depend on experienced modeling designers and domain experts; and (3) they are domain-specific and therefore easier to understand and reuse. Two KBSs were developed in this study: Heuristic-Based Technique (HBT) and Entity Instance Pattern WordNet (EIPW). The goals of this study are (1) to find effective approaches that can improve the novice designers’ performance in developing conceptual models by integrating pattern-based technique and various modeling techniques, (2) to evaluate whether those selected six modeling rules are effective in HBT, and (3) to validate whether the proposed KBSs are effective in creating quality conceptual models. To assess the potential of the KBSs to benefit practice, empirical testing was conductedon tasks of different sizes. The empirical results indicate that novice designers’ overall performance increased by 30.9~46.0 % when using EIPW, and increased by 33.5~34.9% when using HBT, compared with the cases of no tools.Ph.D., Information Studies -- Drexel University, 201

Expert Finding in Disparate Environments

Providing knowledge workers with access to experts and communities-of-practice is central to expertise sharing, and crucial to effective organizational performance, adaptation, and even survival. However, in complex work environments, it is difficult to know who knows what across heterogeneous groups, disparate locations, and asynchronous work. As such, where expert finding has traditionally been a manual operation there is increasing interest in policy and technical infrastructure that makes work visible and supports automated tools for locating expertise. Expert finding, is a multidisciplinary problem that cross-cuts knowledge management, organizational analysis, and information retrieval. Recently, a number of expert finders have emerged; however, many tools are limited in that they are extensions of traditional information retrieval systems and exploit artifact information primarily. This thesis explores a new class of expert finders that use organizational context as a basis for assessing expertise and for conferring trust in the system. The hypothesis here is that expertise can be inferred through assessments of work behavior and work derivatives (e.g., artifacts). The Expert Locator, developed within a live organizational environment, is a model-based prototype that exploits organizational work context. The system associates expertise ratings with expert’s signaling behavior and is extensible so that signaling behavior from multiple activity space contexts can be fused into aggregate retrieval scores. Post-retrieval analysis supports evidence review and personal network browsing, aiding users in both detection and selection. During operational evaluation, the prototype generated high-precision searches across a range of topics, and was sensitive to organizational role; ranking true experts (i.e., authorities) higher than brokers providing referrals. Precision increased with the number of activity spaces used in the model, but varied across queries. The highest performing queries are characterized by high specificity terms, and low organizational diffusion amongst retrieved experts; essentially, the highest rated experts are situated within organizational niches