The Concept of Representation Capability of Databases and its Application in IS Development

The representation capability of an information system in general and a database in particular seems an important and yet elusive concept, which is concerned with, in our view, how a database ever becomes capable of representing real-world objects accurately or otherwise. To explore how to approach and then define this concept, we explore what is meant and required by the statement that a database connection (i.e., a connection between database constructs such as entities in an Entity-relationship (ER) diagram and relations in a relational schema that are made available by a database) refers to, represents and accurately represents a real-world relation respectively. This approach is proven to be insightful and effective. We also find a sufficient and necessary condition for a database connection to be able to accurately represent a real-world relation, which is that the information content of the database connection includes the real-world relation. All these make the concept of representation capability of a database approachable and definable. Furthermore, another different and yet related concept, namely the representation capacity of a database, can also be defined based on the representation capability of a database, which is ‘all the real-world relations that can be represented by the constructs that are made possible and available by the database’. Our theoretical work draws on semiotics, the semantic theory of information presented by Dretske and the information channel theory by Barwise and Seligman, and our practical work involves an information system’s development

A Conceptual Framework for Modelling Spatial Relations

Various approaches lie behind the modelling of spatial relations, which is a heterogeneous and interdisciplinary field. In this paper, we introduce a conceptual framework to describe the characteristics of various models and how they relate each other. A first categorization is made among three representation levels: geometric, computational, and user. At the geometric level, spatial objects can be seen as point-sets and relations can be formally defined at the mathematical level. At the computational level, objects are represented as data types and relations are computed via spatial operators. At the user level, objects and relations belong to a context-dependent user ontology. Another way of providing a categorization is following the underlying geometric space that describes the relations: we distinguish among topologic, projective, and metric relations. Then, we consider the cardinality of spatial relations, which is defined as the number of objects that participate in the relation. Another issue is the granularity at which the relation is described, ranging from general descriptions to very detailed ones. We also consider the dimension of the various geometric objects and the embedding space as a fundamental way of categorizing relations

Does construct overload truly overload the performance? - An experimental study of experienced data modeler.

학위논문 (박사)-- 서울대학교 대학원 경영대학 경영학과, 2017. 8. 박진수.A principal activity in information systems development involves building a conceptual model of domain that an information system is intended to support. Such models are created using a conceptual-modeling grammar fundamental means to specifying information systems requirement. However, the actual usage of grammar is poorly understood and some issues regarding conceptual grammar such as construct overload still remain unsolved. With regard to construct overload in conceptual modeling, past studies have had some deficiencies in research methods and even have presented contradicting results. In this paper, we experimented to test whether construct overload enables conceptual models users to understand a domain more efficiently. To acquire a more complete and accurate understanding of construct overload, our study focused on three major pointsthe evaluation of conceptual modeling grammar semantics, research participants and domain familiarity. This papers key contribution is that it is one of the first studies to investigate practitioners aspects of construct overload employing different degrees of domain familiarity by investigating the cognitive processes of practitioner. In addition, this research reconciles conflicting outcomes by examining practical directions for model variation. The result of study will broaden the perspective on usability in the context of the conceptual model and may serve as an ontological guidance to construct overload when modelers create a conceptual model.1. Introduction 2 2. Theory and Related Work 5 2.1. Theory 6 Theory of Ontological Clarity 7 Feynman-Tufte Principle 7 Mayers Cognitive Theory of Multimedia Learning 8 Information Processing Theory 8 Theory of Visual Attention 9 2.2. Related Work 9 Ontological Clarity 13 Domain Familiarity 13 3. Proposition Development 14 4. Research Method 18 4.1. Design and Measures 19 4.2. Materials 20 Personal Profile and Training Materials 20 Conceptual Models 21 Understanding Task Materials 27 4.3. Participants 31 4.4. Procedures 32 4.5. Results 33 Data Scoring 33 Quantitative Data Analysis 33 5. Cognitive Process Tracing Study 36 5.1. Design and Measures 36 5.2. Materials 37 5.3. Participants 38 5.4. Procedures 38 5.5. Coding Scheme 39 5.6. Analysis of Protocol Data 40 5.7. Analysis of Eye-tracking Data 45 Scan Path 48 Focus and Heat Map 52 Quantitative Data Analysis of Key Performance Indicators 56 6. Discussion 63 6.1. Conclusion 63 6.2. Implication 63 6.3. Limitations and Future Research Directions 65 Reference 66 Appendix A 73 Summary of Information Processing Coding Typology 73 Appendix B 75 Glossary of Eye Tracking Technique 75 Focus Map of Unfamiliar Domain (Waste Processing System) 75Docto

New and extended parameterization of the thermodynamic model AIOMFAC: calculation of activity coefficients for organic-inorganic mixtures containing carboxyl, hydroxyl, carbonyl, ether, ester, alkenyl, alkyl, and aromatic functional groups

We present a new and considerably extended parameterization of the thermodynamic activity coefficient model AIOMFAC (Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients) at room temperature. AIOMFAC combines a Pitzer-like electrolyte solution model with a UNIFAC-based group-contribution approach and explicitly accounts for interactions between organic functional groups and inorganic ions. Such interactions constitute the salt-effect, may cause liquid-liquid phase separation, and affect the gas-particle partitioning of aerosols. The previous AIOMFAC version was parameterized for alkyl and hydroxyl functional groups of alcohols and polyols. With the goal to describe a wide variety of organic compounds found in atmospheric aerosols, we extend here the parameterization of AIOMFAC to include the functional groups carboxyl, hydroxyl, ketone, aldehyde, ether, ester, alkenyl, alkyl, aromatic carbon-alcohol, and aromatic hydrocarbon. Thermodynamic equilibrium data of organic-inorganic systems from the literature are critically assessed and complemented with new measurements to establish a comprehensive database. The database is used to determine simultaneously the AIOMFAC parameters describing interactions of organic functional groups with the ions H^+, Li^+, Na^+, K^+, NH_(4)^+, Mg^(2+), Ca^(2+), Cl^−, Br^−, NO_(3)^−, HSO_(4)^−, and SO_(4)^(2−). Detailed descriptions of different types of thermodynamic data, such as vapor-liquid, solid-liquid, and liquid-liquid equilibria, and their use for the model parameterization are provided. Issues regarding deficiencies of the database, types and uncertainties of experimental data, and limitations of the model, are discussed. The challenging parameter optimization problem is solved with a novel combination of powerful global minimization algorithms. A number of exemplary calculations for systems containing atmospherically relevant aerosol components are shown. Amongst others, we discuss aqueous mixtures of ammonium sulfate with dicarboxylic acids and with levoglucosan. Overall, the new parameterization of AIOMFAC agrees well with a large number of experimental datasets. However, due to various reasons, for certain mixtures important deviations can occur. The new parameterization makes AIOMFAC a versatile thermodynamic tool. It enables the calculation of activity coefficients of thousands of different organic compounds in organic-inorganic mixtures of numerous components. Models based on AIOMFAC can be used to compute deliquescence relative humidities, liquid-liquid phase separations, and gas-particle partitioning of multicomponent mixtures of relevance for atmospheric chemistry or in other scientific fields

Design and evaluation of a consulting system for database design

Database design is a difficult problem for non-expert designers. It is desirable to assist such designers during the problem solving process by means of a knowledge based (KB) system. Although a number of prototype KB systems have been proposed, there are many shortcomings. Firstly, few have incorporated sufficient expertise in modeling relationships, particularly higher order relationships. Secondly, there does not seem to be any published empirical study that experimentally tested the effectiveness of any of these KB tools. Thirdly, problem solving behavior of non-experts, whom the systems were intended to assist, has not been one of the bases for system design. In this project, a consulting system, called CODA, for conceptual database design that addresses the above short comings was developed and empirically validated. More specifically, the CODA system incorporates (a) findings on why non-experts commit errors and (b) heuristics for modeling relationships. Two approaches to knowledge base implementation were used and compared in this project, namely system restrictiveness and decisional guidance (Silver 1990). The Restrictive system uses a proscriptive approach and limits the designer\u27s choices at various design phases by forcing him/her to follow a specific design path. The Guidance system approach, which is less restrictive, involves providing context specific, informative and suggestive guidance throughout the design process. Both the approaches would prevent erroneous design decisions. The main objectives of the study are to evaluate (1) whether the knowledge-based system is more effective than the system without a knowledge-base and (2) which approach to knowledge implementation - whether Restrictive or Guidance - is more effective. To evaluate the effectiveness of the knowledge base itself, the systems were compared with a system that does not incorporate the expertise (Control). An experimental procedure using student subjects was used to test the effectiveness of the systems. The subjects solved a task without using the system (pre-treatment task) and another task using one of the three systems, viz. Control, Guidance or Restrictive (experimental task). Analysis of experimental task scores of those subjects who performed satisfactorily in the pre-treatment task revealed that the knowledge based approach to database design support lead to more accurate solutions than the control system. Among the two KB approaches, Guidance approach was found to lead to better performance when compared to the Control system. It was found that the subjects perceived the Restrictive system easier to use than the Guidance system