Implicit Trust in a Data Model

Abstract : As an assessment of databases used to store Maritime Situational Awareness data, the hypothesis is posed that a database built upon a data model, utilizing international standards, recognized and accepted data modelling concepts, best practices, etc. would be more trusted by the community utilizing the data contained within the database. In this work, the validity of this hypothesis was investigated and assessed by decomposing trust into the sub-components: predictability, dependability, faith, reliability, robustness, familiarity, understandability, explication of intention, usefulness, competence, self-confidence, and reputation. An analysis of how these components are expressed in the context of a database system and in particular, how they impact the data model, was performed. The analysis indicates that reliability, understandability, usefulness, familiarity and reputation are the components that capture the concept of trust in a data model. These components were then applied in an analysis of the National Information Exchange Model-Maritime data model, essentially grading the model against the applicable trust components. Results vary from a poor grade on aspects of reliability, to excellent in terms of familiarity and reputation.

[1]  Paul T. Groth,et al.  An Architecture for Provenance Systems , 2006 .

[2]  Daniel L. Moody,et al.  Complexity effects on end user understanding of data models : An experimental comparison of large data model representation methods , 2002, ECIS.

[3]  Wang Chiew Tan,et al.  An annotation management system for relational databases , 2004, The VLDB Journal.

[4]  N. Moray,et al.  Trust in automation. Part II. Experimental studies of trust and human intervention in a process control simulation. , 1996, Ergonomics.

[5]  Stuart K. Card,et al.  The cost structure of sensemaking , 1993, INTERCHI.

[6]  William C. McGee.  On user criteria for data model evaluation , 1976, TODS.

[7]  Genny Tortora,et al.  Data Model Comprehension: An Empirical Comparison of ER and UML Class Diagrams , 2008, 2008 16th IEEE International Conference on Program Comprehension.

[8]  Geert Poels,et al.  Measures for Assessing Dynamic Complexity Aspects of Object-Oriented Conceptual Schemes , 2000, ER.

[9]  Anthony W. Isenor,et al.  Utilizing Arc Marine Concepts for Designing a Geospatially Enabled Database to Support Rapid Environmental Assessment , 2009 .

[10]  John A. Hoxmeier.  Dimensions of Database Quality , 2005, Encyclopedia of Information Science and Technology.

[11]  Justus D. Naumann,et al.  The Effectiveness of Data Representation Characteristics on User Validation , 1985, ICIS.

[12]  Ronald Maier,et al.  Benefits and Quality of Data Modelling - Results of an Empirical Analysis , 1996, ER.

[13]  D. R. Danielson,et al.  How do users evaluate the credibility of Web sites?: a study with over 2,500 participants , 2003, DUX '03.

[14]  P. Goillau,et al.  Guidelines for Trust in Future ATM Systems: A Literature Review , 2003 .

[15]  S. Gregor,et al.  Measuring Human-Computer Trust , 2000 .

[16]  Matthew West.  Developing High Quality Data Models , 2011 .

[17]  Martha E. Crosby,et al.  The effect of graphic style on data model interpretation , 1999, Inf. Syst. J..

[18]  Ewald A. Kaluscha,et al.  Empirical research in on-line trust: a review and critical assessment , 2003, Int. J. Hum. Comput. Stud..

[19]  Chris Dwyer.  Comprehensive maritime awareness (CMA) joint capabilities technology demonstration (JCTD) , 2007, SPIE Defense + Commercial Sensing.

[20]  Eui Park,et al.  MEASURING TRUST OF HUMAN OPERATORS IN NEW GENERATION RESCUE ROBOTS , 2008 .

[21]  Walter L. Perry,et al.  EXPLORING INFORMATION SUPERIORITY A Methodology for Measuring the Quality of Information and Its Impact on Shared Awareness , 2004 .

[22]  Felix Naumann,et al.  Information Quality: How Good Are Off-The-Shelf DBMS? , 2004, ICIQ.

[23]  Thomas B. Sheridan,et al.  TRUSTWORTHINESS OF COMMAND AND CONTROL SYSTEMS , 1988 .

[24]  J. Llinas,et al.  Studies and Analyses of Aided Adversarial Decision Making. Phase 2: Research on Human Trust in Automation , 1998 .

[25]  Toshiyuki Inagaki,et al.  Laboratory studies of trust between humans and machines in automated systems , 1999 .

[26]  Jennifer Golbeck,et al.  Trust on the World Wide Web: A Survey , 2006, Found. Trends Web Sci..

[27]  Daniel L. Moody,et al.  A Multi-Level Architecture for Representing Enterprise Data Models , 1997, ER.

[28]  Paul T. Groth,et al.  Calculating the Trust of Event Descriptions using Provenance , 2010, SWPM@ISWC.

[29]  Ronald Maier,et al.  Organizational concepts and measures for the evaluation of data modeling , 2001 .

[30]  Yolanda Gil,et al.  A survey of trust in computer science and the Semantic Web , 2007, J. Web Semant..

[31]  Jock D. Mackinlay,et al.  The cost-of-knowledge characteristic function: display evaluation for direct-walk dynamic information visualizations , 1994, CHI Conference Companion.

[32]  Graeme Simsion.  Data Modeling Theory and Practice , 2007 .

[33]  Mario Piattini,et al.  Metrics for data warehouse conceptual models understandability , 2007, Inf. Softw. Technol..

[34]  Mario Piattini,et al.  Metrics for controlling database complexity , 2001 .

[35]  Janet E. Miller,et al.  Development of Metrics for Trust in Automation , 2010 .

[36]  Bonnie M. Muir,et al.  Trust in automation. I: Theoretical issues in the study of trust and human intervention in automated systems , 1994 .

[37]  Thomas B. Sheridan,et al.  Telerobotics, Automation, and Human Supervisory Control , 2003 .

[38]  Anany Levitin,et al.  Quality dimensions of a conceptual view , 1995 .

[39]  Daniel L. Moody,et al.  Metrics for Evaluating the Quality of Entity Relationship Models , 1998, ER.

[40]  Diane M. Strong,et al.  Beyond Accuracy: What Data Quality Means to Data Consumers , 1996, J. Manag. Inf. Syst..