The meaning of a conceptual model is derived from the linguistic semantics of the modeling language used, the intension of the modeling concepts employed, and an explicit specification of these concepts that is understood within a community of practice. The triptych paradigm of conceptual modeling reflects these three dimensions of meaning as represented in the triptych wings of ‘Language Wing’, ‘Model Wing’, and ‘Meaning Wing’. In this paper, we investigate how the specification, that is, the ‘grounding’ of the modeling concepts, can be achieved based on these triptych wings. Our considerations should help the development of information systems to avoid costly future system changes that could occur due to the lack of a comprehensive understanding of an application and its inherent semantics during modeling. When developing a system, specifying the concepts used for modeling as precisely as possible increases the likelihood that the resulting information system will meet the application requirements.

Modern technologies, especially social networks, contribute to the rapid evolution and spread of fake news. Although the creation of fake news is a serious issue, it is the believability of fake news and subsequent actions that produce negative outcomes that can be harmful to individuals and society. Prior research has focused primarily on the role of confirmation bias in explaining the believability of fake news, but other biases are likely. In this research, we use theories of truth and a taxonomy of 10 cognitive biases to conduct an exploratory, qualitative survey of social media users. Five cognitive biases (herd, framing, overconfidence, confirmation, and anchoring) emerge as the most influential. We then propose a Cognitive Bias Mitigation Model of methods that could reduce the believability of fake news. The mitigation methods are grouped according to three themes as they relate to the five biases.

Although the knowledge about human genomics is available to all scientists, information about this scientific breakthrough can often be difficult to fully comprehend and share. A Conceptual Schema of the Human Genome was previously developed to assist in describing human genome-related knowledge, by representing a holistic view of the relevant concepts regarding its biology and underlying mechanisms. This model should become helpful for any researcher who works with human genomics data. We, therefore, perform the process of ontological unpacking on a portion of the model, to facilitate domain understanding and data exchange among heterogeneous systems. The ontological unpacking is a transformation of an input conceptual model into an enriched model based on a foundational ontology. The preliminary analysis and enrichment process are supported by the ontological conceptual modeling language OntoUML, which has been applied previously to complex models to gain ontological clarity. The value of the used method is first assessed from a theoretical point of view: the transformation results in significant, diverse modeling implications regarding the characterization of biological entities, the representation of their changes over time, and, more specifically, the description of chemical compounds. Since the ontological unpacking process is costly, an empirical evaluation is conducted to study the practical implications of applying it in a real learning setting. A particularly complex domain such as metabolic pathways is either described by adopting a traditional conceptual model or explained through an ontologically unpacked model obtained from a traditional model. Our research is evidence that including a strong ontological foundation in traditional conceptual models is useful. It contributes to designing models that convey biological domains better than the original models.

Effective implementation of strategic data-driven health analysis initiatives is heavily dependent on the quality of the electronic medical records that serve as the foundation from which to improve clinical decisions and, in turn, the quality of care. Although there is a large body of research on the quality of healthcare data, a systematical understanding of the methods used to address the issues of data quality is missing. This study analyzes research articles in health information systems/healthcare informatics on data quality to derive a set of dimensions for understanding data quality. Issues related to each dimension are identified and methods used to address them summarized. The issues and methods can inform healthcare professionals of how to improve data practices.

The digitalization of human society continues at a relentless rate. However, to develop modern information technologies, the increasing complexity of the real-world must be modeled, suggesting the general need to reconsider how to carry out conceptual modeling. This research proposes that the often-overlooked notion of “system” should be a separate, and core, conceptual modeling construct and argues for incorporating it and related concepts, such as emergence, into existing approaches to conceptual modeling. The work conducts a synthesis of the ontology of systems and general systems theory. These modeling foundations are then used to propose a CESM+ template for conducing systems-grounded conceptual modeling. Several new conceptual modeling notations are introduced. The systemist modeling is then applied to a case study on the development of a citizen science platform. The case demonstrates the potential contributions of the systemist approach and identifies specific implications of explicit modeling with systems for theory and practice. The paper provides recommendations for how to incorporate systems into existing projects and suggests fruitful opportunities for future conceptual modeling research.

As COVID-19 continues to wreak havoc in everyday lives, the need to limit the spread of the virus remains a challenge, even with advances in medical knowledge, patient care, and vaccine development and distribution. Furthermore, COVID-19 is one in a recent series of airborne diseases, and probably not the last, given the ongoing encroachment of humans into animal habitat. This paper addresses the challenge of managing physical distancing, a highly effective, yet unnatural and contentious, mitigation strategy against infectious diseases. It presents a Pandemic Tech Stack and proposes that physical distancing management technologies are underutilized to fight pandemics. The latter can help ensure that people remain apart when they need to, support the transfer of activities to an online format, and, ultimately, facilitate the gradual reopening of our economies. The challenges associated with the development and use of these technologies are identified and discussed from both the technical and socio-psychological perspectives.

Both conceptual modeling and machine learning have long been recognized as important areas of research. With the increasing emphasis on digitizing and processing large amounts of data for business and other applications, it would be helpful to consider how these areas of research can complement each other. To understand how they can be paired, we provide an overview of machine learning foundations and development cycle. We then examine how conceptual modeling can be applied to machine learning and propose a framework for incorporating conceptual modeling into data science projects. The framework is illustrated by applying it to a healthcare application. For the inverse pairing, machine learning can impact conceptual modeling through text and rule mining, as well as knowledge graphs. The pairing of conceptual modeling and machine learning in this way should help lay the foundations for future research.

Developer creativity is vital for software companies to innovate and survive. Studies on social media have yielded mixed results about its impact on creativity due to the ubiquitous nature of social media. This research differentiates the effects of informational and socializing social media usage on both incremental and radical creativity and explore the moderating role of a developer's openness to experience. Based on a survey of software developers, the authors show that openness positively moderates the impact of informational social media usage on incremental and radical creativity and negatively moderates the impact of socializing social media usage on both types of creativity. There is a stronger positive moderation for the relationship between informational social media usage and radical creativity compared to incremental creativity. The results provide a foundation for understanding explanations of the paradoxical effect of social media usage on creativity.

As activities are increasingly being digitalized in business and society, organizations have sought ways to effectively and competitively, use data. Business intelligence and analytics (BI&A) systems which support managerial decision-making continue to be developed and used. Given the importance of these systems, it would be useful to have a comprehensive and mature guide to support their development and improvement. This research proposes a BI&A Competitive Advantage Maturity Model to identify the main technical and non-technical dimensions of a system to support business intelligence and analysis. The model is based on work systems theory and related research. It maps descriptive characteristics of its main dimensions across analytic adoption stages of aspirational, experienced, and transformed. The development of the model employed a modified Delphi study technique, design science research, and citation analysis.

Much research has focused on understanding design science research and providing guidelines for its successful execution. However, there is still a need for more work on the raison d’être of design science research, which is the development of artefacts that can be applied to solve real-world problems. The value of design science research thus far has been to provide practically applicable solutions, as well as a contribution to knowledge. This paper proposes aesthetics, in addition to truth and utility, as a way to advance this raison d’être. We identify the design science triad of analytics, synthetics, and aesthetics and justify why the aesthetics quality attribute should be appreciated. We then identify twelve quality criteria for articulating and assessing aesthetics in design science research. Doing so enables us to both recognise and achieve artful design so that rigour, utility, and aesthetic value can collectively justify a valuable design science research contribution.

A particularly difficult, but important, challenge in the design and development of contemporary information systems is dealing with complexity. Although complexity has been richly discussed from various perspectives in the literature, there is limited guidance on how to address complexity in information systems design. This research analyzes different approaches to handling complexity and finds that there exists a plurality of ways in which to address complexity that are dependent upon the given situation. This analysis results in the derivation of a framework for addressing complexity in information systems. The framework explicitly recognizes implications and limitations of decomposition, inner-outer environments, abstractions, and decentralization, and the role of Ontology. Application of the framework is intended to enable information researchers to identify and adapt applicable strategies for managing complexity in any domain.

Domain ontologies and conceptual models similarly capture and represent concepts from the real world for inclusion in an information system. This paper examines challenges of conceptual modeling and domain ontology development when mapping to high-level ontologies. The intent is to reconcile apparent differences and position some of the inherent challenges in these closely-coupled areas of research, while providing insights into recognizing and resolving modeling difficulties.

In certain situations, syntactically valid, but incorrect, data entered into a database can result in near-immediate, catastrophic financial losses for an organization. Examples include: omitting zeros in prices of goods on e-commerce sites; and financial fraud where data is directly entered into databases, bypassing application-level financial checks. Such “dangerous data” can, and should, be detected, because it deviates substantially from the statistical properties of existing data. Detection of this kind of problem requires comparing individual data items to a large amount of existing data in the database at run-time. Furthermore, the identification of errors is probabilistic, rather than deterministic, in nature. This research proposes part-whole validation as an approach to addressing the dangerous data situation. Part-whole validation addresses fundamental issues in database management, for example, integrity maintenance. Illustrative and representative examples are first defined, and analyzed. Then, an architecture for part-whole validation is presented and implemented in a prototype to illustrate the feasibility of the research.

Substantial work in knowledge engineering has focused on eliciting knowledge and representing it in a computational form. However, before elicited knowledge can be represented, it must be integrated and transformed so the knowledge engineer can understand it. This research identifies the need to separate knowledge representation into human comprehension and computational reasoning and shows that this will lead to better knowledge representation. Modeling of human comprehension is called conceptual knowledge representation. The Conceptual Knowledge Representation Scheme is developed and validated by conducting a combined qualitative/quantitative repeated-measures experiment comparing the Conceptual Knowledge Representation Scheme to two computation-oriented ones. The results demonstrate that the Conceptual Knowledge Representation Scheme better facilitates human comprehension than existing representation schemes. Four principles of the Conceptual Knowledge Representation Scheme emerge that help to attain effective knowledge representation. These are: (1) a focus on human comprehension only, (2) design around natural language, (3) addition of constructs common in the domain, and (4) constructs for representing abstract versions of detailed concepts.

Privacy concerns and practices, especially those dealing with the acquisition and use of consumer personal information by corporations, are at the forefront of business and social issues associated with the information age. This research examines the privacy policies of large U.S. companies to assess the substance and quality of their stated information practices. Six factors are identified that indicate the extent to which a firm is dependent upon consumer personal information, and therefore more likely to develop high quality privacy statements. The study’s findings provide practical and theoretical implications for information privacy issues, particularly for consumers who need to determine whether or not to disclose their personal identifying information to firms. The results illustrate the complexity involved in managing personal private information.

There is a significant disconnect between the ideals of ‘on-demand’ business and the way in which we currently develop information systems. Growing competition, globalization, increasing regulation...

Database design is difficult because it involves a database designer understanding an application and translating the design requirements into a conceptual model. However, the designer may have little or no knowledge about the application or task for which the database is being designed. This research presents a methodology for supporting database design creation and evaluation that makes use of domain-specific knowledge about an application stored in the form of domain ontologies. The methodology is implemented in a prototype system, the Ontology Management and Database Design Environment. Initial testing of the prototype illustrates that the incorporation and use of ontologies is effective in creating entity-relationship models.

Much of the research that deals with understanding the real world and representing it in a conceptual model uses some form of the entity-relationship model as a means of representation. This research proposes an ontology for classifying relationship verb phrases based upon the domain and context of the application within which the relationship appears. The classification categories to which the verb phrases are mapped were developed based upon prior research in databases, ontologies, and linguistics. The usefulness of the ontology for comparing relationships when used in conjunction with an entity ontology is discussed. Together, these ontologies can be effective in comparing two conceptual database designs for integration and validation. Empirical testing of the ontology on a number of relationships from different application domains and contexts illustrates the usefulness of the research.

A great deal of work on automating systems design and development has been carried out, especially in the database area. Systems that semi-automate the database design process have been developed. These systems are interactive in that they may need to ask the user (usually, a database designer) for clarification. The result is that the system asks questions to the user that make the system look less intelligent than it should. This general type of problem has long been recognized with a proposed approach to overcoming it being the incorporation of common sense knowledge into a design system. The View Creation System is an expert system that plays the role of a database designer. With it, a user knowing little about database technology can express his or her database design requirements, which are represented by an entity-relationship model and then translated into a normalized relational model. The system contains a great deal of knowledge about database design, but little, if any, about the user’s application. This forces the user to specify many trivial facts that would be known by any human designer. To overcome this limitation, a Common Sense Business Reasoner is being developed that has a knowledge base containing general knowledge about the world and a reasoning tool to apply this knowledge to a database design task. An empirical study is carried out to simulate and assess the effectiveness of adding the Common Sense Business Reasoner to the View Creation System.

Although database design tools have been developed that attempt to automate (or semiautomate) the design process, these tools do not have the capability to capture common sense knowledge about business applications and store it in a context-specific manner. As a result, they rely on the user to provide a great deal of "trivial" details and do not function as well as a human designer who usually has some general knowledge of how an application might work based on his or her common sense knowledge of the real world. Common sense knowledge could be used by a database design system to validate and improve the quality of an existing design or even generate new designs. This requires that context-specific information about different database design applications be stored and generalized into information about specific application domains (e.g., pharmacy, daycare, hospital, university, manufacturing). Such information should be stored at the appropriate level of generality in a hierarchically structured knowledge base so that it can be inherited by the subdomains below. For this to occur, two types of learning must take place. First, knowledge about a particular application domain that is acquired from specific applications within that domain are generalized into a domain node (e.g., entities, relationships, and attributes from various hospital applications are generalized to a hospital node). This is referred to as within domain learning. Second, the information common to two (or more) related application domain nodes is generalized to a higher-level node; for example, knowledge from the car rental and video rental domains may be generalized to a rental node. This is called across domain learning. This paper presents a methodology for learning across different application domains based on a distance measure. The parameters used in this methodology were refined by testing on a set of representative cases; empirical testing provided further validation.

Research devoted to developing knowledge-based tools for database design has demonstrated that it is possible to encode a great deal of process knowledge about database design in a (knowledge-based) computer program. However, experience with these tools shows that the contribution of an expert human designer extends beyond his or her knowledge of database design techniques. The paper discusses the application of an approach, called Naive Semantics (NS), to simulate the contributions made to a design based on the designer's general knowledge. Naive semantics involves the use of an extensible store of generally understood knowledge about the world. The paper describes the types of information that could be included in a Naive Semantics knowledge base and how that knowledge might be applied to increase the effectiveness of automated database design systems. Results of various implementations of ontologies based on Naive Semantics are discussed.

Much of the work on conceptual modeling involves the use of an entity-relationship model in which binary relationships appear as associations between two entities. Relationships involving more than two entities are considered rare and, therefore, have not received adequate attention. This research provides a general framework for the analysis of relationships in which binary relationships simply become a special case. The framework helps a designer to identify ternary and other higher-degree relationships that are commonly represented, often inappropriately, as either entities or binary relationships. Generalized rules are also provided for representing higher-degree relationships in the relational model. This uniform treatment of relationships should significantly ease the burden on a designer by enabling him or her to extract more information from a real-world situation and represent it properly in a conceptual design.

Conceptual models or semantic data models were developed to capture the meaning of an application domain as perceived by someone. Moreover, concepts employed in semantic data models have recently been adopted in object-oriented approaches to systems analysis and design. To employ conceptual modeling constructs effectively, their meanings have to be defined rigorously. Often, however, rigorous definitions of these constructs are missing. This situation occurs especially in the case of the relationship construct. Empirical evidence shows that use of relationships is often problematical as a way of communicating the meaning of an application domain. For example, users of conceptual modeling methodologies are frequently confused about whether to show an association between things via a relationship, an entity, or an attribute. Because conceptual models are intended to capture knowledge about a real-world domain, we take the view that the meaning of modeling constructs should be sought in models of reality. Accordingly, we use ontology, which is the branch of philosophy dealing with models of reality, to analyze the meaning of common conceptual modeling constructs. Our analysis provides a precise definition of several conceptual modeling constructs. Based on our analysis, we derive rules for the use of relationships in entity-relationship conceptual modeling. Moreover, we show how the rules resolve ambiguities that exist in current practice and how they can enrich the capacity of an entity-relationship conceptual model to capture knowledge about an application domain.

Although it is possible to encode a great deal of process knowledge about database design into a system, experience has shown that the contribution of a human designer extends beyond his or her knowledge of database design techniques. The next step in the evolution of automated database design tools is to incorporate knowledge and reasoning capabilities to support this higher level of participation. Doing so requires some understanding of what different terms mean. This paper presents an ontology that can be used as a surrogate for the meaning of words in a database design system to simulate the contributions that a designer would make based on his or her general knowledge. The ontology classifies a term into one or more categories such as person, abstract good or tradable document. It is comprised of a semantic network, a knowledge base containing information on the meaning of terms that have been classified, an expert system knowledge-acquisition component, and a distance measure for assessing the distance between the meanings of terms. The ontology was tested by different types of users on a variety of problems and was shown to be quite effective.

Automated database design systems embody knowledge about the database design process. However, their lack of knowledge about the domains for which databases are being developed significantly limits their usefulness. A methodology for acquiring and using general world knowledge about business for database design has been developed and implemented in a system called the Common Sense Business Reasoner, which acquires facts about application domains and organizes them into a a hierarchical, context-dependent knowledge base. This knowledge is used to make intelligent suggestions to a user about the entities, attributes, and relationships to include in a database design. A distance function approach is employed for integrating specific facts, obtained from individual design sessions, into the knowledge base (learning) and for applying the knowledge to subsequent design problems (reasoning).

It is often difficult to obtain a good conceptual understanding of a legacy database, especially when there is a lack of documentation. Database reverse engineering attempts to provide solutions for this problem. It is the part of system maintenance work that produces a sufficient understanding of a legacy database and its application domain to allow appropriate changes to be made. However, research on database reverse engineering has largely ignored design and evaluation issues of their methods (i.e., foundations and processes). This research proposes a framework for the design and evaluation of reverse engineering methods of relational databases. This framework consists of eight criteria: 1) the situation chosen as the basis for reverse engineering, 2) the conceptual model chosen to represent the reverse engineering results, 3) the prerequisites of the database to be reverse engineered, 4) the thoroughness of domain semantics acquisition, 5) rules and heuristics employed by the reverse engineering process, 6) performance efficiency of the reverse engineering process, 7) completeness and robustness and 8) validation. These criteria are important to be considered in designing reverse engineering methods, so that they can perform reverse engineering for a broad range of legacy databases at a high level of automation and provide a conceptual schema that is semantically rich and correct.

Semantic integrity constraints are used to help ensure that a database accurately reflects the real world in structure and content. Existing database design methodologies, however, do not often include a comprehensive procedure for identifying such constraints and incorporating them into a design. A number of knowledge-based systems for database design have been developed with varying degrees of emphasis on, and approaches to, this task. A representative sample of these systems is reviewed for their ability to identify, represent and validate integrity constraints. In order to examine how constraints are represented, a six-dimensional model of semantic integrity constraints based on that proposed by Fernandez et al. [32] is presented. Mechanisms used by the prototype systems to implement each of the dimensions are examined. This analysis is used to identify apparently successful approaches as well as areas where further research on incorporating and representing semantic integrity constraints in knowledge-based database design systems is needed.

Although high-level data abstractions have been recognized as very important for modelling complex applications, one abstraction that is not widely incorporated into design methodologies is that of sets. This research analyzes sets as abstractions of individual members, in order to develop a list of criteria for a ‘good’ set modelling construct, referred to as a grouping. In addition, ways in which the member-of relationship, mapping individual members onto a set can be enriched with additional semantics, are proposed within an object-oriented context.

Database design is a complex and time-consuming process. In order to automate database design, an understanding of the nature of expertise that goes into the design process is needed. Although a number of expert systems have been developed to assist or replace a database designer, database design expertise has not been examined in any detail. This paper proposes a conceptual framework for explaining this type of expertise. The components of the framework are applied to each phase of the design process and used to provide guidelines for the level of expertise developers might strive to obtain. Several representative systems are analyzed, based on the framework, to explore the degree to which expertise is being captured. Implications for the future development of database design expert systems are discussed.

Organizational databases are pervaded with data of poor quality. However, there has not been an analysis of the data quality literature that provides an overall understanding of the state-of-art research in this area. Using an analogy between product manufacturing and data manufacturing, this paper develops a framework for analyzing data quality research, and uses it as the basis for organizing the data quality literature. This framework consists of seven elements: management responsibilities, operation and assurance costs, research and development, production, distribution, personnel management, and legal function. The analysis reveals that most research efforts focus on operation and assurance costs, research and development, and production of data products. Unexplored research topics and unresolved issues are identified and directions for future research provided.>

A new data abstraction, called Materialization, is introduced to model a situation that occurs frequently in the real world and has important implications for database design. Materialization is the relationship between two entity types, one that represents a conceptual object, for example, a TV Model, and one that represents its corresponding concrete objects, in this case, actual TV Sets. The Materialization construct is formally defined and contrasted with other well-known data abstractions. Its design implications are presented in terms of the entity-relationship model and its translation into a relational model. Guidelines are offered for the proper employment of this relationship in database design methodologies, and a discussion is provided of why this constitutes an important data modeling construct.>

The Entity-Relationship (E-R) model is often recommended for use in the database design process because its concepts are perceived to be both natural and easy to use. This paper describes a methodology for the design of a relational database based on the E-R model. The methodology starts with the identification of the basic E-R constructs that occur in an application resulting in an initial database design. Next, ways of capturing certain semantics of an application through data abstraction and procedures for identifying potential design problems are discussed. The translation of the E-R model into a set of normalized relations completes the design process. This methodology builds on previous work on the Extended Entity-Relationship Model and on the automation of database design.

The View Creation System (VCS) is an expert system that engages a user in a dialogue about the information requirements for some application, develops an Entity-Relationship model for the user's database view, and then converts the E-R model to a set of Fourth Normal Form relations. This paper describes the knowledge base of VCS. That is, it presents a formal methodology, capable of mechanization as a computer program, for accepting requirements from a user, identifying and resolving inconsistencies, redundancies, and ambiguities, and ultimately producing a normalized relational representation. Key aspects of the methodology are illustrated by applying VCS's knowledge base to an actual database design task.