Extended Entity-relationship Model Research Articles

Designing PostGIS Database System with Fuzzy Theory to Support Accessibility Tools for Urban Pedestrians

The combination of fuzzy logic and crowdsourcing can be a powerful tool for generating geospatial data for pedestrians with mobility challenges in urban areas. Although potentially useful, information about the accessibility of paths that is generated through crowdsourcing is susceptible to a high degree of imprecision. Spatial data management is required for such systems, which supports the management of uncertain data. Fuzzy theory allows us to model ambiguous information. To fill this gap, an improved method based on a fuzzy relational PostGIS database (FPostGIS) is proposed. The method includes extensions to represent imprecise data within an entity-relationship (ER) data model specifically tailored for path accessibility, and a set of steps for the derivation of FPostGIS from this extended ER model. According to the case study, this methodology has been applied in the design and development of decision support application within the Maps for Easy Paths (MEP) project. This application stores and retrieves accessibility information about a particular path and allows performing spatial operations and analysis inside the database.

Conceptual Data Modeling: Entity-Relationship Models as Thinging Machines

Data modeling is a process of developing a model to design and develop a data system that supports an organization's various business processes. A conceptual data model represents a technology-independent specification of structure of data to be stored within a database. The model aims at providing richer expressiveness and incorporating a set of semantics to (a) support the design, control, and integrity parts of the data stored in data management structures and (b) coordinate viewing of connections and ideas on a database. The described structure of the data is often represented in an entity-relationship (ER) model, which was one of the first data-modeling techniques and is likely to continue to be a popular way of characterizing entity classes, attributes and relationships. This paper is an attempt to examine the basic ER modeling notions to analyze the concepts to which they refer as well as ways to represent them. In such a mission, we apply a new modeling methodology (thinging machine; TM) to ER in terms of its fundamental building constructs, representation entities, relationships and attributes. The goal of this venture is to further the understanding of data models and enrich their semantics. Three specific contributions to modeling in this context are incorporated: (a) using the TM model's five generic actions to inject processing in the ER structure; (b) relating the single ontological element of TM modeling (i.e., a thing/machine or thimac) to ER entities and relationships; and (c) proposing a high-level integrated, extended ER model that includes structural and time-oriented notions (e.g., events or behavior).

Construction of Liver Fibrosis Diagnosis Ontology From Fuzzy Extended ER Modeling

Liver fibrosis diagnoses is a critical and core research study field due to its importance to the patient's life. Moreover, electronic health records (EHR) contain wealthy semantics connected to liver diseases yet ontological implementation is still a challenge. Ontology however, can play critical roles in E-health as a formalization of medical terminologies and decision support system knowledge base. But since clinical data contains a lot of data that is imprecise and vague, classical approaches of ontology construction would not be fruitful. However, Fuzzy ontology, an extension of the crisp ontology that requires different development methodology, can be implemented in this field due to its previous success in modeling semantic knowledge in various domains. In this article, the authors construct a fuzzy ontology by using a fuzzy extended entity relationship (EER) data model for liver fibrosis diagnosis. The resulting ontology is complete and consistent because it is based on a formal methodology of mapping the EER model into a fuzzy ontology.

Comparison of Conceptual Models of XML Based on Extended Entity Relationship Model

Comparison of Conceptual Models of XML Based on Extended Entity Relationship Model

Concepts and evaluation of the extended entity-relationship approach to database design in a multi-paradigm information system modeling tool

Different approaches to information system (IS) development are based on different data models. The selection of a data model for conceptual design, among other things, depends on the problem domain, the knowledge, and the personal preferences of an IS designer. In some situations, a simultaneous usage of different approaches to the conceptual database design and IS development may lead to the most appropriate solutions. In our previous research we have developed a tool that provides an evolutive and incremental approach to IS development, which is based on the form type data model. The approaches based on the Extended Entity-Relationship (EER) and class data models are broadly accepted throughout the community of IS designers. In order to support the simultaneous usage of approaches based on the form type, EER and class data models, we have developed the Multi-Paradigm Information System Modeling Tool (MIST). In this paper, we present a part of our MIST tool that supports EER approach to a database design. MIST components currently provide a formal specification of an EER database schema specification and its transformation into the relational data model, or the class model. Also, MIST allows generation of Structured Query Language code for a database creation and procedural code for implementing database constraints. In addition, Java code that stores and processes data from the database, may be generated from the class model. In this paper, we present the evaluation study of the MIST EER domain-specific language. Users' perceptions of language quality characteristics are used for the evaluation.

Consultas con agrupamiento basado en similitud

The fuzzy sets allow for the representation of vague concepts, where membership of an object could be gradual. The use of this theory in the data bases has been proposed. As result of that, the fuzzy relational model GEFRED has emerged, as well as the fuzzy extended Entity-Relationship model FuzzyEER and the fuzzy querying language FSQL, among others. These extensions include some forms of fuzzy attributes, such as the so-called Type 3. These attributes have values in a set of labels provided with a fuzzy similarity relation, which is an extension of equivalence relations in the fuzzy set theory. The implications of such attributes in partitioned query operators have not been previously explored. The contribution of this paper is on the syntactic and semantic definition of an extension to SQL, in order to allow for partitioned queries with Type 3 fuzzy attributes in grouping criteria. This extension increases the expressive power of the database language with respect to previous proposals

Formal translation from fuzzy EER model to fuzzy XML model

XML has been the de facto standard of data representation and exchange over the Web. In addition, fuzzy data are inherent in the real-world applications. Although fuzzy information has been extensively investigated in the context of relational database model, the classical relational database model and its fuzzy extension to date do not satisfy the need of modeling and processing complex objects with imprecision and uncertainty on the Web. Based on fuzzy sets, this paper concentrates on fuzzy information modeling in the EER (enhanced entity-relationship or extended entity-relationship) model and the fuzzy XML model. In particular, the formal approach to mapping the fuzzy EER model to the fuzzy DTD (document type definition) model is developed.

Construction of fuzzy OWL ontologies from fuzzy EER models: A semantics-preserving approach

Currently, many research studies have been concentrated on construction of fuzzy ontologies from different sources. Over the years, standard and variations of the entity-relationship (ER) model has widespread use, and also some approaches have been proposed for modeling fuzzy information in ER and extended entity-relationship (EER) models. Therefore,constructing fuzzy ontologies from fuzzy EER models may be valuable to the development of fuzzy ontologies, and in turn the constructed fuzzy ontologies may be useful for reasoning about the fuzzy EER models. In this paper, we develop a semantics-preserving approach and an automated tool for constructing fuzzy ontologies from fuzzy EER models, and investigate how to apply the constructed fuzzy ontologies to reason on fuzzy EER models. The constructed fuzzy ontologies are represented by the fuzzy OWL language, which is a fuzzy extension of the W3C recommendation standard ontology language OWL (Web Ontology Language). Firstly, we give the formalization of fuzzy EER models. On this basis, we propose an approach for constructing fuzzy OWL ontologies from fuzzy EER models, which can transform fuzzy EER models (including the structure and instance information of fuzzy EER models) into fuzzy OWL ontologies. Also, we give the proof for the semantics preservation of the transformation, and provide a detailed transformation example. Furthermore, we implement a prototype tool FEER2FOnto, which can automatically construct fuzzy OWL ontologies from fuzzy EER models. Finally, we investigate how to reason on fuzzy EER models with the constructed fuzzy OWL ontologies, and propose the reasoning approach and provide a reasoning example.

Querying incomplete data over extended ER schemata

AbstractSince Chen's Entity-Relationship (ER) model, conceptual modeling has been playing a fundamental role in relational data design. In this paper we consider an extended ER (EER) model enriched with cardinality constraints, disjointness assertions, and is a relations among both entities and relationships. In this setting, we consider the case of incomplete data, which is likely to occur, for instance, when data from different sources are integrated. In such a context, we address the problem of providing correct answers to conjunctive queries by reasoning on the schema. Based on previous results about decidability of the problem, we provide a query answering algorithm that performs rewriting of the initial query into a recursive Datalog query encoding the information about the schema. We finally show extensions to more general settings.

Mapping OWL to the Entity Relationship and Extended Entity Relationship models

This paper presents mapping rules to conceptually model an Entity Relationship (ER) diagram and Extended Entity Relationship (EER) diagram from OWL by identifying ER and EER constructs in OWL. OWL has been designed for the semantic web, but data in OWL format is not easy to manipulate or query. The conceptual view of OWL presented in this paper is necessary to understand OWL and OWL data, and will be used to eventually map OWL data to the relational model. Once in the relational model, OWL data can avail of mature relational database technology.

Letter from the editors

Hello, scholars, and welcome to volume 39(2) of Data Base! We have lots of interesting perspectives on the world of IT in these pages, and plenty of interesting developments to share with you. In this issue we feature the work of a number of leading scholars, including an article by Miguel Aguirre-Urreta and George Marakas on evolving conceptual modeling methods in the software development process. This critical examination of the extended entity relationship model as an alternative to more mature conceptual modeling formalisms, such as the UML of the object orientation, provides us with important new perspectives on the evolving practice of software engineering. This sort of work fills a niche that Data Base seeks to serve in the emergent area of software design and testing. You'll see more work of this sort very soon in Data Base, as part of a forthcoming special issue on systems testing later in the year. Also in these pages, we feature Arik Ragowsky and David Gefen, with an important article on ERP and the strategic orientation of firms. Data Base is edited in a town that features a number of high profile ERP implementations, and the topic has been of long standing interest to us from both strategic and operational perspectives. These colleagues give us the useful theoretical lens of the Strategic Grid through which to view ERP implementation projects, and this provides a critical contribution to our understanding of the design and implementation of these complex and mission-critical systems in the firm. We also have a fascinating study on virtual teams by Rosalie Ocker and Jerry Fjermestad. This emergent organizational form gets a lot of attention in our research and our teaching, as IT development globalizes. But, how well do we really understand the factors that lead to better performance in teams separated by geographical distances? These virtual teams are supported by information systems implementations designed to support work groups, but deep investigation into the use of such systems and the success factors arising from them remains to be conducted, until now. It appears that in using asynchronous communications, high performing teams don't just exchange ideas and information - they argue about things. They debate, and closely examine pros and cons of various suggestions and proposals as part of solving their workgroup problems and completing their assigned tasks. Such systems-supported teams, it seems, spend far less time brainstorming and exploring dimensions of problems than they do making critical commentaries and debating solutions to the problems, often in classic argumentative styles. In addition to the leading-edge work you find in this issue, you can expect more of the same soon to come in these pages. Your Data Base is engaging innovation and evolution in order to both better serve you as a research outlet and better serve the scientific community of information systems researchers with expanded and innovative venues for scholarly discourse. Watch closely for coming developments; we intend, for example, to begin the practice of actively soliciting measure validation research. It is our view that the reluctance of many information systems journals to welcome and nurture the scale development process, specifically for its own benefits and values, has served to stifle innovation and creativity in the development of new measures for important new constructs in our literature. This, in turn, can only hamper the precision with which we as scholars operationalize these important constructs in our research. Particularly at a time when new measure development techniques and practices are emerging in our literature, it is critical that we welcome and promulgate important work that serves to demonstrate not only the methods for, but also the validated outcomes of, operationalizations of critical information systems constructs. For that purpose, Managing Editor Andy Schwarz soon will be taking an important Senior Editor role specific to the solicitation, review and publication of measure development articles in our pages. As part of that process, we will have a new Managing Editor to introduce to you with the advent of our next issue. There are additional developments yet ahead, including an issue showcasing the research produced by the Chinese chapter of the AIS, an issue dedicated to HCI research, and much more of interest to the scholarly community. We will be informing you of these developments in more detail as they reach fruition. Meanwhile, continue to send us your best work. We are easily accessible at our global online submission and review portal, located through links at http://the-detabase.org, or directly accessed at Editorial Manager via http://www.editorialmanager.com/sigmisdb/. We look forward to hearing from you!

Mapping XML Schema to Entity Relationship and Extended Entity Relationship Models

In this paper, we conceptually model an Entity Relationship (ER) diagram and Extended Entity Relationship (EER) diagram from XML Schema. This conceptual view of XML Schema is a necessary step in understanding XML data, and can easily be used to translate XML data to the relational model. Once in the relational model, XML data can avail of mature relational database technology.

Translating relational schema into XML schema definition with data semantic preservation and XSD graph

Many legacy systems have been created by using relational database operating not for the Internet expression. Since the relational database is not an efficient way for data explosion, electronic transfer of data, and electronic business on the Web, we introduce a methodology in which a relational schema will be translated to an Extensible Markup Language (XML) schema definition for creating an XML database that is a simple and efficient format on the Web. We apply the Indirect Schema Translation Method that is a semantic-based methodology in this project. The mechanism is that the Relational Schema will be translated into the conceptual model, an Extended Entity Relationship (EER) Model using Reverse Engineering. Afterward, the EER model will be mapped to an XML Schema Definition Language (XSD) Graph as an XML conceptual schema using Semantic Transformation. Finally, the XSD Graph will be mapped into the XSD as an XML logical schema in the process of Forward Engineering, and the data semantics of participation, cardinality, generalization, aggregation, categorization, N-ary and U-ary relationship are preserved in the translated XML schema definition.

An Empirical Perspective of Using Ternary Relationships in Database Conceptual Modelling

To apply Extended Entity Relationship Model (EER) is a good method for representing requirements on information systems, because of its high level of abstraction. Although it is very close to the user, it is not so trivial when some constructs, such as higher order relationships, are used. This paper describes the characterisation and several important results of an experiment performed at our university in order to show some of the difficu lties found when novice students and practitioners use ternary relationships. Some special topics in identifying ternary relationships such as the importance of the domain of text and the intersection data are also investigated. In order to guide and help users in the design task, these results are introduced in PANDORA Case Tool, a research project which tries to serve as a methodological assistance tool.

Extracting the extended entity-relationship model from a legacy relational database

The maintenance of an existing database depends on the depth of understanding of its characteristics. Such an understanding is easily lost when the developers disperse. The situation becomes worse when the related documentation is missing. This paper addresses this issue by extracting the extended entity-relationship schema from the relational schema. We developed algorithms that investigate characteristics of an existing legacy database in order to identify candidate keys of all relations in the relational schema, to locate foreign keys, and to decide on the appropriate links between the given relations. Based on this analysis, a graph consistent with the entity-relationship diagram is derived to contain all possible uniary and binary relationships between the given relations. The minimum and maximum cardinalities of each link in the mentioned graph are determined, and extra links within the graph are identified and categorized, if any. The latter information is necessary to optimize foreign keys related information. Finally, the last steps in the process involve~(when applicable) suggesting improvements on the original conceptual design, deciding on relationships with attributes, many-to-many and n-ary ( n⩾3) relationships, and identifying is-a links. User involvement in the process is minimized to the case of having multiple choices, where the system does not have the semantic knowledge required to decide on a certain choice.

Conceptual design of fuzzy object-oriented databases using extended entity-relationship model

Entity-relationship–extended entity-relationship models play a crucial role in the conceptual design of relational databases as well as object-oriented databases. Recently, several approaches have been proposed to enhance object-oriented databases (OODBs) using fuzzy set theory. In this paper, we introduce a fuzzy extended entity-relationship model to cope with imperfect as well as complex objects in the real world at a conceptual level. In particular, we provide the formal approach to mapping a fuzzy extended entity-relationship model to a fuzzy object-oriented database schema. © 2001 John Wiley & Sons, Inc.

The impact of data models and task complexity on end-user performance: an experimental investigation

The purpose of this study was to investigate similarities and differences in the quality of data representations produced by end-users using the relational model (RM), the extended entity-relationship model (EERM), and the object-oriented model (OOM). By performing laboratory experiments using MIS major students, quality was evaluated on five constructs of a data model (i.e. entity/object, descriptor, identifier, relationship and generalization hierarchy) and six facets of a relationship (i.e. unary one-to-one, unary one-to-many, binary one-to-one, binary one-to-many, binary many-to-many and ternary many-to-many-to-many).The research focused on two major issues: data model design and data model conversion. The first issue investigated the differences in user performance between the RM, the EERM and the OOM. The second investigated the differences in user performance between the RM and the relational conversions of the EERM and the OOM models. For the first issue, EERM and OOM scored much higher than the RM in correctness scores of binary one-to-many and binary many-to-many relationships, but only the EERM led to significance. The RM and OOM scored much higher than EERM for unary one-to-one relationships, however, only the RM resulted in significance. The OOM required significantly less time for task completion than EERM. For the second issue, RM and the relational conversion of OOM scored significantly higher than the relational conversion of EERM for unary one-to-one relationships.

Expressing casual relationships in conceptual database schemas

Conceptual schema design is a crucial phase in the database design process. The quality of the final database (regardless of logical implementation model) is dependent largely upon the quality of the conceptual schema. Since conceptual schemas serve as formal representations of the requirements specification for a database, it is critical that a schema capture the requirements as completely and unambiguously as possible. Many studies have shown that semantic models, such as the Extended Entity–Relationship model, are better for conceptual database design than traditional models such as relational, hierarchical, and network models. This is primarily because of their ability to capture explicitly many “natural” cognitive relationship types that are likely to occur in requirements specifications, e.g., association, generalization/specialization, and aggregation. However, the relationships that can be specified in a semantic model represent only a subset of the relationships that are likely to be used by people in describing an application environment. Thus, using current semantic models for conceptual database design may result in abstractions of application environments in which some important information from the requirements is either not represented or is represented inappropriately.This paper seeks to help bridge the gap between requirements specifications and data modeling by hypothesizing the need for supporting additional cognitive relationship types in conceptual models. In the paper, we demonstrate the need for one such relationship type, causation. Specifically, we investigate the effects of the lack of constructs in semantic models for capturing causation on analysts' ability to express causal relationships mentioned in a requirements document.We found that subjects not familiar with data modeling expressed causal relationships better in their representations than did subjects who had some prior exposure to data modeling. This seems to indicate that the lack of constructs for capturing causation in semantic models hinders the ability of people trained in data modeling techniques to recognize and express causal relationships in conceptual schemas. The results also suggest the need to develop semantic models that provide constructs for capturing causation and other cognitive relationships.

F-metric: a WWW-based framework for intelligent formulation and analysis of metric queries

As an organization matures, quantitative techniques are employed to make software project management more systematic, informed and under control. This is typically done through the collection and analysis of software metrics to measure the performance of projects. While many organizations utilize software metrics to analyze project issues and answer management queries, the manner in which such queries are answered varies from organization to organization. As the number of metrics grows, interpretation of raw metrics in the context of management goals becomes difficult. The method and tool that we developed attempts to bridge the gap between the project managers' mental model of a project and raw software metrics. Based on industrial surveys, we identified types of queries about the project progress, resources and schedule that project managers often ask during project execution. Next, we built a conceptual model for project metrics relevant to those queries, justifying our selection of a core set of test and evaluation metrics. We defined a project management query language (PMQL for short), based on concepts of an extended Entity-Relationship model, which we used to formulate project queries at a conceptual level. Finally, we present a flexible WWW-based framework which can be used by experts to formulate and update management queries in PMQL, modify heuristics based on which queries are evaluated, as well as to dynamically display solutions to existing queries. The flexibility of our framework means that new queries may be added, heuristics to queries may be changed, and display of results can be changed on-the-fly via mutual cooperation between managers and technical experts of the organization without any changes to code.

An extended entity-relationship model for geographic applications

A special-purpose extension of the Entity-Relationship model for the needs of conceptual modeling of geographic applications, called the Geo-ER Model, is presented. Handling properties associated to objects not because of the objects' nature but because of the objects' position , calls for dealing -at the semantic modeling level-with space, location and dimensionality of objects, spatial relationships, space-depending attributes, and scale and generalization of representations. In order to accomplish this in the framework of ER and its derivatives, we introduce special entity sets, relationships, and add new constructs. The rationale as well as examples of usage of the Geo-ER model from actual projects are presented.