Entity-relationship Data 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.

Database development, design and trending tools

It is very important for database management system (DBMS) users to make sure there is illustration of the logical view of data using a precise data model; it includes hierarchical, network and relational models. The commercially available DBMS are based on the models but the relational product seems to have overshadowed other models. There is an effort been made to break the monopoly of the relational model which resulted in new data models like Object-oriented data model, Entity-Relationship data model, Functional data model, and Semantic data model. Also, an advance in the area of networking has given rise to database servers and its descendant, distributed databases. With this development, geographically distributed organization has its communication bills reduced significantly. For future research, database development tools may depend on the involvement of programming language researchers in databases, the impact of the object technology on the dominating relational model and whether the attempts to integrate knowledge and data bases ultimately yield commercially affordable products.

MODELLING AND VISUALIZING KNOWLEDGE ON THE REFERENCE SYSTEM AND VARIATIONS BASED ON THE MODEL OF PGE – PRODUCT GENERATION ENGINEERING FOR DECISION SUPPORT

AbstractIn 2019, Dyson had to cancel its ambitious electric car project after having already 500 Million pounds spent. This example shows how difficult it is to assess the consequences of decisions on development targets as cost, risk, and innovation potential. Knowledge about references, variation types and their impact on development targets can help to increase the maturity of the decision basis. The model of PGE - product generation engineering describes these interrelations using the reference system. This contribution deals with the question of how knowledge about the impact of variation types and characteristics of reference system elements on new product generations can be made usable through modelling and visualization. Therefore, characteristics of reference system elements and their impacts on common development targets are collected through literature research. To process this knowledge base in technical information systems, an Entity-Relationship data model is developed. Through the implementation of a VR visualization, the data model is validated and a first visualisation approach is shown. The findings of this work can be used to systematise research on impact factors in PGE and to develop further digital methods.

A dictionary for translation from natural to formal data model language

AbstractThe paper describes our current research activities and results related to developing knowledge‐based systems to support the creation of entity‐relationship (ER) models. The authors based obtaining an ER model in textual form on translation from one language into another, that is, from an English controlled natural language into the formalized language of an ER data model. Our translation method consisted of creating translation rules of sentential form parts into ER model constructs based on the textual and character patterns detected in the business descriptions. To enable the computer analyses necessary for creating translation mechanisms, we created a linguistic corpus that contains lists of the business descriptions and the texts of other business materials. From the corpus, we then created a specific dictionary and linguistic rules to automate the business descriptions' translation into the ER data model language. Before that, however, the corpus was enriched by adding annotations to the words related to ER data model constructs. In this paper, we also present the main issues uncovered during the translation process and offer a possible solution with utility evaluation: applying information‐extraction performance measures to a set of sentences from the corpus.

Ontology-based reasoning for entity-relationship data model semantic evaluation

Ontology-based reasoning for entity-relationship data model semantic evaluation

The functional requirements for community information

Purpose – The purpose of this paper is to consider the nature of community information (CI) and proposes a data model, based on the entity-relationship approach adopted in the Functional Requirements for Bibliographic Records (FRBR), which may assist with the development of future metadata standards for CI systems. Design/methodology/approach – The two main data structure standards for CI, namely the element set developed by the Alliance of Information and Referral Systems (AIRS) and the MARC21 Format for CI, are compared by means of a mapping exercise, after which an entity-relationship data model is constructed, at a conceptual level, based on the definitions of CI found in the literature. Findings – The AIRS and MARC21 data structures converge to a fair degree, with MARC21 providing for additional detail in several areas. However, neither structure is systematically and unambiguously defined, suggesting the need for a data model. An entity-relationship data modelling approach, similar to that taken in FRBR, yielded a model that could be used as the basis for future standards development and research. It was found to effectively cover both the AIRS and MARC21 element sets. Originality/value – No explicit data model exists for CI, and there has been little discussion reported about what data elements are required to support CI seeking.

CrowdLearn: Crowd-sourcing the Creation of Highly-structured E-Learning Content

While nowadays there is a plethora of Learning Content Management Systems, the collaborative, community-based creation of rich e-learning content is still not sufficiently well supported. Few attempts have been made to apply crowd-sourcing and wiki-approaches for the creation of e-learning content. However, the paradigm is only applied to unstructured, textual content and cannot be used in SCORM-compliant systems. To address this issue we developed the CrowdLearn concept to exploit the wisdom, creativity and productivity of the crowd for the creation of rich, deep-semantically structured e-learning content. The CrowdLearn concept combines the wiki style for collaborative content authoring with SCORM requirements for re-usability. Therefore, it enables splitting the learning material into Learning Objects (LOs) with an adjustable level of granularity. In order to realize the CrowdLearn concept, a novel data model called WikiApp is devised. The WikiApp data model is a refinement of the traditional entity-relationship data model with further emphasis on collaborative social activities and structured content authoring. We implement and evaluate the CrowdLearn approach with SlideWiki – an educational platform dealing with presentations and assessment tests. The article also comprises results of a usability evaluation with real students.

Data Modeling of Web Site Structure: An Oracle Prototype

Web page design is essentially an intuitive approach. However, as Web pages become more complex in their structure and content, it is not easy to create and maintain their layout structure consistently. This paper outlines an approach to create an entity-relationship data model of Web site and Web page constructs for storage in a relational database. The approach also outlines the generation of Web pages from database repository. The approach details are illustrated by a prototype in Oracle to show the efficacy of the concepts.

AN AUTOMATIC METHOD FOR THE DESIGN OF MULTIDIMENSIONAL SCHEMAS FROM OBJECT ORIENTED DATABASES

A data warehouse (DW) is a large data repository system designed for decision-making purposes. Its design relies on a specific model called multidimensional. This multidimensional model supports analyses of huge volumes of data that trace the enterprise's activities over time. Several design methods were proposed to build multidimensional schemas from either the relational data model or the entity-relationship data model. Almost all proposals that treated the object-oriented data model assume the presence of the data source UML class-diagram. However, in practice, either such a diagram does not exist or is obsolete due to multiple changes/evolutions of the information system. Furthermore, these few proposals require an intense manual intervention of the designer, which requires a high expertise both in the DW domain and in the object database domain. To overcome these disadvantages, this work proposes an automatic DW schema design method starting from an object database (schema and its instances). This method applies a set of extraction rules to identify multidimensional concepts and to generate star schemas. It is defined for the standard ODMG model and, thus, can be adapted with slight changes for other object database models. In addition, its extraction rules have the merit of being independent of the domain semantics. Furthermore, they automatically generate schemas classified according to their analytical potential; this classification helps the DW designer in selecting the most relevant schemas among the generated ones. Finally, being automatic, our method is supported by a toolset that also prepares for the automatic generation of the Extract Transform and Load procedures used to load the DW.

An interactive tool to improve learning of data modeling

Relational and entity-relationship data models are important concepts covered in database and systems analysis and design courses. Entity-relationship model is widely used in conceptual database de...

Construction of the Optimal Milling Parameters Database for Radar Key Parts

In this paper, the study is carried out on the milling parameters optimization and cutting database development of radar key parts in electronics industry. The method is proposed in detail on parameters optimization. The material removal rate is chosen as optimization objective. The cutting constraints include machining tool power, tool life, surface quality, deformation and strength of milling cutter and the chatter stability. The genetic algorithm is selected as global optimal method. At last, it presents the solutions of cutting database based on Web, which include general structure, basic function and entity relationship data model.

HAPPI: an online database of comprehensive human annotated and predicted protein interactions

BackgroundHuman protein-protein interaction (PPIs) data are the foundation for understanding molecular signalling networks and the functional roles of biomolecules. Several human PPI databases have become available; however, comparisons of these datasets have suggested limited data coverage and poor data quality. Ongoing collection and integration of human PPIs from different sources, both experimentally and computationally, can enable disease-specific network biology modelling in translational bioinformatics studies.ResultsWe developed a new web-based resource, the Human Annotated and Predicted Protein Interaction (HAPPI) database, located at . The HAPPI database was created by extracting and integrating publicly available protein interaction databases, including HPRD, BIND, MINT, STRING, and OPHID, using database integration techniques. We designed a unified entity-relationship data model to resolve semantic level differences of diverse concepts involved in PPI data integration. We applied a unified scoring model to give each PPI a measure of its reliability that can place each PPI at one of the five star rank levels from 1 to 5. We assessed the quality of PPIs contained in the new HAPPI database, using evolutionary conserved co-expression pairs called "MetaGene" pairs to measure the extent of MetaGene pair and PPI pair overlaps. While the overall quality of the HAPPI database across all star ranks is comparable to the overall qualities of HPRD or IntNetDB, the subset of the HAPPI database with star ranks between 3 and 5 has a much higher average quality than all other human PPI databases. As of summer 2008, the database contains 142,956 non-redundant, medium to high-confidence level human protein interaction pairs among 10,592 human proteins. The HAPPI database web application also provides …” should be “The HAPPI database web application also provides hyperlinked information of genes, pathways, protein domains, protein structure displays, and sequence feature maps for interactive exploration of PPI data in the database.ConclusionHAPPI is by far the most comprehensive public compilation of human protein interaction information. It enables its users to fully explore PPI data with quality measures and annotated information necessary for emerging network biology studies.

Data management and data base implementation for GMO monitoring

The paper specifies the requirements on a data management system for GMO monitoring by showing which data have to be hold for which purpose. The necessary conditions, plausibilities and dependencies are presented. An Entity Relationship data model is designed conceptionally and logically considering both questionnaires and networks as data sources and common statistical software packages (SPSS, SAS). The physical implementation of this data model will be described.

Modeling software measurement data

This paper proposes a method for specifying models of software data sets in order to capture the definitions and relationships among software measures. We believe a method of defining software data sets is necessary to ensure that software data are trustworthy. Software companies introducing a measurement program need to establish procedures to collect and store trustworthy measurement data. Without appropriate definitions it is difficult to ensure data values are repeatable and comparable. Software metrics researchers need to maintain collections of software data sets. Such collections allow researchers to assess the generality of software engineering phenomena. Without appropriate safeguards, it is difficult to ensure that data from different sources are analyzed correctly. These issues imply the need for a standard method of specifying software data sets so they are fully documented and can be exchanged with confidence. We suggest our method of defining data sets can be used as such a standard. We present our proposed method in terms of a conceptual entity-relationship data model that allows complex software data sets to be modeled and their data values stored. The standard can, therefore, contribute both to the definition of a company measurement program and to the exchange of data sets among researchers.

Evaluating Completeness of Conceptual Business Process Models (CBPMs): A Metric Based on Case Studies

Conceptual models representing data and business processes bridge the gap between requirements specified by end-users and software systems specifications used to fulfill these requirements. More often than not, the entire systems development methodology is driven by a specific conceptual model used in a given situation. For example, a well-known database system development methodology uses the Entity-Relationship data model to capture end-user requirements and to drive the development of the ensuing database application.Several models have been proposed and some have been used as front-end inputs for automated tools to help in systems development. The question facing systems development managers is: which conceptual model to adopt? One important criterion for the effectiveness of a model is its completeness. The completeness of conceptual models has been widely accepted as an important attribute for efficient development of an effective system. Our survey of existing work on evaluating and comparing existing conceptual business process models indicates that almost no work has been done on empirically evaluating completeness of the models in real business situations. The primary contribution of this research is the development of a metric to measure the level of completeness of conceptual business process models (CBPMs). A case study demonstrates the use of this metric to evaluate the completeness of a CBPM - the Integrated Definition Standards Model.

RE-Analyzer: From source code to structured analysis

The RE-Analyzer is an automated, reverse engineering system providing a high level of integration with a computer-aided software engineering (CASE) tool. Specifically, legacy code is transformed into abstractions within a structured analysis methodology. The abstractions are based on data flow diagrams, state transition diagrams, and entity-relationship data models. Since the resulting abstractions can be browsed and modified within a CASE tool environment, a broad range of software engineering activities are supported, including program understanding, reengineering, and redocumentation. In addition, diagram complexity is reduced through the application of control partitioning: algorithmic technique for managing complexity by partitioning source code modules into smaller yet semantically coherent units. This approach also preserves the information content of the original source code. It is in contrast to other reverse engineering techniques that produce only structure charts and thus suffer from loss of information, unmanaged complexity, and a lack of correspondence to structured analysis abstractions. The RE-Analyzer has been implemented and currently supports the reverse engineering of software written in the C language. It has been integrated with a CASE tool based on the VIEWS method.

GRAQULA: A graphical query language for entity-relationship or relational databases

GRAQULA is a graphical language for querying and updating a database. One version of GRAQULA provides a user interface for the entity-relationship data model, and another version (with almost identical syntax) provides a user interface for the relational model. Each version is relationally complete, and each depicts relationships (or expected joins) graphically, GRAQULA provides logical operations (e.g. negation) on graphical objects; these operations have user-specified scopes, allow nesting, and can involve existential or universal quantification. Aggregates (e.g. average) also have user-specified scopes. Queries can invoke other queries, and users and queries can pass parameters to queries. The design reflects a specified set of goals, including expressive power, consistency, and limitation of required memorization.

Formal Specification of a Data Dictionary for an Extended ER Data Model

A formal definition of the data dictionary for an extended entity-relationship data model is described. Extensions should allow greater semantic expressiveness and more precise modelling, while retaining ease of use, intuitiveness and flexibility. Basic concepts of the model are formally defined using the Z notation, and an enhanced graphical notation is also proposed. Furthermore, a simple transformation of the extended ER schema to a relational one is formally described as well.

Deductive-ER: deductive entity-relationship data model and its data language

The entity-relationship (ER) data model is a well known semantic data model in database design. The paper develops a deductive-ER data model, Deductive-ER, which applies the ER approach to the design of deductive databases. Based on such a model, a deductive-ER data language, called Deductive-ER, is constructed, which provides capabilities of definition and manipulation of extensional database components, intensional database components (including virtual entities, virtual relationships, and virtual attributes), hybrid components, generalization hierarchies, and integrity constraints. It supports complex data objects, including tuple-valued, list-valued, text-valued, set-valued, and null-valued attributes. Moreover, it allows recursive definitions. Deductive-ER integrates two different flavoured languages: an ER-based conventional data language and a Horn-clause-based logic data language. It takes advantage of both and provides a simple and powerful interface. The implementation of Deductive-ER is also discussed.

Extending the entity-relationship model to capture dynamic behaviour

The entity-relationship data model is extended by structural integrity constraints. For each entity and relationship, a list of update operations is defined, together with appropriate integrity rules. These rules are defined according to the localisation principle, which states that an update operation performed on any object in the schema should directly affect only those objects in its immediate vicinity. The update, once started, propagates through the database, respecting local integrity rules for each affected object. The integrity rules are used for algorithmic design of integrity-preserving update procedures, to be utilised in database application programs, instead of the primitive update operations supported by the DBMS itself. These procedures may be generated directly from the specification, opening the way to an automated design tool.