Enhanced Entity-relationship Model Research Articles

An Enhanced Entity Model for Converting Relational to Non-Relational Documents in Hospital Management System Based on Cloud Computing

In the past few years, several cloud services such as IBM cloud services, Microsoft service, Amazon web services as well as Google cloud platform services related databases are rising rapidly all over the world. In the case of a Relational database, it is easy to handle only a small amount of data. So to overcome the shortcomings, this paper proposes a Hospital Management System (HMS) using Mongo DB as a database. The Mongo DB is the classification of a document-related database that comes under the category of NoSQL database which is referred to as a non-relational database. Mongo DB aims in reducing the gap among two different types of scalable key-value databases such as fast and high scalable key-value databases. Also, this database reduces the time delay during the working of four operational modes such as selection, insertion, deletion, and updation. Furthermore, the Enhanced Entity-Relationship Model (EERM) for the HMS that is designed to manage the entire section of the hospital which includes the reception section, casualty section, details regarding the medical treatment, and employees. The HMS stores three million tuple entities and is loaded into the Mongo DB database. Also, the operation based on selection, insertion, deletion, and updation mode is evaluated. Therefore, the experimental analysis reveals that the proposed HMS using Mongo DB provides is highly efficient with less time delay, thus obtaining an effective system.

History places

This article presents a project-based case study that was developed for students with diverse backgrounds and varied inclinations for engaging technical topics. The project, called History Places, requires that student teams develop a vision for a kind of digital library, propose a conceptual model, and use the model to derive a logical model and information retrieval specification. From these two design representations, students implement a data-driven Web site that enables users to browse content and search by exact and best-match queries. The project brief contains a set of general requirements that promote creative solutions, while also bounding the complexity of the solution space. The article includes teaching notes and a conceptual model, expressed as an enhanced entity-relationship model in UML. The model, consisting of approximately ten entities, contains binary, unary, ternary, and specialization/generalization relationships. The article concludes with some reflections based on the experiences of using this project in six classes over four years.

Modelling concepts and database implementation techniques for complex biological data

Biological data such as protein structure and function, DNA sequences, and metabolic pathways require conceptual modelling characteristics that are not available in the widely used Entity-Relationship (ER) model and its variants, such as the Enhanced-Entity Relationship (EER) model. In particular, three constructs that occur frequently in bioinformatics data are ordered relationships, functional processes, and 3-dimensional (3D) structures. In this paper, we suggest a solution to this problem, requiring only minimal changes to the EER model by introducing specialised formal relationships for ordering, processes and molecular spatial structure. We show how these new concepts can be implemented in relational databases.

Relaxing Constraints in Enhanced Entity-Relationship Models Using Fuzzy Quantifiers

While various articles about fuzzy entity relationship (ER) and enhanced entity relationship (EER) models have recently been published, not all examine how the constraints expressed in the model may be relaxed. In this paper, our aim is to relax the constraints which can be expressed in a conceptual model using the modeling tool, so that these constraints can be made more flexible. We will also study new constraints that are not considered in classic EER models. We use the fuzzy quantifiers which have been widely studied in the context of fuzzy sets and fuzzy query systems for databases. In addition, we shall examine the representation of these constraints in an EER model and their practical repercussions. The following constraints are studied: the fuzzy participation constraint, the fuzzy cardinality constraint, the fuzzy completeness constraint to represent classes and subclasses, the fuzzy cardinality constraint on overlapping specializations, fuzzy disjoint and fuzzy overlapping constraints on specializations, fuzzy attribute-defined specializations, fuzzy constraints in union types or categories and fuzzy constraints in shared subclasses. We shall also demonstrate how fuzzy (min, max) notation can substitute the fuzzy cardinality constraint but not the fuzzy participation constraint. All these fuzzy constraints have a new meaning, they offer greater expressiveness in conceptual design, and are included in the so-called fuzzy EER model.

MEER—An EER model enhanced with structure methods

Entity relationship (ER) schemas include cardinality constraints, that restrict the dependencies among entities within a relationship type. The cardinality constraints have direct impact on the application maintenance, since insertions or deletions of entities or relationships might affect related entities. Indeed, maintenance of a system or of a database can be strengthened to enforce consistency with respect to the cardinality constraints in a schema. Yet, once an ER schema is translated into a logical database schema, or translated within a system, the direct correlation between the cardinality constraints and maintenance transactions is lost, since the components of the ER schema might be decomposed among those of the logical database schema or the target system. In this paper, a full solution to the enforcement of cardinality constraints in EER schemas is given. We extend the enhanced ER (EER) data model with structure-based update methods that are fully defined by the cardinality constraints. The structure methods are provably terminating and cardinality faithful, i.e., they do not insert new inconsistencies and can only decrease existing ones. A refined approach towards measuring the cardinality consistency of a database is introduced. The contribution of this paper is in the automatic creation of update methods, and in building the formal basis for proving their correctness.

Reasoning about ER models in a deductive environment

In this paper we present an approach to represent schema information, application data and integrity constraints as a logic program in form of Datalog. The schema information is supplied as an enhanced entity relationship (EER) model which is transformed by a one-to-one mapping into a set of ground facts. The application data corresponding to the schema is also represented by ground facts in a single table. In order to check whether the application data conforms to the given schema, generic functional and inclusion dependencies are introduced, which can be mapped into concrete dependencies using the schema information. The concrete dependencies are used for checking the consistency between application data and the schema. The formulation of the constraints based on functional and inclusion dependencies led to a small extension of the EER model by allowing identifying attributes in relationship types. This extension leads to both simpler constraints and simpler EER application models. Furthermore, we provide a meta EER model which can be used to check whether the application EER model is a structural valid EER model. Any application EER diagram is an instance of the meta EER diagram which can be specified using the proposed application data representation. The same integrity rules can be used to check the conformance between the application data and the application EER diagram, the meta EER diagram and the application EER diagram, and finally it can be used to check the meta EER model itself.

Identifying, Classifying, and Resolving Semantic Conflicts in Distributed Heterogeneous Databases

During the past three decades, most medium and large organizations have seen a dramatic proliferation of databases and their associated database management systems. While these databases are useful in supporting their different activities, organizations soon discover the need to access and share data across these systems. Under current technology, this integration is usually not possible due to the heterogeneity of the database management systems, the platforms that these system reside on, and the semantics of the different databases. In this paper we address the issue of semantic conflicts identification and resolution in distributed heterogeneous databases. Using a real world example of heterogeneous databases obtained from various Department of Defense functional applications, we develop a framework for identifying and classifying semantic heterogeneity for databases represented in the Enhanced Entity Relationship Model. We also propose solutions for resolving each type of semantic conflict presented in the classification framework.

A CASE tool for conceptual database design

The paper describes a Computer Aided Software Engineering (CASE) tool to support conceptual database modelling. One popular approach for conceptual database modelling is use of the Entity-Relationship (ER) model. The paper proposes the use of an Enhanced Entity-Relationship (EER) model for conceptual database modelling. This Enhanced Entity-Relationship model extends the Entity-Relationship model by incorporating the generalization/specialization, aggregation and categorization abstractions. The CASE tool, which is based on the Enhanced Entity-Relationship model, is known as the Enhanced Entity-Relationship Diagrammer (EERD). In addition, the CASE tool supports direct visual query and update based on the EERM.