Enterprise Data Modeling Using the Entity-Relationship Model
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Data Warehouse: an Integrated Decision Support Database Whose Content Is Derived from the Various Operational Databases
1 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in DATABASE MANAGEMENT Topic Objective: At the end of this topic student will be able to: Understand the Contrasting basic concepts Understand the Database Server and Database Specified Understand the USER Clause Definition/Overview: Data: Stored representations of objects and events that have meaning and importance in the users environment. Information: Data that have been processed in such a way that they can increase the knowledge of the person who uses it. Metadata: Data that describes the properties or characteristics of end-user data and the context of that data. Database application: An application program (or set of related programs) that is used to perform a series of database activities (create, read, update, and delete) on behalf of database users. WWW.BSSVE.IN Data warehouse: An integrated decision support database whose content is derived from the various operational databases. Constraint: A rule that cannot be violated by database users. Database: An organized collection of logically related data. Entity: A person, place, object, event, or concept in the user environment about which the organization wishes to maintain data. Database management system: A software system that is used to create, maintain, and provide controlled access to user databases. www.bsscommunitycollege.in www.bssnewgeneration.in www.bsslifeskillscollege.in 2 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in Data dependence; data independence: With data dependence, data descriptions are included with the application programs that use the data, while with data independence the data descriptions are separated from the application programs. Data warehouse; data mining: A data warehouse is an integrated decision support database, while data mining (described in the topic introduction) is the process of extracting useful information from databases. -
Arcgis Marine Data Model Reference
ArcGIS Marine Data Model Reference ArcGIS Marine Data Model (June 2003) This document provides an Dawn J. Wright, Oregon State U. overview of the ArcGIS Marine Patrick N. Halpin, Duke U. Data Model. This model focuses on Michael Blongewicz, DHI important features of the ocean realm, both natural and manmade, Steve Grisé, ESRI Redlands with a view towards the many Joe Breman, ESRI Redlands applications of marine GIS data. ArcGIS Data Models TABLE OF CONTENTS Acknowledgements ................................................................................................................... 4 Introduction ............................................................................................................................... 5 Why a Marine Data Model?.................................................................................................... 6 Intended Audience and Scope of the Model............................................................................ 8 The Process of Building a Data Model ..................................................................................... 10 Final Data Model Content, Purpose and Use........................................................................ 14 Data Model Description ........................................................................................................... 16 Overview ............................................................................................................................. 16 Conceptual Framework.................................................................................................... -
A Model-To-Model Transformation of a Generic Relational Database Schema Into a Form Type Data Model
Proceedings of the Federated Conference on Computer Science DOI: 10.15439/2016F408 and Information Systems pp. 1577–1580 ACSIS, Vol. 8. ISSN 2300-5963 A Model-to-Model Transformation of a Generic Relational Database Schema into a Form Type Data Model Sonja Ristić, Slavica Kordić, Milan Čeliković, Vladimir Dimitrieski, Ivan Luković University of Novi Sad, Faculty of Technical Sciences, Trg D. Obradovića 6, 21000 Novi Sad, Serbia Email: {sdristic, slavica, milancel, dimitrieski, ivan}@uns.ac.rs engineering and review different database meta-models Abstract—An important phase of a data-oriented software (MM) that are used in the database reengineering process system reengineering is a database reengineering process and, applied in IIS*Studio. In [5] we propose an MD approach to in particular, its subprocess – a database reverse engineering data structure conceptualization phase of database reverse process. In this paper we present one of the model-to-model engineering process that is conducted through a chain of transformations from a chain of transformations aimed at transformation of a generic relational database schema into a M2M transformations. In this paper we present the final step form type data model. The transformation is a step of the data of the conceptualization phasethe M2M transformation of structure conceptualization phase of a model-driven database a generic relational database schema into a form type model. reverse engineering process that is implemented in IIS*Studio The form type concept and the IIS*Studio architecture are development environment. given in Section 2. Classifications of form types and relation schemes are described in Section 3. The transformation of a I. -
Chapter 2: Database System Concepts and Architecture Define
Chapter 2: Database System Concepts and Architecture define: data model - set of concepts that can be used to describe the structure of a database data types, relationships and constraints set of basic operations - retrievals and updates specify behavior - set of valid user-defined operations categories: high-level (conceptual data model) - provides concepts the way a user perceives data - entity - real world object or concept to be represented in db - attribute - some property of the entity - relationship - represents and interaction among entities representational (implementation data model) - hide some details of how data is stored, but can be implemented directly - record-based models like relational are representational low-level (physical data model) - provides details of how data is stored - record formats - record orderings - access path (for efficient search) schemas and instances: database schema - description of the data (meta-data) defined at design time each object in schema is a schema construct EX: look at TOY example - top notation represents schema schema constructs: cust ID; order #; etc. database state - the data in the database at any particular time - also called set of instances an instance of data is filled when database is populated/updated EX: cust name is a schema construct; George Grant is an instance of cust name difference between schema and state - at design time, schema is defined and state is the empty state - state changes each time data is inserted or updated, schema remains the same Three-schema architecture -
Data Models for Home Services
__________________________________________PROCEEDING OF THE 13TH CONFERENCE OF FRUCT ASSOCIATION Data Models for Home Services Vadym Kramar, Markku Korhonen, Yury Sergeev Oulu University of Applied Sciences, School of Engineering Raahe, Finland {vadym.kramar, markku.korhonen, yury.sergeev}@oamk.fi Abstract An ultimate penetration of communication technologies allowing web access has enriched a conception of smart homes with new paradigms of home services. Modern home services range far beyond such notions as Home Automation or Use of Internet. The services expose their ubiquitous nature by being integrated into smart environments, and provisioned through a variety of end-user devices. Computational intelligence require a use of knowledge technologies, and within a given domain, such requirement as a compliance with modern web architecture is essential. This is where Semantic Web technologies excel. A given work presents an overview of important terms, vocabularies, and data models that may be utilised in data and knowledge engineering with respect to home services. Index Terms: Context, Data engineering, Data models, Knowledge engineering, Semantic Web, Smart homes, Ubiquitous computing. I. INTRODUCTION In recent years, a use of Semantic Web technologies to build a giant information space has shown certain benefits. Rapid development of Web 3.0 and a use of its principle in web applications is the best evidence of such benefits. A traditional database design in still and will be widely used in web applications. One of the most important reason for that is a vast number of databases developed over years and used in a variety of applications varying from simple web services to enterprise portals. In accordance to Forrester Research though a growing number of document, or knowledge bases, such as NoSQL is not a hype anymore [1]. -
Metamodeling the Enhanced Entity-Relationship Model
Metamodeling the Enhanced Entity-Relationship Model Robson N. Fidalgo1, Edson Alves1, Sergio España2, Jaelson Castro1, Oscar Pastor2 1 Center for Informatics, Federal University of Pernambuco, Recife(PE), Brazil {rdnf, eas4, jbc}@cin.ufpe.br 2 Centro de Investigación ProS, Universitat Politècnica de València, València, España {sergio.espana,opastor}@pros.upv.es Abstract. A metamodel provides an abstract syntax to distinguish between valid and invalid models. That is, a metamodel is as useful for a modeling language as a grammar is for a programming language. In this context, although the Enhanced Entity-Relationship (EER) Model is the ”de facto” standard modeling language for database conceptual design, to the best of our knowledge, there are only two proposals of EER metamodels, which do not provide a full support to Chen’s notation. Furthermore, neither a discussion about the engineering used for specifying these metamodels is presented nor a comparative analysis among them is made. With the aim at overcoming these drawbacks, we show a detailed and practical view of how to formalize the EER Model by means of a metamodel that (i) covers all elements of the Chen’s notation, (ii) defines well-formedness rules needed for creating syntactically correct EER schemas, and (iii) can be used as a starting point to create Computer Aided Software Engineering (CASE) tools for EER modeling, interchange metadata among these tools, perform automatic SQL/DDL code generation, and/or extend (or reuse part of) the EER Model. In order to show the feasibility, expressiveness, and usefulness of our metamodel (named EERMM), we have developed a CASE tool (named EERCASE), which has been tested with a practical example that covers all EER constructors, confirming that our metamodel is feasible, useful, more expressive than related ones and correctly defined. -
Ontology Transformation: from Requirements to Conceptual Model*
Justas Trinkunas, Olegas Vasilecas Ontology Transformation: from Requirements to .. SCIeNTIfIC PAPeRS, UNIVeRSITy of Latvia, 2009. Vol. 751 COMPUTER SCIENCE AND Information TECHNOLOGIES 52–64 P. Ontology Transformation: from Requirements to Conceptual Model* Justas Trinkunas1 and Olegas Vasilecas2, 3 1 Information Systems Research Laboratory, Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius-40, Lithuania, [email protected] 2 Information Systems Research Laboratory, Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius-40, Lithuania, [email protected] 3 Department of Computer Science, Faculty of Natural Sciences, Klaipeda University, Herkaus Manto 84, LT-92294 Klaipeda, [email protected] Information systems are increasingly complex, especially in the enormous growth of the volume of data, different structures, different technologies, and the evolving requirements of the users. Consequently, current applications require an enormous effort of design and development. The fast-changing requirements are the main problem in creating and/or modifying conceptual data models. To improve this process, we proposed to reuse already existing knowledge for conceptual modelling. In the paper, we have analysed reusable knowledge models. We present our method for creating conceptual models from various knowledge models. Keywords: transformation, ontology, data modelling. 1 Introduction Most of information systems analysts have at least once considered how many times they have to analyse the same problems, create and design the same things. Most of them ask, is it possible to reuse already existing knowledge? Our answer is yes. We believe that knowledge can and should be reused for information systems development. -
Mapping Conceptual Models to Database Schemas
Chapter 4 Mapping Conceptual Models to Database Schemas David W. Embley and Wai Yin Mok 4.1 Introduction The mapping of a conceptual-model instance to a database schema is fun- damentally the same for all conceptual models. A conceptual-model instance describes the relationships and constraints among the various data items. Given the relationships and constraints, the mappings group data items to- gether into flat relational schemas for relational databases and into nested relational schemas for object-based and XML storage structures. Although we are particularly interested in the basic principles behind the mappings, we take the approach of first presenting them in Sections 4.2 and 4.3 in terms of mapping an ER model to a relational database. In these sections, for the ER constructs involved, we (1) provide examples of the constructs and say what they mean, (2) give rules for mapping the constructs to a relational schema and illustrate the rules with examples, and (3) state the underlying principles. We then take these principles and show in Section 4.4 how to apply them to UML. This section on UML also serves as a guide to applying the principles to other conceptual models. In Section 4.5 we ask and answer the question about the circumstances under which the mappings yield normalized relational database schemas. In Section 4.6 we extend the mappings beyond flat relations for relational databases and show how to map conceptual-model instances to object-based and XML storage structures. We provide pointers to additional readings in Section 4.7. Throughout, we use an application in which we assume that we are designing a database for a bed and breakfast service. -
Gis Data Models
GIS DATA MODELS CONCEPTUAL MODEL OF SPATIAL INFORMATION 1. Introduction GIS does not store a map in any conventional sense, nor it stores a particular image or view of geographic area. Instead, a GIS stores the data from which we can draw a desired view to suit a particular purpose known as geographic data. There are two types of data in GIS; spatial data and non-spatial data (Attribute data). Non-spatial data include information about the features. For example, name of roads, schools, forests etc., population or census data for the region concerned etc. Non-spatial or attribute data is that qualifies the spatial data. It describes some aspects of the spatial data, not specified by its geometry alone. A geographical information system essentially integrates the above two types of data and allows user to derive new data for planning. Spatial models are important in that way in which information is represented affects the type of analysis that can be performed and the type of graphic display that can be performed and the type of analysis that can be obtained. In GIS systems there is a major distinction between what are usually referred to as vector GIS and raster GIS. These two approaches to spatial data processing, often to be found in the same GIS package, reflect two different methods of spatial modeling: the former focusing on discrete objects that are to be described, and the latter concerned primarily with recording what is to be found at a predetermined set of locations that may be grid cells or points. -
Define Conceptual Schema in Dbms
Define Conceptual Schema In Dbms Earthshaking Garv hydroplane early while Esteban always tub his liquidations oversimplified sorrily, he theologised so industrially. Devolution or tyrannical, Rinaldo never bottle-feed any stereochromy! Visitorial Barney sometimes politicises his cannibal dreadfully and landscapes so ago! In knowing the same data projects, while control module or schema conceptual. Implementation data model is used at local level. One badge to calculate cardinality constraints. Its functionality is its limit the number and entity occurrences associated in a relationship. Parallel database systems are used in the applications that chapter to query extremely large databases or a have people process an extremely large orchard of transactions per second. SALARY stretch a number. Data compression and data encryption techniques. Dynamic properties, for example, operations or rules defining new database states based on applied state changes. The scheduler is illicit for ensuring that concurrent operations on the database software without conflicting with me another. It also allows any differences in entity names, attributes names, attributes order, data types, and manual on, to pay determined. Thus, by inspecting the articulation set therefore a decomposition hypergraph, one immediately infers that certain MDs hold in on original relation. This is off very strong within which floor have developed our decomposition algorithm. Elementary FDs and multiple elementary MDs have and simple structure appealing to intuition and certain formal properties ACM Transactions on Database Systems. DOB is then date. Guarino is arguing for. We use cookies to hum the best possible experience equip our website. The information about the mapping requests among various schema levels are included in quality system catalog of DBMS. -
The Layman's Guide to Reading Data Models
Data Architecture & Engineering Services The Layman’s Guide to Reading Data Models A data model shows a data asset’s structure, including the relationships and constraints that determine how data will be stored and accessed. 1. Common Types of Data Models Conceptual Data Model A conceptual data model defines high-level relationships between real-world entities in a particular domain. Entities are typically depicted in boxes, while lines or arrows map the relationships between entities (as shown in Figure 1). Figure 1: Conceptual Data Model Logical Data Model A logical data model defines how a data model should be implemented, with as much detail as possible, without regard for its physical implementation in a database. Within a logical data model, an entity’s box contains a list of the entity’s attributes. One or more attributes is designated as a primary key, whose value uniquely specifies an instance of that entity. A primary key may be referred to in another entity as a foreign key. In the Figure 2 example, each Employee works for only one Employer. Each Employer may have zero or more Employees. This is indicated via the model’s line notation (refer to the Describing Relationships section). Figure 2: Logical Data Model Last Updated: 03/30/2021 OIT|EADG|DEA|DAES 1 The Layman’s Guide to Reading Data Models Physical Data Model A physical data model describes the implementation of a data model in a database (as shown in Figure 3). Entities are described as tables, Attributes are translated to table column, and Each column’s data type is specified. -
Managing Data Models Is Based on a “Where-Used” Model Versus the Traditional “Transformation-Mapping” Model
Data Model Management Whitemarsh Information Systems Corporation 2008 Althea Lane Bowie, Maryland 20716 Tele: 301-249-1142 Email: [email protected] Web: www.wiscorp.com Data Model Management Table of Contents 1. Objective ..............................................................1 2. Topics Covered .........................................................1 3. Architectures of Data Models ..............................................1 4. Specified Data Model Architecture..........................................3 5. Implemented Data Model Architecture.......................................7 6. Operational Data Model Architecture.......................................10 7. Conclusions...........................................................10 8. References............................................................11 Copyright 2006, Whitemarsh Information Systems Corporation Proprietary Data, All Rights Reserved ii Data Model Management 1. Objective The objective of this Whitemarsh Short Paper is to present an approach to the creation and management of data models that are founded on policy-based concepts that exist across the enterprise. Whitemarsh Short Papers are available from the Whitemarsh website at: http://www.wiscorp.com/short_paper_series.html Whitemarsh’s approach to managing data models is based on a “where-used” model versus the traditional “transformation-mapping” model. This difference brings a very great benefit to data model management. 2. Topics Covered The topics is this paper include: ! Architectures of Data