The Integrated Data Model: a Database Perspective
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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.................................................................................................... -
Data Model Standards and Guidelines, Registration Policies And
Data Model Standards and Guidelines, Registration Policies and Procedures Version 3.2 ● 6/02/2017 Data Model Standards and Guidelines, Registration Policies and Procedures Document Version Control Document Version Control VERSION D ATE AUTHOR DESCRIPTION DRAFT 03/28/07 Venkatesh Kadadasu Baseline Draft Document 0.1 05/04/2007 Venkatesh Kadadasu Sections 1.1, 1.2, 1.3, 1.4 revised 0.2 05/07/2007 Venkatesh Kadadasu Sections 1.4, 2.0, 2.2, 2.2.1, 3.1, 3.2, 3.2.1, 3.2.2 revised 0.3 05/24/07 Venkatesh Kadadasu Incorporated feedback from Uli 0.4 5/31/2007 Venkatesh Kadadasu Incorporated Steve’s feedback: Section 1.5 Issues -Change Decide to Decision Section 2.2.5 Coordinate with Kumar and Lisa to determine the class words used by XML community, and identify them in the document. (This was discussed previously.) Data Standardization - We have discussed on several occasions the cross-walk table between tabular naming standards and XML. When did it get dropped? Section 2.3.2 Conceptual data model level of detail: changed (S) No foreign key attributes may be entered in the conceptual data model. To (S) No attributes may be entered in the conceptual data model. 0.5 6/4/2007 Steve Horn Move last paragraph of Section 2.0 to section 2.1.4 Data Standardization Added definitions of key terms 0.6 6/5/2007 Ulrike Nasshan Section 2.2.5 Coordinate with Kumar and Lisa to determine the class words used by XML community, and identify them in the document. -
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. -
Using Telelogic DOORS and Microsoft Visio to Model and Visualize Complex Business Processes
Using Telelogic DOORS and Microsoft Visio to Model and Visualize Complex Business Processes “The Business Driven Application Lifecycle” Bob Sherman Procter & Gamble Pharmaceuticals [email protected] Michael Sutherland Galactic Solutions Group, LLC [email protected] Prepared for the Telelogic 2005 User Group Conference, Americas & Asia/Pacific http://www.telelogic.com/news/usergroup/us2005/index.cfm 24 October 2005 Abstract: The fact that most Information Technology (IT) projects fail as a result of requirements management problems is common knowledge. What is not commonly recognized is that the widely haled “use case” and Object Oriented Analysis and Design (OOAD) phenomenon have resulted in little (if any) abatement of IT project failures. In fact, ten years after the advent of these methods, every major IT industry research group remains aligned on the fact that these projects are still failing at an alarming rate (less than a 30% success rate). Ironically, the popularity of use case and OOAD (e.g. UML) methods may be doing more harm than good by diverting our attention away from addressing the real root cause of IT project failures (when you have a new hammer, everything looks like a nail). This paper asserts that, the real root cause of IT project failures centers around the failure to map requirements to an accurate, precise, comprehensive, optimized business model. This argument will be supported by a using a brief recap of the history of use case and OOAD methods to identify differences between the problems these methods were intended to address and the challenges of today’s IT projects. -
Principles of the Concept-Oriented Data Model Alexandr Savinov
Principles of the Concept-Oriented Data Model Alexandr Savinov Institute of Mathematics and Computer Science Academy of Sciences of Moldova Academiei 5, MD-2028 Chisinau, Moldova Fraunhofer Institute for Autonomous Intelligent System Schloss Birlinghoven, 53754 Sankt Augustin, Germany http://www.conceptoriented.com [email protected] Principles of the Concept-Oriented Data Model Alexandr Savinov Institute of Mathematics and Computer Science, Academy of Sciences of Moldova Academiei 5, MD-2028 Chisinau, Moldova Fraunhofer Institute for Autonomous Intelligent System Schloss Birlinghoven, 53754 Sankt Augustin, Germany http://www.conceptoriented.com [email protected] In the paper a new approach to data representation and manipulation is described, which is called the concept-oriented data model (CODM). It is supposed that items represent data units, which are stored in concepts. A concept is a combination of superconcepts, which determine the concept’s dimensionality or properties. An item is a combination of superitems taken by one from all the superconcepts. An item stores a combination of references to its superitems. The references implement inclusion relation or attribute- value relation among items. A concept-oriented database is defined by its concept structure called syntax or schema and its item structure called semantics. The model defines formal transformations of syntax and semantics including the canonical semantics where all concepts are merged and the data semantics is represented by one set of items. The concept-oriented data model treats relations as subconcepts where items are instances of the relations. Multi-valued attributes are defined via subconcepts as a view on the database semantics rather than as a built-in mechanism. -
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. -
The Importance of Data Models in Enterprise Metadata Management
THE IMPORTANCE OF DATA MODELS IN ENTERPRISE METADATA MANAGEMENT October 19, 2017 © 2016 ASG Technologies Group, Inc. All rights reserved HAPPINESS IS… SOURCE: 10/18/2017 TODAY SHOW – “THE BLUE ZONE OF HAPPINESS” – DAN BUETTNER • 3 Close Friends • Get a Dog • Good Light • Get Religion • Get Married…. Stay Married • Volunteer • “Money will buy you Happiness… well, it’s more about Financial Security” • “Our Data Models are now incorporated within our corporate metadata repository” © 2016 ASG Technologies Group, Inc. All rights reserved 3 POINTS TO REMEMBER SOURCE: 10/19/2017 DAMA NYC – NOONTIME SPEAKER SLOT – MIKE WANYO – ASG TECHNOLOGIES 1. Happiness is individually sought and achievable © 2016 ASG Technologies Group, Inc. All rights reserved 3 POINTS TO REMEMBER SOURCE: 10/19/2017 DAMA NYC – NOONTIME SPEAKER SLOT – MIKE WANYO – ASG TECHNOLOGIES 1. Happiness is individually sought and achievable 2. Data Models can in be incorporated into your corporate metadata repository © 2016 ASG Technologies Group, Inc. All rights reserved 3 POINTS TO REMEMBER SOURCE: 10/19/2017 DAMA NYC – NOONTIME SPEAKER SLOT – MIKE WANYO – ASG TECHNOLOGIES 1. Happiness is individually sought and achievable 2. Data Models can in be incorporated into your corporate metadata repository 3. ASG Technologies can provide an overall solution with services to accomplish #2 above and more for your company. © 2016 ASG Technologies Group, Inc. All rights reserved AGENDA Data model imports to the metadata collection View and search capabilities Traceability of physical and logical -
Towards the Universal Spatial Data Model Based Indexing and Its Implementation in Mysql
Towards the universal spatial data model based indexing and its implementation in MySQL Evangelos Katsikaros Kongens Lyngby 2012 IMM-M.Sc.-2012-97 Technical University of Denmark Informatics and Mathematical Modelling Building 321, DK-2800 Kongens Lyngby, Denmark Phone +45 45253351, Fax +45 45882673 [email protected] www.imm.dtu.dk IMM-M.Sc.: ISSN XXXX-XXXX Summary This thesis deals with spatial indexing and models that are able to abstract the variety of existing spatial index solutions. This research involves a thorough presentation of existing dynamic spatial indexes based on R-trees, investigating abstraction models and implementing such a model in MySQL. To that end, the relevant theory is presented. A thorough study is performed on the recent and seminal works on spatial index trees and we describe their basic properties and the way search, deletion and insertion are performed on them. During this effort, we encountered details that baffled us, did not make the understanding the core concepts smooth or we thought that could be a source of confusion. We took great care in explaining in depth these details so that the current study can be a useful guide for a number of them. A selection of these models were later implemented in MySQL. We investigated the way spatial indexing is currently engineered in MySQL and we reveal how search, deletion and insertion are performed. This paves the path to the un- derstanding of our intervention and additions to MySQL's codebase. All of the code produced throughout this research was included in a patch against the RDBMS MariaDB. -
Enterprise Data Modeling Using the Entity-Relationship Model
Database Systems Session 3 – Main Theme Enterprise Data Modeling Using The Entity/Relationship Model Dr. Jean-Claude Franchitti New York University Computer Science Department Courant Institute of Mathematical Sciences Presentation material partially based on textbook slides Fundamentals of Database Systems (6th Edition) by Ramez Elmasri and Shamkant Navathe Slides copyright © 2011 1 Agenda 11 SessionSession OverviewOverview 22 EnterpriseEnterprise DataData ModelingModeling UsingUsing thethe ERER ModelModel 33 UsingUsing thethe EnhancedEnhanced Entity-RelationshipEntity-Relationship ModelModel 44 CaseCase StudyStudy 55 SummarySummary andand ConclusionConclusion 2 Session Agenda Session Overview Enterprise Data Modeling Using the ER Model Using the Extended ER Model Case Study Summary & Conclusion 3 What is the class about? Course description and syllabus: » http://www.nyu.edu/classes/jcf/CSCI-GA.2433-001 » http://cs.nyu.edu/courses/fall11/CSCI-GA.2433-001/ Textbooks: » Fundamentals of Database Systems (6th Edition) Ramez Elmasri and Shamkant Navathe Addition Wesley ISBN-10: 0-1360-8620-9, ISBN-13: 978-0136086208 6th Edition (04/10) 4 Icons / Metaphors Information Common Realization Knowledge/Competency Pattern Governance Alignment Solution Approach 55 Agenda 11 SessionSession OverviewOverview 22 EnterpriseEnterprise DataData ModelingModeling UsingUsing thethe ERER ModelModel 33 UsingUsing thethe EnhancedEnhanced Entity-RelationshipEntity-Relationship ModelModel 44 CaseCase StudyStudy 55 SummarySummary andand ConclusionConclusion -
HANDLE - a Generic Metadata Model for Data Lakes
HANDLE - A Generic Metadata Model for Data Lakes Rebecca Eichler, Corinna Giebler, Christoph Gr¨oger, Holger Schwarz, and Bernhard Mitschang In: Big Data Analytics and Knowledge Discovery (DaWaK) 2020 BIBTEX: @inproceedingsfeichler2020handle, title=fHANDLE-A Generic Metadata Model for Data Lakesg, author=fEichler, Rebecca and Giebler, Corinna and Gr{\"o\}ger,Christoph and Schwarz, Holger and Mitschang, Bernhardg, booktitle=fInternational Conference on Big Data Analytics and Knowledge Discovery (DaWaK) 2020g, pages=f73{88g, year=f2020g, organization=fSpringerg, doi=f10.1007/978-3-030-59065-9 7g g c by Springer Nature This is the author's version of the work. The final authenticated version is avail- able online at: https://doi.org/10.1007/978-3-030-59065-9 7 HANDLE - A Generic Metadata Model for Data Lakes Rebecca Eichler1, Corinna Giebler1, Christoph Gr¨oger2, Holger Schwarz1, and Bernhard Mitschang1 1 University of Stuttgart, Universit¨atsstraße38, 70569 Stuttgart, Germany [email protected] 2 Robert Bosch GmbH, Borsigstraße 4, 70469 Stuttgart, Germany [email protected] Abstract The substantial increase in generated data induced the de- velopment of new concepts such as the data lake. A data lake is a large storage repository designed to enable flexible extraction of the data's value. A key aspect of exploiting data value in data lakes is the collec- tion and management of metadata. To store and handle the metadata, a generic metadata model is required that can reflect metadata of any potential metadata management use case, e.g., data versioning or data lineage. However, an evaluation of existent metadata models yields that none so far are sufficiently generic. -
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. -
XV. the Entity-Relationship Model
Information Systems Analysis and Design csc340 XV. The Entity-Relationship Model The Entity-Relationship Model Entities, Relationships and Attributes Cardinalities, Identifiers and Generalization Documentation of E-R Diagrams and Business Rules 2002 John Mylopoulos The Entity-Relationship Model -- 1 Information Systems Analysis and Design csc340 The Entity Relationship Model The Entity-Relationship (ER) model is a conceptual data model, capable of describing the data requirements for a new information system in a direct and easy to understand graphical notation. Data requirements are described in terms of a conceptualconceptual (or, ER) schema. ER schemata are comparable to class diagrams. 2002 John Mylopoulos The Entity-Relationship Model -- 2 Information Systems Analysis and Design csc340 The Constructs of the E-R Model AND/XOR 2002 John Mylopoulos The Entity-Relationship Model -- 3 Information Systems Analysis and Design csc340 Entities These represent classes of objects (facts, things, people,...) that have properties in common and an autonomous existence. City, Department, Employee, Purchase and Sale are examples of entities for a commercial organization. An instance of an entity is an object in the class represented by the entity. Stockholm, Helsinki, are examples of instances of the entity City, and the employees Peterson and Johanson are examples of instances of the Employee entity. The E-R model is very different from the relational model in which it is not possible to represent an object without knowing its properties (an employee is represented by a tuple containing the name, surname, age, and other attributes.) 2002 John Mylopoulos The Entity-Relationship Model -- 4 Information Systems Analysis and Design csc340 Examples of Entities 2002 John Mylopoulos The Entity-Relationship Model -- 5 Information Systems Analysis and Design csc340 Relationships They represent logical links between two or more entities.