Deriving the Logical Data Model from the Business Model
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												  Data Model Standards and Guidelines, Registration Policies AndData 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.
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												  Data Warehouse Logical Modeling and DesignData Warehouse 1. Methodological Framework Logical Modeling and Design • Conceptual Design & Logical Design (6) • Design Phases and schemata derivations 2. Logical Modelling: The Multidimensionnal Model Bernard ESPINASSE • Problematic of the Logical Design Professeur à Aix-Marseille Université (AMU) • The Multidimensional Model: fact, measures, dimensions Ecole Polytechnique Universitaire de Marseille 3. Implementing a Dimensional Model in ROLAP • Star schema January, 2020 • Snowflake schema • Aggregates and views 4. Logical Design: From Fact schema to ROLAP Logical schema Methodological framework • From fact schema to relational star-schema: basic rules Logical Modeling: The Multidimensional Model • Examples towards Relational Star Schema • Examples towards Relational Snowflake Schema Logical Design : From Fact schema to ROLAP Logical schema • Advanced logical modelling ROLAP schema in MDX for Mondrian 5. ROLAP schema in MDX for Mondrian Bernard ESPINASSE - Data Warehouse Logical Modelling and Design 1 Bernard ESPINASSE - Data Warehouse Logical Modelling and Design 2 • Livres • Golfarelli M., Rizzi S., « Data Warehouse Design : Modern Principles and Methodologies », McGrawHill, 2009. • Kimball R., Ross, M., « Entrepôts de données : guide pratique de modélisation dimensionnelle », 2°édition, Ed. Vuibert, 2003, ISBN : 2- 7117-4811-1. • Franco J-M., « Le Data Warehouse ». Ed. Eyrolles, Paris, 1997. ISBN 2- 212-08956-2. • Conceptual Design & Logical Design • Cours • Life-Cycle • Course of M. Golfarelli M. and S. Rizzi, University of Bologna
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												  ENTITY-RELATIONSHIP MODELING of SPATIAL DATA for GEOGRAPHIC INFORMATION SYSTEMS Hugh WENTITY-RELATIONSHIP MODELING OF SPATIAL DATA FOR GEOGRAPHIC INFORMATION SYSTEMS Hugh W. Calkins Department of Geography and National Center for Geographic Information and Analysis, State University of New York at Buffalo Amherst, NY 14261-0023 Abstract This article presents an extension to the basic Entity-Relationship (E-R) database modeling approach for use in designing geographic databases. This extension handles the standard spatial objects found in geographic information systems, multiple representations of the spatial objects, temporal representations, and the traditional coordinate and topological attributes associated with spatial objects and relationships. Spatial operations are also included in this extended E-R modeling approach to represent the instances where relationships between spatial data objects are only implicit in the database but are made explicit through spatial operations. The extended E-R modeling methodology maps directly into a detailed database design and a set of GIS function. 1. Introduction GIS design (planning, design and implementing a geographic information system) consists of several activities: feasibility analysis, requirements determination, conceptual and detailed database design, and hardware and software selection. Over the past several years, GIS analysts have been interested in, and have used, system design techniques adopted from software engineering, including the software life-cycle model which defines the above mentioned system design tasks (Boehm, 1981, 36). Specific GIS life-cycle models have been developed for GIS by Calkins (1982) and Tomlinson (1994). Figure 1 is an example of a typical life-cycle model. GIS design models, which describe the implementation procedures for the life-cycle model, outline the basic steps of the design process at a fairly abstract level (Calkins, 1972; Calkins, 1982).
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												  Data Analytics EMEA Insurance Data Analytics Study ContentsA little less conversation, a lot more action – tactics to get satisfaction from data analytics EMEA Insurance data analytics study Contents Foreword 01 Introduction from the authors 04 Vision and strategy 05 A disconnect between analytics and business strategies 05 Articulating a clear business case can be tricky 07 Tactical projects are trumping long haul strategic wins 09 The ever evolving world of the CDO 10 Assets and capability 12 Purple People are hard to find 12 Data is not always accessible or trustworthy 14 Agility and traditional insurance are not natural bedfellows 18 Operationalisation and change management 23 Operating models have no clear winner 23 The message is not always loud and clear 24 Hearts and minds do not change overnight 25 Are you ready to become an IDO? 28 Appendix A – The survey 30 Appendix B – Links to publications 31 Appendix C – Key contacts 32 A little less conversation, a lot more action – tactics to get satisfaction from data analytics | EMEA Insurance data analytics study Foreword The world is experiencing the fastest pace of data expansion and technological change in history. Our work with the World Economic Forum in 2015 identified that, within financial services, insurance is the industry which is most ripe for disruption from innovation owing to the significant pressure across the value chain. To build on this work, our report ‘Turbulence ahead – The future of general insurance’, set out various innovations transforming the industry and subsequent scenarios for The time is now the future. It identified that innovation within the insurance industry is no longer led by insurers themselves.
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												  Constructing a Meta Data Architecture0-471-35523-2.int.07 6/16/00 12:29 AM Page 181 CHAPTER 7 Constructing a Meta Data Architecture This chapter describes the key elements of a meta data repository architec- ture and explains how to tie data warehouse architecture into the architec- ture of the meta data repository. After reviewing these essential elements, I examine the three basic architectural approaches for building a meta data repository and discuss the advantages and disadvantages of each. Last, I discuss advanced meta data architecture techniques such as closed-loop and bidirectional meta data, which are gaining popularity as our industry evolves. What Makes a Good Architecture A sound meta data architecture incorporates five general characteristics: ■ Integrated ■ Scalable ■ Robust ■ Customizable ■ Open 181 0-471-35523-2.int.07 6/16/00 12:29 AM Page 182 182 Chapter 7 It is important to understand that if a company purchases meta data access and/or integration tools, those tools define a significant portion of the meta data architecture. Companies should, therefore, consider these essential characteristics when evaluating tools and their implementation of the technology. Integrated Anyone who has worked on a decision support project understands that the biggest challenge in building a data warehouse is integrating all of the dis- parate sources of data and transforming the data into meaningful informa- tion. The same is true for a meta data repository. A meta data repository typically needs to be able to integrate a variety of types and sources of meta data and turn the resulting stew into meaningful, accessible business and technical meta data.
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												  MDA Transformation Process of a PIM Logical Decision-Making from Nosql Database to Big Data Nosql PSMInternational Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-1, October 2019 MDA Transformation Process of A PIM Logical Decision-Making from NoSQL Database to Big Data NoSQL PSM Fatima Kalna, Abdessamad Belangour, Mouad Banane, Allae Erraissi databases managed by these systems fall into four categories: Abstract: Business Intelligence or Decision Support System columns, documents, graphs and key-value [3]. Each of them (DSS) is IT for decision-makers and business leaders. It describes offering specific features. For example, in a the means, the tools and the methods that make it possible to document-oriented database like MongoDB [4], data is stored collect, consolidate, model and restore the data, material or immaterial, of a company to offer a decision aid and to allow a in tables whose lines can be nested. This organization of the decision-maker to have an overview of the activity being treated. data is coupled to operators that allow access to the nested Given the large volume, variety, and data velocity we entered the data. The work we are presenting aims to assist a user in the era of Big Data. And since most of today's BI tools are designed to implementation of a massive database on a NoSQL database handle structured data. In our research project, we aim to management system. To do this, we propose a transformation consolidate a BI system for Big Data. In continuous efforts, this process that ensures the transition from the NoSQL logic paper is a progress report of our first contribution that aims to apply the techniques of model engineering to propose a universal model to a NoSQL physical model ie.
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												  A Logical Design Methodology for Relational Databases Using the Extended Entity-Relationship ModelA Logical Design Methodology for Relational Databases Using the Extended Entity-Relationship Model TOBY J. TEOREY Computing Research Laboratory, Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, Michigan 48109-2122 DONGQING YANG Computer Science and Technology, Peking Uniuersity, Beijing, The People’s Republic of China JAMES P. FRY Computer and Information Systems, Graduate School of Business Administration, The University of Michigan, Ann Arbor, Michigan 48109-1234 A database design methodology is defined for the design of large relational databases. First, the data requirements are conceptualized using an extended entity-relationship model, with the extensions being additional semantics such as ternary relationships, optional relationships, and the generalization abstraction. The extended entity- relationship model is then decomposed according to a set of basic entity-relationship constructs, and these are transformed into candidate relations. A set of basic transformations has been developed for the three types of relations: entity relations, extended entity relations, and relationship relations. Candidate relations are further analyzed and modified to attain the highest degree of normalization desired. The methodology produces database designs that are not only accurate representations of reality, but flexible enough to accommodate future processing requirements. It also reduces the number of data dependencies that must be analyzed, using the extended ER model conceptualization, and maintains data
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												  An Enterprise Information System Data Architecture GuideAn Enterprise Information System Data Architecture Guide Grace Alexandra Lewis Santiago Comella-Dorda Pat Place Daniel Plakosh Robert C. Seacord October 2001 TECHNICAL REPORT CMU/SEI-2001-TR-018 ESC-TR-2001-018 Pittsburgh, PA 15213-3890 An Enterprise Information System Data Architecture Guide CMU/SEI-2001-TR-018 ESC-TR-2001-018 Grace Alexandra Lewis Santiago Comella-Dorda Pat Place Daniel Plakosh Robert C. Seacord October 2001 COTS-Based Systems Unlimited distribution subject to the copyright. This report was prepared for the SEI Joint Program Office HQ ESC/DIB 5 Eglin Street Hanscom AFB, MA 01731-2116 The ideas and findings in this report should not be construed as an official DoD position. It is published in the interest of scientific and technical information exchange. FOR THE COMMANDER Norton L. Compton, Lt Col, USAF SEI Joint Program Office This work is sponsored by the U.S. Department of Defense. The Software Engineering Institute is a federally funded research and development center sponsored by the U.S. Department of Defense. Copyright 2001 by Carnegie Mellon University. Requests for permission to reproduce this document or to prepare derivative works of this document should be addressed to the SEI Licensing Agent. NO WARRANTY THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT INFRINGEMENT.
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												  ER Studio Data Architect Quick Start GuideProduct Documentation ER Studio Data Architect Quick Start Guide Version 11.0 © 2015 Embarcadero Technologies, Inc. Embarcadero, the Embarcadero Technologies logos, and all other Embarcadero Technologies product or service names are trademarks or registered trademarks of Embarcadero Technologies, Inc. All other trademarks are property of their respective owners. Embarcadero Technologies, Inc. is a leading provider of award-winning tools for application developers and database professionals so they can design systems right, build them faster and run them better, regardless of their platform or programming language. Ninety of the Fortune 100 and an active community of more than three million users worldwide rely on Embarcadero products to increase productivity, reduce costs, simplify change management and compliance and accelerate innovation. The company's flagship tools include: Embarcadero® Change Manager™, CodeGear™ RAD Studio, DBArtisan®, Delphi®, ER/Studio®, JBuilder® and Rapid SQL®. Founded in 1993, Embarcadero is headquartered in San Francisco, with offices located around the world. Embarcadero is online at www.embarcadero.com. March, 2015 Embarcadero Technologies 2 CONTENTS Introducing ER/Studio Data Architect............................................................................ 5 Notice for Developer Edition Users ................................................................................... 5 Product Benefits by Audience ............................................................................................ 5 What's
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												  ER/Studio Enterprise Team EditionER/Studio Enterprise Team Edition THE ULTIMATE COLLABORATIVE ENTERPRISE DATA ARCHITECTURE AND MODELING SOLUTION With many organizations seeing a significant increase in data, along with more emphasis on compliance to industry and government regulations, it’s clear that having a solid strategy for data management is extremely important. ER/Studio Enterprise Team edition provides a comprehensive solution that empowers users to easily define models and metadata, establish a foundation for data governance initiatives, and define an enterprise architecture to effectively manage data across the whole organization. THE CHALLENGE OF MANAGING AND UTILIZING ENTERPRISE DATA Physically capturing and properly integrating data is challenging, especially for ER/Studio gives data management professionals the metadata unstructured content. Incorporating the new data, interpreting it correctly, and foundation to quickly respond to business process demands, reduce making it available to decision makers in a timely manner poses three distinct the risk of noncompliance, and deliver more actionable insight. challenges for data management professionals: Many organizations must deal with both relational and NoSQL data, • Leveraging enterprise data as an organizational asset as well as a broad landscape of platforms. ER/Studio Enterprise Team • Improving and managing data quality edition continues to build on its support of strategic enterprise systems including Teradata, Netezza, and Azure, as well as Big Data platform • Clearly and effectively communicating data throughout an organization support for Hadoop Hive and MongoDB, giving organizations an To address these issues, ER/Studio Enterprise Team edition gives data modelers interpretive and collaborative enterprise advantage for leveraging their and data architects the capabilities needed to analyze, document, and share data residing in diverse locations, from data centers to mobile platforms.
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												  Data Architect I Class Code:Class 0317 Code: 0317State Classification Job Description Data ArchitectSalary Group: I B28 Data Architect I Class Code:Class 0317 Code: 0317 CLASS TITLE CLASS CODE SALARY GROUP SALARY RANGE DATA ARCHITECT I 0317 B28 $83,991 - $142,052 DATA ARCHITECT II 0318 B30 $101,630 - $171,881 GENERAL DESCRIPTION Performs highly complex (senior-level) data analysis and data architecture work. Work involves data modeling; implementing and managing database systems, data warehouses, and data analytics; and designing strategies and setting standards for operations, programming, and security. May supervise the work of others. Works under limited supervision, with moderate latitude for the use of initiative and independent judgment. EXAMPLES OF WORK PERFORMED Determines database requirements by analyzing business operations, applications, and programming; reviewing business objectives; and evaluating current systems. Obtains data model requirements, develops and implements data models for new projects, and maintains existing data models and data architectures. Develops data structures for data warehouses and data mart projects and initiatives; and supports data analytics and business intelligence systems. Implements corresponding database changes to support new and modified applications, and ensures new designs conform to data standards and guidelines; are consistent, normalized, and perform as required; and are secure from unauthorized access or update. Provides guidelines on creating data models and various standards relating to governed data. Reviews changes to technical and business metadata, realizing their impacts on enterprise applications, and ensures the impacts are communicated to appropriate parties. Establishes measures to chart progress related to the completeness and quality of metadata for enterprise information; to support reduction of data redundancy and fragmentation and elimination of unnecessary movement of data; and to improve data quality.
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												  Ermrest: an Entity-Relationship Data Storage Service for Web-Based, Data-Oriented CollaborationERMrest: an entity-relationship data storage service for web-based, data-oriented collaboration. Karl Czajkowski, Carl Kesselman, Robert Schuler, Hongsuda Tangmunarunkit Information Sciences Institute Viterbi School of Engineering University of Southern California Marina del Rey, CA 90292 Email: fkarlcz,carl,schuler,[email protected] Abstract—Scientific discovery is increasingly dependent on a are hard to evolve over time, and make it difficult to search scientist’s ability to acquire, curate, integrate, analyze, and share for specific data values. large and diverse collections of data. While the details vary from domain to domain, these data often consist of diverse digital In previous work, we have argued for an alternative ap- assets (e.g. image files, sequence data, or simulation outputs) that proach based on scientific asset management [5]. We separate are organized with complex relationships and context which may the “science data” (e.g. microscope images, sequence data, evolve over the course of an investigation. In addition, discovery flow cytometry data) from the “metadata” (e.g. references, is often collaborative, such that sharing of the data and its provenance, properties, and contextual relationships). We have organizational context is highly desirable. Common systems for also defined a data-oriented architecture which expresses col- managing file or asset metadata hide their inherent relational laboration as the manipulation of shared data resources housed structures, while traditional relational database systems do not extend to the distributed collaborative environment often seen in complementary object (asset) and relational (metadata) in scientific investigations. To address these issues, we introduce stores [6]. The metadata encode not only properties and refer- ERMrest, a collaborative data management service which allows ences of individual assets, but relationships among assets and general entity-relationship modeling of metadata manipulated other domain-specific elements such as experiments, protocol by RESTful access methods.