Relational Model of Temporal Data Based on 6Th Normal Form
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Sixth Normal Form
3 I January 2015 www.ijraset.com Volume 3 Issue I, January 2015 ISSN: 2321-9653 International Journal for Research in Applied Science & Engineering Technology (IJRASET) Sixth Normal Form Neha1, Sanjeev Kumar2 1M.Tech, 2Assistant Professor, Department of CSE, Shri Balwant College of Engineering &Technology, DCRUST University Abstract – Sixth Normal Form (6NF) is a term used in relational database theory by Christopher Date to describe databases which decompose relational variables to irreducible elements. While this form may be unimportant for non-temporal data, it is certainly important when maintaining data containing temporal variables of a point-in-time or interval nature. With the advent of Data Warehousing 2.0 (DW 2.0), there is now an increased emphasis on using fully-temporalized databases in the context of data warehousing, in particular with next generation approaches such as Anchor Modeling . In this paper, we will explore the concepts of temporal data, 6NF conceptual database models, and their relationship with DW 2.0. Further, we will also evaluate Anchor Modeling as a conceptual design method in which to capture temporal data. Using these concepts, we will indicate a path forward for evaluating a possible translation of 6NF-compliant data into an eXtensible Markup Language (XML) Schema for the purpose of describing and presenting such data to disparate systems in a structured format suitable for data exchange. Keywords :, 6NF,SQL,DKNF,XML,Semantic data change, Valid Time, Transaction Time, DFM I. INTRODUCTION Normalization is the process of restructuring the logical data model of a database to eliminate redundancy, organize data efficiently and reduce repeating data and to reduce the potential for anomalies during data operations. -
The Design of Multidimensional Data Model Using Principles of the Anchor Data Modeling: an Assessment of Experimental Approach Based on Query Execution Performance
WSEAS TRANSACTIONS on COMPUTERS Radek Němec, František Zapletal The Design of Multidimensional Data Model Using Principles of the Anchor Data Modeling: An Assessment of Experimental Approach Based on Query Execution Performance RADEK NĚMEC, FRANTIŠEK ZAPLETAL Department of Systems Engineering Faculty of Economics, VŠB - Technical University of Ostrava Sokolská třída 33, 701 21 Ostrava CZECH REPUBLIC [email protected], [email protected] Abstract: - The decision making processes need to reflect changes in the business world in a multidimensional way. This includes also similar way of viewing the data for carrying out key decisions that ensure competitiveness of the business. In this paper we focus on the Business Intelligence system as a main toolset that helps in carrying out complex decisions and which requires multidimensional view of data for this purpose. We propose a novel experimental approach to the design a multidimensional data model that uses principles of the anchor modeling technique. The proposed approach is expected to bring several benefits like better query execution performance, better support for temporal querying and several others. We provide assessment of this approach mainly from the query execution performance perspective in this paper. The emphasis is placed on the assessment of this technique as a potential innovative approach for the field of the data warehousing with some implicit principles that could make the process of the design, implementation and maintenance of the data warehouse more effective. The query performance testing was performed in the row-oriented database environment using a sample of 10 star queries executed in the environment of 10 sample multidimensional data models. -
Powerdesigner 16.6 Data Modeling
SAP® PowerDesigner® Document Version: 16.6 – 2016-02-22 Data Modeling Content 1 Building Data Models ...........................................................8 1.1 Getting Started with Data Modeling...................................................8 Conceptual Data Models........................................................8 Logical Data Models...........................................................9 Physical Data Models..........................................................9 Creating a Data Model.........................................................10 Customizing your Modeling Environment........................................... 15 1.2 Conceptual and Logical Diagrams...................................................26 Supported CDM/LDM Notations.................................................27 Conceptual Diagrams.........................................................31 Logical Diagrams............................................................43 Data Items (CDM)............................................................47 Entities (CDM/LDM)..........................................................49 Attributes (CDM/LDM)........................................................55 Identifiers (CDM/LDM)........................................................58 Relationships (CDM/LDM)..................................................... 59 Associations and Association Links (CDM)..........................................70 Inheritances (CDM/LDM)......................................................77 1.3 Physical Diagrams..............................................................82 -
Translating Data Between Xml Schema and 6Nf Conceptual Models
Georgia Southern University Digital Commons@Georgia Southern Electronic Theses and Dissertations Graduate Studies, Jack N. Averitt College of Spring 2012 Translating Data Between Xml Schema and 6Nf Conceptual Models Curtis Maitland Knowles Follow this and additional works at: https://digitalcommons.georgiasouthern.edu/etd Recommended Citation Knowles, Curtis Maitland, "Translating Data Between Xml Schema and 6Nf Conceptual Models" (2012). Electronic Theses and Dissertations. 688. https://digitalcommons.georgiasouthern.edu/etd/688 This thesis (open access) is brought to you for free and open access by the Graduate Studies, Jack N. Averitt College of at Digital Commons@Georgia Southern. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of Digital Commons@Georgia Southern. For more information, please contact [email protected]. 1 TRANSLATING DATA BETWEEN XML SCHEMA AND 6NF CONCEPTUAL MODELS by CURTIS M. KNOWLES (Under the Direction of Vladan Jovanovic) ABSTRACT Sixth Normal Form (6NF) is a term used in relational database theory by Christopher Date to describe databases which decompose relational variables to irreducible elements. While this form may be unimportant for non-temporal data, it is certainly important for data containing temporal variables of a point-in-time or interval nature. With the advent of Data Warehousing 2.0 (DW 2.0), there is now an increased emphasis on using fully-temporalized databases in the context of data warehousing, in particular with approaches such as the Anchor Model and Data Vault. In this work, we will explore the concepts of temporal data, 6NF conceptual database models, and their relationship with DW 2.0. Further, we will evaluate the Anchor Model and Data Vault as design methods in which to capture temporal data. -
Denormalization Strategies for Data Retrieval from Data Warehouses
Decision Support Systems 42 (2006) 267–282 www.elsevier.com/locate/dsw Denormalization strategies for data retrieval from data warehouses Seung Kyoon Shina,*, G. Lawrence Sandersb,1 aCollege of Business Administration, University of Rhode Island, 7 Lippitt Road, Kingston, RI 02881-0802, United States bDepartment of Management Science and Systems, School of Management, State University of New York at Buffalo, Buffalo, NY 14260-4000, United States Available online 20 January 2005 Abstract In this study, the effects of denormalization on relational database system performance are discussed in the context of using denormalization strategies as a database design methodology for data warehouses. Four prevalent denormalization strategies have been identified and examined under various scenarios to illustrate the conditions where they are most effective. The relational algebra, query trees, and join cost function are used to examine the effect on the performance of relational systems. The guidelines and analysis provided are sufficiently general and they can be applicable to a variety of databases, in particular to data warehouse implementations, for decision support systems. D 2004 Elsevier B.V. All rights reserved. Keywords: Database design; Denormalization; Decision support systems; Data warehouse; Data mining 1. Introduction houses as issues related to database design for high performance are receiving more attention. Database With the increased availability of data collected design is still an art that relies heavily on human from the Internet and other sources and the implemen- intuition and experience. Consequently, its practice is tation of enterprise-wise data warehouses, the amount becoming more difficult as the applications that data- of data that companies possess is growing at a bases support become more sophisticated [32].Cur- phenomenal rate. -
A Comprehensive Analysis of Sybase Powerdesigner 16.0
white paper A Comprehensive Analysis of Sybase® PowerDesigner® 16.0 InformationArchitect vs. ER/Studio XE2 Version 2.0 www.sybase.com TABLe OF CONTENtS 1 Introduction 1 Product Overviews 1 ER/Studio XE2 3 Sybase PowerDesigner 16.0 4 Data Modeling Activities 4 Overview 6 Types of Data Model 7 Design Layers 8 Managing the SAM-LDM Relationship 10 Forward and Reverse Engineering 11 Round-trip Engineering 11 Integrating Data Models with Requirements and Processes 11 Generating Object-oriented Models 11 Dependency Analysis 17 Model Comparisons and Merges 18 Update Flows 18 Required Features for a Data Modeling Tool 18 Core Modeling 25 Collaboration 27 Interfaces & Integration 29 Usability 34 Managing Models as a Project 36 Dependency Matrices 37 Conclusions 37 Acknowledgements 37 Bibliography 37 About the Author IntrOduCtion Data modeling is more than just database design, because data doesn’t just exist in databases. Data does not exist in isolation, it is created, managed and consumed by business processes, and those business processes are implemented using a variety of applications and technologies. To truly understand and manage our data, and the impact of changes to that data, we need to manage more than just models of data in databases. We need support for different types of data models, and for managing the relationships between data and the rest of the organization. When you need to manage a data center across the enterprise, integrating with a wider set of business and technology activities is critical to success. For this reason, this review will use the InformationArchitect version of Sybase PowerDesigner rather than their DataArchitect™ version. -
Assessment of Helical Anchors Bearing Capacity for Offshore Aquaculture Applications
The University of Maine DigitalCommons@UMaine Electronic Theses and Dissertations Fogler Library Summer 8-23-2019 Assessment of Helical Anchors Bearing Capacity for Offshore Aquaculture Applications Leon D. Cortes Garcia University of Maine, [email protected] Follow this and additional works at: https://digitalcommons.library.umaine.edu/etd Recommended Citation Cortes Garcia, Leon D., "Assessment of Helical Anchors Bearing Capacity for Offshore Aquaculture Applications" (2019). Electronic Theses and Dissertations. 3094. https://digitalcommons.library.umaine.edu/etd/3094 This Open-Access Thesis is brought to you for free and open access by DigitalCommons@UMaine. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of DigitalCommons@UMaine. For more information, please contact [email protected]. ASSESSMENT OF HELICAL ANCHORS BEARING CAPACITY FOR OFFSHORE AQUACULTURE APPLICATIONS By Leon D. Cortes Garcia B.S. National University of Colombia, 2016 A THESIS Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science (in Civil Engineering) The Graduate School The University of Maine August 2019 Advisory Committee: Aaron P. Gallant, Professor of Civil Engineering, Co‐Advisor Melissa E. Landon, Professor of Civil Engineering, Co‐Advisor Kimberly D. Huguenard, Professor of Civil Engineering Copyright 2019 Leon D. Cortes Garcia All Rights Reserved ii ASSESSMENT OF HELICAL ANCHORS BEARING CAPACITY FOR OFFSHORE AQUACULTURE APPLICATIONS. By Leon Cortes Garcia Thesis Advisor(s): Dr. Aaron Gallant, Dr. Melissa Landon An Abstract of the Thesis Presented in Partial Fulfillment of the Requirements for the Degree of Master of Science (in Civil Engineering) August 2019 Aquaculture in Maine is an important industry with expected growth in the coming years to provide food in an ecological and environmentally sustainable way. -
Persistent Staging Area Models for Data Warehouses
https://doi.org/10.48009/1_iis_2012_121-132 Issues in Information Systems Volume 13, Issue 1, pp. 121-132, 2012 PERSISTENT STAGING AREA MODELS FOR DATA WAREHOUSES V. Jovanovic, Georgia Southern University, Statesboro, USA , [email protected] I. Bojicic, Fakultet Organizacioninh Nauka, Beograd, Serbia [email protected] C. Knowles, Georgia Southern University, Statesboro, USA, [email protected] M. Pavlic, Sveuciliste Rijeka/Odjel Informatike, Rijeka, Croatia, [email protected] ABSTRACT The paper’s scope is conceptual modeling of the Data Warehouse (DW) staging area seen as a permanent system of records of interest to regulatory/auditing compliance. The context is design of enterprise data warehouses compliant with the next generation of DW architecture that is DW 2.0. We address temporal DW aspect and review relevant alternative models i.e. compliant with DW 2.0, namely the Data Vault (DV) and the Anchor Model (AM). The principal novelty is a Conceptual DV Model and its comparison with the Anchor Model. Keywords: Conceptual Data Vault, Persistent Staging Area, Data Warehouse, Anchor Model, System of Records, Staging Area Design, DW Compliance, Next Generation DW Architecture, DW 2.0 INTRODUCTION The new DW architecture 2.0 [14] is an attempt to define a standard for the next generation of Data Warehouses (DW). One of the explicit requirements in the DW 2.0 is to support changes over time (while preserving the entire history of data and its structure). A DW 2.0 staging area may be explicitly recognized as a System of Records [15] or an Enterprise DW, see [4], and [20]. -
Normalization Exercises
DATABASE DESIGN: NORMALIZATION NOTE & EXERCISES (Up to 3NF) Tables that contain redundant data can suffer from update anomalies, which can introduce inconsistencies into a database. The rules associated with the most commonly used normal forms, namely first (1NF), second (2NF), and third (3NF). The identification of various types of update anomalies such as insertion, deletion, and modification anomalies can be found when tables that break the rules of 1NF, 2NF, and 3NF and they are likely to contain redundant data and suffer from update anomalies. Normalization is a technique for producing a set of tables with desirable properties that support the requirements of a user or company. Major aim of relational database design is to group columns into tables to minimize data redundancy and reduce file storage space required by base tables. Take a look at the following example: StdSSN StdCity StdClass OfferNo OffTerm OffYear EnrGrade CourseNo CrsDesc S1 SEATTLE JUN O1 FALL 2006 3.5 C1 DB S1 SEATTLE JUN O2 FALL 2006 3.3 C2 VB S2 BOTHELL JUN O3 SPRING 2007 3.1 C3 OO S2 BOTHELL JUN O2 FALL 2006 3.4 C2 VB The insertion anomaly: Occurs when extra data beyond the desired data must be added to the database. For example, to insert a course (CourseNo), it is necessary to know a student (StdSSN) and offering (OfferNo) because the combination of StdSSN and OfferNo is the primary key. Remember that a row cannot exist with NULL values for part of its primary key. The update anomaly: Occurs when it is necessary to change multiple rows to modify ONLY a single fact. -
Sql Query Optimization for Highly Normalized Big Data
DECISION MAKING AND BUSINESS INTELLIGENCE SQL QUERY OPTIMIZATION FOR HIGHLY NORMALIZED BIG DATA Nikolay I. GOLOV Lecturer, Department of Business Analytics, School of Business Informatics, Faculty of Business and Management, National Research University Higher School of Economics Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation E-mail: [email protected] Lars RONNBACK Lecturer, Department of Computer Science, Stocholm University Address: SE-106 91 Stockholm, Sweden E-mail: [email protected] This paper describes an approach for fast ad-hoc analysis of Big Data inside a relational data model. The approach strives to achieve maximal utilization of highly normalized temporary tables through the merge join algorithm. It is designed for the Anchor modeling technique, which requires a very high level of table normalization. Anchor modeling is a novel data warehouse modeling technique, designed for classical databases and adapted by the authors of the article for Big Data environment and a massively parallel processing (MPP) database. Anchor modeling provides flexibility and high speed of data loading, where the presented approach adds support for fast ad-hoc analysis of Big Data sets (tens of terabytes). Different approaches to query plan optimization are described and estimated, for row-based and column- based databases. Theoretical estimations and results of real data experiments carried out in a column-based MPP environment (HP Vertica) are presented and compared. The results show that the approach is particularly favorable when the available RAM resources are scarce, so that a switch is made from pure in-memory processing to spilling over from hard disk, while executing ad-hoc queries. -
Database Normalization
Outline Data Redundancy Normalization and Denormalization Normal Forms Database Management Systems Database Normalization Malay Bhattacharyya Assistant Professor Machine Intelligence Unit and Centre for Artificial Intelligence and Machine Learning Indian Statistical Institute, Kolkata February, 2020 Malay Bhattacharyya Database Management Systems Outline Data Redundancy Normalization and Denormalization Normal Forms 1 Data Redundancy 2 Normalization and Denormalization 3 Normal Forms First Normal Form Second Normal Form Third Normal Form Boyce-Codd Normal Form Elementary Key Normal Form Fourth Normal Form Fifth Normal Form Domain Key Normal Form Sixth Normal Form Malay Bhattacharyya Database Management Systems These issues can be addressed by decomposing the database { normalization forces this!!! Outline Data Redundancy Normalization and Denormalization Normal Forms Redundancy in databases Redundancy in a database denotes the repetition of stored data Redundancy might cause various anomalies and problems pertaining to storage requirements: Insertion anomalies: It may be impossible to store certain information without storing some other, unrelated information. Deletion anomalies: It may be impossible to delete certain information without losing some other, unrelated information. Update anomalies: If one copy of such repeated data is updated, all copies need to be updated to prevent inconsistency. Increasing storage requirements: The storage requirements may increase over time. Malay Bhattacharyya Database Management Systems Outline Data Redundancy Normalization and Denormalization Normal Forms Redundancy in databases Redundancy in a database denotes the repetition of stored data Redundancy might cause various anomalies and problems pertaining to storage requirements: Insertion anomalies: It may be impossible to store certain information without storing some other, unrelated information. Deletion anomalies: It may be impossible to delete certain information without losing some other, unrelated information. -
A Developer's Guide to Data Modeling for SQL Server
Praise for A Developer’s Guide to Data Modeling for SQL Server “Eric and Joshua do an excellent job explaining the importance of data modeling and how to do it correctly. Rather than relying only on academic concepts, they use real-world ex- amples to illustrate the important concepts that many database and application develop- ers tend to ignore. The writing style is conversational and accessible to both database design novices and seasoned pros alike. Readers who are responsible for designing, imple- menting, and managing databases will benefit greatly from Joshua’s and Eric’s expertise.” —Anil Desai, Consultant, Anil Desai, Inc. “Almost every IT project involves data storage of some kind, and for most that means a relational database management system (RDBMS). This book is written for a database- centric audience (database modelers, architects, designers, developers, etc.). The authors do a great job of showing us how to take a project from its initial stages of requirements gathering all the way through to implementation. Along the way we learn how to handle some of the real-world design issues that typically surface as we go through the process. “The bottom line here is simple. This is the book you want to have just finished read- ing when your boss says ‘We have a new project I would like your help with.’” —Ronald Landers, Technical Consultant, IT Professionals, Inc. “The Data Model is the foundation of the application. I’m pleased to see additional books being written to address this critical phase. This book presents a balanced and pragmatic view with the right priorities to get your SQL server project off to a great start and a long life.” —Paul Nielsen, SQL Server MVP, SQLServerBible.com “This is a truly excellent introduction to the database design methodology that will work for both novices and advanced designers.