Data Models – Database System Architecture
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ACS-3902 Ron Mcfadyen Slides Are Based on Chapter 5 (7Th Edition)
ACS-3902 Ron McFadyen Slides are based on chapter 5 (7th edition) (chapter 3 in 6th edition) ACS-3902 1 The Relational Data Model and Relational Database Constraints • Relational model – Ted Codd (IBM) 1970 – First commercial implementations available in early 1980s – Widely used ACS-3902 2 Relational Model Concepts • Database is a collection of relations • Implementation of relation: table comprising rows and columns • In practice a table/relation represents an entity type or relationship type (entity-relationship model … later) • At intersection of a row and column in a table there is a simple value • Row • Represents a collection of related data values • Formally called a tuple • Column names • Columns may be referred to as fields, or, formally as attributes • Values in a column are drawn from a domain of values associated with the column/field/attribute ACS-3902 3 Relational Model Concepts 7th edition Figure 5.1 ACS-3902 4 Domains • Domain – Atomic • A domain is a collection of values where each value is indivisible • Not meaningful to decompose further – Specifying a domain • Name, data type, rules – Examples • domain of department codes for UW is a list: {“ACS”, “MATH”, “ENGL”, “HIST”, etc} • domain of gender values for UW is the list (“male”, “female”) – Cardinality: number of values in a domain – Database implementation & support vary ACS-3902 5 Domain example - PostgreSQL CREATE DOMAIN posint AS integer CHECK (VALUE > 0); CREATE TABLE mytable (id posint); INSERT INTO mytable VALUES(1); -- works INSERT INTO mytable VALUES(-1); -- fails https://www.postgresql.org/docs/current/domains.html ACS-3902 6 Domain example - PostgreSQL CREATE DOMAIN domain_code_type AS character varying NOT NULL CONSTRAINT domain_code_type_check CHECK (VALUE IN ('ApprovedByAdmin', 'Unapproved', 'ApprovedByEmail')); CREATE TABLE codes__domain ( code_id integer NOT NULL, code_type domain_code_type NOT NULL, CONSTRAINT codes_domain_pk PRIMARY KEY (code_id) ) ACS-3902 7 Relation • Relation schema R – Name R and a list of attributes: • Denoted by R (A1, A2, ...,An) • E.g. -
An Information-Theoretic Metric of System Complexity with Application
An Information-Theoretic Metric Douglas Allaire1 of System Complexity With e-mail: [email protected] Application to Engineering Qinxian He John Deyst System Design Karen Willcox System complexity is considered a key driver of the inability of current system design practices to at times not recognize performance, cost, and schedule risks as they emerge. Aerospace Computational Design Laboratory, We present here a definition of system complexity and a quantitative metric for measuring Department of Aeronautics and Astronautics, that complexity based on information theory. We also derive sensitivity indices that indi- Massachusetts Institute of Technology, cate the fraction of complexity that can be reduced if more about certain factors of a sys- Cambridge, MA 02139 tem can become known. This information can be used as part of a resource allocation procedure aimed at reducing system complexity. Our methods incorporate Gaussian pro- cess emulators of expensive computer simulation models and account for both model inadequacy and code uncertainty. We demonstrate our methodology on a candidate design of an infantry fighting vehicle. [DOI: 10.1115/1.4007587] 1 Introduction methodology identifies the key contributors to system complexity and provides quantitative guidance for resource allocation deci- Over the years, engineering systems have become increasingly sions aimed at reducing system complexity. complex, with astronomical growth in the number of components We define system complexity as the potential for a system to and their interactions. With this rise in complexity comes a host exhibit unexpected behavior in the quantities of interest. A back- of new challenges, such as the adequacy of mathematical models ground discussion on complexity metrics, uncertainty sources in to predict system behavior, the expense and time to conduct complex systems, and related work presented in Sec. -
SQL Server Column Store Indexes Per-Åke Larson, Cipri Clinciu, Eric N
SQL Server Column Store Indexes Per-Åke Larson, Cipri Clinciu, Eric N. Hanson, Artem Oks, Susan L. Price, Srikumar Rangarajan, Aleksandras Surna, Qingqing Zhou Microsoft {palarson, ciprianc, ehans, artemoks, susanpr, srikumar, asurna, qizhou}@microsoft.com ABSTRACT SQL Server column store indexes are “pure” column stores, not a The SQL Server 11 release (code named “Denali”) introduces a hybrid, because they store all data for different columns on new data warehouse query acceleration feature based on a new separate pages. This improves I/O scan performance and makes index type called a column store index. The new index type more efficient use of memory. SQL Server is the first major combined with new query operators processing batches of rows database product to support a pure column store index. Others greatly improves data warehouse query performance: in some have claimed that it is impossible to fully incorporate pure column cases by hundreds of times and routinely a tenfold speedup for a store technology into an established database product with a broad broad range of decision support queries. Column store indexes are market. We’re happy to prove them wrong! fully integrated with the rest of the system, including query To improve performance of typical data warehousing queries, all a processing and optimization. This paper gives an overview of the user needs to do is build a column store index on the fact tables in design and implementation of column store indexes including the data warehouse. It may also be beneficial to build column enhancements to query processing and query optimization to take store indexes on extremely large dimension tables (say more than full advantage of the new indexes. -
9. SQL FUNDAMENTALS What Is SQL?
ROHINI COLLEGE OF ENGINEERING & TECHNOLOGY 9. SQL FUNDAMENTALS What is SQL? • SQL stands for Structured Query Language • SQL allows you to access a database • SQL is an ANSI standard computer language • SQL can execute queries against a database • SQL can retrieve data from a database • SQL can insert new records in a database • SQL can delete records from a database • SQL can update records in a database • SQL is easy to learn SQL is an ANSI (American National Standards Institute) standard computer language for accessing and manipulating database systems. SQL statements are used to retrieve and update data in a database. • SQL works with database programs like MS Access, DB2, Informix, MS SQL Server, Oracle, Sybase, etc. There are many different versions of the SQL language, but to be in compliance with the ANSI standard, they must support the same major keywords in a similar manner (such as SELECT, UPDATE, DELETE, INSERT, WHERE, and others). SQL Database Tables • A database most often contains one or more tables. Each table is identified by a name (e.g. "Customers" or "Orders"). Tables contain records (rows) with data. Below is an example of a table called "Persons": CS8492-DATABASE MANAGEMENT SYSTEMS ROHINI COLLEGE OF ENGINEERING & TECHNOLOGY SQL Language types: Structured Query Language(SQL) as we all know is the database language by the use of which we can perform certain operations on the existing database and also we can use this language to create a database. SQL uses certain commands like Create, Drop, Insert, etc. to carry out the required tasks. These SQL commands are mainly categorized into four categories as: 1. -
When Relational-Based Applications Go to Nosql Databases: a Survey
information Article When Relational-Based Applications Go to NoSQL Databases: A Survey Geomar A. Schreiner 1,* , Denio Duarte 2 and Ronaldo dos Santos Mello 1 1 Departamento de Informática e Estatística, Federal University of Santa Catarina, 88040-900 Florianópolis - SC, Brazil 2 Campus Chapecó, Federal University of Fronteira Sul, 89815-899 Chapecó - SC, Brazil * Correspondence: [email protected] Received: 22 May 2019; Accepted: 12 July 2019; Published: 16 July 2019 Abstract: Several data-centric applications today produce and manipulate a large volume of data, the so-called Big Data. Traditional databases, in particular, relational databases, are not suitable for Big Data management. As a consequence, some approaches that allow the definition and manipulation of large relational data sets stored in NoSQL databases through an SQL interface have been proposed, focusing on scalability and availability. This paper presents a comparative analysis of these approaches based on an architectural classification that organizes them according to their system architectures. Our motivation is that wrapping is a relevant strategy for relational-based applications that intend to move relational data to NoSQL databases (usually maintained in the cloud). We also claim that this research area has some open issues, given that most approaches deal with only a subset of SQL operations or give support to specific target NoSQL databases. Our intention with this survey is, therefore, to contribute to the state-of-art in this research area and also provide a basis for choosing or even designing a relational-to-NoSQL data wrapping solution. Keywords: big data; data interoperability; NoSQL databases; relational-to-NoSQL mapping 1. -
Relational Database Design Chapter 7
Chapter 7: Relational Database Design Chapter 7: Relational Database Design First Normal Form Pitfalls in Relational Database Design Functional Dependencies Decomposition Boyce-Codd Normal Form Third Normal Form Multivalued Dependencies and Fourth Normal Form Overall Database Design Process Database System Concepts 7.2 ©Silberschatz, Korth and Sudarshan 1 First Normal Form Domain is atomic if its elements are considered to be indivisible units + Examples of non-atomic domains: Set of names, composite attributes Identification numbers like CS101 that can be broken up into parts A relational schema R is in first normal form if the domains of all attributes of R are atomic Non-atomic values complicate storage and encourage redundant (repeated) storage of data + E.g. Set of accounts stored with each customer, and set of owners stored with each account + We assume all relations are in first normal form (revisit this in Chapter 9 on Object Relational Databases) Database System Concepts 7.3 ©Silberschatz, Korth and Sudarshan First Normal Form (Contd.) Atomicity is actually a property of how the elements of the domain are used. + E.g. Strings would normally be considered indivisible + Suppose that students are given roll numbers which are strings of the form CS0012 or EE1127 + If the first two characters are extracted to find the department, the domain of roll numbers is not atomic. + Doing so is a bad idea: leads to encoding of information in application program rather than in the database. Database System Concepts 7.4 ©Silberschatz, Korth and Sudarshan 2 Pitfalls in Relational Database Design Relational database design requires that we find a “good” collection of relation schemas. -
The BRAIN in Space a Teacher’S Guide with Activities for Neuroscience This Page Intentionally Left Blank
Educational Product National Aeronautics and Teachers Grades 5–12 Space Administration The BRAIN in Space A Teacher’s Guide With Activities for Neuroscience This page intentionally left blank. The Brain in Space A Teacher’s Guide With Activities for Neuroscience National Aeronautics and Space Administration Life Sciences Division Washington, DC This publication is in the Public Domain and is not protected by copyright. Permission is not required for duplication. EG-1998-03-118-HQ This page intentionally left blank. This publication was made possible by the National Acknowledgments Aeronautics and Space Administration, Cooperative Agreement No. NCC 2-936. Principal Investigator: Walter W.Sullivan, Jr., Ph.D. Neurolab Education Program Office of Operations and Planning Morehouse School of Medicine Atlanta, GA Writers: Marlene Y. MacLeish, Ed.D. Director, Neurolab Education Program Morehouse School of Medicine Atlanta, GA Bernice R. McLean, M.Ed. Deputy Director, Neurolab Education Program Morehouse School of Medicine Atlanta, GA Graphic Designer and Illustrator: Denise M.Trahan, B.A. Atlanta, GA Technical Director: Perry D. Riggins Neurolab Education Program Morehouse School of Medicine Atlanta, GA This page intentionally left blank. Contributors This publication was developed for the National Aeronautics and Space Administration (NASA) under a Cooperative Agreement with the Morehouse School of Medicine (MSM). Many individu- als and organizations contributed to the production of this curriculum. We acknowledge their support and contributions. Organizations: Joseph Whittaker, Ph.D. Atlanta Public School System Neurolab Education Program Advisory Board: Society for Neuroscience Gene Brandt The Dana Alliance for Brain Initiatives Milton C. Clipper, Jr. Mary Anne Frey, Ph.D. NASA Headquarters: Charles A. -
Data Dictionary a Data Dictionary Is a File That Helps to Define The
Cleveland | v. 216.369.2220 • Columbus | v. 614.291.8456 Data Dictionary A data dictionary is a file that helps to define the organization of a particular database. The data dictionary acts as a description of the data objects or items in a model and is used for the benefit of the programmer or other people who may need to access it. A data dictionary does not contain the actual data from the database; it contains only information for how to describe/manage the data; this is called metadata*. Building a data dictionary provides the ability to know the kind of field, where it is located in a database, what it means, etc. It typically consists of a table with multiple columns that describe relationships as well as labels for data. A data dictionary often contains the following information about fields: • Default values • Constraint information • Definitions (example: functions, sequence, etc.) • The amount of space allocated for the object/field • Auditing information What is the data dictionary used for? 1. It can also be used as a read-only reference in order to obtain information about the database 2. A data dictionary can be of use when developing programs that use a data model 3. The data dictionary acts as a way to describe data in “real-world” terms Why is a data dictionary needed? One of the main reasons a data dictionary is necessary is to provide better accuracy, organization, and reliability in regards to data management and user/administrator understanding and training. Benefits of using a data dictionary: 1. -
Relational Algebra
Relational Algebra Instructor: Shel Finkelstein Reference: A First Course in Database Systems, 3rd edition, Chapter 2.4 – 2.6, plus Query Execution Plans Important Notices • Midterm with Answers has been posted on Piazza. – Midterm will be/was reviewed briefly in class on Wednesday, Nov 8. – Grades were posted on Canvas on Monday, Nov 13. • Median was 83; no curve. – Exam will be returned in class on Nov 13 and Nov 15. • Please send email if you want “cheat sheet” back. • Lab3 assignment was posted on Sunday, Nov 5, and is due by Sunday, Nov 19, 11:59pm. – Lab3 has lots of parts (some hard), and is worth 13 points. – Please attend Labs to get help with Lab3. What is a Data Model? • A data model is a mathematical formalism that consists of three parts: 1. A notation for describing and representing data (structure of the data) 2. A set of operations for manipulating data. 3. A set of constraints on the data. • What is the associated query language for the relational data model? Two Query Languages • Codd proposed two different query languages for the relational data model. – Relational Algebra • Queries are expressed as a sequence of operations on relations. • Procedural language. – Relational Calculus • Queries are expressed as formulas of first-order logic. • Declarative language. • Codd’s Theorem: The Relational Algebra query language has the same expressive power as the Relational Calculus query language. Procedural vs. Declarative Languages • Procedural program – The program is specified as a sequence of operations to obtain the desired the outcome. I.e., how the outcome is to be obtained. -
Introduction to Cyber-Physical System Security: a Cross-Layer Perspective
IEEE TRANSACTIONS ON MULTI-SCALE COMPUTING SYSTEMS, VOL. 3, NO. 3, JULY-SEPTEMBER 2017 215 Introduction to Cyber-Physical System Security: A Cross-Layer Perspective Jacob Wurm, Yier Jin, Member, IEEE, Yang Liu, Shiyan Hu, Senior Member, IEEE, Kenneth Heffner, Fahim Rahman, and Mark Tehranipoor, Senior Member, IEEE Abstract—Cyber-physical systems (CPS) comprise the backbone of national critical infrastructures such as power grids, transportation systems, home automation systems, etc. Because cyber-physical systems are widely used in these applications, the security considerations of these systems should be of very high importance. Compromise of these systems in critical infrastructure will cause catastrophic consequences. In this paper, we will investigate the security vulnerabilities of currently deployed/implemented cyber-physical systems. Our analysis will be from a cross-layer perspective, ranging from full cyber-physical systems to the underlying hardware platforms. In addition, security solutions are introduced to aid the implementation of security countermeasures into cyber-physical systems by manufacturers. Through these solutions, we hope to alter the mindset of considering security as an afterthought in CPS development procedures. Index Terms—Cyber-physical system, hardware security, vulnerability Ç 1INTRODUCTION ESEARCH relating to cyber-physical systems (CPS) has In addition to security concerns, CPS privacy is another Rrecently drawn the attention of those in academia, serious issue. Cyber-physical systems are often distributed industry, and the government because of the wide impact across wide geographic areas and typically collect huge CPS have on society, the economy, and the environment [1]. amounts of information used for data analysis and decision Though still lacking a formal definition, cyber-physical making. -
Oracle Database Advanced Application Developer's Guide, 11G Release 2 (11.2) E17125-03
Oracle® Database Advanced Application Developer's Guide 11g Release 2 (11.2) E17125-03 August 2010 Oracle Database Advanced Application Developer's Guide, 11g Release 2 (11.2) E17125-03 Copyright © 1996, 2010, Oracle and/or its affiliates. All rights reserved. Primary Author: Sheila Moore Contributing Authors: D. Adams, L. Ashdown, M. Cowan, J. Melnick, R. Moran, E. Paapanen, J. Russell, R. Strohm, R. Ward Contributors: D. Alpern, G. Arora, C. Barclay, D. Bronnikov, T. Chang, L. Chen, B. Cheng, M. Davidson, R. Day, R. Decker, G. Doherty, D. Elson, A. Ganesh, M. Hartstein, Y. Hu, J. Huang, C. Iyer, N. Jain, R. Jenkins Jr., S. Kotsovolos, V. Krishnaswamy, S. Kumar, C. Lei, B. Llewellyn, D. Lorentz, V. Moore, K. Muthukkaruppan, V. Moore, J. Muller, R. Murthy, R. Pang, B. Sinha, S. Vemuri, W. Wang, D. Wong, A. Yalamanchi, Q. Yu This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this software or related documentation is delivered to the U.S. -
Cyber-Physical Systems, a New Formal Paradigm to Model Redundancy and Resiliency Mario Lezoche, Hervé Panetto
Cyber-Physical Systems, a new formal paradigm to model redundancy and resiliency Mario Lezoche, Hervé Panetto To cite this version: Mario Lezoche, Hervé Panetto. Cyber-Physical Systems, a new formal paradigm to model redun- dancy and resiliency. Enterprise Information Systems, Taylor & Francis, 2020, 14 (8), pp.1150-1171. 10.1080/17517575.2018.1536807. hal-01895093 HAL Id: hal-01895093 https://hal.archives-ouvertes.fr/hal-01895093 Submitted on 13 Oct 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Cyber-Physical Systems, a new formal paradigm to model redundancy and resiliency Mario Lezoche, Hervé Panetto Research Centre for Automatic Control of Nancy (CRAN), CNRS, Université de Lorraine, UMR 7039, Boulevard des Aiguillettes B.P.70239, 54506 Vandoeuvre-lès-Nancy, France. e-mail: {mario.lezoche, herve.panetto}@univ-lorraine.fr Cyber-Physical Systems (CPS) are systems composed by a physical component that is controlled or monitored by a cyber-component, a computer-based algorithm. Advances in CPS technologies and science are enabling capability, adaptability, scalability, resiliency, safety, security, and usability that will far exceed the simple embedded systems of today. CPS technologies are transforming the way people interact with engineered systems.