...3 Question 1 3-7 4 Question 2 8 5 Question 3 9 6 Question 4 10-11 7 References 12 Database Question 1: Data Model/Database Model Flat file Flat file data model is model where data was store as a plain text file in a single table. Each line of the table hold each record with field and it is separated by commas or tabs. It cannot contain multiple tables like relational database. All the data stored by flat file, a computer file system will be store in a single...
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...Relational Model of Shared Data Banks Abstract How does one decide best practice for a database management design? Who has used mathematics and science as a basis to assist organizations with a baseline relational model for database management? Well for over the last forty years credit has been given to entrepreneur Edgar F. Codd. The evolution of relational databases remain ever changing, however the foundation laid by Cobb remains best practice for the vast majority of organizations. Simple principles defined in his writing “A Relational Model of Data for Large Shared Data Banks” exhibits pros and cons in comparison to other databases. Although databases range in size, durability, and ease all plays a significant role in an organization growth, success, and failure. Relational Model of Shared Data Banks The paper “A Relational Model of Data for Large Shared Data Banks”, written by E. F. Codd explains the advantages of a relational database model primarily in comparison to the tree-structure and network model. The author points out the importance of derivability, redundancy and consistency of relations within the relational model versus other prototypes. One of Cobb’s example cites, “The network model, on the other hand, has spawned a number of confusions, not the least of which is mistaking the derivation of connection for the derivation of relations” (Cobb, 1970, p. 377). Cobb’s overall goal is to make sure readers see how effective and how successful an organization...
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...? Codd’s Relational Model Databases Database systems store information in every conceivable business environment. From large tracking databases such as airline reservation systems to ATM application, database systems store and distribute the data that we depend on. Relational Database Model So far, the most popular data storage model is the relational database. The relational database model was invented by Dr. Edward E. Codd in 1970 A relational database store data in the form of relations (or Tables) Tables are made up of rows (tuples) and columns (fields). Relational Database Model It is important to understand the relational database model because the today's’ widely used SQL evolved to service this concepts. Dr. Codd defined 13 rules, oddly enough referred to as Codd’s 12 rules, that define the relational model. Codd’s 12 rules 0. A relational DBMS must be able to manage databases entirely through its relational capabilities. 1. The Information Rule : All information in a relational database (including table and column names) is representable explicitly as values in tables. 2 Guaranteed Access : Every value in a relational database is guaranteed to be accessible by using a combination of the table name, primary key value, and column name. 3 Systematic Null Value Support : The DBMS provides systematic support for the treatment of null values (unknown, not available, missing or inapplicable...
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...forms, visit my Database Normalization eBook page. Definition of terms Now, first a few definitions about database normalization: I cite a few as I have found them in the Hyper dictionary on the Internet, though you may find a number of differing definitions around. We are dealing with the "Relational Model" as the basis for our relational databases. The relational model as I see it is Definition: A data model introduced by E.F. Codd in 1959/1970, particularly well suited for business data management. In this model, data are organized in tables. The set of names of the columns is called the "schema" of the table.". The work of E. F. Codd, and also Chris Date, is based on relational algebra, which is well outside the scope of this article. Database model: The product (outcome) of the database design process which aims to identify and organize the required data conceptually and logically. A database model tells you what information is to be contained in a particular database, how the information will be used, and how the items in the database will be related to each other. A well thought-out database model reduces the need for changes. Future systems may re-use parts of existing models, which should lower development costs. Database Normalization: A series of steps followed to obtain a database...
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...References 13 Appendix A 14 Example Normal Form Diagrams 15 Abstract In relational databases, normalization is a process that is used to eliminate redundancy, reduce the potential for anomalies during data processing, and maintain data consistency and integrity through out the database. It is a design technique used primarily as a guide in designing relational databases. Normalization is essentially a two step process that puts data into tabular form by removing repeating groups and then removes duplicated data from the relational tables. The theory of normalization is based on the concept of normal forms introduced by Edgar F. Codd in 1970. I choosed this topic because this is one of the areas in my role as a database administrator where I’m weak. During my training as a database administrator, I was never really offered a database design class, however, while at work, I have been occasionally given the challenging task of reviewing a data model and giving a sign – off. I have been involved in various projects in the design of the database and luckily for me, most of my tasks have been to transform the logical model into a physical model. Writing this paper will give me the opportunity to further understand the concept of database design from a normalization perspective while adding to my knowledge base in database...
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...Modern Database Applications | [Type the document title] | | | Contents 1. Gray, J. (2009). Jim Gray on eScience: A transformed scientific method. The Fourth Paradigm: Data-intensive scientific discovery 2 2. Rowley, J. (2007). Wisdom hierarchy: Representations of the DIKW hierarchy. Journal of Information Science 3 3. Goldman, N. (2013). Towards practical, high-capacity, low-maintenance information storage in synthesized DNA. 4 4. Gray, J. (1981). The transaction concept: virtues and limitations. In: VLDB '81: Proceedings of the seventh international conference on Very Large Data Bases 5 5. Codd, E. F. (1970). A Relational Model of Data for Large Shared Data Banks. Communications of the ACM 7 6. Chen, P. (1976). The entity-relationship model: Toward a unified view of data. ACM Transactions on Database Systems 8 1. Gray, J. (2009). Jim Gray on eScience: A transformed scientific method. The Fourth Paradigm: Data-intensive scientific discovery Gray states that there is need to distinguish data-intensive science from computational science; he defines an emerging fourth paradigm for scientific exploration. This paradigm is derived from the deluge of data being produced within scientific research fields, and the necessity for tools which can be utilised within the whole research cycle; data capture, curation, analysis and visualisation. He identified that currently the data being produced is not being organised, or published in a systematic...
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...Why Normalization Failed to Become the Ultimate Guide for Database Designers? Marin Fotache Alexandru Ioan Cuza University of Iasi, Romania Faculty of Economics and Business Administration Dept. of Business Information Systems Blvd. Carol I, nr. 22, Iasi, 700505, Romania Tel: + 40 744 497 654, Fax: + 40 232 217 000 fotache@uaic.ro ABSTRACT With an impressive theoretical foundation, normalization was supposed to bring rigor and relevance into such a slippery domain as database design is. Almost every database textbook treats normalization in a certain extent, usually suggesting that the topic is so clear and consolidated that it does not deserve deeper discussions. But the reality is completely different. After more than three decades, normalization not only has lost much of its interest in the research papers, but also is still looking for practitioners to apply it effectively. Despite the vast amount of database literature, comprehensive books illustrating the application of normalization to effective real-world applications are still waited. This paper reflects the point of 1 view of an Information Systems academic who incidentally has been for almost twenty years a practitioner in developing database applications. It outlines the main weaknesses of normalization and offers some explanations about the failure of a generous framework in becoming the so much needed universal guide for database designers. Practitioners might be interested in finding out (or confirming)...
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...Jonathan Hindman PT2520 Chapter 4 10/18/13 Cardinality: Cardinality refers to the number of allowed instances of a relationship. In the usual cardinality of one to many, for instance, each record on the one side can have zero to any number of records on the many side. Cardinality can be more specific however. Each patron at a library can have only 20 items checked out at once. This has a cardinality of 0 to 20. Composite keys: In database design, a compound key is a key that consists of 2 or more attributes that uniquely identify an entity occurrence. Each attribute that makes up the compound key is a simple key in its own right. Crow’s feet notation: A type of notation for entity relationships in entity relation diagrams that depicts the many side of a relationship with a three-pronged end called a “crows foot.” This type of notation provides more information about the cardinality of a relationship than the arrow notation for relationships. Domain entities: Those database entities that relate directly to the business problem under consideration. Entity relation diagrams: A diagram that shows entities, their attributes, and the relationships among them. Linking entity: An entity used to resolve a many-to-many relationship into two one-to-many relationships. Logical entity: The design of a database without regard to the physical implementation of the database. Lookup entity: An entity used to store lookup values such as state names or zip codes. Maximum cardinality:...
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...Relational Databases Jason A. Mizener Professor Merkel Intro to Information Technology 17 August 2014 Relational Databases As technology as grown throughout the years, so has the information it utilizes. Early on databases were organized through the hierarchical model, which was the earliest representation of the parent-child structure (each parent can have multiple children, but each child can only have one parent). Shortly thereafter the network database model was incorporated to more effectively represent complex data relationships. Large amounts of data became better organized, which improved database performance. Additionally, the parent-child structure was improved to where children could now have multiple parents. Despite these advances, databases continued to grow increasingly cumbersome and complex, and further advances were necessary to keep everything manageable. The relational database, designed in 1970 by E. F. Codd, represented a major breakthrough in database technology for both designers and users alike. The relational model is very simple, yet very effective in concept. As strange as it may be to think, the relational database pioneered the concept of using tables to hold and organize data. An Excel spreadsheet is probably the most obvious example of a relational database that the end user can relate to today. Essentially, a relational database is the logical view, rather than the physical view, of information. (Carlos Coronel, 2013) ...
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...one of tools that can assist managers in making sound business decisions. OLAP is a powerful technology behind many Business Intelligence (BI) applications. It offers many capabilities for data discovery, report viewing, complex analytical calculations, and planning (Olap.com, n.d.). In other words, OLAP is a “computer-enhanced multidimensional analysis” (Achor, 2002). The term OLAP was created by E.F. Codd in 1993. According to Codd and associates, OLAP is made up of many speculative “what-if” and/or “why” data model scenarios conducted within the context of the specific historical basis (Codd, Codd and Salley, 1993). Under these scenarios, the values of major parameters are changed to show potential variances in “supply, production, the economy, sales, marketplace, costs, and/or other environmental and internal factors” (Codd, Codd and Salley, 1993, p.6). These variable groups or dimensions make up a base for the company’s planning, analysis and reporting activities (Bogue, 2005). OLAP tools do not keep individual transaction records in a row-by-column format, like relational databases. Instead, they store consolidated information in multidimensional cubes (Olap.com, n.d.). When necessary, analysts use operations called consolidation, “drill-down”, and “slicing and dicing” to generate a worksheet-like display of viewpoints (O'Brien &...
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...Larry Ellison is the founder and CEO of the enterprise software company Oracle Corporation. He is also a well-known American billionaire and philanthropist. Lawrence Joseph Ellison, better known as Larry Ellison, was born in New York City on August 17, 1944 to a young unwed Jewish mother. In order to provide her son with a better upbringing, Ellison's mother gave him to Lillian Spellman Ellison and Louis Ellison, her aunt and uncle in Chicago. They formally adopted Ellison when he was only nine months old. | Lawrence was raised in a two-bedroom apartment on the city's South Side. As a boy, Larry Ellison showed an independent, rebellious streak and often clashed with his adoptive father. From an early age, he showed a strong aptitude for math and science, and was named science student of the year at the University of Illinois. During the final exams in his second year, Larry Ellison's adoptive mother died, and he dropped out of school. He enrolled at the University of Chicago the following fall, but dropped out again after the first semester. His adoptive father was now convinced that Larry would never make anything of himself, but the seemingly aimless young man had already learned the rudiments of computer programming in Chicago. He took this skill with him to Berkeley, California, arriving with just enough money for fast food and a few tanks of gas. For the next eight years, Ellison bounced from job to job, working as a technician for Fireman's Fund and Wells Fargo bank...
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...A relational database solution or relational databases have helped many companies gain more information about their operations by supporting simple operator queries, answering questions such as "What customer had the largest demand?" of "What customer ordered the largest shipment?" They are built to manage relationships and are ideal for starting contextual or genealogical information about processes. Before I continue I would like to explain what a relational database is and what it is derived from. Databases have been a staple of business computing from the very beginning of the digital era. In fact, the relational database was born in 1970 when E.F. Codd, a researcher at IBM, wrote a paper outlining the process. A database is an application that can store and retrieve data very rapidly. The relational bit refers to how the data is stored in the database and how it is organized. Since then, relational databases have grown in popularity to become the standard. A relational database allows you to easily find specific information. It also allows you to sort based on any field and generate reports that contain only certain fields from each record. Relational databases use tables to store information. With a relational database, you can quickly compare information because of the arrangement of data in columns. The relational database model takes advantage of this uniformity to build completely new tables out of required information from existing tables. In other words, it uses...
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...Applications. 2d ed. Redwood City, CA: Benjamin Cummings, 1994. Bruce, T. A. Designing Quality Databases with IDEF1X Information Models. New York: Dorset House, 1992. Chen, P. P-S. “The Entity-Relationship Model—Toward a Unified View of Data.” ACM Transactions on Database Systems 1 (March 1976): 9–36. Codd, E. F. “A Relational Model of Data for Large Relational Databases.” Communications of the ACM 13 (6) (1970): 77–87. Dutka, A. F., and H. H. Hanson. Fundamentals of Data Normalization. Reading, MA: Addison-Wesley, 1989. Finkelstein, R. “Breaking the Rules Has a Price.” Database Programming & Design 1 (June 1988): 11–14. Fleming, C. C., and B. von Halle. “An Overview of Logical Data Modeling.” Data Resource Management 1 (1) (1990): 5–15. Fowler, M. UML Distilled: A Brief Guide to the Object Modeling Language. 2d ed. Reading, MA: Addison-Wesley, 2000. Gibson, M., C. Hughes, and W. Remington. “Tracking the Trade-Offs with Inverted Lists.” Database Programming & Design 2 (January 1989): 28–34. Gottesdiener, E. “Turning Rules into Requirements.” Application Development Trends 6 (7) (1999): 37–50. Hay, D. Data Model Patterns: Conventions of Thought. New York: Dorset House, 1996. Hoffer, J. A., V. Ramesh, and H. Topi. Modern Database Management. 10th ed. Upper Saddle River, NJ: Prentice Hall, 2011. Inmon, W. H. “Using the Generic Data Model.” Accessed January 12, 2004, www.dmreview.com/master.cfm?NavID=55&EdID=4820. Kimball, R., and M. Ross. The Data Warehouse...
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...1.2 What is the most commonly used type of database? Ans. The most commonly used type of database is Relational Database. 1.7 Define the terms data and information. Explain how the two terms differ. Ans. There is a subtle difference between data and information. Data are the facts or details from which information is derived. Individual pieces of data are rarely useful alone. For data to become information, data needs to be put into context. 1.22 Define the term database. Ans. Database is a collection of data in an organized form which allows a user for fast storage and retrieval of required data. 1.27 Is Microsoft Access a DBMS? Why or why not? Ans. Microsoft Access is not just a DBMS it is also an Application Generator. Although Microsoft Access contains a DBMS engine that creates, processes, and administers the database, it also contains form, report, and query components that are the Microsoft Access application Generator. 1.33 Name the components of an enterprise-class database system. Ans. These are the components of an enterprise-class database system: 1. Applications Running Over Corporate Network 2. E-Commerce Applications on Web Server 3. Web Portal with Reporting Applications 4. XML Web Services Applications 5. Mobile Apps 1.37 List several consequences of a poorly designed database. Ans. These are the consequences of a poorly designed database: 1. Redundancy 2. Improper Tables or Many Tables for same Object 3. Null Values...
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...cr. (4,50 ECTS cr.) Teaching methods (Full-time, daytime studies): Lectures - 16 h per semestre Laboratory works - 32 h per semestre Individual work - 72 h per semester Course aim Understandig of models and system of information resourses. Jelena Mamčenko Introduction to Data Modeling and MSAccess CONTENT 1 Introduction to Data Modeling ............................................................................................................... 5 1.1 Data Modeling Overview ............................................................................................................... 5 1.1.1 Methodology .......................................................................................................................... 6 1.1.2 Data Modeling In the Context of Database Design................................................................ 6 1.1.3 Components of A Data Model................................................................................................ 6 1.1.4 Why is Data Modeling Important? ......................................................................................... 6 1.1.5 Summary ................................................................................................................................ 7 1.2 The Entity-Relationship Model ...................................................................................................... 7 1.2.1 Basic Constructs of E-R Modeling......................................................
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