...Statistical Databases Jaideep Srivastava and Hung Q. Ngo, Department of Computer Science, University of Minnesota, 200 Union street, EE/CS Building, room 4-192, Minneapolis, MN 55455 e-mail: srivasta, hngo @cs.umn.edu, ¡ 1 Introduction A statistical database management system (SDBMS) is a database management system that can model, store and manipulate data in a manner well suited to the needs of users who want to perform statistical analyses on the data. Statistical databases have some special characteristics and requirements that are not supported by existing commercial database management systems. For example, while basic aggregation operations like SUM and AVG are part of SQL, there is no support for other commonly used operations like variance and co-variance. Such computations, as well as more advanced ones like regression and principal component analysis, are usually performed using statistical packages and libraries, such as SAS [1] and SPSS [2]. From the end user’s perspective, whether the statistical calculations are being performed in the database or in a statistical package can be quite transparent, especially from a functionality viewpoint. However, once the datasets to be analyzed grow beyond a certain size, the statistical package approach becomes infeasible, either due to its inability to handle large volumes of data, or the unacceptable computation times which make interactive analysis impossible. With the increasing sophistication of data collection instrumentation...
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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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...different programs as and when required . It seems that the video rental shop is present at home . Research is going on to improve the quality of VoD-related services . Some of the VoD-related applications are providing Video Films on Demand , Local news and Weather Forecasting , Games , music and Leisure , Education and Remote Learning Facilities , Home Shopping and Other Consumer Services , Banking etc . This system has three main components . These are Video server : it stores and provides access to programs Data delivery network : it is the linking source for subscriber Set-top box : It is the interface between TV equipments and VoD services MPEG (Motion Picture Experts Group ) is a group of people who work for ISO (International Standards Organization ) to generate digital video standards i .e producing sequences of images in time and to generate audio compression . Various kind of MPEG are ranked on the basis of the speed (in CD-ROM data rates ) at which they deliver video to consumer devices . For example , the speed of MPEG-1 was 1 .5 Mbit /s , MPEG-2 with improved resolution deliver broadcast and HDTV quality video . Over past few years , a considerable growth in the multimedia communication has been recorded due to exponential rise in consumer demands . There is a trend in allowing customers to select films /video sequences remotely and have them streamed directly to the user per specific requirements . The multimedia industry aims at developing a database...
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...Basic Relational Model References: Chapter 3: The Relational Data Model & Relational Database Constraints From Elmasri and avathe; Pearson ew International Edition, 6th Edition 1 Outline • The Relational Data Model and Relational Database Constraints • Relational Model Constraints and Relational Database Schemas • Update Operations, Transactions, and Dealing with Constraint Violations 2 The Relational Data Model and Relational Database Constraints • Relational model – First commercial implementations available in early 1980s – Has been implemented in a large number of commercial systems • Hierarchical and network models – Preceded the relational model 3 Relational Model Concepts • Represents data as a collection of relations • Table of values – Row • Represents a collection of related data values • Fact that typically corresponds to a real-world entity or relationship • Tuple – Table name and column names • Interpret the meaning of the values in each row attribute 4 Relational Model Concepts (contd) 5 Domains, Attributes, Tuples, and Relations • Domain D – Set of atomic values • Atomic – Each value indivisible • Specifying a domain – Data type specified for each domain 6 Domains, Attributes, Tuples, and Relations (contd) • Relation schema R – Denoted by R(A1, A2, ...,An) – Made up of a relation name R and a list of attributes, A1, A2, ..., An • Attribute Ai – Name of a role played by some...
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...released its own system called Essbase using the OLAP theoretical background and functionality. In 1998, Microsoft stepped in for upgrading and advancement in the OLAP technology. Microsoft worked on the mainstream idea and developed highly advanced online analytical system that is deployed in many large organizations today. Types of OLAP There are 3 types of the on-line analytical systems each with different properties according to the level of use. Multi-dimensional OLAP Multi-dimensional OLAP is also known as MOLAP in short. It is a classic type of leading on-line analytical systems. Instead of storing the data in relational form, it has the ability to store data in the multi-dimensional array that is highly optimized. This makes the data storage more efficient and quick. . Relational OLAP Relational OLAP (ROLAP) works similar to the relational...
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...1.Introduction Relational database design theory is mainly based on a class of constraints called Functional Dependencies (FDs).FDs are a generalization of keys. This theory defines when a relation is in normal form (e.g., in Third Normal Form or 3NF) for a given set of FDs. It is usually a sign of bad DB design if a schema contains relations that violate the normal form requirements. If a normal form is violated, data is stored redundantly, and information about different concepts is intermixed. To reduce the redundancy in a given relation we need to decompose it in two or more sub relations . Decomposition into highest normal forms required maintaining properties like loss less join decomposition and dependency preserving decomposition. To normalize a relation R we have to remove certain dependencies that exist in it like partial dependency& transitive dependency. Basic definitions 1.1 Functional dependency: In a given table, an attribute Y is said to have a functional dependency on a set of attributes X (written X → Y) if and only if each X value is associated with precisely one Y value. For example, in an "Employee" table that includes the attributes "Employee ID" and "Employee Date of Birth", the functional dependency {Employee ID} → {Employee Date of Birth} would hold. It follows from the previous two sentences that each {Employee ID} is associated with precisely one {Employee Date of Birth}. 1.2 Trivial functional dependency A trivial functional dependency...
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...MGT321 Management Info Systems Database Models Assignment The organization I am using is for a database model is a Fortune 500 behavioral marketing company that focuses on grocery shopper’s behaviors and trends to create targeted brand media to the shopper at the point-of-sale (POS). This organization employs 800 people across the United States, with their corporate headquarters being in Florida, employees in remote offices, ones who work from home, as well as international employees in 4 countries; Germany, the United Kingdom, Japan and France. We will call this company “Mayani Marketing Inc.” The database model chosen for this company is Relational, based on the fact that most of the departmental data is already based in Oracle, Microsoft Access and SQL Server and MySQL. The type of data this company specializes in is shopping basket information. These transactions are captured in the millions per day over a 20,000+ network nationwide, and are encrypted to provide security to the shopper identities, which are never linked to their shopping basket data (the use of frequent shopper cards and loyalty programs help this data collection). There is a data warehouse that is comprised of multiple IBM Blade servers as well as two Netezza TwinFin servers that are the platform of the data warehouse structure. Mayani is also taking advantage of Netezza's partnership of i-class advanced analytics, which partners the data warehousing with SAS and Micro Strategy to harness all of...
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...International Journal of Database Management Systems ( IJDMS ), Vol.3, No.1, February 2011 RDBNorma: - A semi-automated tool for relational database schema normalization up to third normal form. Y. V. Dongare1, P. S. Dhabe2 and S. V. Deshmukh3 1 Department of Computer Engg., Vishwakarma Institute of Info.Technology, Pune, India 2 yashwant_dongre@yahoo.com Department of Computer Engg., Vishwakarma Institute of Technology, Pune, India dhabeps@gmail.com supriya_vd2005@yahoo.com 3 Department of Computer Engg., Pune Vidhyarthi Griha’s College of E&T, Pune, India Abstract— In this paper a tool called RDBNorma is proposed, that uses a novel approach to represent a relational database schema and its functional dependencies in computer memory using only one linked list and used for semi-automating the process of relational database schema normalization up to third normal form. This paper addresses all the issues of representing a relational schema along with its functional dependencies using one linked list along with the algorithms to convert a relation into second and third normal form by using above representation. We have compared performance of RDBNorma with existing tool called Micro using standard relational schemas collected from various resources. It is observed that proposed tool is at least 2.89 times faster than the Micro and requires around half of the space than Micro to represent a relation. Comparison is done by entering all the attributes and functional...
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...FILE ORGANIZATION TERMS AND CONCEPTS THE DATA HIERARCHY A computer system organizes data in a hierarchy that starts with bits and bytes and progresses to fields, records, files, and databases * A bit represents the smallest unit of data a computer can handle * A group of bits, called a byte, represents a single character, which can be a letter, a number, or another symbol (A,2?,S) * A grouping of characters into a word, a group of words, or a complete number (such as a person’s name or age) is called a field Ex: employee Last name, Customer Account number * A group of related fields, such as the student’s name, the course taken, the date, and the grade, comprises a record; Ex: There will be one record for every one * A group of records of the same type is called a file. Ex: Employee Benefits file, Employee payroll file * Database: A group of related files about a specific entity Ex: HR database PROBLEMS WITH THE TRADITIONAL FILE ENVIRONMENT In most organizations, systems tended to grow independently without a company-wide plan. Accounting, finance, manufacturing, human resources, and sales and marketing all developed their own systems and data files. Figure 6-2 illustrates the traditional approach to information processing * In the company as a whole, this process led to multiple master files created, maintained, and operated by separate divisions or departments. As this process goes on for 5 or 10 years, the organization is saddled...
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...Relational Database: Design This paper evaluates four similar web databases and concludes with an overview of considerations for designing web databases. The web databases reviewed are: (a) the Academy of Motion Pictures and Arts (Academy, 2013), (b) IMDb (IMDb, 2013), (c) Complete Index to World Film (Complete Index, 2013), and (d) Labor Film Database (Labor Film, 2013). Table 1 summarizes each database in terms of purpose, target audience, functions, and usability. Table 1 Summary of Web Database Reviews Academy IMDb CITWF Labor Films Purpose • Digital artifact preservation for movie industry • Movie industry research • Search vast amounts of information on just about any film, game or actor • Search fair amount of information on just about any film in the world • Educate • Research Audience • Journalists • Filmmakers • Students • Hobbyists • General public • Film buffs • Journalists • Hobbyists • Gamers • Students • Researchers • Film buffs • Hobbyists • Researchers • Activists • General public Functions • Basic and advanced searches • Six advanced query functions using: fill-in-the-box, check-a-square, and pre-filled drop-down database values • Collects information in user-defined lists. • Watch lists • Simple Search: user selects film or person from a drop-down box and enters a search phrase • Advanced Search: user builds multiple-condition • One-size-fits-all search via a drop-down box of extensive search categories • On-demand streaming •...
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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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...118 IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.6, June 2010 Indexing and Querying Semistructured Data Views of Relational Database B.M. Monjurul Alom, Frans Henskens and Michael Hannaford School of Electrical Engineering. & Computer Science, University of Newcastle, AUSTRALIA that contains a XML data repository at its core [22]. It is difficult to achieve high query performance using XML data repositories, since queries are answered by traversing many individual element-to-element links requiring multiple index lookups [23] . In the case of XML data, queries are even more complex because they may contain regular path expressions [24]. Thus additional flexibility is needed in order to traverse data whose structure is irregular or partially unknown to the user. Another option for managing semistructured data is to store and query it with a relational database [22]. In the database community many researchers argue that the relational (and objectrelational) model, due to its maturity and widespread usage, is still the best option [25]. XML query processing is much more complicated than traditional query processing methods because of the structure of XML [1]. A path expression specifies patterns of selection predicates on multiple elements related by a tree structure named Query Tree Pattern (QTP). Consequently, In order to process an XML query, all occurrences of its related QTP should be distinguished in the XML document. This is an expensive...
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...Ling Liu and M. Tamer Özsu (Eds.), Springer-Verlag, 2009. Synonyms computational ontology, semantic data model, ontological engineering Definition In the context of computer and information sciences, an ontology defines a set of representational primitives with which to model a domain of knowledge or discourse. The representational primitives are typically classes (or sets), attributes (or properties), and relationships (or relations among class members). The definitions of the representational primitives include information about their meaning and constraints on their logically consistent application. In the context of database systems, ontology can be viewed as a level of abstraction of data models, analogous to hierarchical and relational models, but intended for modeling knowledge about individuals, their attributes, and their relationships to other individuals. Ontologies are typically specified in languages that allow abstraction away from data structures and implementation strategies; in practice, the languages of ontologies are closer in expressive power to first-order logic than languages used to model databases. For this reason, ontologies are said to be at the "semantic" level, whereas database schema are models of data at the "logical" or "physical" level. Due to their independence from lower level data models, ontologies are used for integrating heterogeneous databases, enabling interoperability among disparate systems, and specifying interfaces to independent...
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...Section 1 1. The study of computer based Information Systems (IS) is not just limited to traditional computers and servers, but also includes such devices as: a. Network Routers b. Mobile Phones c. Telecommunication Switches d. All of the Above 2. Information Technology (IT) refers only to the information processing software programs of a computer system: True or False 3. Information Systems are used in the business world to support an organization’s: a. Decision Making b. Business processes and operations c. Strategies for competitive advantage d. All of the above e. None of the above 4. The barriers a Business must deal with, such as competitors, substitutes, customers, supplier, etc are often referred to as: a. Competitive Strategies b. Competitive Forces c. Strategic Information Systems d. None of the above 5. Using IT to help a company make radical changes or improvements in their business processes is part of: a. Leveraging Investment in IT b. Agile Company c. Value Chain d. Business Process Reengineering e. Creating Switching Costs 6. The main processing component of a computer is called: a. Primary Storage b. Magnetic Disks c. Central Processing Unit d. Semiconductor Memory 7. Which of the following is a quantitative model typically used by a DSS: a. Sensitivity analysis b. What-if analysis c. Goal-seeking analysis d. All of the above 8. Information can be saved in many ways with a computer, the main memory is called: ...
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...The REA Accounting Model: Intellectual Heritage and Prospects for Progress Cheryl L. Dunn Florida State University William E. McCarthy Michigan State University Send page proofs to: William E. McCarthy Department of Accounting N270 North Business Complex Michigan State University East Lansing, MI 48824 Acknowledgments: The authors would like to acknowledge the helpful comments of three anonymous referees and the editor on two earlier versions of this paper. Helpful pointers into the literature were provided by William Schrader and Stephen Zeff. We would also like to acknowledge comments received at the Michigan State University 1995 Summer AIS Colloquium and at the 1995 Workshop on Semantic Modeling of Accounting Phenomena. Financial support was received from Arthur Andersen LLP and from the Departments of Accounting at Grand Valley State University and Michigan State University. The REA Accounting Model: Intellectual Heritage and Prospects for Progress ABSTRACT: Researchers often equate database accounting models in general and the Resources-Events-Agents (REA) accounting model in particular with events accounting as proposed by Sorter (1969). In fact, REA accounting, database accounting, and events accounting are very different. Because REA accounting has become a popular topic in AIS research, it is important to agree on exactly what is meant by certain ideas, both in concept and in historical origin. This article clarifies the intellectual...
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