(12) United States Patent (10) Patent No.: US 8.458,105 B2 Nolan Et Al
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Data Warehouse: an Integrated Decision Support Database Whose Content Is Derived from the Various Operational Databases
1 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in DATABASE MANAGEMENT Topic Objective: At the end of this topic student will be able to: Understand the Contrasting basic concepts Understand the Database Server and Database Specified Understand the USER Clause Definition/Overview: Data: Stored representations of objects and events that have meaning and importance in the users environment. Information: Data that have been processed in such a way that they can increase the knowledge of the person who uses it. Metadata: Data that describes the properties or characteristics of end-user data and the context of that data. Database application: An application program (or set of related programs) that is used to perform a series of database activities (create, read, update, and delete) on behalf of database users. WWW.BSSVE.IN Data warehouse: An integrated decision support database whose content is derived from the various operational databases. Constraint: A rule that cannot be violated by database users. Database: An organized collection of logically related data. Entity: A person, place, object, event, or concept in the user environment about which the organization wishes to maintain data. Database management system: A software system that is used to create, maintain, and provide controlled access to user databases. www.bsscommunitycollege.in www.bssnewgeneration.in www.bsslifeskillscollege.in 2 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in Data dependence; data independence: With data dependence, data descriptions are included with the application programs that use the data, while with data independence the data descriptions are separated from the application programs. Data warehouse; data mining: A data warehouse is an integrated decision support database, while data mining (described in the topic introduction) is the process of extracting useful information from databases. -
Bi-Directional Transformation Between Normalized Systems Elements and Domain Ontologies in OWL
Bi-directional Transformation between Normalized Systems Elements and Domain Ontologies in OWL Marek Suchanek´ 1 a, Herwig Mannaert2, Peter Uhnak´ 3 b and Robert Pergl1 c 1Faculty of Information Technology, Czech Technical University in Prague, Thakurova´ 9, Prague, Czech Republic 2Normalized Systems Institute, University of Antwerp, Prinsstraat 13, Antwerp, Belgium 3NSX bvba, Wetenschapspark Universiteit Antwerpen, Galileilaan 15, 2845 Niel, Belgium Keywords: Ontology, Normalized Systems, Transformation, Model-driven Development, Ontology Engineering, Software Modelling. Abstract: Knowledge representation in OWL ontologies gained a lot of popularity with the development of Big Data, Artificial Intelligence, Semantic Web, and Linked Open Data. OWL ontologies are very versatile, and there are many tools for analysis, design, documentation, and mapping. They can capture concepts and categories, their properties and relations. Normalized Systems (NS) provide a way of code generation from a model of so-called NS Elements resulting in an information system with proven evolvability. The model used in NS contains domain-specific knowledge that can be represented in an OWL ontology. This work clarifies the potential advantages of having OWL representation of the NS model, discusses the design of a bi-directional transformation between NS models and domain ontologies in OWL, and describes its implementation. It shows how the resulting ontology enables further work on the analytical level and leverages the system design. Moreover, due to the fact that NS metamodel is metacircular, the transformation can generate ontology of NS metamodel itself. It is expected that the results of this work will help with the design of larger real-world applications as well as the metamodel and that the transformation tool will be further extended with additional features which we proposed. -
A Survey and Classification of Controlled Natural Languages
A Survey and Classification of Controlled Natural Languages ∗ Tobias Kuhn ETH Zurich and University of Zurich What is here called controlled natural language (CNL) has traditionally been given many different names. Especially during the last four decades, a wide variety of such languages have been designed. They are applied to improve communication among humans, to improve translation, or to provide natural and intuitive representations for formal notations. Despite the apparent differences, it seems sensible to put all these languages under the same umbrella. To bring order to the variety of languages, a general classification scheme is presented here. A comprehensive survey of existing English-based CNLs is given, listing and describing 100 languages from 1930 until today. Classification of these languages reveals that they form a single scattered cloud filling the conceptual space between natural languages such as English on the one end and formal languages such as propositional logic on the other. The goal of this article is to provide a common terminology and a common model for CNL, to contribute to the understanding of their general nature, to provide a starting point for researchers interested in the area, and to help developers to make design decisions. 1. Introduction Controlled, processable, simplified, technical, structured,andbasic are just a few examples of attributes given to constructed languages of the type to be discussed here. We will call them controlled natural languages (CNL) or simply controlled languages.Basic English, Caterpillar Fundamental English, SBVR Structured English, and Attempto Controlled English are some examples; many more will be presented herein. This article investigates the nature of such languages, provides a general classification scheme, and explores existing approaches. -
A Conceptual Framework for Constructing Distributed Object Libraries Using Gellish
A Conceptual Framework for Constructing Distributed Object Libraries using Gellish Master's Thesis in Computer Science Michael Rudi Henrichs [email protected] Parallel and Distributed Systems group Faculty of Electrical Engineering, Mathematics and Computer Science Delft University of Technology June 1, 2009 Student Michael Rudi Henrichs Studentnumber: 9327103 Oranjelaan 8 2264 CW Leidschendam [email protected] MSc Presentation June 2, 2009 at 14:00 Lipkenszaal (LB 01.150), Faculty EWI, Mekelweg 4, Delft Committee Chair: Prof. Dr. Ir. H.J. Sips [email protected] Member: Dr. Ir. D.H.J. Epema [email protected] Member: Ir. N.W. Roest [email protected] Supervisor: Dr. K. van der Meer [email protected] Idoro B.V. Zonnebloem 52 2317 LM Leiden The Netherlands Parallel and Distributed Systems group Department of Software Technology Faculty of Electrical Engineering, Mathematics and Computer Science Delft University of Technology Mekelweg 4 2826 CD Delft The Netherlands www.ewi.tudelft.nl Sponsors: This master's thesis was typeset with MiKTEX 2.7, edited on TEXnicCenter 1 beta 7.50. Illustrations and diagrams were created using Microsoft Visio 2003 and Corel Paint Shop Pro 12.0. All running on an Acer Aspire 6930. Copyright c 2009 by Michael Henrichs, Idoro B.V. Cover photo and design by Michael Henrichs c 2009 http:nnphoto.lemantle.com All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without the prior permission of the author. -
Data Models for Home Services
__________________________________________PROCEEDING OF THE 13TH CONFERENCE OF FRUCT ASSOCIATION Data Models for Home Services Vadym Kramar, Markku Korhonen, Yury Sergeev Oulu University of Applied Sciences, School of Engineering Raahe, Finland {vadym.kramar, markku.korhonen, yury.sergeev}@oamk.fi Abstract An ultimate penetration of communication technologies allowing web access has enriched a conception of smart homes with new paradigms of home services. Modern home services range far beyond such notions as Home Automation or Use of Internet. The services expose their ubiquitous nature by being integrated into smart environments, and provisioned through a variety of end-user devices. Computational intelligence require a use of knowledge technologies, and within a given domain, such requirement as a compliance with modern web architecture is essential. This is where Semantic Web technologies excel. A given work presents an overview of important terms, vocabularies, and data models that may be utilised in data and knowledge engineering with respect to home services. Index Terms: Context, Data engineering, Data models, Knowledge engineering, Semantic Web, Smart homes, Ubiquitous computing. I. INTRODUCTION In recent years, a use of Semantic Web technologies to build a giant information space has shown certain benefits. Rapid development of Web 3.0 and a use of its principle in web applications is the best evidence of such benefits. A traditional database design in still and will be widely used in web applications. One of the most important reason for that is a vast number of databases developed over years and used in a variety of applications varying from simple web services to enterprise portals. In accordance to Forrester Research though a growing number of document, or knowledge bases, such as NoSQL is not a hype anymore [1]. -
SQL from Wikipedia, the Free Encyclopedia Jump To: Navigation
SQL From Wikipedia, the free encyclopedia Jump to: navigation, search This article is about the database language. For the airport with IATA code SQL, see San Carlos Airport. SQL Paradigm Multi-paradigm Appeared in 1974 Designed by Donald D. Chamberlin Raymond F. Boyce Developer IBM Stable release SQL:2008 (2008) Typing discipline Static, strong Major implementations Many Dialects SQL-86, SQL-89, SQL-92, SQL:1999, SQL:2003, SQL:2008 Influenced by Datalog Influenced Agena, CQL, LINQ, Windows PowerShell OS Cross-platform SQL (officially pronounced /ˌɛskjuːˈɛl/ like "S-Q-L" but is often pronounced / ˈsiːkwəl/ like "Sequel"),[1] often referred to as Structured Query Language,[2] [3] is a database computer language designed for managing data in relational database management systems (RDBMS), and originally based upon relational algebra. Its scope includes data insert, query, update and delete, schema creation and modification, and data access control. SQL was one of the first languages for Edgar F. Codd's relational model in his influential 1970 paper, "A Relational Model of Data for Large Shared Data Banks"[4] and became the most widely used language for relational databases.[2][5] Contents [hide] * 1 History * 2 Language elements o 2.1 Queries + 2.1.1 Null and three-valued logic (3VL) o 2.2 Data manipulation o 2.3 Transaction controls o 2.4 Data definition o 2.5 Data types + 2.5.1 Character strings + 2.5.2 Bit strings + 2.5.3 Numbers + 2.5.4 Date and time o 2.6 Data control o 2.7 Procedural extensions * 3 Criticisms of SQL o 3.1 Cross-vendor portability * 4 Standardization o 4.1 Standard structure * 5 Alternatives to SQL * 6 See also * 7 References * 8 External links [edit] History SQL was developed at IBM by Donald D. -
Universidad Carlos III De Madrid Escuela Politécnica Superior
Universidad Carlos III de Madrid Escuela Politécnica Superior Ingeniería en Informática Proyecto Fin de Carrera DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Autor: Verónica Casado Manzanero Tutor: Anabel Fraga Vázquez Diciembre, 2009 DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Agradecimientos En primer lugar querría dar las gracias a mis padres, por su apoyo, comprensión, generosidad, por darme todo lo que necesito y más, y sobre todo, por enseñarme a ser como soy y servirme de ejemplo para convertirme en mejor persona cada día. También me gustaría recordar a mi hermana por ayudarme siempre en lo he necesitado con esa gran dosis de paciencia que sé que ha de tener. A mis amigas Carol y Raquel, por permanecer siempre a mi lado a pesar de estar semanas sin vernos. A mi novio Marcos, por pasarme esa paciencia y tranquilidad suya que tanto aprecio, por apoyarme en todo momento, por creer en mí y por permanecer a mi lado durante estos seis largos años. Por último, agradecerle a mi tutora Anabel toda la ayuda prestada, tanto en las asignaturas como en este proyecto. Gracias a su inestimable ayuda y entusiasmo he podido completar con éxito el trabajo aquí propuesto. Ha sido un verdadero placer trabajar con ella. Verónica Casado Manzanero 3/254 Universidad Carlos III de Madrid DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Verónica Casado Manzanero 4/254 Universidad Carlos III de Madrid DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Contenido 1. Introducción y motivación ......................................................................................... -
DATABASE and KNOWLEDGE-BASE SYSTEMS VOLUME I: CLASSICAL DATABASE SYSTEMS Jeffrey D
PRINCIPLES OF DATABASE AND KNOWLEDGE-BASE SYSTEMS VOLUME I: CLASSICAL DATABASE SYSTEMS Jeffrey D. Ullman STANFORD UNIVERSITY COMPUTER SCIENCE PRESS TABLE OF CONTENTS Chapter 1: Databases, Object Bases, and Knowledge Bases 1.1: The Capabilities of a DBMS 2 1.2: Basic Database System Terminology 7 1.3: Database Languages 12 1.4: Modern Database System Applications 18 1.5: Object-base Systems 21 1.6: Knowledge-base Systems 23 1.7: History and Perspective 28 Bibliographie Notes 29 Chapter 2: Data Models for Database Systems 32 2.1: Data Models 32 2.2: The Entity-relationship Model 34 2.3: The Relational Data Model 43 2.4: Operations in the Relational Data Model 53 2.5: The Network Data Model 65 2.6: The Hierarchical Data Model 72 2.7: An Object-Oriented Model 82 Exercises 87 Bibliographie Notes 94 Chapter 3: Logic as a Data Model 96 3.1: The Meaning of Logical Rules 96 3.2: The Datalog Data Model 100 3.3: Evaluating Nonrecursive Rules 106 3.4: Computing the Meaning of Recursive Rules 115 3.5: Incremental Evaluation of Least Fixed Points 124 3.6: Negations in Rule Bodies 128 3.7: Relational Algebra and Logic 139 3.8: Relational Calculus 145 3.9: Tuple Relational Calculus 156 3.10: The Closed World Assumption 161 Exercises 164 Bibliographie Notes 171 VIII TABLE OF CONTENTS Chapter 4: Relational Query Languages 174 4.1: General Remarks Regarding Query Languages 174 4.2: ISBL: A "Pure" Relational Algebra Language 177 4.3: QUEL: A Tuple Relational Calculus Language 185 4.4: Query-by-Example: A DRC Language 195 4.5: Data Definition in QBE 207 4.6: -
Datalog Educational System V4.2 User's Manual
Universidad Complutense de Madrid Datalog Educational System Datalog Educational System V4.2 User’s Manual Fernando Sáenz-Pérez Grupo de Programación Declarativa (GPD) Departamento de Ingeniería del Software e Inteligencia Artificial (DISIA) Universidad Complutense de Madrid (UCM) September, 25th, 2016 Fernando Sáenz-Pérez 1/341 Universidad Complutense de Madrid Datalog Educational System Copyright (C) 2004-2016 Fernando Sáenz-Pérez Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in Appendix A, in the section entitled " Documentation License ". Fernando Sáenz-Pérez 2/341 Universidad Complutense de Madrid Datalog Educational System Contents 1. Introduction........................................................................................................................... 9 1.1 Novel Extensions in DES ......................................................................................... 10 1.2 Highlights for the Current Version ........................................................................ 11 1.3 Features of DES in Short .......................................................................................... 11 1.4 Future Enhancements............................................................................................... 14 1.5 Related Work............................................................................................................ -
Ontology Languages – a Review
International Journal of Computer Theory and Engineering, Vol.2, No.6, December, 2010 1793-8201 Ontology Languages – A Review V. Maniraj, Dr.R. Sivakumar 1) Logical Languages Abstract—Ontologies have been becoming a hot research • First order predicate logic topic for the application in artificial intelligence, semantic web, Software Engineering, Library Science and information • Rule based logic Architecture. Ontology is a formal representation of set of concepts within a domain and relationships between those • concepts. It is used to reason about the properties of that Description logic domain and may be used to define the domain. An ontology language is a formal language used to encode the ontologies. A 2) Frame based Languages number of research languages have been designed and released • Similar to relational databases during the past few years by the research community. They are both proprietary and standard based. In this paper a study has 3) Graph based Languages been reported on the different features and issues of these • languages. This paper also addresses the challenges for Semantic network research community in the further development of ontology languages. • Analogy with the web is rationale for the semantic web I. INTRODUCTION Ontology engineering (or ontology building) is a subfield II. BACKGROUND of knowledge engineering that studies the methods and CycL1 in computer science and artificial intelligence is an methodologies for building ontologies. It studies the ontology language used by Doug Lenat’s Cye artificial ontology development process, the ontology life cycle, the intelligence project. Ramanathan V. Guna was instrumental methods and methodologies for building ontologies, and the in the design of the language. -
A Query Facility for Allegro Redacted for Privacy Abstract Approved: Earl F
AN ABSTRACT OF THE THESIS OF Richard Goodemoot for the degree of Master of Science in Computer Science presented on June 11 1986. Title: A Query Facility for Allegro Redacted for Privacy Abstract approved: Earl F. Ecklund, Jr. Allegro is a network database management system being developed at Oregon State University. This project adds a user friendly query facility to the system. The user is presented with pictorial display of the network records and a query interface modeled on the QueryByExample system.By request the user may be shown the network sets of the queryschema. When necessary the user may specify query navigationofthe network schema. While implemented and functional, this facility should be considered as a feasibility study for a full query system on a network data base. To provide the desired display this facility is implemented on a system separate from the main Allegro system and uses a communication interface to it. This facility is a Smalitalk implementation. A Query Facility for Allegro by Richard Goodemoot A THESIS submitted to Oregon State University In partial fulfillment of the requirements for the degree of Master of Science Completed June 11, 1986 Commencement June 1987 APPROVED: / Redacted for Privacy Adjunct Professor of Computer Science in Charge of Major Redacted for Privacy on behalf of Walter Rudd Chairman of Department of Computer Science Redacted for Privacy Dean of Gradtfate School Date thesis is presented June 11, 1986 Typed by Richard Goodemoot TABLE OF CONTENTS Page 1 INTRODUCTION 1 1.1 Network Database -
Ontology-Based Design of Space Systems
Ontology-Based Design of Space Systems Christian Hennig 1, Alexander Viehl 2, Benedikt Kämpgen 2, and Harald Eisenmann 1 1Airbus Defence and Space, Space Systems, Friedrichshafen, Germany {christian.hennig,harald.eisenmann}@airbus.com 2FZI Research Center for Information Technology, Karlsruhe, Germany {viehl,kaempgen}@fzi.de Abstract. In model-based systems engineering a model specifying the system's design is shared across a variety of disciplines and used to ensure the consisten- cy and quality of the overall design. Existing implementations for describing these system models exhibit a number of shortcomings regarding their approach to data management. In this emerging applications paper, we present the appli- cation of an ontology for space system design that provides increased semantic soundness of the underlying standardized data specification, enables reasoners to identify problems in the system, and allows the application of operational knowledge collected over past projects to the system to be designed. Based on a qualitative evaluation driven by data derived from an actual satellite design pro- ject, a reflection on the applicability of ontologies in the overall model-based systems engineering approach is pursued. Keywords: Space Systems, Systems Engineering, MBSE, ECSS-E-TM-10-23, Conceptual Data Model, OWL, Reasoning. 1 Introduction The industrial setting for producing systems to be deployed in space, such as satel- lites, launch vehicles, or science spacecraft, involves a multitude of engineering disci- plines. Each involved discipline has its own view on the system to be built, along with its own models, based on its own model semantics. For forming a consistent picture of the system, information from all relevant discipline-specific models is integrated towards an interdisciplinary system model, forming the practice of model-based sys- tems engineering (MBSE).