Requirements for XML Document Database Systems Airi Salminen Frank Wm
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Oracle Database VLDB and Partitioning Guide, 11G Release 2 (11.2) E25523-01
Oracle® Database VLDB and Partitioning Guide 11g Release 2 (11.2) E25523-01 September 2011 Oracle Database VLDB and Partitioning Guide, 11g Release 2 (11.2) E25523-01 Copyright © 2008, 2011, Oracle and/or its affiliates. All rights reserved. Contributors: Hermann Baer, Eric Belden, Jean-Pierre Dijcks, Steve Fogel, Lilian Hobbs, Paul Lane, Sue K. Lee, Diana Lorentz, Valarie Moore, Tony Morales, Mark Van de Wiel 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 is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, the following notice is applicable: U.S. GOVERNMENT RIGHTS Programs, software, databases, and related documentation and technical data delivered to U.S. Government customers are "commercial computer software" or "commercial technical data" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, the use, duplication, disclosure, modification, and adaptation shall be subject to the restrictions and license terms set forth in the applicable Government contract, and, to the extent applicable by the terms of the Government contract, the additional rights set forth in FAR 52.227-19, Commercial Computer Software License (December 2007). -
Data Warehouse Fundamentals for Storage Professionals – What You Need to Know EMC Proven Professional Knowledge Sharing 2011
Data Warehouse Fundamentals for Storage Professionals – What You Need To Know EMC Proven Professional Knowledge Sharing 2011 Bruce Yellin Advisory Technology Consultant EMC Corporation [email protected] Table of Contents Introduction ................................................................................................................................ 3 Data Warehouse Background .................................................................................................... 4 What Is a Data Warehouse? ................................................................................................... 4 Data Mart Defined .................................................................................................................. 8 Schemas and Data Models ..................................................................................................... 9 Data Warehouse Design – Top Down or Bottom Up? ............................................................10 Extract, Transformation and Loading (ETL) ...........................................................................11 Why You Build a Data Warehouse: Business Intelligence .....................................................13 Technology to the Rescue?.......................................................................................................19 RASP - Reliability, Availability, Scalability and Performance ..................................................20 Data Warehouse Backups .....................................................................................................26 -
Health Sensor Data Management in Cloud
Special Issue - 2015 International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 NCRTS-2015 Conference Proceedings Health Sensor Data Management in Cloud Rashmi Sahu Department of Computer Science and Engineering BMSIT,Avallahalli,Yelahanka,Bangalore Visveswariya Technological University Abstract--Wearable sensor devices with cloud computing uses its software holds 54% of patient records in US and feature have great impact in our daily lives. This 2.5% of patient records in world wide.[9] technology provides services to acquire, consume and share personal health information. Apart from that we can be How resource wastage can be refused by using cloud connected with smart phones through which we can access technology information through sensor devices equipped with our smart phone. Now smartphones has been resulted in the new ways. It is getting embedded with sensor devices such as Suppose there are 3 Hospitals A,B,C.Each hospital cameras, microphones, accelerometers, proximity sensors, maintains their own network database server,they have GPS etc. through which we can track information and management department and softwares,maintainance significant parameter about physiology. Some of the department and softwares.They organizes their own data wearable tech devices are popular today like Jawbone Up and they maintained by their own.But there is resource and Fitbit Flex, HeartMath Inner Balance Sensor, wastage,means three different health organizations Tinke.This paper is survey in area of medical field that utilizing resources having paid and costs three times of represents why cloud technologies used in medical field and single plus waste of data space also.so why can’t we how health data managed in cloud. -
Study Material for B.Sc.Cs Dataware Housing and Mining Semester - Vi, Academic Year 2020-21
STUDY MATERIAL FOR B.SC.CS DATAWARE HOUSING AND MINING SEMESTER - VI, ACADEMIC YEAR 2020-21 UNIT CONTENT PAGE Nr I DATA WARE HOUSING 03 II BUSINESS ANALYSIS 10 III DATA MINING 18 IV ASSOCIATION RULE MINING AND CLASSIFICATION 35 V CLUSTER ANALYSIS 53 Page 1 of 66 STUDY MATERIAL FOR B.SC.CS DATAWARE HOUSING AND MINING SEMESTER - VI, ACADEMIC YEAR 2020-21 UNIT I: DATA WAREHOUSING Data warehousing Components: ->Overall Architecture Data warehouse architecture is Based on a relational database management system server that functions as the central repository (a central location in which data is stored and managed) for informational data In the data warehouse architecture, operational data and processing is separate and data warehouse processing is separate. Central information repository is surrounded by a number of key components. These key components are designed to make the entire environment- (i) functional, (ii) manageable and (iii) accessible by both the operational systems that source data into warehouse by end-user query and analysis tools. Page 2 of 66 STUDY MATERIAL FOR B.SC.CS DATAWARE HOUSING AND MINING SEMESTER - VI, ACADEMIC YEAR 2020-21 The source data for the warehouse comes from the operational applications As data enters the data warehouse, it is transformed into an integrated structure and format The transformation process may involve conversion, summarization, filtering, and condensation of data Because data within the data warehouse contains a large historical component the data warehouse must b capable of holding and managing large volumes of data and different data structures for the same database over time. ->Data Warehouse Database Central data warehouse database is a foundation for data warehousing environment. -
Public 1 Agenda
© 2013 SAP AG. All rights reserved. Public 1 Agenda Welcome Agenda • Introduction to Dobler Consulting • SAP IQ Roadmap – What to Expect • Q&A Presenters • Courtney Claussen - SAP IQ Product Management • Peter Dobler - CEO Dobler Consulting Closing © 2013 SAP AG. All rights reserved. Public 2 Introduction to Dobler Consulting Dobler Consulting is a leading information technology and database services company, offering a broad spectrum of services to their clients; acting as your Trusted Adviser, Provide License Sales, Architectural Review and Design Consulting, Optimization Services, High Availability review and enablement, Training and Cross Training, and lastly ongoing support and preventative maintenance. Founded in 2000, the Tampa consulting firm specializes in SAP/Sybase, Microsoft SQL Server, and Oracle. Visit us online at www.doblerconsulting.com, or contact us at 813 322 3240, or [email protected]. © 2013 SAP AG. All rights reserved. Public 3 Your Data is Your DNA, Dobler Consulting Focus Areas Strategic Database Consulting SAP VAR D&T DBA Database Managed Training Services Programs Cross-Platform Expertise SAP Sybase® SQL Server® Oracle® © 2013 SAP AG. All rights reserved. Public 4 What’s Ahead ISUG-TECH Annual Conference April 14-17, Atlanta • Register at http://my.isug.com/conference/registration • Early Bird ending 2/28/14 (free hotel room with registration) SAPPHIRENOW Annual Conference June 3-5, Orlando • Come visit our kiosk in the exhibition hall © 2013 SAP AG. All rights reserved. Public 5 SAP IQ Roadmap Dobler Events Webinar Courtney Claussen / SAP IQ Product Management February 27, 2014 Disclaimer This presentation outlines our general product direction and should not be relied on in making a purchase decision. -
Database Machines in Support of Very Large Databases
Rochester Institute of Technology RIT Scholar Works Theses 1-1-1988 Database machines in support of very large databases Mary Ann Kuntz Follow this and additional works at: https://scholarworks.rit.edu/theses Recommended Citation Kuntz, Mary Ann, "Database machines in support of very large databases" (1988). Thesis. Rochester Institute of Technology. Accessed from This Thesis is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For more information, please contact [email protected]. Rochester Institute of Technology School of Computer Science Database Machines in Support of Very large Databases by Mary Ann Kuntz A thesis. submitted to The Faculty of the School of Computer Science. in partial fulfillment of the requirements for the degree of Master of Science in Computer Systems Management Approved by: Professor Henry A. Etlinger Professor Peter G. Anderson A thesis. submitted to The Faculty of the School of Computer Science. in partial fulfillment of the requirements for the degree of Master of Science in Computer Systems Management Approved by: Professor Henry A. Etlinger Professor Peter G. Anderson Professor Jeffrey Lasky Title of Thesis: Database Machines In Support of Very Large Databases I Mary Ann Kuntz hereby deny permission to reproduce my thesis in whole or in part. Date: October 14, 1988 Mary Ann Kuntz Abstract Software database management systems were developed in response to the needs of early data processing applications. Database machine research developed as a result of certain performance deficiencies of these software systems. -
A Methodology for Evaluating Relational and Nosql Databases for Small-Scale Storage and Retrieval
Air Force Institute of Technology AFIT Scholar Theses and Dissertations Student Graduate Works 9-1-2018 A Methodology for Evaluating Relational and NoSQL Databases for Small-Scale Storage and Retrieval Ryan D. Engle Follow this and additional works at: https://scholar.afit.edu/etd Part of the Databases and Information Systems Commons Recommended Citation Engle, Ryan D., "A Methodology for Evaluating Relational and NoSQL Databases for Small-Scale Storage and Retrieval" (2018). Theses and Dissertations. 1947. https://scholar.afit.edu/etd/1947 This Dissertation is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, please contact [email protected]. A METHODOLOGY FOR EVALUATING RELATIONAL AND NOSQL DATABASES FOR SMALL-SCALE STORAGE AND RETRIEVAL DISSERTATION Ryan D. L. Engle, Major, USAF AFIT-ENV-DS-18-S-047 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio DISTRIBUTION STATEMENT A. Approved for public release: distribution unlimited. AFIT-ENV-DS-18-S-047 The views expressed in this paper are those of the author and do not reflect official policy or position of the United States Air Force, Department of Defense, or the U.S. Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. i AFIT-ENV-DS-18-S-047 A METHODOLOGY FOR EVALUATING RELATIONAL AND NOSQL DATABASES FOR SMALL-SCALE STORAGE AND RETRIEVAL DISSERTATION Presented to the Faculty Department of Systems and Engineering Management Graduate School of Engineering and Management Air Force Institute of Technology Air University Air Education and Training Command In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Ryan D. -
XML Normal Form (XNF)
Ryan Marcotte www.cs.uregina.ca/~marcottr CS 475 (Advanced Topics in Databases) March 14, 2011 Outline Introduction to XNF and motivation for its creation Analysis of XNF’s link to BCNF Algorithm for converting a DTD to XNF Example March 14, 2011 Ryan Marcotte 2 March 14, 2011 Ryan Marcotte 3 Introduction XML is used for data storage and exchange Data is stored in a hierarchical fashion Duplicates and inconsistencies may exist in the data store March 14, 2011 Ryan Marcotte 4 Introduction Relational databases store data according to some schema XML also stores data according to some schema, such as a Document Type Definition (DTD) Obviously, some schemas are better than others A normal form is needed that reduces the amount of storage needed while ensuring consistency and eliminating redundancy March 14, 2011 Ryan Marcotte 5 Introduction XNF was proposed by Marcelo Arenas and Leonid Libkin (University of Toronto) in a 2004 paper titled “A Normal Form for XML Documents” Recognized a need for good XML data design as “a lot of data is being put on the web” “Once massive web databases are created, it is very hard to change their organization; thus, there is a risk of having large amounts of widely accessible, but at the same time poorly organized legacy data.” March 14, 2011 Ryan Marcotte 6 Introduction XNF provides a set of rules that describe well-formed DTDs Poorly-designed DTDs can be transformed into well- formed ones (through normalization – just like relational databases!) Well-formed DTDs avoid redundancies and update -
Model-Based XML to Relational Database Mapping Choices
International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3S, October 2019 Model-based XML to Relational Database Mapping Choices Emyliana Song, Su-Cheng Haw, Fang-Fang Chua Type Definition file (DTD) or XML schema to define Abstract— Extensible Markup Language (XML) technology structure of XML document. For model-based mapping, is widely used for data exchange and data representation in both DTD and XML schema is not needed. online and offline mode. This structured format language able to be transformed into other formats and share information The rest of the paper is organized as follows. Existing and across platforms. XML is simple; however, it is designed to related approaches on model-based mapping schemes are accommodate changes. For this paper, a study on reviewed in section 2. Section 3 discussed the performance transformation of XML document into relational database is evaluation carried out in the experiment of selected conducted. Crucial part of this process is how to maintain the approaches. Experimental results and analysis of the hierarchy and relationships between data in the document into findings are presented in section 4. And lastly, Section 5 database. Approaches that are discussed in this paper each uses own unique way of data storing technique and database design. conclude the paper. Therefore, each algorithm is assessed with three datasets constitute of small, medium and large size XML file. The II. LITERATURE REVIEW efficiency of the algorithms is being tested on time taken for data storing and query execution process. At the end of the Throughout the years, numerous mapping schemes have evaluation, we discuss factors that affect algorithm performance been proposed to resolve issues on transforming XML to and present suggestions to improve mapping scheme for future relational database structure. -
VLDB Prerequisite for the Success of Digital India 02 Content
VLDB Prerequisite for the success of Digital India 02 Content Foreword 05 Introduction to Very Large Database 06 Adoption of VLDB 07 Overview of Digital India Programme 10 How VLDB Can Enable Digital India Programme 12 Key VLDB Challenges and Solutions 13 Conclusion 20 References 21 Contacts 21 03 VLDB | Prerequisite for the success of Digital India 04 VLDB | Prerequisite for the success of Digital India Foreword A few decades ago, data was considered a byproduct To tap ongoing momentum of digitizing India, there is a of algorithms or processes, not quite an integral great need to develop an atmosphere of impregnable part. But as the algorithms started being used for association between government, industry and businesses, it was realized that data generated is common man. A new kind of professional has not just a byproduct, rather an essential part of the emerged, the data scientist, who possesses the skills of process. Personal desktops also began using client- software programmer, statistician and artist to extract server databases regularly. Two decades later, we see the data. With time, the data generated and processed databases being involved in activities we perform on a will further increase and new solutions will have to be daily basis. The presence of the “Industrial Revolution devised, but this first step is essential in ensuring that of Data” is being felt all over the world, from science the whole country moves towards digitization as one. to the arts, from business to government. Digital information increases tenfold every five years that The purpose of this report is to promote discussions results in a vast amount of data being shared. -
XML in Oracle 9I
Geoff Lee Senior Product Manager Oracle Corporation XML in Oracle9i A Technical Overview Agenda ! Survey ! Technical Overview ! Summary ! Q & A Agenda ! Survey ! Technical Overview ! Summary ! Q & A XML in Oracle9i - Overview ! XML and its family of standards are vital to the future of e-Business ! Oracle9i XML Developer’s Kits support the family of XML standards to provide a complete XML-enabled Internet application platform ! Oracle9i Database Native XML support enables fast, flexible, and scalable storage and retrieval of XML data and documents ! XML Messaging and Transformation support in AQ provide a centralized, easy to manage, secure infrastructure for global messaging Agenda ! Survey ! Technical Overview – XML – XDK – Database Native XML – XML Messaging ! Summary ! Q & A Application Requirements 1 Internet Content 2 Internet Application Management Development ConsolidateConsolidate InternetInternet contentcontent MakeMake webweb sitesite transactional,transactional, BuildBuild dynamicdynamic webweb sites/portalssites/portals secure,secure, scalablescalable andand availableavailable 5 3 Enterprise Application Business Intelligence Integration Capture,Capture, analyze,analyze, andand shareshare IntegrateIntegrate webweb sites,sites, ERP,ERP, businessbusiness intelligenceintelligence legacylegacy systems,systems, supplierssuppliers 4 Mobile Information Access MakeMake webweb sitessites accessibleaccessible fromfrom anyany mobilemobile devicedevice Internet N-Tier Architecture Web Browser HTTP Listener Dispatcher Web Server HTML Application -
Big Data Query Optimization -Literature Survey
Big Data Query Optimization -Literature Survey Anuja S. ( [email protected] ) SRM Institute of Science and Technology Malathy C. SRM Institute of Science and Technology Research Keywords: Big data, Parallelism, optimization, hadoop, and map reduce. Posted Date: July 12th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-655386/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/9 Abstract In today's world, most of the private and public sector organizations deal with massive amounts of raw data, which includes information and knowledge in their secret layer. In addition, the format, scale, variety, and velocity of generated data make it more dicult to use the algorithms in an ecient manner. This complexity necessitates the use of sophisticated methods, strategies, and algorithms to solve the challenges of managing raw data. Big data query optimization (BDQO) requires businesses to dene, diagnose, forecast, prescribe, and cognize hidden growth opportunities and guiding them toward achieving market value. BDQO uses advanced analytical methods to extract information from an increasingly growing volume of data, resulting in a reduction in the diculty of the decision-making process. Hadoop, Apache Hive, No SQL, Map Reduce, and HPCC are the technologies used in big data applications to manage large data. It is less costly to consume data for query processing because big data provides scalability. However, small businesses will never be able to query large databases. Joining tables with millions of tuples could take hours. Parallelism, which solves the problem by using more processors, may be a potential solution.