Xml Schema to Xslt
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Informal Data Transformation Considered Harmful
Informal Data Transformation Considered Harmful Eric Daimler, Ryan Wisnesky Conexus AI out the enterprise, so that data and programs that depend on that data need not constantly be re-validated for ev- ery particular use. Computer scientists have been develop- ing techniques for preserving data integrity during trans- formation since the 1970s (Doan, Halevy, and Ives 2012); however, we agree with the authors of (Breiner, Subrah- manian, and Jones 2018) and others that these techniques are insufficient for the practice of AI and modern IT sys- tems integration and we describe a modern mathematical approach based on category theory (Barr and Wells 1990; Awodey 2010), and the categorical query language CQL2, that is sufficient for today’s needs and also subsumes and unifies previous approaches. 1.1 Outline To help motivate our approach, we next briefly summarize an application of CQL to a data science project undertaken jointly with the Chemical Engineering department of Stan- ford University (Brown, Spivak, and Wisnesky 2019). Then, in Section 2 we review data integrity and in Section 3 we re- view category theory. Finally, we describe the mathematics of our approach in Section 4, and conclude in Section 5. We present no new results, instead citing a line of work summa- rized in (Schultz, Spivak, and Wisnesky 2017). Image used under a creative commons license; original 1.2 Motivating Case Study available at http://xkcd.com/1838/. In scientific practice, computer simulation is now a third pri- mary tool, alongside theory and experiment. Within -
Automating the Capture of Data Transformations from Statistical Scripts in Data Documentation Jie Song George Alter H
C2Metadata: Automating the Capture of Data Transformations from Statistical Scripts in Data Documentation Jie Song George Alter H. V. Jagadish University of Michigan University of Michigan University of Michigan Ann Arbor, Michigan Ann Arbor, Michigan Ann Arbor, Michigan [email protected] [email protected] [email protected] ABSTRACT CCS CONCEPTS Datasets are often derived by manipulating raw data with • Information systems → Data provenance; Extraction, statistical software packages. The derivation of a dataset transformation and loading. must be recorded in terms of both the raw input and the ma- nipulations applied to it. Statistics packages typically provide KEYWORDS limited help in documenting provenance for the resulting de- data transformation, data documentation, data provenance rived data. At best, the operations performed by the statistical ACM Reference Format: package are described in a script. Disparate representations Jie Song, George Alter, and H. V. Jagadish. 2019. C2Metadata: Au- make these scripts hard to understand for users. To address tomating the Capture of Data Transformations from Statistical these challenges, we created Continuous Capture of Meta- Scripts in Data Documentation. In 2019 International Conference data (C2Metadata), a system to capture data transformations on Management of Data (SIGMOD ’19), June 30-July 5, 2019, Am- in scripts for statistical packages and represent it as metadata sterdam, Netherlands. ACM, New York, NY, USA, 4 pages. https: in a standard format that is easy to understand. We do so by //doi.org/10.1145/3299869.3320241 devising a Structured Data Transformation Algebra (SDTA), which uses a small set of algebraic operators to express a 1 INTRODUCTION large fraction of data manipulation performed in practice. -
OWL 2 Web Ontology Language Quick Reference Guide
OWL 2 Web Ontology Language Quick Reference W3C Proposed Recommendation 22 September Guide 2009 OWL 2 Web Ontology Language Quick Reference Guide W3C Proposed Recommendation 22 September 2009 This version: http://www.w3.org/TR/2009/PR-owl2-quick-reference-20090922/ Latest version: http://www.w3.org/TR/owl2-quick-reference/ Previous version: http://www.w3.org/TR/2009/WD-owl2-quick-reference-20090611/ (color-coded diff) Editors: Jie Bao, Rensselaer Polytechnic Institute Elisa F. Kendall, Sandpiper Software, Inc. Deborah L. McGuinness, Rensselaer Polytechnic Institute Peter F. Patel-Schneider, Bell Labs Research, Alcatel-Lucent Contributors: Li Ding, Rensselaer Polytechnic Institute Ankesh Khandelwal, Rensselaer Polytechnic Institute This document is also available in these non-normative formats: PDF version, Reference Card. Copyright © 2009 W3C® (MIT, ERCIM, Keio), All Rights Reserved. W3C liability, trademark and document use rules apply. Abstract The OWL 2 Web Ontology Language, informally OWL 2, is an ontology language for the Semantic Web with formally defined meaning. OWL 2 ontologies provide classes, properties, individuals, and data values and are stored as Semantic Web documents. OWL 2 ontologies can be used along with information written in RDF, and OWL 2 ontologies themselves are primarily exchanged as RDF documents. The OWL 2 Document Overview describes the overall state of OWL 2, and should be read before other OWL 2 documents. Page 1 of 15 http://www.w3.org/TR/2009/PR-owl2-quick-reference-20090922/ OWL 2 Web Ontology Language Quick Reference W3C Proposed Recommendation 22 September Guide 2009 This document provides a non-normative quick reference guide to the OWL 2 language. -
What Is a Data Warehouse?
What is a Data Warehouse? By Susan L. Miertschin “A data warehouse is a subject oriented, integrated, time variant, nonvolatile, collection of data in support of management's decision making process.” https: //www. bus iness.auc. dk/oe kostyr /file /What_ is_a_ Data_ Ware house.pdf 2 What is a Data Warehouse? “A copy of transaction data specifically structured for query and analysis” 3 “Data Warehousing is the coordination, architected, and periodic copying of data from various sources, both inside and outside the enterprise, into an environment optimized for analytical and informational processing” ‐ Alan Simon Data Warehousing for Dummies 4 Business Intelligence (BI) • “…implies thinking abstractly about the organization, reasoning about the business, organizing large quantities of information about the business environment.” p. 6 in Giovinazzo textbook • Purpose of BI is to define and execute a strategy 5 Strategic Thinking • Business strategist – Always looking forward to see how the company can meet the objectives reflected in the mission statement • Successful companies – Do more than just react to the day‐to‐day environment – Understand the past – Are able to predict and adapt to the future 6 Business Intelligence Loop Business Intelligence Figure 1‐1 p. 2 Giovinazzo • Encompasses entire loop shown Business Strategist • Data Storage + ETC = OLAP Data Mining Reports Data Warehouse Data Storage • Data WhWarehouse + Tools (yellow) = Extraction,Transformation, Cleaning DiiDecision Support CRM Accounting Finance HR System 7 The Data Warehouse Decision Support Systems Central Repository Metadata Dependent Data DtData Mar t EtExtrac tion DtData Log Administration Cleansing/Tranformation External Extraction Source Extraction Store Independent Data Mart Operational Environment Figure 1-2 p. -
POLITECNICO DI TORINO Repository ISTITUZIONALE
POLITECNICO DI TORINO Repository ISTITUZIONALE Rethinking Software Network Data Planes in the Era of Microservices Original Rethinking Software Network Data Planes in the Era of Microservices / Miano, Sebastiano. - (2020 Jul 13), pp. 1-175. Availability: This version is available at: 11583/2841176 since: 2020-07-22T19:49:25Z Publisher: Politecnico di Torino Published DOI: Terms of use: Altro tipo di accesso This article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository Publisher copyright (Article begins on next page) 08 October 2021 Doctoral Dissertation Doctoral Program in Computer and Control Enginering (32nd cycle) Rethinking Software Network Data Planes in the Era of Microservices Sebastiano Miano ****** Supervisor Prof. Fulvio Risso Doctoral examination committee Prof. Antonio Barbalace, Referee, University of Edinburgh (UK) Prof. Costin Raiciu, Referee, Universitatea Politehnica Bucuresti (RO) Prof. Giuseppe Bianchi, University of Rome “Tor Vergata” (IT) Prof. Marco Chiesa, KTH Royal Institute of Technology (SE) Prof. Riccardo Sisto, Polytechnic University of Turin (IT) Politecnico di Torino 2020 This thesis is licensed under a Creative Commons License, Attribution - Noncommercial- NoDerivative Works 4.0 International: see www.creativecommons.org. The text may be reproduced for non-commercial purposes, provided that credit is given to the original author. I hereby declare that, the contents and organisation of this dissertation constitute my own original work and does not compromise in any way the rights of third parties, including those relating to the security of personal data. ........................................ Sebastiano Miano Turin, 2020 Summary With the advent of Software Defined Networks (SDN) and Network Functions Virtualization (NFV), software started playing a crucial role in the computer net- work architectures, with the end-hosts representing natural enforcement points for core network functionalities that go beyond simple switching and routing. -
Data Warehousing on AWS
Data Warehousing on AWS March 2016 Amazon Web Services – Data Warehousing on AWS March 2016 © 2016, Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document is provided for informational purposes only. It represents AWS’s current product offerings and practices as of the date of issue of this document, which are subject to change without notice. Customers are responsible for making their own independent assessment of the information in this document and any use of AWS’s products or services, each of which is provided “as is” without warranty of any kind, whether express or implied. This document does not create any warranties, representations, contractual commitments, conditions or assurances from AWS, its affiliates, suppliers or licensors. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. Page 2 of 26 Amazon Web Services – Data Warehousing on AWS March 2016 Contents Abstract 4 Introduction 4 Modern Analytics and Data Warehousing Architecture 6 Analytics Architecture 6 Data Warehouse Technology Options 12 Row-Oriented Databases 12 Column-Oriented Databases 13 Massively Parallel Processing Architectures 15 Amazon Redshift Deep Dive 15 Performance 15 Durability and Availability 16 Scalability and Elasticity 16 Interfaces 17 Security 17 Cost Model 18 Ideal Usage Patterns 18 Anti-Patterns 19 Migrating to Amazon Redshift 20 One-Step Migration 20 Two-Step Migration 20 Tools for Database Migration 21 Designing Data Warehousing Workflows 21 Conclusion 24 Further Reading 25 Page 3 of 26 Amazon Web Services – Data Warehousing on AWS March 2016 Abstract Data engineers, data analysts, and developers in enterprises across the globe are looking to migrate data warehousing to the cloud to increase performance and lower costs. -
SLA Information Technology Division Metadata for Video: Too Much
3/1/2019 Metadata for Video: Too Much Content, Not Enough Information | SLA Information Technology Division Home About Us » Events » Sections » Enter search keyword SLA Information Technology Division Awards » Making Edgier Easier. We're IT! Current b/ITe (v31n5) » b/ITe Archives Virtual Events What’s New Categorized | Uncategorized Metadata for Video: Too Much Content, Not Enough Information Posted on 31 August 2012. by Wayne Pender, McGill University, 2012 Joe Ann Clifton Student Award Winner (Published September 1, 2012) Abstract Television news libraries have struggled with cataloguing, organizing and storing visual materials for efficient search and retrieval for years. This task has been complicated by the emergence of digital video and the exponential growth in the holdings of television news libraries. In parallel, the ability for non-professionals to shoot, edit and publish videos of increasing production value and complexity on the web has flooded the Internet with videos that appeal to a wide audience and subject interest. The present survey looks at the metadata protocols and practices in place for an internal audience in professional operations and on display for the general public on the Internet. The study finds that the lack of a common metadata schema can make much of the material inaccessible. Literature pertaining to this area is reviewed and future direction is discussed. http://it.sla1.org/2012/08/metadata/ 1/10 3/1/2019 Metadata for Video: Too Much Content, Not Enough Information | SLA Information Technology Division Keywords: metadata, video, XML, RDF, MXF, television, news, YouTube Paper Searching and retrieving visual content is problematic. The search for specific moving images on film and video has long been a task for television news professionals, but now with wide spread availability of video resources on the Internet it has become a task for anyone. -
Instruction for Using XML Notepad
Using XML Notepad to Read, Edit, and Parse FGDC-CSDGM XML Metadata. Currently many tools exist that allow a user to work with XML metadata files. However, most of the main metadata creation/editing tools do not provide a means of validating a metadata record for compliance with the FGDC-CSDGM standard or its variants, such as the Biological Data Profile (BDP) standard. One of the most reliable ways to validate metadata files (that is, check files for completion and/or errors) is with the USGS Metadata Parser (MP) utilities. The MP tool and the other tools distributed in the package were developed by Peter Schweitzer and are freely available online (http://geology.usgs.gov/tools/metadata/tools/doc/mp.html). Once configured properly, MP can process metadata files and be used to produce a text file with a list of all errors found. A user can then use this list of errors to find and correct the problems using a text editor or the metadata editor of their choice. The challenge with this method is that finding and correcting the error within a metadata editor based solely on the error message from MP can be confusing and frustrating. Additionally, if there are many errors or complex problems to correct, the process will require multiple iterations of running MP and subsequently correcting errors in a metadata editor. All in all, the process can be fairly time consuming and onerous, especially for those without a detailed understanding of the FGDC-CSDGM standard. The methodology for metadata editing and validation described in this document tries to address some of these problems with an alternative approach. -
Cisco XML Schemas
CHAPTER 14 Cisco XML Schemas This chapter contains information about common XML schemas. The structure and allowable content of the XML request and response instances supported by the Cisco IOS XR XML application programming interface (API) are documented by means of XML schemas (.xsd files). The XML schemas are documented using the standard World Wide Web Consortium (W3C) XML schema language, which provides a much more powerful and flexible mechanism for describing schemas than can be achieved using Document Type Definitions (DTDs). The set of XML schemas consists of a small set of common high-level schemas and a larger number of component-specific schemas as described in this chapter. For more information on the W3C XML Schema standard, see this URL: http://www.w3.org/XML/Schema This chapter contains these sections: • XML Schema Retrieval, page 14-135 • Common XML Schemas, page 14-136 • Component XML Schemas, page 14-136 XML Schema Retrieval The XML schemas that belong to the features in a particular package are obtained as a .tar file from cisco.com. To retrieve the XML schemas, you must: 1. Click this URL to display the Downloads page: http://tools.cisco.com/support/downloads/go/Redirect.x?mdfid=268437899 Note Select Downloads. Only customer or partner viewers can access the Download Software page. Guest users will get an error. 2. Select Cisco IOS XR Software. 3. Select IOS XR XML Schemas. 4. Select the XML schema for your platform. Once untarred, all the XML schema files appear as a flat directory of .xsd files and can be opened with any XML schema viewing application, such as XMLSpy. -
Modularization of XHTML in XML Schema Modularization of XHTML™ in XML Schema
Modularization of XHTML in XML Schema Modularization of XHTML™ in XML Schema Modularization of XHTML™ in XML Schema W3C Working Draft - 22 March 2001 This version: http://www.w3.org/TR/2001/WD-xhtml-m12n-schema-20010322 (Single HTML file [p.1] , PostScript version, PDF version, ZIP archive, or Gzip’d TAR archive) Latest version: http://www.w3.org/TR/xhtml-m12n-schema Editors: Daniel Austin, Mozquito Technologies AG Shane McCarron, Applied Testing and Technology Copyright ©2001 W3C® (MIT, INRIA, Keio), All Rights Reserved. W3C liability, trademark, document use and software licensing rules apply. Abstract This document describes a methodology for the modularization of XHTML using XML Schema. Modularization of XHTML allows document authors to modify and extend XHTML in a conformant way. Status of This Document This section describes the status of this document at the time of its publication. Other documents may supersede this document. The latest status of this document series is maintained at the W3C. This is the first public "Working Draft" of "Modularization of XHTML in XML Schema" for review by members of the W3C and other interested parties in the general public. It is a stand-alone document to ease its review. Once the methodology described in this document become mature, it will be integrated into a future document forthcoming from the HTML Working Group. This document is still in its early stage, and may be updated, replaced, or obsoleted by other documents at any time. Publication of this Working Draft does not imply endorsement by the W3C, and it is inappropriate to use W3C Working Drafts as reference material or to cite them as other than "work in progress". -
The Development of Algorithms for On-Demand Map Editing for Internet and Mobile Users with Gml and Svg
THE DEVELOPMENT OF ALGORITHMS FOR ON-DEMAND MAP EDITING FOR INTERNET AND MOBILE USERS WITH GML AND SVG Miss. Ida K.L CHEUNG a, , Mr. Geoffrey Y.K. SHEA b a Department of Land Surveying & Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, email: [email protected] b Department of Land Surveying & Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, email: [email protected] Commission VI, PS WG IV/2 KEY WORDS: Spatial Information Sciences, GIS, Research, Internet, Interoperability ABSTRACT: The widespread availability of the World Wide Web has led to a rapid increase in the amount of data accessing, sharing and disseminating that gives the opportunities for delivering maps over the Internet as well as small mobile devices. In GIS industry, many vendors or companies have produced their own web map products which have their own version, data model and proprietary data formats without standardization. Such problem has long been an issue. Therefore, Geographic Markup Language (GML) was designed to provide solutions. GML is an XML grammar written in XML Schema for the modelling, transport, and storage of geographic information including both spatial and non-spatial properties of geographic features. GML is developed by Open GIS Consortium in order to promote spatial data interoperability and open standard. Since GML is still a newly developed standard, this promising research field provides a standardized method to integrate web-based mapping information in terms of data modelling, spatial data representation mechanism and graphic presentation. As GML is not for data display, SVG is an ideal vector graphic for displaying geographic data. -
Master Data Simplified
MASTER THE BEST IN MASTER DATA GOVERNANCE DATA SIMPLIFICATION & MANAGEMENT SIMPLIFIED CHARLIE MASSOGLIA & ANBARASAN MURUGAN ‘A MUST READ FOR DATA ENTHUISIASTS’ - AUSTIN DAVIS About the Authors Charlie Massoglia VP & CIO, Chain-Sys Corporation Former CIO for Dawn Food Products For 13+ years. 25+ years experience with a variety of ERP systems. Extensive experience in system migrations & conversions. Participated in 9 acquisitions ranging from a single US location to 14 sites in 11 countries. Author of numerous technical books, articles, presentations, and seminars globally. Anbarasan Murugan Product Lead, Master Data Management Master Data Simplification & Governance expert. Industry experience of more than 11 years. Chief Technical Architect for more than 10 products TM within the Chain Sys Platform . Has designed complex analytical & transactional Master data processes for Fortune 500 companies. Master Data Simplified An Introduction to Master Data Simplification, Governance, and Management By Charles L. Massoglia VP & CIO Chain●Sys Corporation [email protected] and Anbarasan Murugan Product Manager Chain●Sys Corporation [email protected] No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without written permission of the publisher. For information regarding permission, write to Chain-Sys Corporation, Attention: Permissions Department, 325 S. Clinton Street, Suite 205, Grand Ledge, MI 48837 Trademarks: Chain●Sys Platform is a trademark of Chain-Sys Corporation in the United States and other countries and may not be used without permission.