PREMIS Data Dictionary for Preservation Metadata
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EY Fall02.Pdf
THE JOURNAL OF THE School of Forestry & Environmental Studies EnvironmentYale Fall 2002 LMS Software Changing the Face of Forest Management Inside: Reflections on the Johannesburg Summit, page 36 letters It is a magnificent production, well balanced I write to express my disappointment with the and with outstanding texts and pictures. I liked tone of the new Yale F&ES journal. Cover particularly Dean Speth’s message: “Did 9/11 headlines, such as “Hidden Dangers,”and its really change everything?” I have circulated the accompanying article that point up risks without journal to our graduate students and to various adequate discussion of the rationale, histories, staff members, ending in the library. I am tradeoffs and contexts for those risks signals that eagerly awaiting the next issue. Thank you for the school has decided to follow the “histrionic your effort. model”of raising environmental awareness (and, GERARDO BUDOWSKI,YC ’56,PH.D.1962 I am sure, funding). This contrasts with the SENIOR PROFESSOR traditional academic model, which seeks DEPARTMENT NATURAL RESOURCES AND PEACE sobriety, balance and accuracy over hysteria. UNIVERSITY FOR PEACE While I agree that there is a place for emotion SAN JOSE,COSTA RICA and metaphor to help generate public concern about environmental issues, I do not want to see academic institutions—and particularly Yale— go down this slippery path. Leave the emotion The first edition of Environment: Yale was very The inaugural issue of Environment: and “necessary” distortions in context to the impressive—congratulations. Yale elicited many responses. Because environmental NGOs. Nonetheless, I found the of space limitations, only a representa- MARK DAMIAN DUDA,M.E.S.’85 coverage of Dr. -
Artificial Intelligence: Distinguishing Between Types & Definitions
19 NEV. L.J. 1015, MARTINEZ 5/28/2019 10:48 AM ARTIFICIAL INTELLIGENCE: DISTINGUISHING BETWEEN TYPES & DEFINITIONS Rex Martinez* “We should make every effort to understand the new technology. We should take into account the possibility that developing technology may have im- portant societal implications that will become apparent only with time. We should not jump to the conclusion that new technology is fundamentally the same as some older thing with which we are familiar. And we should not hasti- ly dismiss the judgment of legislators, who may be in a better position than we are to assess the implications of new technology.”–Supreme Court Justice Samuel Alito1 TABLE OF CONTENTS INTRODUCTION ............................................................................................. 1016 I. WHY THIS MATTERS ......................................................................... 1018 II. WHAT IS ARTIFICIAL INTELLIGENCE? ............................................... 1023 A. The Development of Artificial Intelligence ............................... 1023 B. Computer Science Approaches to Artificial Intelligence .......... 1025 C. Autonomy .................................................................................. 1026 D. Strong AI & Weak AI ................................................................ 1027 III. CURRENT STATE OF AI DEFINITIONS ................................................ 1029 A. Black’s Law Dictionary ............................................................ 1029 B. Nevada ..................................................................................... -
SQL Server Column Store Indexes Per-Åke Larson, Cipri Clinciu, Eric N
SQL Server Column Store Indexes Per-Åke Larson, Cipri Clinciu, Eric N. Hanson, Artem Oks, Susan L. Price, Srikumar Rangarajan, Aleksandras Surna, Qingqing Zhou Microsoft {palarson, ciprianc, ehans, artemoks, susanpr, srikumar, asurna, qizhou}@microsoft.com ABSTRACT SQL Server column store indexes are “pure” column stores, not a The SQL Server 11 release (code named “Denali”) introduces a hybrid, because they store all data for different columns on new data warehouse query acceleration feature based on a new separate pages. This improves I/O scan performance and makes index type called a column store index. The new index type more efficient use of memory. SQL Server is the first major combined with new query operators processing batches of rows database product to support a pure column store index. Others greatly improves data warehouse query performance: in some have claimed that it is impossible to fully incorporate pure column cases by hundreds of times and routinely a tenfold speedup for a store technology into an established database product with a broad broad range of decision support queries. Column store indexes are market. We’re happy to prove them wrong! fully integrated with the rest of the system, including query To improve performance of typical data warehousing queries, all a processing and optimization. This paper gives an overview of the user needs to do is build a column store index on the fact tables in design and implementation of column store indexes including the data warehouse. It may also be beneficial to build column enhancements to query processing and query optimization to take store indexes on extremely large dimension tables (say more than full advantage of the new indexes. -
When Relational-Based Applications Go to Nosql Databases: a Survey
information Article When Relational-Based Applications Go to NoSQL Databases: A Survey Geomar A. Schreiner 1,* , Denio Duarte 2 and Ronaldo dos Santos Mello 1 1 Departamento de Informática e Estatística, Federal University of Santa Catarina, 88040-900 Florianópolis - SC, Brazil 2 Campus Chapecó, Federal University of Fronteira Sul, 89815-899 Chapecó - SC, Brazil * Correspondence: [email protected] Received: 22 May 2019; Accepted: 12 July 2019; Published: 16 July 2019 Abstract: Several data-centric applications today produce and manipulate a large volume of data, the so-called Big Data. Traditional databases, in particular, relational databases, are not suitable for Big Data management. As a consequence, some approaches that allow the definition and manipulation of large relational data sets stored in NoSQL databases through an SQL interface have been proposed, focusing on scalability and availability. This paper presents a comparative analysis of these approaches based on an architectural classification that organizes them according to their system architectures. Our motivation is that wrapping is a relevant strategy for relational-based applications that intend to move relational data to NoSQL databases (usually maintained in the cloud). We also claim that this research area has some open issues, given that most approaches deal with only a subset of SQL operations or give support to specific target NoSQL databases. Our intention with this survey is, therefore, to contribute to the state-of-art in this research area and also provide a basis for choosing or even designing a relational-to-NoSQL data wrapping solution. Keywords: big data; data interoperability; NoSQL databases; relational-to-NoSQL mapping 1. -
Data Dictionary a Data Dictionary Is a File That Helps to Define The
Cleveland | v. 216.369.2220 • Columbus | v. 614.291.8456 Data Dictionary A data dictionary is a file that helps to define the organization of a particular database. The data dictionary acts as a description of the data objects or items in a model and is used for the benefit of the programmer or other people who may need to access it. A data dictionary does not contain the actual data from the database; it contains only information for how to describe/manage the data; this is called metadata*. Building a data dictionary provides the ability to know the kind of field, where it is located in a database, what it means, etc. It typically consists of a table with multiple columns that describe relationships as well as labels for data. A data dictionary often contains the following information about fields: • Default values • Constraint information • Definitions (example: functions, sequence, etc.) • The amount of space allocated for the object/field • Auditing information What is the data dictionary used for? 1. It can also be used as a read-only reference in order to obtain information about the database 2. A data dictionary can be of use when developing programs that use a data model 3. The data dictionary acts as a way to describe data in “real-world” terms Why is a data dictionary needed? One of the main reasons a data dictionary is necessary is to provide better accuracy, organization, and reliability in regards to data management and user/administrator understanding and training. Benefits of using a data dictionary: 1. -
USER GUIDE Optum Clinformatics™ Data Mart Database
USER GUIDE Optum Clinformatics Data Mart Database 1 | P a g e TABLE OF CONTENTS TOPIC PAGE # 1. REQUESTING DATA 3 Eligibility 3 Forms 3 Contact Us 4 2. WHAT YOU WILL NEED 4 SAS Software 4 VPN 5 3. ABSTRACTS, MANUSCRIPTS, THESES, AND DISSERTATIONS 5 Referencing Optum Data 5 Optum Review 5 4. DATA USER SET-UP AND ACCESS INFORMATION 6 Server Log-In After Initial Set-Up 6 Server Access 6 Establishing a Connection to Enterprise Guide 7 Instructions to Add SAS EG to the Cleared Firewall List 8 How to Proceed After Connection 8 5. BEST PRACTICES FOR DATA USE 9 Saving Programs and Back-Up Procedures 9 Recommended Coding Practices 9 6. APPENDIX 11 Version Date: 27-Feb-17 2 | P a g e Optum® ClinformaticsTM Data Mart Database The Optum® ClinformaticsTM Data Mart is an administrative health claims database from a large national insurer made available by the University of Rhode Island College of Pharmacy. The statistically de-identified data includes medical and pharmacy claims, as well as laboratory results, from 2010 through 2013 with over 22 million commercial enrollees. 1. REQUESTING DATA The following is a brief outline of the process for gaining access to the data. Eligibility Must be an employee or student at the University of Rhode Island conducting unfunded or URI internally funded projects. Data will be made available to the following users: 1. Faculty and their research team for projects with IRB approval. 2. Students a. With a thesis/dissertation proposal approved by IRB and the Graduate School (access request form, see link below, must be signed by Major Professor). -
Activant Prophet 21
Activant Prophet 21 Understanding Prophet 21 Databases This class is designed for… Prophet 21 users that are responsible for report writing System Administrators Operations Managers Helpful to be familiar with SQL Query Analyzer and how to write basic SQL statements Objectives Explain the difference between databases, tables and columns Extract data from different areas of the system Discuss basic SQL statements using Query Analyzer Use Prophet 21 to gather SQL Information Utilize Data Dictionary This course will NOT cover… Basic Prophet 21 functionality Seagate’s Crystal Reports Definitions Columns Contains a single piece of information (fields) Record (rows) One complete set of columns Table A collection of rows View Virtual table created for easier data extraction Database Collection of information organized for easy selection of data SQL Query A graphical user interface used to extract Analyzer data SQL Query Analyzer Accessed through Microsoft SQL Server SQL Server Tools Enterprise Manager Perform administrative functions such as backing up and restoring databases and maintaining SQL Logins Profiler Run traces of activity in your system Basic SQL Commands sp_help sp_help <table_name> select * from <table_name> Select <field_name> from <table_name> Most Common Reporting Areas Address and Contact tables Order Processing Inventory Purchasing Accounts Receivable Accounts Payable Production Orders Address and Contact tables Used in conjunction with other tables These tables hold every address/contact in the -
For a Casual Faith and This Is No Time to Go It Alone
NO TIME UNITARIAN UNIVERSALIST ASSOCIATION Annual Report FOR A Fiscal Year 2018 CASUAL FAITH TABLE OF CON- TENTS A letter from Rev. Susan Frederick-Gray 1 Time to... Equip Congregations for Health and Vitality 4 Train and Support Leaders 10 Advance UU Values and Justice 14 Organizational and Institutional Change 18 Grow New Congregations and Communities 22 Leadership 23 Financial Performance 24 Contributors 26 Congregations Individuals Legacy Society In memorium 76 Beacon Press and Skinner House 79 Our Unitarian Universalist Principles 80 Two themes came to define my first year as your UUA President – This is TABLE No Time for a Casual Faith and This is No Time to go it Alone. This is a defining time in our nation and for our planet. The challenges, opportunities and crises that mark this time impact our own lives and our congregations and communities. Unfortunately, in times of crises and change None of this could happen without your OF CON- — when rhetoric of fear and defensiveness collective support, as congregations and dominate — it is all too common for people individuals. The UUA is the embodiment and institutions to break down, or to turn of the covenant we make to each other as inward and protective. But it is precisely in Unitarian Universalists to build something times of change and urgency when we need stronger than any of us could be alone. more courage, more love, more commitment When the UUA shows up for congregations in order to nurture the hope that is found following hurricanes and wildfires, when in seeing the possibilities that live within we help congregations find and call new TENTS humanity and community. -
Uniform Data Access Platform for SQL and Nosql Database Systems
Information Systems 69 (2017) 93–105 Contents lists available at ScienceDirect Information Systems journal homepage: www.elsevier.com/locate/is Uniform data access platform for SQL and NoSQL database systems ∗ Ágnes Vathy-Fogarassy , Tamás Hugyák University of Pannonia, Department of Computer Science and Systems Technology, P.O.Box 158, Veszprém, H-8201 Hungary a r t i c l e i n f o a b s t r a c t Article history: Integration of data stored in heterogeneous database systems is a very challenging task and it may hide Received 8 August 2016 several difficulties. As NoSQL databases are growing in popularity, integration of different NoSQL systems Revised 1 March 2017 and interoperability of NoSQL systems with SQL databases become an increasingly important issue. In Accepted 18 April 2017 this paper, we propose a novel data integration methodology to query data individually from different Available online 4 May 2017 relational and NoSQL database systems. The suggested solution does not support joins and aggregates Keywords: across data sources; it only collects data from different separated database management systems accord- Uniform data access ing to the filtering options and migrates them. The proposed method is based on a metamodel approach Relational database management systems and it covers the structural, semantic and syntactic heterogeneities of source systems. To introduce the NoSQL database management systems applicability of the proposed methodology, we developed a web-based application, which convincingly MongoDB confirms the usefulness of the novel method. Data integration JSON ©2017 Elsevier Ltd. All rights reserved. 1. Introduction solution to retrieve data from heterogeneous source systems and to deliver them to the user. -
Product Master Data Management Reference Guide
USAID GLOBAL HEALTH SUPPLY CHAIN PROGRAM Procurement and Supply Management PRODUCT MASTER DATA MANAGEMENT REFERENCE GUIDE Version 1.0 February 2020 The USAID Global Health Supply Chain Program-Procurement and Supply Management (GHSC-PSM) project is funded under USAID Contract No. AID-OAA-I-15-0004. GHSC-PSM connects technical solutions and proven commercial processes to promote efficient and cost-effective health supply chains worldwide. Our goal is to ensure uninterrupted supplies of health commodities to save lives and create a healthier future for all. The project purchases and delivers health commodities, offers comprehensive technical assistance to strengthen national supply chain systems, and provides global supply chain leadership. GHSC-PSM is implemented by Chemonics International, in collaboration with Arbola Inc., Axios International Inc., IDA Foundation, IBM, IntraHealth International, Kuehne + Nagel Inc., McKinsey & Company, Panagora Group, Population Services International, SGS Nederland B.V., and University Research Co., LLC. To learn more, visit ghsupplychain.org DISCLAIMER: The views expressed in this publication do not necessarily reflect the views of the U.S. Agency for International Development or the U.S. government. Contents Acronyms ....................................................................................................................................... 3 Executive Summary ...................................................................................................................... 4 Background -
Master Data Management: Dos & Don’Ts
Master Data Management: dos & don’ts Keesjan van Unen, Ad de Goeij, Sander Swartjes, and Ard van der Staaij Master Data Management (MDM) is high on the agenda for many organizations. At Board level too, people are now fully aware that master data requires specific attention. This increasing attention is related to the concern for the overall quality of data. By enhancing the quality of master data, disrup- tions to business operations can be limited and therefore efficiency is increased. Also, the availability of reliable management information will increase. However, reaching this goal is not an entirely smooth process. MDM is not rocket science, but it does have specific problems that must be addressed in the right way. This article describes three of these attention areas, including several dos and don’ts. C.J.W.A. van Unen is a senior manager at KPMG Management Consulting, IT Advisory. [email protected] A.S.M de Goeij RE is a manager at KPMG Management Consulting, IT Advisory. [email protected] S. Swartjes is an advisor at KPMG Risk Consulting, IT Advisory. [email protected] A.J. van der Staaij is an advisor at KPMG Risk Consulting, IT Advisory. [email protected] Compact_ 2012 2 International edition 1 Treat master data as an asset Introduction Do not go for gold immediately Every organization has to deal with master data, and seeks Although it may be very tempting, you should not go for ways to manage the quality of this specific group of data in the ‘ideal model’ when (re)designing the MDM organiza- an effective and efficient manner. -
The Pharmacologist 2 0 0 9 December
Vol. 51 Number 4 The Pharmacologist 2 0 0 9 December 2009 Year In Review Presidential Torch Passed From Awards Winners in 2009 Past-President Joe Beavo to President Brian Cox ASPET Launches New Website ASPET Participates In Habitat For Humanity in New Orleans Also Inside this Issue: ASPET Holds Student/Postdoc Focus Group ASPET Election Nominees 2009 Contributors EB 2010 Program Grid MAPS Meeting Summary & Abstracts A Publication of the American Society for 101 Pharmacology and Experimental Therapeutics - ASPET Volume 51 Number 4, 2009 The Pharmacologist is published and distributed by the American Society for Pharmacology and Experimental Therapeutics. The PHARMACOLOGIST EDITOR Suzie Thompson EDITORIAL ADVISORY BOARD News Suzanne G. Laychock, PhD John S. Lazo, PhD Richard R. Neubig, PhD Year In Review . page 103 COUNCIL ASPET Election Nominees . page 104 President Brian M. Cox, PhD 2009 Contributors . page 107 President-Elect EB 2010 Grid . page 109 James R. Halpert, PhD Past President Joe A. Beavo, PhD Secretary/Treasurer Features David R. Sibley, PhD Secretary/Treasurer-Elect Bryan F. Cox, PhD Journals . page 110 Past Secretary/Treasurer Public Affairs & Government Relations . page 112 Susan G. Amara, PhD Councilors Chapter News Suzanne G. Laychock, PhD Mid-Atlantic Chapter Meeting . page 114 John S. Lazo, PhD Richard R. Neubig, PhD Members in the News . page 132 Chair, Board of Publications Trustees Staff News . page 132 James E. Barrett, PhD Chair, Program Committee New ASPET Members . page 133 Jack Bergman, PhD In Sympathy . page 137 Chair, Long Range Planning Committee Joe A. Beavo, PhD Obituary Executive Officer Ira W. Hillyard . page 138 Christine K.