'Aggregated data' diaporamas de présentation

Chapter 34

Chapter 34. OLAP Transparencies. Chapter 34 - Objectives. The purpose of Online Analytical Processing (OLAP). The relationship between OLAP and data warehousing. The key features of OLAP applications . Chapter 34 - Objectives. How to represent multi-dimensional data.

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Scientific Creativity, Logic, and Chance:

Scientific Creativity, Logic, and Chance:. The Integration of Product, Person, and Process Research Traditions. Introduction. The Metasciences History of Science Philosophy of Science Sociology of Science Psychology of Science. The Two Psychologies of Science. Experimental

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C ontinuous Q uality I mprovement

MIECHV Grantee Meeting. C ontinuous Q uality I mprovement. Michelle P. Hill mhill@pa.gov 717.772.4850. 6/5/2013. Departments of Education and Public Welfare | www.education.state.pa.us | www.dpw.state.pa.us. Session Agenda. Introduction to Quality Improvement

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Doc MET 400 (2015) & D ata validation Training course

Doc MET 400 (2015) & D ata validation Training course. Training course on European Union Trade in Goods Statistics. Table of content. Introduction Doc MET400 : what is it ? Link with legislation Versions Structure of Doc MET 400 document Contact persons

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Data Mining: Data Preparation

Data Mining: Data Preparation. Data Preprocessing. Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Why Data Preprocessing?. Data in the real world is dirty

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Data Mining: Data Preparation

Data Mining: Data Preparation. Data Preprocessing. Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Why Data Preprocessing?. Data in the real world is dirty

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Data Mining: Data Preparation

Data Mining: Data Preparation. Data Preprocessing. Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Why Data Preprocessing?. Data in the real world is dirty

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Fall VAMAP Conference November, 2004

Fall VAMAP Conference November, 2004. SCHEV Biopsy of a Data Warehouse. Marina Moschos Wendell Pai Sumi Shahid. Outline. Phase 1: Building a data warehouse infrastructure. Phase 2: Designing and implementing a web-based collection system.

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Prescription opioid overdose & misuse in Oregon

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Node Clustering in Wireless Sensor Networks by Considering Structural Characteristics of the Network Graph

Node Clustering in Wireless Sensor Networks by Considering Structural Characteristics of the Network Graph. Nikos Dimokas 1 Dimitrios Katsaros 1,2 Yannis Manolopoulos 1. 1 Informatics Dept., Aristotle University, Thessaloniki, Greece

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IHA Formation - 1996

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Workshop on Statistical Organization and Management for SADC Member States Luanda, 2-6 December 2006 --------- by Awa

Workshop on Statistical Organization and Management for SADC Member States Luanda, 2-6 December 2006 --------- by Awa Thiongane UNECA/ACS. Contents. Types of Statistical Databases Micro level Statistical Databases The ECA Statistical Database Features of the ECA Statistical Database

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MySQL and Hadoop

MySQL and Hadoop. MySQL SF Meetup 2012 Chris Schneider. About Me . Chris Schneider, Data Architect @ Ning.com (a Glam Media Company) Spent the last ~2 years working with Hadoop (CDH) Spent the last 10 years building MySQL architecture for multiple companies chriss@glam.com.

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INVESTING IN HYDROPOWER PLANTS f OR SOCIOECONOMIC DEVELOPMENT in Nepal

INVESTING IN HYDROPOWER PLANTS f OR SOCIOECONOMIC DEVELOPMENT in Nepal. European Institute for Asian Studies Brussels 14 February 2014. www.gwiwater.org. By Valerie NDARUZANIYE. FOCUS OF PRESENTATION. Major hydroelectric i ndustry in the World Major Rivers in Nepal

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Jeffery S. Horsburgh

Linking HIS and GIS How to support the objective, transparent and robust calculation and publication of SWSI?. Jeffery S. Horsburgh. CUAHSI HIS Sharing hydrologic data. Combining GIS and HIS Demo Dynamic Display of Water Temperature. Physical Implementation. ArcMap Documents.

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Overview of Archiving of Microdata Session 4 United Nations Statistics Division Demographic Statistics Section

Overview of Archiving of Microdata Session 4 United Nations Statistics Division Demographic Statistics Section. Overview of Presentation. What are microdata? Why disseminate microdata? Data files for archiving Preparing the data sets Data security Tools for archiving of microdata

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Educational Linkedscape What is it?

Educational Linkedscape What is it?. The Educational Linkedscape links all educational related data in the educational landscape. Kennisnet, Jeroen Hamers. Educational Linkedscape What is it?. The Educational Linkedscape links all educational related data in the educational landscape .

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Data Analysis in Game D esign

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Metadata Driven Statistical Data Warehouse System at the Hungarian Central Statistical Office

Metadata Driven Statistical Data Warehouse System at the Hungarian Central Statistical Office. Imre Pap Senior IT Advisor Hungarian Central Statistical Office. META. THE DW STRUCTURE. DISSEMINATION db . Oracle EXPRESS multidimensional. Web browser. Meta Database. Subject Matters.

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