'Structured data' presentation slideshows

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Introduction to the Semantic Web and Linked Data

Introduction to the Semantic Web and Linked Data

Library of Congress BIBFRAME Pilot Training for Catalogers. Introduction to the Semantic Web and Linked Data. Module 1 - Part 1 The Semantic Web and Linked Data Concepts: A basic overview. Overview. Context Goals of the presentation Learning objectives Outline of the content.

By richard_edik
(266 views)

GS1 Mobile Com Intro & Update

GS1 Mobile Com Intro & Update

GS1 Mobile Com Intro & Update. September 2008. How to use these slides. These slides give background information and current status about the GS1 Mobile Com initiative: Slides 4 – 11: GS1 and Mobile Commerce: why is GS1 involved in Mobile Commerce?

By arleen
(468 views)

Medicare & Medicaid EHR Incentives NPRM

Medicare & Medicaid EHR Incentives NPRM

Medicare & Medicaid EHR Incentives NPRM. Implementing the American Reinvestment & Recovery Act of 2009. Overview. American Reinvestment & Recovery Act – February 2009 EHR Incentive NPRM on Display – December 30, 2009; published January 13, 2010 NPRM Comment Period Closes – March 15, 2010.

By daniel_millan
(315 views)

Keyword Search on Structured and Semi-Structured Data

Keyword Search on Structured and Semi-Structured Data

Keyword Search on Structured and Semi-Structured Data. Yi Chen Wei Wang Ziyang Liu Xuemin Lin. Arizona State University, USA. University of New South Wales & NICTA, Australia. Traditional Data Access Methods. Databases / XML data Structured, with rich meta-data

By Renfred
(274 views)

How to Manage Unstructured SQL Server Data

How to Manage Unstructured SQL Server Data

How to Manage Unstructured SQL Server Data. Steve Jones SQLServerCentral Red Gate Software. Agenda. Structured and unstructured data Filestream Filetable. Types of Data. Structured Semi-structured Unstructured. 3. Structured Data. “normal” RDBMS data Format is known and defined

By Gabriel
(269 views)

Introduction to Distributed Storage Systems

Introduction to Distributed Storage Systems

Introduction to Distributed Storage Systems . Harry Xu CS 239, Fall 2019. Problems and Challenges. Extremely large amounts of data are available these days FB Social: 721M vertices, 68.7B edges in May 2011 Google Maps: 20 petabytes of data Where to put them Single machine? Servers?

By alta
(32 views)

SPARQL1.1: An introduction

SPARQL1.1: An introduction

SPARQL1.1: An introduction. @ AxelPolleres Digital Enterprise Research Institute, National University of Ireland, Galway. These slides are provided under creative commons Attribution- NonCommercial-ShareAlike 3.0 Unported License!. What is SPARQL?. Query Language for RDF

By chibale
(155 views)

LabVIEW Object Oriented Programming (LVOOP)

LabVIEW Object Oriented Programming (LVOOP)

LabVIEW Object Oriented Programming (LVOOP). Introduction of the HGF Base Class Library ( H elmholtz, G SI, F AIR) NI Big Physics Round Table February 2nd 2009 Holger Brand. Introduction to LVOOP What is LabVIEW? Why Object Oriented Programming? Why should use LVOOP?

By shelby
(1727 views)

Empirical Modeling

Empirical Modeling

Empirical Modeling. R.V. Guha. Outline. Data Science  Empirical Modeling Deep dives on some research topics Web scale structured data Teachable learning systems The case for a ` Data Commons ’. Models. Engineering = Modeling Models are essential for building, predicting &

By ura
(220 views)

Paolo Ferragina Dipartimento di Informatica Università di Pisa

Paolo Ferragina Dipartimento di Informatica Università di Pisa

IR. Paolo Ferragina Dipartimento di Informatica Università di Pisa. With my personal touch…. CS276 Information Retrieval and Web Search Christopher Manning and Prabhakar Raghavan Lecture 1: Boolean retrieval. Information Retrieval.

By fionnuala
(163 views)

JSR 73: Data Mining API

JSR 73: Data Mining API

JSR 73: Data Mining API. 資工三 B90902008 林宗澤. Introduction. In JDM, data mining [Mitchell1997, BL1997] includes the functional areas of classification, regression, attribute importance1, clustering, and association.

By nida
(294 views)

Text Mining Chapter 20

Text Mining Chapter 20

Text Mining Chapter 20. Text data . Structured data Unstructured data Text Video Audio. Applications of Text Mining – HR Forms. Employment applications Match with job requirements Processing of applications. Applications, cont. Medical triage/diagnosis

By orly
(600 views)

What we mean by Big Data and Advanced Analytics

What we mean by Big Data and Advanced Analytics

What we mean by Big Data and Advanced Analytics. Small data. Mostly structured data in existing relational databases of standard business applications (e.g., SAP, Oracle). Big data. Massive and multi-dimensional data Dispersed data sources (internal and external)

By Audrey
(905 views)

Component 11 Configuring EHRs

Component 11 Configuring EHRs

Component 11 Configuring EHRs. Unit 8 Data Infrastructure Lecture 1. Data Architecture Defined. Two definitions will help us to define the meaning of the components making up the overall architecture that encompasses an electronic health record.

By dore
(127 views)

Introduction to EPICS V4 Design, Features and Status

Introduction to EPICS V4 Design, Features and Status

Introduction to EPICS V4 Design, Features and Status. Ralph Lange , Spring 2017 EPICS Collaboration Meeting. Supporting the next generation of scientific control systems. Preface: What V4 is not. V4 is not a replacement for V3 V4 does not introduce a new IOC database

By lenci
(318 views)

Text Mining Concepts

Text Mining Concepts

Text Mining Concepts. 85-90 percent of all corporate data is in some kind of unstructured form (e.g., text) Unstructured corporate data is doubling in size every 18 months Tapping into these information sources is not an option, but a need to stay competitive Answer: text mining

By kanoa
(382 views)

The Report

The Report

The Report. Michael Stonebraker Adjunct Professor Massachusetts Institute of Technology (stonebraker@lcs.mit.edu). Context. We have to write one (or we look stupid) We can’t say “we got it right in 1997”. Proposal. Make up a 50 year challenge With a 5-10 year milestone

By gen
(124 views)

Mining Structured vs. Unstructured Data Where is the structure and where did the semantics go?

Mining Structured vs. Unstructured Data Where is the structure and where did the semantics go?

Mining Structured vs. Unstructured Data Where is the structure and where did the semantics go?. Rahim Yaseen SAP Labs LLC. Why Mining works for structured data. For relational data There is no separation of the semantic data model and the logical storage model

By daxia
(245 views)

Getting to 'the 5 stars of Linked Open Data' for Nanoinformatics

Getting to 'the 5 stars of Linked Open Data' for Nanoinformatics

Getting to 'the 5 stars of Linked Open Data' for Nanoinformatics . Mills Davis, Project10x, and Brand Niemann, Semanticommunity.net November 4, 2010. White House Blog: Nanotechnology.

By baird
(205 views)

CS490D: Introduction to Data Mining Prof. Chris Clifton

CS490D: Introduction to Data Mining Prof. Chris Clifton

CS490D: Introduction to Data Mining Prof. Chris Clifton. March 26, 2004 Text Mining. Data Mining in Text. Association search in text corpuses provides suggestive information Groups of related entities Clusters that identify topics Flexibility is crucial

By sofia
(158 views)

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