'Data summarization' presentation slideshows

Data summarization - PowerPoint PPT Presentation


Fast PCA and Bayesian Variable Selection for Large Data Sets Based on SQL and UDFs

Fast PCA and Bayesian Variable Selection for Large Data Sets Based on SQL and UDFs

Fast PCA and Bayesian Variable Selection for Large Data Sets Based on SQL and UDFs. Mario Navas, Carlos Ordonez, Veerabhadran Baladandayuthapani KDD-LDMTA‘10 July 25, 2010. Introduction. Efficient computation of very large datasets for DM, ML and statistics.

By ellery
(121 views)

TERA: PAMS Reporting

TERA: PAMS Reporting

TERA: PAMS Reporting. By Michael McGuire mcguire@ utk.edu https:// tera.usg.utk.edu /. Outline. The Reporting Architecture Selecting a Report to View Report Interface Viewing Report Results Sorting Reports Report Totals Exporting/Analyzing Report Data. The URL. TERA: PAMS Reporting.

By yuma
(159 views)

Excel – Pivot Tables

Excel – Pivot Tables

Excel – Pivot Tables. Dori Baldwin Computer Logic Group. Agenda. All things Pivot Tables Basic Design Using Filters Moving between Rows and Columns Viewing Multiple Data Calculations Grouping Data Pivot Table Chart Drilling into data Formatting. About the Trainer…. Dori.

By ewan
(161 views)

SAS: Managing Memory and Optimizing System Performance

SAS: Managing Memory and Optimizing System Performance

SAS: Managing Memory and Optimizing System Performance. Jacek Czajkowski 09/29/2008. Optimizing System Performance. Optimizing System Performance consists of managing the interplay of the following three critical computer resources: I/O Memory CPU time. Definitions. Performance Statistics

By nura
(131 views)

Chapter 4: Data Mining Primitives, Languages, and System Architectures

Chapter 4: Data Mining Primitives, Languages, and System Architectures

Chapter 4: Data Mining Primitives, Languages, and System Architectures. Data mining primitives: What defines a data mining task? A data mining query language Design graphical user interfaces based on a data mining query language Architecture of data mining systems Summary. Unit II.

By darice
(503 views)

Social Media Marketing Management 社會媒體行銷管理

Social Media Marketing Management 社會媒體行銷管理

探索性因素分析 (Exploratory Factor Analysis). Social Media Marketing Management 社會媒體行銷管理. 1002SMMM11 TLMXJ1A Tue 12,13,14 (19:20-22:10) D325. Min-Yuh Day 戴敏育 Assistant Professor 專任助理教授 Dept. of Information Management , Tamkang University 淡江大學 資訊管理學系 http://mail. tku.edu.tw/myday/ 2013-05-28.

By ismet
(137 views)

项目调研: Hadoop

项目调研: Hadoop

软件体系结构作业 1. 项目调研: Hadoop. 杨晓亮 MG0933047 南京大学计算机科学与技术系 yangxiaoliang2006@gmail.com 2010-3-23. 内容. H adoop 介绍 H adoop 的体系结构 H adoop 的应用. 2014/8/25. 2. 引言. KB 1000 MB 1000,000 GB 1000,000,000 TB 1000,000,000,000 PB 1000,000,000,000,000 … …. 我们今天所要面对的数据量. Google 处理的数据量.

By roana
(142 views)

Characterizing and Exploiting Reference Locality in Data Stream Applications

Characterizing and Exploiting Reference Locality in Data Stream Applications

Characterizing and Exploiting Reference Locality in Data Stream Applications. Feifei Li, Ching Chang, George Kollios, Azer Bestavros Computer Science Department Boston University. Query (e.g. Joins over two streams). Result. Select tuples that maximize the query metrics.

By ekram
(124 views)

Mohammad Qasim Khan Lecturer Department of Computer Science, AIOU

Mohammad Qasim Khan Lecturer Department of Computer Science, AIOU

Mohammad Qasim Khan Lecturer Department of Computer Science, AIOU. History of Computer. The abacus is often wrongly attributed to China. In fact, the oldest surviving abacus was used in 300 B.C. by the Babylonians. The abacus is still in use today, principally in the far east.

By colin
(124 views)

Clustering over Multiple Evolving Streams by Events and Correlations

Clustering over Multiple Evolving Streams by Events and Correlations

Clustering over Multiple Evolving Streams by Events and Correlations. Mi-Yen Yeh, Bi-Ru Dai, Ming-Syan Chen Electrical Engineering, National Taiwan University IEEE Transaction on Knowledge and Data Engineering (TKDE) 2007. Outline. Introduction Data Summarization Similarity Measurement

By micol
(110 views)

ADBIS 2007 Discretization Numbers for Multiple-Instances Problem in Relational Database

ADBIS 2007 Discretization Numbers for Multiple-Instances Problem in Relational Database

ADBIS 2007 Discretization Numbers for Multiple-Instances Problem in Relational Database. Rayner Alfred Dimitar Kazakov Artificial Intelligence Group, Computer Science Department, York University (30 th September, 2007). Overview. Introduction Objectives Experimental Design

By tierra
(81 views)

Sources of Climate Information and the WB Climate Data Portal

Sources of Climate Information and the WB Climate Data Portal

Sources of Climate Information and the WB Climate Data Portal. Michael I. Westphal CC Team ENV SDN Week 26/2/08. Oft-heard Questions. What’s out there? What data are easily accessible? What data are useful and important in the development context?

By christen-conley
(138 views)

项目调研: Hadoop

项目调研: Hadoop

软件体系结构作业 1. 项目调研: Hadoop. 杨晓亮 MG0933047 南京大学计算机科学与技术系 yangxiaoliang2006@gmail.com 2010-3-23. 内容. H adoop 介绍 H adoop 的体系结构 H adoop 的应用. 2014/11/18. 2. 引言. KB 1000 MB 1000,000 GB 1000,000,000 TB 1000,000,000,000 PB 1000,000,000,000,000 … …. 我们今天所要面对的数据量. Google 处理的数据量.

By phillip-whitfield
(142 views)

Information for dissertation/thesis students seeking statistical consulting help

Information for dissertation/thesis students seeking statistical consulting help

Why consulting is a must?\nQuality statistical consulting assures you with high-quality research methods, effective data collection and precisely analyzed findings from your research. \n\nGetting help\nHow long will the entire process take?\nWhat is the availability of statistician?\nHow does statistical consulting work? \nIs the person technically qualified?\nIs it ethical for a doctoral student to use a statistical consultant? \nNeed more help? Be comprehensively and systematically guided by our Highly experienced consultants. \n\nContact Us:\nUK NO: 44-1143520021 \nIndia No: 91-8754446690\nUS NO: 1-972-502-9262 \nEmail: info@statswork.com\nWebsite: http://www.statswork.com/\nLandline: 91-44-42124284

By StatsStatswork
(5 views)

How To Work With Data Summarization Techniques In Data Mining?

How To Work With Data Summarization Techniques In Data Mining?

Data Summarization is a simple term for a short conclusion of a big theory or a paragraph. This is something where you write the code and in the end, you declare the final result in the form of summarizing data. Data summarization has the great importance in the data mining.\n\nRead full blog on :\n\nhttps://www.loginworks.com/blogs/how-to-work-with-data-summarization-techniques-in-data-mining/

By rotansharma
(85 views)

Get Started with Hadoop Hive HiveQL Languages

Get Started with Hadoop Hive HiveQL Languages

Apache Hadoop is the storage system which is written in Java, which is an open-source, fault-tolerant, and scalable framework.

By janbasktraining
(2 views)

Principles of Knowledge Discovery in Data

Principles of Knowledge Discovery in Data

Principles of Knowledge Discovery in Data. Fall 2004. Chapter 5 : Data Summarization. Dr. Osmar R. Zaïane University of Alberta. Source: Dr. Jiawei Han. Summary of Last Chapter. What is the motivation for ad-hoc mining process? What defines a data mining task?

By rpaulette
(0 views)


View Data summarization PowerPoint (PPT) presentations online in SlideServe. SlideServe has a very huge collection of Data summarization PowerPoint presentations. You can view or download Data summarization presentations for your school assignment or business presentation. Browse for the presentations on every topic that you want.