SlideShare une entreprise Scribd logo
1  sur  12
VISUAL DATA MINING
Deborah Adams, Northcentral University
INTRODUCTION
The presentation will review the various aspects of interactive database systems,
which integrates various forms of data making them readily available for access,
communication, etc. Additionally, it will explore how large amounts of data are
subject to the analytic process, while explaining how online analytical processing
performs data analysis on a multidimensional level providing complex calculations,
trend analysis, and sophisticated data modeling. Viewers of this presentation should
receive an understanding of data mining and analytical processing of data that
benefits professionals.
MIS7002-8 Database Administration & Management
2
INTERACTIVE DB SYSTEMS
Data
Services
Architecture
Service-
Enabling
Data Stores
Through ‘service-enabling’
a data store enables access
to web clients/applications
inaccessible external
data by publishing data
services (i.e. Microsoft WCF
Data Services and Oracles’
ODSI).
Data services are
employed on top of
data stores exposing
data/meta data to
consumers through an
external model
displaying a map
between the
store/external model
of the data store.
Carey et al. , 2012
MIS7002-8 Database Administration & Management
3
INTERACTIVE SYSTEMS
MIS7002-8 Database Administration & Management
4
www.vmvizura.si
www.3dmediawn.comwww.interactive.com.hk
PROCESS OF DATA MINING
Data Mining Defined
Data mining - automated application of
processes detecting patterns while
extracting knowledge from data. This
algorithm counts patterns, fits models
from/ to data. It is a step in the concept
of knowledge discovery in databases
(KDD) allowing for data sets to be
analyzed, searching patterns and
discovering rules. Data mining which is
automated makes it easier to apply the
scheme of decision support systems.
Discovery of knowledge from data is
found in techniques like associations,
classification, clustering and trend
analysis.
Attribute Focusing
The end-user is targeted through the use
of algorithms which leads the user
through data analysis. This method was
known in a earlier application of software
process engineering. It has been
applied/discovered interesting patterns
in the NBA.
Data Mining Applications
Take a large amount of computing power
which equates to multiple hours of valuable
time to mine large databases/construct
complex models. The reduction of wait time,
increase productivity and increases
understanding of knowledge discovery
process noted through scalable parallel
systems. Using numerous processors enables
more memory and a larger database to be
utilized and handled in the main memory
attached to the processors.
Goil et al., 1997
MIS7002-8 Database Administration & Management
5
OLAP PROCESS
On-line Analytical Processing—OLAP—
systems enable insight to be gained into the
performance of an enterprise by multiple
views of organized data that reflects the
multidimensional nature of enterprise data.
OLAP provides fast, consistent, interactive
access to multiple views of information. It
answers what if, why, who, and what
questions that create decision support
systems and help to extract knowledge from
data. According to multiple dimensions, OLAP
summarizes, consolidates, views, applies,
formulates to, and synthesizes.
MIS7002-8 Database Administration & Management
6
Goil et al., 1997
www.ctg.Albany.edu
OLAP PROCESS
The total of all possible dimension
combinations is what the data cube
computes, which is useful for answering
OLAP queries that uses an aggregate
combinations of various attributes.
An important function of OLAP queries
is aggregations, which data cube
operators may be helped by.
OLAP systems are required to provide
efficient analytical query processing in
high performance computing.
MIS7002-8 Database Administration & Management
7
Goil et al., 1997
www.searchbusinessanalytics.techtarget.com
VISUAL DATA/IMAGE DB
MIS7002-8 Database Administration & Management
8
www.devart.com
www.aquafold.com
 Gaze data/eye movements are
complex, eye movements are
created moment to moment
through interacting processes—
cognitive, perceptual, and motor.
 Eye movements are revealing and
can be used to study the
dynamics of cognitive systems.
‘Gaze’ actively gathers world
information, while binding
objects in the physical realm to
internal cognitive programs in a
moment by moment fashion.
It is critical to decipher momentary eye
movement data for the understanding
of how external sensory data
with internal cognitive processes.
Yu et al., 2012
DECISIONS MADE FROM DATA
MIS7002-8 Database Administration & Management
9
 Data combined with other data
provides improved results to
the public.
 We are able to get and use
data for better results for
companies and individuals.
 Products from data brokers prevent
fraud, improve product offerings &
deliver tailored consumer
advertisements.
 Brokers foster competition enabling
small businesses to pitch innovative
products to unreachable
consumers.
Information about individuals is complied by brokers from
online and offline sources—email, personal websites,
social media posts, U.S. Census records, retailers, DMV
records, and real estate records—using progressive
analytic tools, for selling to other brokers and businesses.
Anthes, 2015
BENEFITS OF DATA MINING
Mountains of data
 There are numerous types of data
collected across industries, states, and
governments.
 Data-informed decision-making over
the last decade has become a
movement due to so much data that
is available.
 Google the term ‘decision making’ or
‘data’ there are more than 50 million
entries.
Formats
 Accounts need to be either in the
same or complementary format for
translating into a common format.
 Most times different suppliers of data
use different formats.
 Data must be cleaned—edited, tested
for correctness and consistency.
Data 4 knowledge
 Translating knowledge from data
requires assessment, interpretation,
and access to sources of data and the
continuous accumulation of data.
 It is essential to use data for decision-
making. Data mining is the search
method for said process.
MIS7002-8 Database Administration & Management
10
Jianping et al., 2010; Schoors, 2000
CONCLUSION
Numerous, mostly all, businesses/professions benefit from data mining (i.e. medical,
education, legal, banking, politics, etc.). Having access to data in a visual format as well as
textual assists in the analytical process. Data contained in data-mines must be edited and
tested to ensure accuracy and consistency. The data accessed is typically utilized for decisions
and knowledge purposes like projecting, training, forecasting, budgeting, planning, etc. This
presentation provides information and examples of data mining its processes and how it is
used/benefits individuals/businesses.
MIS7002-8 Database Administration & Management
11
REFERENCES
Anthes, G. (2015). Data Brokers Are Watching You. Communications of The ACM, 58(1), 28-30. doi:10.1145/2686740
Carey, M.J., Onose, N., Petropoulos, M. (2012). Data Services. Communications of the ACM, 55(6), 86-97.
doi:10.1145/2184319.2184340
Goil, S., & Choudhary, A. (1997). High performance OLAP and data mining on parallel computers. Data Mining and
Knowledge Discovery, 1(4), 391-417. doi:http://dx.doi.org/10.1023/A:1009777418785
Jianping, S., Cooley, V. E., Reeves, P., Burt, W. L., Ryan, L., Rainey, J. M., & Wenhui, Y. (2010). Using data for decision-
making: perspectives from 16 principals in Michigan, USA. International Review of Education / Internationale Zeitschrift
Für Erziehungswissenschaft, 56(4), 435-456. doi:10.1007/s11159-010-9172-x
Schoors, K. (2000). A note on building a database on russian banks: Fieldwork against the odds. Post - Communist
Economies,12(2), 241-249. Retrieved from
http://search.proquest.com.proxy1.ncu.edu/docview/222605083?accountid=28180
Yu, C., Yurovsky, D., & Xu, T. (. (2012). Visual data mining: An exploratory approach to analyzing temporal patterns of eye
movements. Infancy, 17(1), 33-60. doi:10.1111/j.1532-7078.2011.00095.x
12

Contenu connexe

Tendances

LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...ijdpsjournal
 
Information economics and big data
Information economics and big dataInformation economics and big data
Information economics and big dataMark Albala
 
Sameer Kumar Das International Conference Paper 53
Sameer Kumar Das International Conference Paper 53Sameer Kumar Das International Conference Paper 53
Sameer Kumar Das International Conference Paper 53Mr.Sameer Kumar Das
 
Big Data, Business Intelligence and Data Analytics
Big Data, Business Intelligence and Data AnalyticsBig Data, Business Intelligence and Data Analytics
Big Data, Business Intelligence and Data AnalyticsSystems Limited
 
Business_Analytics_Presentation_Luke_Caratan
Business_Analytics_Presentation_Luke_CaratanBusiness_Analytics_Presentation_Luke_Caratan
Business_Analytics_Presentation_Luke_CaratanLuke Caratan
 
Big Data, NoSQL, NewSQL & The Future of Data Management
Big Data, NoSQL, NewSQL & The Future of Data ManagementBig Data, NoSQL, NewSQL & The Future of Data Management
Big Data, NoSQL, NewSQL & The Future of Data ManagementTony Bain
 
Big Data: Opportunities, Strategy and Challenges
Big Data: Opportunities, Strategy and ChallengesBig Data: Opportunities, Strategy and Challenges
Big Data: Opportunities, Strategy and ChallengesGregg Barrett
 
Big Data Analytics: Recent Achievements and New Challenges
Big Data Analytics: Recent Achievements and New ChallengesBig Data Analytics: Recent Achievements and New Challenges
Big Data Analytics: Recent Achievements and New ChallengesEditor IJCATR
 
A Review on Classification of Data Imbalance using BigData
A Review on Classification of Data Imbalance using BigDataA Review on Classification of Data Imbalance using BigData
A Review on Classification of Data Imbalance using BigDataIJMIT JOURNAL
 
An Comprehensive Study of Big Data Environment and its Challenges.
An Comprehensive Study of Big Data Environment and its Challenges.An Comprehensive Study of Big Data Environment and its Challenges.
An Comprehensive Study of Big Data Environment and its Challenges.ijceronline
 

Tendances (19)

LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...
 
Information economics and big data
Information economics and big dataInformation economics and big data
Information economics and big data
 
Big data analytics
Big data analyticsBig data analytics
Big data analytics
 
Sameer Kumar Das International Conference Paper 53
Sameer Kumar Das International Conference Paper 53Sameer Kumar Das International Conference Paper 53
Sameer Kumar Das International Conference Paper 53
 
Data Management
Data ManagementData Management
Data Management
 
Taming the data beast
Taming the data beastTaming the data beast
Taming the data beast
 
Big Data, Business Intelligence and Data Analytics
Big Data, Business Intelligence and Data AnalyticsBig Data, Business Intelligence and Data Analytics
Big Data, Business Intelligence and Data Analytics
 
Fraud and Risk in Big Data
Fraud and Risk in Big DataFraud and Risk in Big Data
Fraud and Risk in Big Data
 
Business_Analytics_Presentation_Luke_Caratan
Business_Analytics_Presentation_Luke_CaratanBusiness_Analytics_Presentation_Luke_Caratan
Business_Analytics_Presentation_Luke_Caratan
 
Data Science
Data ScienceData Science
Data Science
 
Big Data, NoSQL, NewSQL & The Future of Data Management
Big Data, NoSQL, NewSQL & The Future of Data ManagementBig Data, NoSQL, NewSQL & The Future of Data Management
Big Data, NoSQL, NewSQL & The Future of Data Management
 
Hadoop Overview
Hadoop OverviewHadoop Overview
Hadoop Overview
 
Big data
Big dataBig data
Big data
 
Big data
Big dataBig data
Big data
 
Big Data: Opportunities, Strategy and Challenges
Big Data: Opportunities, Strategy and ChallengesBig Data: Opportunities, Strategy and Challenges
Big Data: Opportunities, Strategy and Challenges
 
Big Data Analytics: Recent Achievements and New Challenges
Big Data Analytics: Recent Achievements and New ChallengesBig Data Analytics: Recent Achievements and New Challenges
Big Data Analytics: Recent Achievements and New Challenges
 
A Review on Classification of Data Imbalance using BigData
A Review on Classification of Data Imbalance using BigDataA Review on Classification of Data Imbalance using BigData
A Review on Classification of Data Imbalance using BigData
 
An Comprehensive Study of Big Data Environment and its Challenges.
An Comprehensive Study of Big Data Environment and its Challenges.An Comprehensive Study of Big Data Environment and its Challenges.
An Comprehensive Study of Big Data Environment and its Challenges.
 
Big data-analytics-ebook
Big data-analytics-ebookBig data-analytics-ebook
Big data-analytics-ebook
 

En vedette

Visual Data Mining
Visual Data MiningVisual Data Mining
Visual Data MiningCloudNSci
 
A cognitive psychologist's approach to data mining
A cognitive psychologist's approach to data miningA cognitive psychologist's approach to data mining
A cognitive psychologist's approach to data miningmaggiexyz
 
Today's BI and Data Mining ecosystem
Today's BI and Data Mining ecosystemToday's BI and Data Mining ecosystem
Today's BI and Data Mining ecosystemJosep Arroyo
 
[Monthlyhands] SMART TV Report 201302
[Monthlyhands] SMART TV Report 201302[Monthlyhands] SMART TV Report 201302
[Monthlyhands] SMART TV Report 201302HANDSTUDIO
 
Visual data mining with HeatMiner
Visual data mining with HeatMinerVisual data mining with HeatMiner
Visual data mining with HeatMinerCloudNSci
 
4.3 multimedia datamining
4.3 multimedia datamining4.3 multimedia datamining
4.3 multimedia dataminingKrish_ver2
 
Multimedia Data Mining using Deep Learning
Multimedia Data Mining using Deep LearningMultimedia Data Mining using Deep Learning
Multimedia Data Mining using Deep LearningBhagyashree Barde
 
마인즈랩 통합 VOC 관리 솔루션 소개_20151030
마인즈랩 통합 VOC 관리 솔루션 소개_20151030마인즈랩 통합 VOC 관리 솔루션 소개_20151030
마인즈랩 통합 VOC 관리 솔루션 소개_20151030Taejoon Yoo
 
Deep Learning Cases: Text and Image Processing
Deep Learning Cases: Text and Image ProcessingDeep Learning Cases: Text and Image Processing
Deep Learning Cases: Text and Image ProcessingGrigory Sapunov
 
Data compression introduction
Data compression introductionData compression introduction
Data compression introductionRahul Khanwani
 
Image encryption and decryption
Image encryption and decryptionImage encryption and decryption
Image encryption and decryptionAashish R
 
마인즈랩 회사소개서 V2.3_한국어버전
마인즈랩 회사소개서 V2.3_한국어버전마인즈랩 회사소개서 V2.3_한국어버전
마인즈랩 회사소개서 V2.3_한국어버전Taejoon Yoo
 
Social media mining PPT
Social media mining PPTSocial media mining PPT
Social media mining PPTChhavi Mathur
 
speech processing and recognition basic in data mining
speech processing and recognition basic in  data miningspeech processing and recognition basic in  data mining
speech processing and recognition basic in data miningJimit Rupani
 

En vedette (20)

Visual Data Mining
Visual Data MiningVisual Data Mining
Visual Data Mining
 
Audio mining
Audio miningAudio mining
Audio mining
 
A cognitive psychologist's approach to data mining
A cognitive psychologist's approach to data miningA cognitive psychologist's approach to data mining
A cognitive psychologist's approach to data mining
 
Today's BI and Data Mining ecosystem
Today's BI and Data Mining ecosystemToday's BI and Data Mining ecosystem
Today's BI and Data Mining ecosystem
 
[Monthlyhands] SMART TV Report 201302
[Monthlyhands] SMART TV Report 201302[Monthlyhands] SMART TV Report 201302
[Monthlyhands] SMART TV Report 201302
 
Visual data mining with HeatMiner
Visual data mining with HeatMinerVisual data mining with HeatMiner
Visual data mining with HeatMiner
 
4.3 multimedia datamining
4.3 multimedia datamining4.3 multimedia datamining
4.3 multimedia datamining
 
Multimedia Data Mining using Deep Learning
Multimedia Data Mining using Deep LearningMultimedia Data Mining using Deep Learning
Multimedia Data Mining using Deep Learning
 
마인즈랩 통합 VOC 관리 솔루션 소개_20151030
마인즈랩 통합 VOC 관리 솔루션 소개_20151030마인즈랩 통합 VOC 관리 솔루션 소개_20151030
마인즈랩 통합 VOC 관리 솔루션 소개_20151030
 
Data compression
Data compressionData compression
Data compression
 
Image Encryption in java ppt.
Image Encryption in java ppt.Image Encryption in java ppt.
Image Encryption in java ppt.
 
Deep Learning Cases: Text and Image Processing
Deep Learning Cases: Text and Image ProcessingDeep Learning Cases: Text and Image Processing
Deep Learning Cases: Text and Image Processing
 
Data compression
Data compression Data compression
Data compression
 
Data compression introduction
Data compression introductionData compression introduction
Data compression introduction
 
Image encryption and decryption
Image encryption and decryptionImage encryption and decryption
Image encryption and decryption
 
Data compression
Data compressionData compression
Data compression
 
Social Data Mining
Social Data MiningSocial Data Mining
Social Data Mining
 
마인즈랩 회사소개서 V2.3_한국어버전
마인즈랩 회사소개서 V2.3_한국어버전마인즈랩 회사소개서 V2.3_한국어버전
마인즈랩 회사소개서 V2.3_한국어버전
 
Social media mining PPT
Social media mining PPTSocial media mining PPT
Social media mining PPT
 
speech processing and recognition basic in data mining
speech processing and recognition basic in  data miningspeech processing and recognition basic in  data mining
speech processing and recognition basic in data mining
 

Similaire à Visual Data Mining

DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATA
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATADATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATA
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATAijseajournal
 
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...ijdpsjournal
 
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANC...
LEVERAGING CLOUD BASED BIG DATA ANALYTICS  IN KNOWLEDGE MANAGEMENT FOR ENHANC...LEVERAGING CLOUD BASED BIG DATA ANALYTICS  IN KNOWLEDGE MANAGEMENT FOR ENHANC...
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANC...ijdpsjournal
 
Big data (word file)
Big data  (word file)Big data  (word file)
Big data (word file)Shahbaz Anjam
 
BIG DATA IN CLOUD COMPUTING REVIEW AND OPPORTUNITIES
BIG DATA IN CLOUD COMPUTING REVIEW AND OPPORTUNITIESBIG DATA IN CLOUD COMPUTING REVIEW AND OPPORTUNITIES
BIG DATA IN CLOUD COMPUTING REVIEW AND OPPORTUNITIESijcsit
 
Big Data in Cloud Computing Review and Opportunities
Big Data in Cloud Computing Review and OpportunitiesBig Data in Cloud Computing Review and Opportunities
Big Data in Cloud Computing Review and OpportunitiesAIRCC Publishing Corporation
 
A SURVEY OF BIG DATA ANALYTICS
A SURVEY OF BIG DATA ANALYTICSA SURVEY OF BIG DATA ANALYTICS
A SURVEY OF BIG DATA ANALYTICSijistjournal
 
Big data analytics and large-scale computers
Big data analytics and large-scale computersBig data analytics and large-scale computers
Big data analytics and large-scale computersShubhamKhurana20
 
IRJET- Big Data Management and Growth Enhancement
IRJET- Big Data Management and Growth EnhancementIRJET- Big Data Management and Growth Enhancement
IRJET- Big Data Management and Growth EnhancementIRJET Journal
 
Introduction to Big Data
Introduction to Big DataIntroduction to Big Data
Introduction to Big DataAkshata Humbe
 
Data Warehouse: A Primer
Data Warehouse: A PrimerData Warehouse: A Primer
Data Warehouse: A PrimerIJRTEMJOURNAL
 
The Comparison of Big Data Strategies in Corporate Environment
The Comparison of Big Data Strategies in Corporate EnvironmentThe Comparison of Big Data Strategies in Corporate Environment
The Comparison of Big Data Strategies in Corporate EnvironmentIRJET Journal
 
Getting down to business on Big Data analytics
Getting down to business on Big Data analyticsGetting down to business on Big Data analytics
Getting down to business on Big Data analyticsThe Marketing Distillery
 
Data modeling techniques used for big data in enterprise networks
Data modeling techniques used for big data in enterprise networksData modeling techniques used for big data in enterprise networks
Data modeling techniques used for big data in enterprise networksDr. Richard Otieno
 
The Big Data Importance – Tools and their Usage
The Big Data Importance – Tools and their UsageThe Big Data Importance – Tools and their Usage
The Big Data Importance – Tools and their UsageIRJET Journal
 

Similaire à Visual Data Mining (20)

DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATA
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATADATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATA
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATA
 
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...
 
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANC...
LEVERAGING CLOUD BASED BIG DATA ANALYTICS  IN KNOWLEDGE MANAGEMENT FOR ENHANC...LEVERAGING CLOUD BASED BIG DATA ANALYTICS  IN KNOWLEDGE MANAGEMENT FOR ENHANC...
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANC...
 
Big data (word file)
Big data  (word file)Big data  (word file)
Big data (word file)
 
Unit-1 introduction to Big data.pdf
Unit-1 introduction to Big data.pdfUnit-1 introduction to Big data.pdf
Unit-1 introduction to Big data.pdf
 
Unit-1 introduction to Big data.pdf
Unit-1 introduction to Big data.pdfUnit-1 introduction to Big data.pdf
Unit-1 introduction to Big data.pdf
 
big-data.pdf
big-data.pdfbig-data.pdf
big-data.pdf
 
Complete-SRS.doc
Complete-SRS.docComplete-SRS.doc
Complete-SRS.doc
 
BIG DATA IN CLOUD COMPUTING REVIEW AND OPPORTUNITIES
BIG DATA IN CLOUD COMPUTING REVIEW AND OPPORTUNITIESBIG DATA IN CLOUD COMPUTING REVIEW AND OPPORTUNITIES
BIG DATA IN CLOUD COMPUTING REVIEW AND OPPORTUNITIES
 
Big Data in Cloud Computing Review and Opportunities
Big Data in Cloud Computing Review and OpportunitiesBig Data in Cloud Computing Review and Opportunities
Big Data in Cloud Computing Review and Opportunities
 
A SURVEY OF BIG DATA ANALYTICS
A SURVEY OF BIG DATA ANALYTICSA SURVEY OF BIG DATA ANALYTICS
A SURVEY OF BIG DATA ANALYTICS
 
Big data analytics and large-scale computers
Big data analytics and large-scale computersBig data analytics and large-scale computers
Big data analytics and large-scale computers
 
Big data upload
Big data uploadBig data upload
Big data upload
 
IRJET- Big Data Management and Growth Enhancement
IRJET- Big Data Management and Growth EnhancementIRJET- Big Data Management and Growth Enhancement
IRJET- Big Data Management and Growth Enhancement
 
Introduction to Big Data
Introduction to Big DataIntroduction to Big Data
Introduction to Big Data
 
Data Warehouse: A Primer
Data Warehouse: A PrimerData Warehouse: A Primer
Data Warehouse: A Primer
 
The Comparison of Big Data Strategies in Corporate Environment
The Comparison of Big Data Strategies in Corporate EnvironmentThe Comparison of Big Data Strategies in Corporate Environment
The Comparison of Big Data Strategies in Corporate Environment
 
Getting down to business on Big Data analytics
Getting down to business on Big Data analyticsGetting down to business on Big Data analytics
Getting down to business on Big Data analytics
 
Data modeling techniques used for big data in enterprise networks
Data modeling techniques used for big data in enterprise networksData modeling techniques used for big data in enterprise networks
Data modeling techniques used for big data in enterprise networks
 
The Big Data Importance – Tools and their Usage
The Big Data Importance – Tools and their UsageThe Big Data Importance – Tools and their Usage
The Big Data Importance – Tools and their Usage
 

Plus de Doctoral Student, NCU (14)

architectures of erp systems
architectures of erp systemsarchitectures of erp systems
architectures of erp systems
 
Migrating to the Cloud
Migrating to the CloudMigrating to the Cloud
Migrating to the Cloud
 
Ethics and Decision Making
Ethics and Decision MakingEthics and Decision Making
Ethics and Decision Making
 
Organizational intelligence
Organizational intelligenceOrganizational intelligence
Organizational intelligence
 
Taxonomy of collaborative applications
Taxonomy of collaborative applicationsTaxonomy of collaborative applications
Taxonomy of collaborative applications
 
Researcher vs. Analyst
Researcher vs. AnalystResearcher vs. Analyst
Researcher vs. Analyst
 
Information Systems
Information SystemsInformation Systems
Information Systems
 
Strategic Management
Strategic Management Strategic Management
Strategic Management
 
Adams dbtm7101 8 putting it all together
Adams dbtm7101 8 putting it all togetherAdams dbtm7101 8 putting it all together
Adams dbtm7101 8 putting it all together
 
Theories of Communication
Theories of CommunicationTheories of Communication
Theories of Communication
 
Strategies of Fiscal Management
Strategies of Fiscal ManagementStrategies of Fiscal Management
Strategies of Fiscal Management
 
Cim 634 seeing the future d adams
Cim 634 seeing the future d adamsCim 634 seeing the future d adams
Cim 634 seeing the future d adams
 
Pathway to incarceration
Pathway to incarcerationPathway to incarceration
Pathway to incarceration
 
Prudence Crandall
Prudence Crandall Prudence Crandall
Prudence Crandall
 

Dernier

Understanding Accommodations and Modifications
Understanding  Accommodations and ModificationsUnderstanding  Accommodations and Modifications
Understanding Accommodations and ModificationsMJDuyan
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introductionMaksud Ahmed
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingTechSoup
 
Sociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning ExhibitSociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning Exhibitjbellavia9
 
General Principles of Intellectual Property: Concepts of Intellectual Proper...
General Principles of Intellectual Property: Concepts of Intellectual  Proper...General Principles of Intellectual Property: Concepts of Intellectual  Proper...
General Principles of Intellectual Property: Concepts of Intellectual Proper...Poonam Aher Patil
 
Magic bus Group work1and 2 (Team 3).pptx
Magic bus Group work1and 2 (Team 3).pptxMagic bus Group work1and 2 (Team 3).pptx
Magic bus Group work1and 2 (Team 3).pptxdhanalakshmis0310
 
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdfUGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdfNirmal Dwivedi
 
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
Kodo Millet  PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...Kodo Millet  PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...pradhanghanshyam7136
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsTechSoup
 
How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17Celine George
 
Python Notes for mca i year students osmania university.docx
Python Notes for mca i year students osmania university.docxPython Notes for mca i year students osmania university.docx
Python Notes for mca i year students osmania university.docxRamakrishna Reddy Bijjam
 
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptxBasic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptxDenish Jangid
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdfQucHHunhnh
 
Micro-Scholarship, What it is, How can it help me.pdf
Micro-Scholarship, What it is, How can it help me.pdfMicro-Scholarship, What it is, How can it help me.pdf
Micro-Scholarship, What it is, How can it help me.pdfPoh-Sun Goh
 
How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17Celine George
 
PROCESS RECORDING FORMAT.docx
PROCESS      RECORDING        FORMAT.docxPROCESS      RECORDING        FORMAT.docx
PROCESS RECORDING FORMAT.docxPoojaSen20
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdfQucHHunhnh
 
Dyslexia AI Workshop for Slideshare.pptx
Dyslexia AI Workshop for Slideshare.pptxDyslexia AI Workshop for Slideshare.pptx
Dyslexia AI Workshop for Slideshare.pptxcallscotland1987
 

Dernier (20)

Understanding Accommodations and Modifications
Understanding  Accommodations and ModificationsUnderstanding  Accommodations and Modifications
Understanding Accommodations and Modifications
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introduction
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy Consulting
 
Sociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning ExhibitSociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning Exhibit
 
General Principles of Intellectual Property: Concepts of Intellectual Proper...
General Principles of Intellectual Property: Concepts of Intellectual  Proper...General Principles of Intellectual Property: Concepts of Intellectual  Proper...
General Principles of Intellectual Property: Concepts of Intellectual Proper...
 
Magic bus Group work1and 2 (Team 3).pptx
Magic bus Group work1and 2 (Team 3).pptxMagic bus Group work1and 2 (Team 3).pptx
Magic bus Group work1and 2 (Team 3).pptx
 
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdfUGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
 
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
Kodo Millet  PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...Kodo Millet  PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The Basics
 
How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17
 
Python Notes for mca i year students osmania university.docx
Python Notes for mca i year students osmania university.docxPython Notes for mca i year students osmania university.docx
Python Notes for mca i year students osmania university.docx
 
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptxBasic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
 
Micro-Scholarship, What it is, How can it help me.pdf
Micro-Scholarship, What it is, How can it help me.pdfMicro-Scholarship, What it is, How can it help me.pdf
Micro-Scholarship, What it is, How can it help me.pdf
 
How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17
 
Spatium Project Simulation student brief
Spatium Project Simulation student briefSpatium Project Simulation student brief
Spatium Project Simulation student brief
 
PROCESS RECORDING FORMAT.docx
PROCESS      RECORDING        FORMAT.docxPROCESS      RECORDING        FORMAT.docx
PROCESS RECORDING FORMAT.docx
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
 
Asian American Pacific Islander Month DDSD 2024.pptx
Asian American Pacific Islander Month DDSD 2024.pptxAsian American Pacific Islander Month DDSD 2024.pptx
Asian American Pacific Islander Month DDSD 2024.pptx
 
Dyslexia AI Workshop for Slideshare.pptx
Dyslexia AI Workshop for Slideshare.pptxDyslexia AI Workshop for Slideshare.pptx
Dyslexia AI Workshop for Slideshare.pptx
 

Visual Data Mining

  • 1. VISUAL DATA MINING Deborah Adams, Northcentral University
  • 2. INTRODUCTION The presentation will review the various aspects of interactive database systems, which integrates various forms of data making them readily available for access, communication, etc. Additionally, it will explore how large amounts of data are subject to the analytic process, while explaining how online analytical processing performs data analysis on a multidimensional level providing complex calculations, trend analysis, and sophisticated data modeling. Viewers of this presentation should receive an understanding of data mining and analytical processing of data that benefits professionals. MIS7002-8 Database Administration & Management 2
  • 3. INTERACTIVE DB SYSTEMS Data Services Architecture Service- Enabling Data Stores Through ‘service-enabling’ a data store enables access to web clients/applications inaccessible external data by publishing data services (i.e. Microsoft WCF Data Services and Oracles’ ODSI). Data services are employed on top of data stores exposing data/meta data to consumers through an external model displaying a map between the store/external model of the data store. Carey et al. , 2012 MIS7002-8 Database Administration & Management 3
  • 4. INTERACTIVE SYSTEMS MIS7002-8 Database Administration & Management 4 www.vmvizura.si www.3dmediawn.comwww.interactive.com.hk
  • 5. PROCESS OF DATA MINING Data Mining Defined Data mining - automated application of processes detecting patterns while extracting knowledge from data. This algorithm counts patterns, fits models from/ to data. It is a step in the concept of knowledge discovery in databases (KDD) allowing for data sets to be analyzed, searching patterns and discovering rules. Data mining which is automated makes it easier to apply the scheme of decision support systems. Discovery of knowledge from data is found in techniques like associations, classification, clustering and trend analysis. Attribute Focusing The end-user is targeted through the use of algorithms which leads the user through data analysis. This method was known in a earlier application of software process engineering. It has been applied/discovered interesting patterns in the NBA. Data Mining Applications Take a large amount of computing power which equates to multiple hours of valuable time to mine large databases/construct complex models. The reduction of wait time, increase productivity and increases understanding of knowledge discovery process noted through scalable parallel systems. Using numerous processors enables more memory and a larger database to be utilized and handled in the main memory attached to the processors. Goil et al., 1997 MIS7002-8 Database Administration & Management 5
  • 6. OLAP PROCESS On-line Analytical Processing—OLAP— systems enable insight to be gained into the performance of an enterprise by multiple views of organized data that reflects the multidimensional nature of enterprise data. OLAP provides fast, consistent, interactive access to multiple views of information. It answers what if, why, who, and what questions that create decision support systems and help to extract knowledge from data. According to multiple dimensions, OLAP summarizes, consolidates, views, applies, formulates to, and synthesizes. MIS7002-8 Database Administration & Management 6 Goil et al., 1997 www.ctg.Albany.edu
  • 7. OLAP PROCESS The total of all possible dimension combinations is what the data cube computes, which is useful for answering OLAP queries that uses an aggregate combinations of various attributes. An important function of OLAP queries is aggregations, which data cube operators may be helped by. OLAP systems are required to provide efficient analytical query processing in high performance computing. MIS7002-8 Database Administration & Management 7 Goil et al., 1997 www.searchbusinessanalytics.techtarget.com
  • 8. VISUAL DATA/IMAGE DB MIS7002-8 Database Administration & Management 8 www.devart.com www.aquafold.com  Gaze data/eye movements are complex, eye movements are created moment to moment through interacting processes— cognitive, perceptual, and motor.  Eye movements are revealing and can be used to study the dynamics of cognitive systems. ‘Gaze’ actively gathers world information, while binding objects in the physical realm to internal cognitive programs in a moment by moment fashion. It is critical to decipher momentary eye movement data for the understanding of how external sensory data with internal cognitive processes. Yu et al., 2012
  • 9. DECISIONS MADE FROM DATA MIS7002-8 Database Administration & Management 9  Data combined with other data provides improved results to the public.  We are able to get and use data for better results for companies and individuals.  Products from data brokers prevent fraud, improve product offerings & deliver tailored consumer advertisements.  Brokers foster competition enabling small businesses to pitch innovative products to unreachable consumers. Information about individuals is complied by brokers from online and offline sources—email, personal websites, social media posts, U.S. Census records, retailers, DMV records, and real estate records—using progressive analytic tools, for selling to other brokers and businesses. Anthes, 2015
  • 10. BENEFITS OF DATA MINING Mountains of data  There are numerous types of data collected across industries, states, and governments.  Data-informed decision-making over the last decade has become a movement due to so much data that is available.  Google the term ‘decision making’ or ‘data’ there are more than 50 million entries. Formats  Accounts need to be either in the same or complementary format for translating into a common format.  Most times different suppliers of data use different formats.  Data must be cleaned—edited, tested for correctness and consistency. Data 4 knowledge  Translating knowledge from data requires assessment, interpretation, and access to sources of data and the continuous accumulation of data.  It is essential to use data for decision- making. Data mining is the search method for said process. MIS7002-8 Database Administration & Management 10 Jianping et al., 2010; Schoors, 2000
  • 11. CONCLUSION Numerous, mostly all, businesses/professions benefit from data mining (i.e. medical, education, legal, banking, politics, etc.). Having access to data in a visual format as well as textual assists in the analytical process. Data contained in data-mines must be edited and tested to ensure accuracy and consistency. The data accessed is typically utilized for decisions and knowledge purposes like projecting, training, forecasting, budgeting, planning, etc. This presentation provides information and examples of data mining its processes and how it is used/benefits individuals/businesses. MIS7002-8 Database Administration & Management 11
  • 12. REFERENCES Anthes, G. (2015). Data Brokers Are Watching You. Communications of The ACM, 58(1), 28-30. doi:10.1145/2686740 Carey, M.J., Onose, N., Petropoulos, M. (2012). Data Services. Communications of the ACM, 55(6), 86-97. doi:10.1145/2184319.2184340 Goil, S., & Choudhary, A. (1997). High performance OLAP and data mining on parallel computers. Data Mining and Knowledge Discovery, 1(4), 391-417. doi:http://dx.doi.org/10.1023/A:1009777418785 Jianping, S., Cooley, V. E., Reeves, P., Burt, W. L., Ryan, L., Rainey, J. M., & Wenhui, Y. (2010). Using data for decision- making: perspectives from 16 principals in Michigan, USA. International Review of Education / Internationale Zeitschrift Für Erziehungswissenschaft, 56(4), 435-456. doi:10.1007/s11159-010-9172-x Schoors, K. (2000). A note on building a database on russian banks: Fieldwork against the odds. Post - Communist Economies,12(2), 241-249. Retrieved from http://search.proquest.com.proxy1.ncu.edu/docview/222605083?accountid=28180 Yu, C., Yurovsky, D., & Xu, T. (. (2012). Visual data mining: An exploratory approach to analyzing temporal patterns of eye movements. Infancy, 17(1), 33-60. doi:10.1111/j.1532-7078.2011.00095.x 12

Notes de l'éditeur

  1. Carey et al. 2012