BIG DATA: SECURE DATA MINING OVER COMPLEX AND HEAVY DATA
|
|
- Oliver Dennis
- 8 years ago
- Views:
Transcription
1 BIG DATA: SECURE DATA MINING OVER COMPLEX AND HEAVY DATA Yashwanth Kumar Dachepalli 1, M. Prasanna 2 1 M.Tech Student, 2 Assistant Professor Department of CSE, Sarada Institute of Technology & Science, Raghunadha Palem, Khammam, Telangana State, India. Abstract: Big data is the new technology to find out the datasets from huge and complex data ware houses. This is the technology which was going with rapid speed through all the domains like technology, engineering and science it is also going through biomedical, biological and physical sciences. Big Data Mining is the technology which is useful for getting the data through large size of datasets or data streams, it may be based on speed, based on size, data difference. This is going to be a best technology in coming years. This the technical paper which is going to discuss about data, data mining and data mining with big data. I. INTRODUCTION The term 'Big Data' appeared for 1st time in 1998 during a Si Graphics (SGI) slide deck by John Mashey with the title of "Big information and also the NextWave of InfraStress". massive data processing was terribly relevant from the start, because the 1st book mentioning 'Big Data' may be a data processing book that appeared conjointly in 1998 by Weiss and Indrukya. However, the first academic paper with the words 'Big Data' within the title appeared slightly later in 2000 during a paper by Diebold.The origin of the term 'Big Data' is owing to the very fact that we have a tendency to area unit making a large quantity of information a day. Usama Fayyad in his invited speak at the KDD BigMine 12Workshop conferred wonderful information numbers concerning net usage, among them the following: each day Google has over one billion queries per day, Twitter has over 250 milion tweets per day, Facebook has more than 800 million updates per day, and YouTube has over four billion views per day. the info made nowadays is calculable within the order of zettabytes, and it's growing around fourhundredth per annum.a new massive supply of information is going to be generated from mobile devices and massive firms as Google, Apple, Facebook, Yahoo area unit setting out to look carefully to the present information to seek out helpful patterns to enhance user expertise. Big data is pervasive, and nevertheless still the notion engenders confusion. Massive information has been wont to convey all styles of ideas, including: Brobdingnagian quantities of information, social media analytics, next generation information management capabilities, period information, and far a lot of. regardless of the label, organizations area unit setting out to perceive and explore the way to method and analyze an enormous array of knowledge in new ways that. In doing therefore, a small, however growing cluster of pioneers is achieving breakthrough business outcomes. In industries throughout the planet, executives acknowledge the requirement to find out a lot of concerning the way to exploit massive information. however despite what appears like unrelenting media attention, it are often exhausting to seek out in-depth data on what organizations area unit very doing. So, we sought-after to raised perceive however organizations read massive information and to what extent they're presently victimisation it to profit their businesses. II. TYPES OF BIG DATA AND SOURCES There square measure 2 forms of huge data: structured and unstructured. Structured knowledge square measure numbers and words which will be simply categorised and analyzed. These knowledge square measure generated by things like network sensors embedded in electronic devices, smart phones, and international positioning system (GPS) devices. Structured knowledge conjointly embrace things like sales figures, account balances, and dealing knowledge. Unstructured knowledge embrace additional advanced info, like client reviews from industrial websites, photos and other transmission, and comments on social networking sites. These knowledge can't simply be separated into classes or analyzed numerically. Unstructured huge knowledge is that the things that humans square measure speech communication, says huge knowledge house vp Tony Jewitt of Plano,Texas. It uses tongue. Analysis of unstructured knowledge depends on keywords, which permit users to filter the data supported searchable terms. The explosive growth of the web in recent years implies that the variability and quantity of big knowledge still grow. a lot of of that growth comes from unstructured knowledge. III. HACE THEOREM. Big information starts with large-volume, heterogeneous, autonomous sources with distributed and redistributed management, and seeks to explore complicated and evolving relationships among information. These characteristics create it associate degree extreme challenge for discovering helpful Copyright 2015.All rights reserved. 3117
2 information from the large information. in an exceedingly naïve sense, we are able to imagine that variety of blind men try to see an enormous artiodactyl mammal, which can be the large information during this context. The goal of every blind person is to draw an image (or conclusion) of the artiodactyl mammal in step with the a part of data he collects throughout the method. as a result of every person s read is limited to his native region, it's not stunning that the blind men can every conclude severally that the artiodactyl mammal feels sort of a rope, a hose, or a wall, betting on the region every of them is restricted to. to form the matter even additional complicated, allow us to assume that the artiodactyl mammal is growing speedily and its create changes perpetually, and every blind person might have his own (possible unreliable and inaccurate) data sources that tell him concerning biased information concerning the camel (e.g., one blind person might exchange his feeling concerning the artiodactyl mammal with another blind person, wherever the changed knowledge is inherently biased). Exploring the large information during this state of affairs is akin to aggregating heterogeneous information from completely different sources (blind men) to assist draw a very best image to reveal the real gesture of the camel in an exceedingly period fashion. Indeed, this task isn't as easy as asking every blind person to explain his feelings concerning the camel so obtaining associate degree knowledgeable to draw one single image with a combined read, regarding that every individual might speak a distinct language (heterogeneous and numerous data sources) and that they might even have privacy considerations about the messages they deliberate within the data exchange method.the term huge information virtually considerations concerning information volumes, HACE theorem suggests that the key characteristics of the large information square measure A. Brobdingnagian with heterogeneous and numerous information sources:- One of the elemental characteristics of the large information is that the Brobdingnagian volume of information depicted by heterogeneous and numerous dimensionalities. This Brobdingnagian volume of information comes from varied sites like Twitter, Myspace, Orkut and LinkedIn etc. B. redistributed control:- Autonomous information sources with distributed and redistributed controls square measure a main characteristic of Big information applications. Being autonomous, every information supply is in a position to get and collect data while not involving (or relying on) any centralized management. this can be like the globe Wide internet (WWW) setting wherever every web server provides an explicit quantity of data and every server is in a position to completely operate while not essentially relying on alternative servers. C. complicated information and information associations:- Multistructure, multisource information is complicated information, samples of complicated data varieties square measure bills of materials, data processing documents, maps, time-series, pictures and video. Such combined characteristics recommend that huge information need a big mind to consolidate information for optimum values. IV. THREE V S IN BIG DATA Doug educator was the primary one talking regarding 3V s in massive information Management Volume: the number of information. maybe the characteristic most related to massive information, volume refers to the mass quantities of information that organizations are attempting to harness to boost decision-making across the enterprise. information volumes continue to increase at associate degree unprecedented rate. Variety: differing kinds of information and data sources. selection is regarding managing the quality of multiple information varieties, including structured, semi-structured and unstructured information. Organizations got to integrate and analyze information from a complex array of each ancient and non-traditional info sources, from at intervals and out of doors the enterprise. With the explosion of sensors, sensible devices and social collaboration technologies, information is being generated in unnumberable forms, including: text, web data, tweets, audio, video, log files and additional. Velocity: information in motion. The speed at that information is formed, processed and analyzed continues to accelerate. Nowadays there ar 2 additional V s Variability:- There ar changes within the structure of the information and the way users wish to interpret that data. Value:- Business price that provides organization a compelling advantage, owing to the power of constructing selections based mostly in answering queries that were antecedently thought-about on the far side reach. V. DATA MINING FOR BIG DATA Generally, data {processing} (sometimes known as information or information discovery) is that the process of analyzing information from totally different perspectives and summarizing it into helpful info - info that may be accustomed increase revenue, cuts costs, or both. Technically, data {processing} is that the process of finding correlations or patterns among dozens of fields in massive relative database. Data mining as a term used for the precise Copyright 2015.All rights reserved. 3118
3 categories of six activities or tasks as follows: 1. Classification 2. Estimation 3. Prediction 4. Association rules 5. Clustering 6. Description A. Classification Classification may be a method of generalizing the information in keeping with totally different instances. many major styles of classification algorithms in data processing area unit call tree, k-nearest neighbor classifier, Naive mathematician, Apriori and AdaBoost. Classification consists of examining the options of a freshly given object and distribution thereto a predefined category. The classification task is characterised by the well-defined categories, and a coaching set consisting of reclassified examples. B. Estimation Estimation deals with unceasingly valued outcomes. Given some input file, we tend to use estimation to return up with a value for a few unknown continuous variables like financial gain, height or mastercard balance. C. Prediction It s a press release concerning the method things can happen within the future, usually however not continually supported expertise or information. Prediction could also be a press release during which some outcome is anticipated. D. Association Rules An association rule may be a rule which means sure association relationships among a collection of objects (such as occur together or one implies the other ) during a information. E. Clustering Clustering may be thought-about the foremost necessary unsupervised learning problem; therefore, as each alternative drawback of this type, it deals with finding a structure during a assortment of unlabelled information. Meeting the challenges conferred by massive information are troublesome. the degree of knowledge is already huge and increasing every day. the rate of its generation and growth is increasing, driven partly by the proliferation of web connected devices. moreover, the variability of knowledge being generated is additionally increasing, and organization s capability to capture and method this information is restricted. Current technology, design, management and analysis approaches area unit unable to deal with the flood of knowledge, and organizations can ought to amendment the manner they have confidence, plan, govern, manage, method and report on information to understand the potential of massive information. A. Privacy, security and trust The Australian Government is committed to protective the privacy rights of its voters and has recently reinforced the Privacy Act (through the passing of the Privacy change (Enhancing Privacy Protection) Bill 2012) to boost the protection of and set clearer boundaries for usage of nonpublic info. Government agencies, once grouping or managing voters information, area unit subject to a spread of legislative controls, and must comply with the variety of acts and laws like the liberty of knowledge Act (1982), the Archives Act (1983), the Telecommunications Act (1997),the Electronic Transactions Act (1999), and also the Intelligence Services Act (2001). These legislative instruments area unit designed to take care of public confidence within the government as a good and secure repository and steward of national info. the employment of massive information by government agencies won't amendment this; rather it should add an extra layer of complexness in terms of managing info security risks. massive information sources, the transport and delivery systems at intervals and across agencies, and also the finish points for this information can all become targets of interest for hackers, each native and international and can ought to be protected. the general public unleash of huge machinereadable information sets, as a part of the open government policy, may doubtless give a chance for unfriendly state and non-state actors to reap sensitive info, or produce a mosaic of exploitable info from apparently innocuous information. This threat can ought to be understood and thoroughly managed. The potential worth of massive information could be a function of the amount of relevant, disparate datasets which will be coupled and analysed to reveal new patterns, trends and insights. trust in government agencies is needed before voters are ready to perceive that such linking and analysis will occur whereas protective the privacy rights of people. B. information management and sharing Accessible info is that the lifeblood of a sturdy democracy and a productive economy.2 Government agencies realize that for information to own any worth it must be determinable, accessible and usable, and also the significance of those requirements solely will increase because the discussion turns towards massive information. Government agencies should accomplish these requirements while still adhering to privacy laws. The processes close the manner information is collected, handled, utilized and managed by agencies can ought to be aligned with all Copyright 2015.All rights reserved. 3119
4 relevant legislative and regulative instruments with a spotlight on making the information out there for analysis in an exceedingly lawful, controlled and significant manner. information conjointly must be correct, complete and timely if it's to be wont to support advanced analysis and deciding. For these reasons, management and governance focus must air creating information open and out there across government via standardised Apis, formats and data. Improved quality of knowledge can turn out tangible advantages in terms of business intelligence, deciding, sustainable cost-savings and productivity enhancements. the present trend towards open information and open government has seen a spotlight on creating information sets out there to the general public, but these open initiatives ought to conjointly place specialize in making information open, out there and standardised at intervals and between agencies in such the way that permits inter-governmental agency use and collaboration to the extent created doable by the privacy laws. C. Technology and analytical systems The emergence of massive information and also the potential to undertake advanced analysis of terribly massive information sets is, primarily, a consequence of recent advances within the technology that enable this. If massive information analytics is to be adopted by agencies, a large amount of stress could also be placed upon current ICT systems and solutions that presently carry the burden of processing, analysing and archiving information. Government agencies can ought to manage these new needs expeditiously in order to deliver internet advantages through the adoption of latest technologies. VI. EXPERIMENTAL RESULTS Before doing data mining we have to upload dataset into application. After uploading dataset we have to apply tyre1(parallel computing). Then we will get the results as shown below. After applying the tyre 2(privacy) we will get the results like as shown below. After applying the tyre2 we have to apply tyre3. When we apply tyre3 then we will get the data through data mining. the results were shown below. VII. Conclusion Use of Integrity may be a important facet in health-care systems. this technique provides information integrity by applying new modification to existing system for higher accuracy measured altogether phases of system. we tend to use straightforward graphical interface for health related applications that is well learnable for country peoples, who are uneducated. this technique is incredibly helpful in rural/remote areas wherever hospitals and health connected facility is on the market far-flung from their home. This newer system also provides SMS alert for the users. we tend to apply recently projected decoding outsourcing with privacy protection to shift clients pairing computation to the cloud server. to guard mheath service providers programs, we tend to expand the branching program tree by victimization the random permutation and randomise the choice thresholds used at the choice branching nodes. This system has future scope on client s privacy protection victimization outsourcing decryption technique. during this system security are often obtained by victimization projected new branching program that replaces existing downside of system. there's any scope in improvement over linear pairing, homomorphic secret writing, multidimentional vary question supported anonymous IBE, decoding outsourcing, private re-encryption for CAM cloud assisted mobile health observation system. Copyright 2015.All rights reserved. 3120
5 REFERENCES [1] Alex Berson and Stephen J.Smith Data Warehousing,Data Mining and OLAP edition [2] Department of Finance and Deregulation Australian Government Big Data Strategy-Issue Paper March [3] NASSCOM Big Data Report [4] Wei Fan and Albert Bifet Mining Big Data:Current Status and Forecast to the Future,Vol 14,Issue 2,2013. [5] Algorithm and approaches to handle large Data-A Survey,IJCSN Vol 2,Issue 3,2013. [6] Xindong Wu, Gong-Quing Wu and Wei Ding Data Mining with Big data, IEEE Transactions on Knoweledge and Data Enginnering Vol 26 No1 Jan [7] Xu Y etal, balancing reducer workload for skewed data using sampling based partioning [8] X. Niuniu and L. Yuxun, Review of Decision Trees, IEEE, [9] Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner Decision Trees-What Are They? [10] Weiss, S.H. and Indurkhya, N. (1998), Predictive Data Mining: A Practical Guide, Morgan Kaufmann Publishers, San Francisco, CA. Copyright 2015.All rights reserved. 3121
A REVIEW REPORT ON DATA MINING
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 4, April 2015,
More informationIJCSIET-ISSUE5-VOLUME2-SERIES3 Page 2
An Enhancement of Data Mining to Web Mining to Big Data 1 A. Vishala, 2 G.JACOB JAYA RAJ 2 Professor, 2 Head of the Department 1,2 SV College of Engineering and Technology Abstract-We are in a world of
More informationLiterature Survey in Data Mining with Big Data
Literature Survey in Data Mining with Big Data 1 Mr.Mohammad Raziuddin & 2 Prof. T.Venkata Ramana Department of CSE SLC's Institute of Engineering and Technology, Hyderabad, India. 1 raziuddin414@gmail.com,
More informationA Survey on Parallel Method for Rough Set using MapReduce Technique for Data Mining
www.ijecs.in International Journal Of Engineering And Computer Science ISSN: 2319-7242 Volume 4 Issue 9 Sep 2015, Page No. 14160-14163 A Survey on Parallel Method for Rough Set using MapReduce Technique
More informationBig Data Strategy Issues Paper
Big Data Strategy Issues Paper MARCH 2013 Contents 1. Introduction 3 1.1 Where are we now? 3 1.2 Why a big data strategy? 4 2. Opportunities for Australian Government agencies 5 2.1 What the future looks
More informationInternational Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014
RESEARCH ARTICLE OPEN ACCESS A Survey of Data Mining: Concepts with Applications and its Future Scope Dr. Zubair Khan 1, Ashish Kumar 2, Sunny Kumar 3 M.Tech Research Scholar 2. Department of Computer
More informationVolume 3, Issue 8, August 2015 International Journal of Advance Research in Computer Science and Management Studies
Volume 3, Issue 8, August 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online at: www.ijarcsms.com An
More informationINTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY
INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK REVIEW ON BIG DATA SECURITY IN CLOUD COMPUTING MISS. ANKITA S. AMBADKAR 1, PROF.
More informationData sets preparing for Data mining analysis by SQL Horizontal Aggregation
Data sets preparing for Data mining analysis by SQL Horizontal Aggregation V.Nikitha 1, P.Jhansi 2, K.Neelima 3, D.Anusha 4 Department Of IT, G.Pullaiah College of Engineering and Technology. Kurnool JNTU
More informationAn Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015
An Introduction to Data Mining for Wind Power Management Spring 2015 Big Data World Every minute: Google receives over 4 million search queries Facebook users share almost 2.5 million pieces of content
More informationAnuradha Bhatia, Faculty, Computer Technology Department, Mumbai, India
Volume 3, Issue 9, September 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Real Time
More informationINTEGRATED APPROACH FOR DATA MINING AND CLOUD MINING:CASE STUDY
INTEGRATED APPROACH FOR DATA MINING AND CLOUD MINING:CASE STUDY 1 MULPURI.KALYAN, 2 ROSLIN COMPUTER SCIENCE &ENGINEERING DEPARTMENT SAVEETHA SCHOOL OF ENGINEERING,CHENNAI Email:kalyanchowdary.5233@gmail.com
More informationKeywords Big Data; OODBMS; RDBMS; hadoop; EDM; learning analytics, data abundance.
Volume 4, Issue 11, November 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Analytics
More informationBIG DATA: BIG BOOST TO BIG TECH
BIG DATA: BIG BOOST TO BIG TECH Ms. Tosha Joshi Department of Computer Applications, Christ College, Rajkot, Gujarat (India) ABSTRACT Data formation is occurring at a record rate. A staggering 2.9 billion
More informationData Refinery with Big Data Aspects
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 7 (2013), pp. 655-662 International Research Publications House http://www. irphouse.com /ijict.htm Data
More information5 Keys to Unlocking the Big Data Analytics Puzzle. Anurag Tandon Director, Product Marketing March 26, 2014
5 Keys to Unlocking the Big Data Analytics Puzzle Anurag Tandon Director, Product Marketing March 26, 2014 1 A Little About Us A global footprint. A proven innovator. A leader in enterprise analytics for
More informationResearch Note What is Big Data?
Research Note What is Big Data? By: Devin Luco Copyright 2012, ASA Institute for Risk & Innovation Keywords: Big Data, Database Management, Data Variety, Data Velocity, Data Volume, Structured Data, Unstructured
More informationIJITE Vol.03 Issue - 03, (March 2015) ISSN: 2321 1776 Impact Factor 3.570
Big data analytics vs Data Mining analytics Vinti Parmar, 1 Department of Computer Science, Indira Gandhi University, Meerpur, Rewari Haryana, INDIA Itisha Gupta Department of Computer Science, Bright
More informationRole of Social Networking in Marketing using Data Mining
Role of Social Networking in Marketing using Data Mining Mrs. Saroj Junghare Astt. Professor, Department of Computer Science and Application St. Aloysius College, Jabalpur, Madhya Pradesh, India Abstract:
More informationINTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY
INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK A SURVEY ON BIG DATA ISSUES AMRINDER KAUR Assistant Professor, Department of Computer
More informationANALYSIS OF WEBSITE USAGE WITH USER DETAILS USING DATA MINING PATTERN RECOGNITION
ANALYSIS OF WEBSITE USAGE WITH USER DETAILS USING DATA MINING PATTERN RECOGNITION K.Vinodkumar 1, Kathiresan.V 2, Divya.K 3 1 MPhil scholar, RVS College of Arts and Science, Coimbatore, India. 2 HOD, Dr.SNS
More informationInternational Journal of Innovative Research in Computer and Communication Engineering
FP Tree Algorithm and Approaches in Big Data T.Rathika 1, J.Senthil Murugan 2 Assistant Professor, Department of CSE, SRM University, Ramapuram Campus, Chennai, Tamil Nadu,India 1 Assistant Professor,
More informationAN EFFICIENT SELECTIVE DATA MINING ALGORITHM FOR BIG DATA ANALYTICS THROUGH HADOOP
AN EFFICIENT SELECTIVE DATA MINING ALGORITHM FOR BIG DATA ANALYTICS THROUGH HADOOP Asst.Prof Mr. M.I Peter Shiyam,M.E * Department of Computer Science and Engineering, DMI Engineering college, Aralvaimozhi.
More informationSPATIAL DATA CLASSIFICATION AND DATA MINING
, pp.-40-44. Available online at http://www. bioinfo. in/contents. php?id=42 SPATIAL DATA CLASSIFICATION AND DATA MINING RATHI J.B. * AND PATIL A.D. Department of Computer Science & Engineering, Jawaharlal
More informationBig Data Driven Knowledge Discovery for Autonomic Future Internet
Big Data Driven Knowledge Discovery for Autonomic Future Internet Professor Geyong Min Chair in High Performance Computing and Networking Department of Mathematics and Computer Science College of Engineering,
More informationTransforming the Telecoms Business using Big Data and Analytics
Transforming the Telecoms Business using Big Data and Analytics Event: ICT Forum for HR Professionals Venue: Meikles Hotel, Harare, Zimbabwe Date: 19 th 21 st August 2015 AFRALTI 1 Objectives Describe
More informationAssociation Technique on Prediction of Chronic Diseases Using Apriori Algorithm
Association Technique on Prediction of Chronic Diseases Using Apriori Algorithm R.Karthiyayini 1, J.Jayaprakash 2 Assistant Professor, Department of Computer Applications, Anna University (BIT Campus),
More informationSunnie Chung. Cleveland State University
Sunnie Chung Cleveland State University Data Scientist Big Data Processing Data Mining 2 INTERSECT of Computer Scientists and Statisticians with Knowledge of Data Mining AND Big data Processing Skills:
More informationInformation Management course
Università degli Studi di Milano Master Degree in Computer Science Information Management course Teacher: Alberto Ceselli Lecture 01 : 06/10/2015 Practical informations: Teacher: Alberto Ceselli (alberto.ceselli@unimi.it)
More informationInternational Journal of Advancements in Research & Technology, Volume 3, Issue 5, May-2014 18 ISSN 2278-7763. BIG DATA: A New Technology
International Journal of Advancements in Research & Technology, Volume 3, Issue 5, May-2014 18 BIG DATA: A New Technology Farah DeebaHasan Student, M.Tech.(IT) Anshul Kumar Sharma Student, M.Tech.(IT)
More informationBig data and its transformational effects
Big data and its transformational effects Professor Fai Cheng Head of Research & Technology September 2015 Working together for a safer world Topics Lloyd s Register Big Data Data driven world Data driven
More informationA Review of Anomaly Detection Techniques in Network Intrusion Detection System
A Review of Anomaly Detection Techniques in Network Intrusion Detection System Dr.D.V.S.S.Subrahmanyam Professor, Dept. of CSE, Sreyas Institute of Engineering & Technology, Hyderabad, India ABSTRACT:In
More informationIndexed Terms: Big Data, benefits, characteristics, definition, problems, unstructured data
Managing Data through Big Data: A Review Harsimran Singh Anand Assistant Professor, PG Dept of Computer Science & IT, DAV College, Amritsar Email id: harsimran_anand@yahoo.com A B S T R A C T Big Data
More informationHow To Use Data Mining For Knowledge Management In Technology Enhanced Learning
Proceedings of the 6th WSEAS International Conference on Applications of Electrical Engineering, Istanbul, Turkey, May 27-29, 2007 115 Data Mining for Knowledge Management in Technology Enhanced Learning
More informationGovernment Technology Trends to Watch in 2014: Big Data
Government Technology Trends to Watch in 2014: Big Data OVERVIEW The federal government manages a wide variety of civilian, defense and intelligence programs and services, which both produce and require
More informationAn Overview of Knowledge Discovery Database and Data mining Techniques
An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,
More informationData Visualization Techniques
Data Visualization Techniques From Basics to Big Data with SAS Visual Analytics WHITE PAPER SAS White Paper Table of Contents Introduction.... 1 Generating the Best Visualizations for Your Data... 2 The
More informationANALYTICS BUILT FOR INTERNET OF THINGS
ANALYTICS BUILT FOR INTERNET OF THINGS Big Data Reporting is Out, Actionable Insights are In In recent years, it has become clear that data in itself has little relevance, it is the analysis of it that
More informationProtect Integrity of Data in Cloud Assisted Privacy Preserving Mobile Health Monitoring
International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 13 (2014), pp. 1329-1334 International Research Publications House http://www. irphouse.com Protect Integrity
More information131-1. Adding New Level in KDD to Make the Web Usage Mining More Efficient. Abstract. 1. Introduction [1]. 1/10
1/10 131-1 Adding New Level in KDD to Make the Web Usage Mining More Efficient Mohammad Ala a AL_Hamami PHD Student, Lecturer m_ah_1@yahoocom Soukaena Hassan Hashem PHD Student, Lecturer soukaena_hassan@yahoocom
More informationMeasure Social Media like a Pro: Social Media Analytics Uncovered SOCIAL MEDIA LIKE SHARE. Powered by
1 Measure Social Media like a Pro: Social Media Analytics Uncovered # SOCIAL MEDIA LIKE # SHARE Powered by 2 Social media analytics were a big deal in 2013, but this year they are set to be even more crucial.
More informationInternational Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 4, Jul-Aug 2015
RESEARCH ARTICLE OPEN ACCESS Data Mining Approach To Big Data Jyothiprasanna Jaladi [1], B.V.Kiranmayee [2], S.Nagini [3] Student of M.Tech(SE) [1], Associate Professor Département Computer Science and
More informationPrediction of Heart Disease Using Naïve Bayes Algorithm
Prediction of Heart Disease Using Naïve Bayes Algorithm R.Karthiyayini 1, S.Chithaara 2 Assistant Professor, Department of computer Applications, Anna University, BIT campus, Tiruchirapalli, Tamilnadu,
More informationDanny Wang, Ph.D. Vice President of Business Strategy and Risk Management Republic Bank
Danny Wang, Ph.D. Vice President of Business Strategy and Risk Management Republic Bank Agenda» Overview» What is Big Data?» Accelerates advances in computer & technologies» Revolutionizes data measurement»
More informationAdobe Insight, powered by Omniture
Adobe Insight, powered by Omniture Accelerating government intelligence to the speed of thought 1 Challenges that analysts face 2 Analysis tools and functionality 3 Adobe Insight 4 Summary Never before
More informationBig Data and Analytics: Challenges and Opportunities
Big Data and Analytics: Challenges and Opportunities Dr. Amin Beheshti Lecturer and Senior Research Associate University of New South Wales, Australia (Service Oriented Computing Group, CSE) Talk: Sharif
More informationBig Data Analytic and Mining with Machine Learning Algorithm
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 4, Number 1 (2014), pp. 33-40 International Research Publications House http://www. irphouse.com /ijict.htm Big Data
More informationIJRCS - International Journal of Research in Computer Science ISSN: 2349-3828
ISSN: 2349-3828 Implementing Big Data for Intelligent Business Decisions Dr. V. B. Aggarwal Deepshikha Aggarwal 1(Jagan Institute of Management Studies, Delhi, India, vbaggarwal@jimsindia.org) 2(Jagan
More informationIntroduction to Data Mining
Introduction to Data Mining Jay Urbain Credits: Nazli Goharian & David Grossman @ IIT Outline Introduction Data Pre-processing Data Mining Algorithms Naïve Bayes Decision Tree Neural Network Association
More informationBig Data Mining: Challenges and Opportunities to Forecast Future Scenario
Big Data Mining: Challenges and Opportunities to Forecast Future Scenario Poonam G. Sawant, Dr. B.L.Desai Assist. Professor, Dept. of MCA, SIMCA, Savitribai Phule Pune University, Pune, Maharashtra, India
More informationData Mining Solutions for the Business Environment
Database Systems Journal vol. IV, no. 4/2013 21 Data Mining Solutions for the Business Environment Ruxandra PETRE University of Economic Studies, Bucharest, Romania ruxandra_stefania.petre@yahoo.com Over
More informationData Warehousing and Data Mining in Business Applications
133 Data Warehousing and Data Mining in Business Applications Eesha Goel CSE Deptt. GZS-PTU Campus, Bathinda. Abstract Information technology is now required in all aspect of our lives that helps in business
More informationA COGNITIVE APPROACH IN PATTERN ANALYSIS TOOLS AND TECHNIQUES USING WEB USAGE MINING
A COGNITIVE APPROACH IN PATTERN ANALYSIS TOOLS AND TECHNIQUES USING WEB USAGE MINING M.Gnanavel 1 & Dr.E.R.Naganathan 2 1. Research Scholar, SCSVMV University, Kanchipuram,Tamil Nadu,India. 2. Professor
More informationBig Data. Fast Forward. Putting data to productive use
Big Data Putting data to productive use Fast Forward What is big data, and why should you care? Get familiar with big data terminology, technologies, and techniques. Getting started with big data to realize
More informationA Hurwitz white paper. Inventing the Future. Judith Hurwitz President and CEO. Sponsored by Hitachi
Judith Hurwitz President and CEO Sponsored by Hitachi Introduction Only a few years ago, the greatest concern for businesses was being able to link traditional IT with the requirements of business units.
More informationData Visualization Techniques
Data Visualization Techniques From Basics to Big Data with SAS Visual Analytics WHITE PAPER SAS White Paper Table of Contents Introduction.... 1 Generating the Best Visualizations for Your Data... 2 The
More informationHadoop for Enterprises:
Hadoop for Enterprises: Overcoming the Major Challenges Introduction to Big Data Big Data are information assets that are high volume, velocity, and variety. Big Data demands cost-effective, innovative
More information2015 Workshops for Professors
SAS Education Grow with us Offered by the SAS Global Academic Program Supporting teaching, learning and research in higher education 2015 Workshops for Professors 1 Workshops for Professors As the market
More informationManifest for Big Data Pig, Hive & Jaql
Manifest for Big Data Pig, Hive & Jaql Ajay Chotrani, Priyanka Punjabi, Prachi Ratnani, Rupali Hande Final Year Student, Dept. of Computer Engineering, V.E.S.I.T, Mumbai, India Faculty, Computer Engineering,
More informationISSN: 2320-1363 CONTEXTUAL ADVERTISEMENT MINING BASED ON BIG DATA ANALYTICS
CONTEXTUAL ADVERTISEMENT MINING BASED ON BIG DATA ANALYTICS A.Divya *1, A.M.Saravanan *2, I. Anette Regina *3 MPhil, Research Scholar, Muthurangam Govt. Arts College, Vellore, Tamilnadu, India Assistant
More informationEXECUTIVE REPORT. Big Data and the 3 V s: Volume, Variety and Velocity
EXECUTIVE REPORT Big Data and the 3 V s: Volume, Variety and Velocity The three V s are the defining properties of big data. It is critical to understand what these elements mean. The main point of the
More informationSocial Media Mining. Data Mining Essentials
Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers
More informationPrivacy: Legal Aspects of Big Data and Information Security
Privacy: Legal Aspects of Big Data and Information Security Presentation at the 2 nd National Open Access Workshop 21-22 October, 2013 Izmir, Turkey John N. Gathegi University of South Florida, Tampa,
More informationAnalyzing Big Data: The Path to Competitive Advantage
White Paper Analyzing Big Data: The Path to Competitive Advantage by Marcia Kaplan Contents Introduction....2 How Big is Big Data?................................................................................
More informationData Mining System, Functionalities and Applications: A Radical Review
Data Mining System, Functionalities and Applications: A Radical Review Dr. Poonam Chaudhary System Programmer, Kurukshetra University, Kurukshetra Abstract: Data Mining is the process of locating potentially
More informationStatistics for BIG data
Statistics for BIG data Statistics for Big Data: Are Statisticians Ready? Dennis Lin Department of Statistics The Pennsylvania State University John Jordan and Dennis K.J. Lin (ICSA-Bulletine 2014) Before
More informationWhite Paper. How Streaming Data Analytics Enables Real-Time Decisions
White Paper How Streaming Data Analytics Enables Real-Time Decisions Contents Introduction... 1 What Is Streaming Analytics?... 1 How Does SAS Event Stream Processing Work?... 2 Overview...2 Event Stream
More informationHow To Use Big Data Effectively
Why is BIG Data Important? March 2012 1 Why is BIG Data Important? A Navint Partners White Paper May 2012 Why is BIG Data Important? March 2012 2 What is Big Data? Big data is a term that refers to data
More informationAn Enterprise Framework for Business Intelligence
An Enterprise Framework for Business Intelligence Colin White BI Research May 2009 Sponsored by Oracle Corporation TABLE OF CONTENTS AN ENTERPRISE FRAMEWORK FOR BUSINESS INTELLIGENCE 1 THE BI PROCESSING
More informationBusiness Analytics and the Nexus of Information
Business Analytics and the Nexus of Information 2 The Impact of the Nexus of Forces 4 From the Gartner Files: Information and the Nexus of Forces: Delivering and Analyzing Data 6 About IBM Business Analytics
More informationEfficient Cloud Computing Load Balancing Using Cloud Partitioning and Game Theory in Public Cloud
Efficient Cloud Computing Load Balancing Using Cloud Partitioning and Game Theory in Public Cloud P.Rahul 1, Dr.A.Senthil Kumar 2, Boney Cherian 3 P.G. Scholar, Department of CSE, R.V.S. College of Engineering
More informationA Study on Effective Business Logic Approach for Big Data Mining
A Study on Effective Business Logic Approach for Big Data Mining T. Sathis Kumar Assistant Professor, Dept of C.S.E, Saranathan College of Engineering, Trichy, Tamil Nadu, India. ABSTRACT: Big data is
More informationManaging Cloud Server with Big Data for Small, Medium Enterprises: Issues and Challenges
Managing Cloud Server with Big Data for Small, Medium Enterprises: Issues and Challenges Prerita Gupta Research Scholar, DAV College, Chandigarh Dr. Harmunish Taneja Department of Computer Science and
More informationCOMP9321 Web Application Engineering
COMP9321 Web Application Engineering Semester 2, 2015 Dr. Amin Beheshti Service Oriented Computing Group, CSE, UNSW Australia Week 11 (Part II) http://webapps.cse.unsw.edu.au/webcms2/course/index.php?cid=2411
More informationData Mining & Data Stream Mining Open Source Tools
Data Mining & Data Stream Mining Open Source Tools Darshana Parikh, Priyanka Tirkha Student M.Tech, Dept. of CSE, Sri Balaji College Of Engg. & Tech, Jaipur, Rajasthan, India Assistant Professor, Dept.
More informationComparison of K-means and Backpropagation Data Mining Algorithms
Comparison of K-means and Backpropagation Data Mining Algorithms Nitu Mathuriya, Dr. Ashish Bansal Abstract Data mining has got more and more mature as a field of basic research in computer science and
More informationBIDM Project. Predicting the contract type for IT/ITES outsourcing contracts
BIDM Project Predicting the contract type for IT/ITES outsourcing contracts N a n d i n i G o v i n d a r a j a n ( 6 1 2 1 0 5 5 6 ) The authors believe that data modelling can be used to predict if an
More informationAuto-Classification for Document Archiving and Records Declaration
Auto-Classification for Document Archiving and Records Declaration Josemina Magdalen, Architect, IBM November 15, 2013 Agenda IBM / ECM/ Content Classification for Document Archiving and Records Management
More informationIntroduction to Data Mining
Introduction to Data Mining 1 Why Data Mining? Explosive Growth of Data Data collection and data availability Automated data collection tools, Internet, smartphones, Major sources of abundant data Business:
More informationDMDSS: Data Mining Based Decision Support System to Integrate Data Mining and Decision Support
DMDSS: Data Mining Based Decision Support System to Integrate Data Mining and Decision Support Rok Rupnik, Matjaž Kukar, Marko Bajec, Marjan Krisper University of Ljubljana, Faculty of Computer and Information
More informationDr. U. Devi Prasad Associate Professor Hyderabad Business School GITAM University, Hyderabad Email: Prasad_vungarala@yahoo.co.in
96 Business Intelligence Journal January PREDICTION OF CHURN BEHAVIOR OF BANK CUSTOMERS USING DATA MINING TOOLS Dr. U. Devi Prasad Associate Professor Hyderabad Business School GITAM University, Hyderabad
More informationMobile Phone APP Software Browsing Behavior using Clustering Analysis
Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management Bali, Indonesia, January 7 9, 2014 Mobile Phone APP Software Browsing Behavior using Clustering Analysis
More informationBuilding Big with Big Data Now companies are in the middle of a renovation that forces them to be analytics-driven to continue being competitive.
Unlocking Big Data Building Big with Big Data Now companies are in the middle of a renovation that forces them to be analytics-driven to continue being competitive. Data analysis provides a complete insight
More informationFor healthcare, change is in the air and in the cloud
IBM Software Healthcare Thought Leadership White Paper For healthcare, change is in the air and in the cloud Scalable and secure private cloud solutions can meet the challenges of healthcare transformation
More informationGrid Density Clustering Algorithm
Grid Density Clustering Algorithm Amandeep Kaur Mann 1, Navneet Kaur 2, Scholar, M.Tech (CSE), RIMT, Mandi Gobindgarh, Punjab, India 1 Assistant Professor (CSE), RIMT, Mandi Gobindgarh, Punjab, India 2
More informationLEVERAGING BIG DATA ANALYTICS THROUGH ANALYTICS-AS-A-SERVICE (AAAS) TOOL
INTERNATIONAL JOURNAL OF PROFESSIONAL ENGINEERING STUDIES Volume VI /Issue 2 / JAN 2016 LEVERAGING BIG DATA ANALYTICS THROUGH ANALYTICS-AS-A-SERVICE (AAAS) TOOL 1 SRAVAN RENTALA, 2 V UMA RANI 1 M.Tech
More informationKeywords- Big data, HACE theorem, Processing Framework, Methodologies, Applications.
Volume 5, Issue 6, June 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Big Data Analytics
More informationInternational Journal of Advanced Engineering Research and Applications (IJAERA) ISSN: 2454-2377 Vol. 1, Issue 6, October 2015. Big Data and Hadoop
ISSN: 2454-2377, October 2015 Big Data and Hadoop Simmi Bagga 1 Satinder Kaur 2 1 Assistant Professor, Sant Hira Dass Kanya MahaVidyalaya, Kala Sanghian, Distt Kpt. INDIA E-mail: simmibagga12@gmail.com
More informationIntegrated email archiving: streamlining compliance and discovery through content and business process management
Make better decisions, faster March 2008 Integrated email archiving: streamlining compliance and discovery through content and business process management 2 Table of Contents Executive summary.........
More informationThe Promise of Industrial Big Data
The Promise of Industrial Big Data Big Data Real Time Analytics Katherine Butler 1 st Annual Digital Economy Congress San Diego, CA Nov 14 th 15 th, 2013 Individual vs. Ecosystem What Happened When 1B
More informationHealthcare Measurement Analysis Using Data mining Techniques
www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 03 Issue 07 July, 2014 Page No. 7058-7064 Healthcare Measurement Analysis Using Data mining Techniques 1 Dr.A.Shaik
More informationFormal Methods for Preserving Privacy for Big Data Extraction Software
Formal Methods for Preserving Privacy for Big Data Extraction Software M. Brian Blake and Iman Saleh Abstract University of Miami, Coral Gables, FL Given the inexpensive nature and increasing availability
More informationBig Data: Opportunities & Challenges, Myths & Truths 資 料 來 源 : 台 大 廖 世 偉 教 授 課 程 資 料
Big Data: Opportunities & Challenges, Myths & Truths 資 料 來 源 : 台 大 廖 世 偉 教 授 課 程 資 料 美 國 13 歲 學 生 用 Big Data 找 出 霸 淩 熱 點 Puri 架 設 網 站 Bullyvention, 藉 由 分 析 Twitter 上 找 出 提 到 跟 霸 凌 相 關 的 詞, 搭 配 地 理 位 置
More informationUnstructured data in the enterprise
Introduction Silverton Consulting, Inc. StorInt Briefing Today, file data is expanding without limit. Some suggest that this data will grow 40X over the next decade, at which time ~80% of all company data
More informationScalable Enterprise Data Integration Your business agility depends on how fast you can access your complex data
Transforming Data into Intelligence Scalable Enterprise Data Integration Your business agility depends on how fast you can access your complex data Big Data Data Warehousing Data Governance and Quality
More informationhow can I comprehensively control sensitive content within Microsoft SharePoint?
SOLUTION BRIEF Information Lifecycle Control for Sharepoint how can I comprehensively control sensitive content within Microsoft SharePoint? agility made possible CA Information Lifecycle Control for SharePoint
More informationReaping the Rewards of Big Data
Reaping the Rewards of Big Data TABLE OF CONTENTS INTRODUCTION: 2 TABLE OF CONTENTS FINDING #1: BIG DATA PLATFORMS ARE ESSENTIAL FOR A MAJORITY OF ORGANIZATIONS TO MANAGE FUTURE BIG DATA CHALLENGES. 4
More informationBig Data Introduction, Importance and Current Perspective of Challenges
International Journal of Advances in Engineering Science and Technology 221 Available online at www.ijaestonline.com ISSN: 2319-1120 Big Data Introduction, Importance and Current Perspective of Challenges
More informationInformation Visualization WS 2013/14 11 Visual Analytics
1 11.1 Definitions and Motivation Lot of research and papers in this emerging field: Visual Analytics: Scope and Challenges of Keim et al. Illuminating the path of Thomas and Cook 2 11.1 Definitions and
More information