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Introduction to data mining and architecture  in hindi
 
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Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 174709 Last moment tuitions
data mining task
 
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Views: 2865 Sailaja NV
KDD ( knowledge data discovery )  in data mining in hindi
 
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Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 61713 Last moment tuitions
Data Mining, Classification, Clustering, Association Rules, Regression, Deviation
 
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Complete set of Video Lessons and Notes available only at http://www.studyyaar.com/index.php/module/20-data-warehousing-and-mining Data Mining, Classification, Clustering, Association Rules, Sequential Pattern Discovery, Regression, Deviation http://www.studyyaar.com/index.php/module-video/watch/53-data-mining
Views: 84228 StudyYaar.com
Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 24874 Red Apple Tutorials
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 103681 LearnEveryone
The 9 Most Common Data Mining Tasks - Cognitir
 
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This cognitorial highlights the 9 most common data mining methods used in practice. For a related video, watch "Supervised vs. Unsupervised Methods": https://www.youtube.com/watch?v=i3itDGwhLq4 This video was created by Cognitir (formerly Import Classes). For additional free resources, visit: www.cognitir.com
Views: 2593 Cognitir
Data Model Examples to Help You Predict the Future
 
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In this video, we're going to look at different ways you can track status in your applications. Ultimately what I'd like to show you, is why updating your database is a bad idea in general. Transcript and code: http://www.deegeu.com/data-model-examples/ In this video, we're going to look at different ways you can track status in your applications. Ultimately what I'd like to show you, is why updating your database is a bad idea in general. There are exceptions, but for most cases you want to just append data. Finally we'll look at some of the benefits of storing a state history in your data model, including predicting the future with data mining! Concepts: Programming, data modeling, data structures, data patterns Social Links: Don't hesitate to contact me if you have any further questions. WEBSITE : [email protected] TWITTER : http://www.twitter.com/deege FACEBOOK: https://www.facebook.com/deegeu.programming.tutorials GOOGLE+ : http://google.com/+Deegeu-programming-tutorials About Me: http://www.deegeu.com/about-programming-tutorial-videos/ Related Videos: https://www.youtube.com/playlist?list=PLZlGOBonMjFVXbUCdvYLEZFAkimS27Aor Media credits: All images are owned by DJ Spiess unless listed below Balloons - Creative Commons CC0 License https://download.unsplash.com/photo-1433838552652-f9a46b332c40
Views: 330 Deege U
What is Data Mining
 
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Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a buzzword, and is frequently misused to mean any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) but is also generalized to any kind of computer decision support system, including artificial intelligence, machine learning, and business intelligence. In the proper use of the word, the key term is discovery[citation needed], commonly defined as "detecting something new". Even the popular book "Data mining: Practical machine learning tools and techniques with Java"(which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting are part of the data mining step, but do belong to the overall KDD process as additional steps.
Views: 52035 John Paul
Data mining  harvesting and analytics. ( All you  need to know)
 
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There is a whirlwind of videos and info on this but none that explained it properly to me. I went online and found out everything I needed to know about the data breaches and the implications of those breaches! I provided some links below in case you wish to educate yourself on whats happening with YOUR data! https://www.quora.com/What-is-the-difference-between-data-analytics-and-data-mining-1 https://www.connotate.com/are-you-screen-scraping-or-data-mining/ http://searchdatamanagement.techtarget.com/definition/data-scrubbing What is the difference between data warehousing and data mining? The main difference between data warehousing and data mining is that data warehousing is the process of compiling and organizing data into one common database, whereas data mining is the process of extracting meaningful data from that database.
Views: 44 Elle's place
What is NAND Flash? MLC vs. TLC, 3D NAND, & More
 
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This video explores an old topic of ours and discusses NAND Flash for SSDs. We talk SLC, MLC, and TLC NAND, discuss which is "best" (and if there is a "best"), and talk about how NAND/SSDs work. Read more in our SSD architecture post here: http://www.gamersnexus.net/guides/1497-ssd-architecture-1-what-is-tlc-nand-mlc-anatomy Like our content? Please consider becoming our Patron to support us: http://www.patreon.com/gamersnexus Help us with your regular Amazon purchases: http://bit.ly/20xUcgz And Newegg! http://bit.ly/T4fEut ** Please like, comment, and subscribe for more! ** Follow us in these locations for more gaming and hardware updates: t: http://www.twitter.com/gamersnexus f: http://www.facebook.com/gamersnexus w: http://www.gamersnexus.net/
Views: 51872 Gamers Nexus
Primitive Meaning
 
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Video shows what primitive means. An original or primary word; a word not derived from another, as opposed to .. A member of a primitive society.. A simple-minded person.. primitive pronunciation. How to pronounce, definition by Wiktionary dictionary. primitive meaning. Powered by MaryTTS
Views: 7048 SDictionary
Introduction to Data Mining: Data Attributes (Part 1)
 
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This video is part two of the vocabulary used in data mining focused on attributes. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8Lh-0 See what our past attendees are saying here: https://hubs.ly/H0f8LXl0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 12215 Data Science Dojo
Lecture - 34 Data Mining and Knowledge Discovery
 
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Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 133547 nptelhrd
Lecture 03 -The Linear Model I
 
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The Linear Model I - Linear classification and linear regression. Extending linear models through nonlinear transforms. Lecture 3 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on April 10, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 239009 caltech
Concept Description Characterization and Comparison
 
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Descriptive & Predictive Data Mining, Concept Description
Views: 3880 Dr.Anamika Bhargava
Bitcoin Q&A: What is Segregated Witness?
 
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What is Segregated Witness (SegWit)? How do you know you’re using SegWit? SegWit fixes transaction malleability and enables further upgrades towards Bitcoin smart contracts. Can transaction malleability lead to denial-of-service (DoS) attacks? Does SegWit put witness data in a different block? Did SegWit change transaction validation? versus? Are there any security risks of invalid blocks? CORRECTION: At ~2:07, I stated the incorrect BIP number for the Bech32 address format, which should be BIP173. BIP176 is about "bits" denomination. https://github.com/bitcoin/bips/blob/master/bip-0173.mediawiki More on SegWit: https://bitcoincore.org/en/2016/01/26/segwit-benefits/ These questions are from the MOOC 9.3 and 9.5 sessions which took place on March 2nd and March 16th, 2018 respectively. Andreas is a teaching fellow with the University of Nicosia. The first course in their Master of Science in Digital Currency degree, DFIN-511: Introduction to Digital Currencies, is offered for free as an open enrollment MOOC course to anyone interested in learning about the fundamental principles. If you want early-access to talks and a chance to participate in the monthly live Q&As with Andreas, become a patron: https://www.patreon.com/aantonop RELATED: The Lightning Network - https://www.youtube.com/playlist?list=PLPQwGV1aLnTurL4wU_y3jOhBi9rrpsYyi SegWit adoption - https://youtu.be/KCsTVTRk6I4 Advanced Bitcoin Scripting Part 1: Transactions and Multi-sig - https://youtu.be/8FeAXjkmDcQ Advanced Bitcoin Scripting Part 2: SegWit, Consensus, and Trustware - https://youtu.be/pQbeBduVQ4I How do I choose a wallet? - https://youtu.be/tN6b62sEpsY Hot vs. cold wallets - https://youtu.be/Aji_E9sw0AE SegWit adoption - https://youtu.be/KCsTVTRk6I4 Secure, tiered storage system - https://youtu.be/uYIVuZgN95M Decentralised exchanges and counterparty risk - https://youtu.be/hi_jaw0dT9M Decentralised exchanges with fiat - https://youtu.be/3Url8tbQEkA How do I secure my bitcoin? - https://youtu.be/vt-zXEsJ61U How do mnemonic seeds work? - https://youtu.be/wWCIQFNf_8g HODLing and the "get free" scheme - https://youtu.be/MhOwmsW1YNI How to get people to care about security - https://youtu.be/Ji1lS9NMz1E Bitcoin as everyday currency - https://youtu.be/xYvvSV4mjH0 Irreversibility and consumer protection - https://youtu.be/R107YWu5XzU Hyperbitcoinization - https://youtu.be/AB5MU5fXKfo Job opportunities with cryptocurrencies - https://youtu.be/89_p4pDlQtI How long until mainstream adoption? - https://youtu.be/y3cKBDBabtA What is the biggest adoption hurdle? - https://youtu.be/jHgyHF3F2TI Andreas M. Antonopoulos is a technologist and serial entrepreneur who has become one of the most well-known and respected figures in bitcoin. Follow on Twitter: @aantonop https://twitter.com/aantonop Website: https://antonopoulos.com/ He is the author of two books: “Mastering Bitcoin,” published by O’Reilly Media and considered the best technical guide to bitcoin; “The Internet of Money,” a book about why bitcoin matters. THE INTERNET OF MONEY, v1: https://www.amazon.co.uk/Internet-Money-collection-Andreas-Antonopoulos/dp/1537000454/ref=asap_bc?ie=UTF8 [NEW] THE INTERNET OF MONEY, v2: https://www.amazon.com/Internet-Money-Andreas-M-Antonopoulos/dp/194791006X/ref=asap_bc?ie=UTF8 MASTERING BITCOIN: https://www.amazon.co.uk/Mastering-Bitcoin-Unlocking-Digital-Cryptocurrencies/dp/1449374042 [NEW] MASTERING BITCOIN, 2nd Edition: https://www.amazon.com/Mastering-Bitcoin-Programming-Open-Blockchain/dp/1491954388 Subscribe to the channel to learn more about Bitcoin & open blockchains! Music: "Unbounded" by Orfan (https://www.facebook.com/Orfan/) Outro Graphics: Phneep (http://www.phneep.com/) Outro Art: Rock Barcellos (http://www.rockincomics.com.br/)
Views: 11636 aantonop
Bitcoin - Hash Pointers and Data Structures
 
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Hash Pointers and Data Structures - Bitcoin and Cryptocurrency Technologies Part 1 - Introduction to Crypto and Cryptocurrencies Learn about cryptographic building blocks ("primitives") and reason about their security. Work through how these primitives can be used to construct simple crypto currencies.
Views: 1044 intrigano
SAXually Explicit Images: Data Mining Large Shape Databases
 
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Google TechTalks May 12, 2006 Eamonn Keogh ABSTRACT The problem of indexing large collections of time series and images has received much attention in the last decade, however we argue that there is potentially great untapped utility in data mining such collections. Consider the following two concrete examples of problems in data mining. Motif Discovery (duplication detection): Given a large repository of time series or images, find approximately repeated patterns/images. Discord Discovery: Given a large repository of time series or images, find the most unusual time series/image. As we will show, both these problems have applications in fields as diverse as anthropology, crime...
Views: 4654 Google
What is pruning a decision tree
 
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What is pruning a decision tree - Find out more explanation for : 'What is pruning a decision tree' only from this channel. Information Source: google
Views: 50 moibrad4 moibrad4
Mod-01 Lec-02 Data Mining, Data assimilation and prediction
 
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Dynamic Data Assimilation: an introduction by Prof S. Lakshmivarahan,School of Computer Science,University of Oklahoma.For more details on NPTEL visit http://nptel.ac.in
Views: 1721 nptelhrd
21. Cryptography: Hash Functions
 
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MIT 6.046J Design and Analysis of Algorithms, Spring 2015 View the complete course: http://ocw.mit.edu/6-046JS15 Instructor: Srinivas Devadas In this lecture, Professor Devadas covers the basics of cryptography, including desirable properties of cryptographic functions, and their applications to security. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 66857 MIT OpenCourseWare
Top 10 Trends In Data Science | Eduonix
 
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Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured, similar to data mining. Data Science is spreading its roots gradually and becoming a hot topic of discussion everywhere. We have made a detailed video, which will tell you about all the recent trends which are going around in Data Science and if you're planning to choose your career in DS you will get a clearer idea about your path. We hope you like this video. The top trends mentioned in the video are: 1. Internet of Things (IoT) 2. Artificial Intelligence 3. Augmented Reality 4. Hyper Personalisation 5. Graph Analytics 6. Machine Intelligence 7. Agile Data Science 8. Behavioral Analytics 9. Journey Sciences 10. The Experience Economy Don't forget to check our new project on Data Science Foundational Program on Kickstarter. This program incorporates everything from beginner-level concepts to real-world implementation along with 4 courses, 2 e-books, Interview preparation guide, multiple labs, numerous practice tests and much more. Read more - https://kck.st/2CuIkay Thank you for watching! We’d love to know your thoughts in the comments section below. Also, don’t forget to hit the ‘like’ button and ‘subscribe’ to ‘Eduonix Learning Solutions’ for regular updates. https://goo.gl/BCmVLG Follow Eduonix on other social networks: ■ Facebook: https://goo.gl/ZqRVjS ■ Twitter: https://goo.gl/oRDaji ■ Google+: https://goo.gl/mfPaxx ■ Instagram: https://goo.gl/7f5DUC | @eduonix ■ Linkedin: https://goo.gl/9LLmmJ ■ Pinterest: https://goo.gl/PczPjp
DATA MINING   2 Text Retrieval and Search Engines   Lesson 6 4 Future of Web Search
 
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https://www.coursera.org/learn/text-retrieval
Views: 23 Ryo Eng
How to Draw a Data Flow Diagram
 
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This KnowledgeKnugget™ is an overview of Data Flow Diagrams. If you want to learn step by step how to create Data Flow Diagrams (DFDs), Context-level DFDs, and Rigorous Physical Process Models (RPPM), take the full 18 lectures 1.5 hours) eCourse “Data Flow Diagrams Simply Put!” at http://businessanalysisexperts.com/product/ecourse-data-flow-diagrams-context-model/. Prefer reading? Try the ebook version at http://businessanalysisexperts.com/product/dataflow-diagramming-example/. DESCRIPTION: Getting from someone’s explanations of how they do their job to usable and accurate workflow descriptions can be a daunting proposition. In this 8 minute KnowledgeKnugget™ (KK™), recognized business analysis expert Tom Hathaway explains what a DFD is, which symbols are allowed, and what each symbol means. A good DFD is the baseline for identifying problems and defining the requirements for any solution from the business perspective. ABOUT THE COURSE: This course answers the following questions: • What is a Data Flow Diagram (DFD)? • What is a Rigorous Physical Process Model? • What is a Context-Level DFD? • Why should I use Data Flow Diagrams? • What symbols can I use on each type of diagram? • How can I drill down into a process? • How can I show internal processes and flows that produce the results? • What does balancing a Data Flow Diagram mean and what is the business value? • What is the most efficient approach to balancing a DFD? • What business value do process specifications offer? • How can I express detailed specifications for processes and data? • What is “metadata" and why do you need it? • What does a fully balanced DFD look like? • What value does a DFD fragment provide? SIGN UP for the full course today at http://businessanalysisexperts.com/product/ecourse-data-flow-diagrams-context-model/. To view more IT requirements training, visit the Business Analysis Learning Store at http://businessanalysisexperts.com/business-analysis-training-store/.
Views: 173757 BA-EXPERTS
Topic Model for Graph Mining | Final Year Projects 2016
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 79 Clickmyproject
Introduction to Theory Of Computation ll What is Symbol, Alphabet, String, Language, Finite Automata
 
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Theory Of Computation Symbol Alphabet String Language Finite Automata TOC Basics Of TOC Introduction To Theory Of Computation 📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5-MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5-MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
What is Graph Analytics?
 
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Cray graph analytics expert Dr. James Maltby discusses graph analytics, how it’s used, and why companies should consider migrating towards a graph analytics platform.
Views: 2561 Cray Inc.
Data Types in Java
 
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Checkout my Full course on JAVA : https://www.udemy.com/java-for-complete-beginers-programming-fundamentals/?couponCode=YTFOR10 Primitive types are the most basic data types available within the Java language. There are 8: boolean, byte, char, short, int, long, float and double. These types serve as the building blocks of data manipulation in Java. Such types serve only one purpose — containing pure, simple values of a kind. Because these data types are defined into the Java type system by default, they come with a number of operations predefined. You can not define a new operation for such primitive types. In the Java type system, there are three further categories of primitives: Numeric primitives: short, int, long, float and double. These primitive data types hold only numeric data. Operations associated with such data types are those of simple arithmetic (addition, subtraction, etc.) or of comparisons (is greater than, is equal to, etc.) Textual primitives: byte and char. These primitive data types hold characters (that can be Unicode alphabets or even numbers). Operations associated with such types are those of textual manipulation (comparing two words, joining characters to make words, etc.). However, byte and char can also support arithmetic operations. Boolean and null primitives: boolean and null. All the primitive types have a fixed size. Thus, the primitive types are limited to a range of values. A smaller primitive type (byte) can contain less values than a bigger one (long).
Views: 17 Fun With Code
Hovindism #1 the definition of evolution (part 2/2)
 
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Kent completely destroys his version of the theory of evolution. References 1. Merriam-Webster. 2004. The Merriam-Webster Dictionary. Merriiam-Webster. 2. Weinberg, S. 2008. Cosmology. Oxford University Press. 3. Hubble, E. 1929. A relation between distance and radial velocity among extra galactic nebulae. PNAS 15(3):168-173. 4. Fixsen DJ, Cheng ES, Gales JM, Mather JC, ShaFer RA, Wright EL. 1996. The Cosmic Microwave Background Spectrum from the Full Cobe1 Firas Data Set. Astrophysical Journal 473:576-587. 5. Reeves H. 1974. On the origin of the light elements. Annuak Review of Astronomy and Astrophysics 12:437-470. 6. The Supernova Cosmology Project. 2003. New Constraints on [..], and w from an Independent Set of 11 High-Redshift Supernovae Observed with the Hubble Space Telescope. Astrophysical Journal. 598:102-137. 7. Kuo HM, Goldhaber G, Perlmutter S. 1998. A study of 42 type Ia supernovae and a resulting measurement of... Physics Reports 307(1):325-331. 8. Ellis GFR. 1979. The homogeneity of the universe. General Relativity and Gravitation 11(4):281-289. 9. Misner CW. 1968. The isotropy of the universe. Astrophysical Journal 151:431-457. 10. Hoyle F. 1946. The synthesis of the elements from hydrogen. Monthly Notices of the Royal Astronomical Society 106:343-383. 11. Seeger PA, Fowler WA, Clayton DD. 1965. Nucleosynthesis of heavy elements by neutron capture. Astrophysical Journal Supplement 11:121-166. 12. Bonnell IA, Bate MR. 2006. Star formation through gravitational collapse and competitive accretion. Monthly Notices of the Royal Astronomical Society 370:488-494. 13. McCaughrean MJ and O'dell CR. 1996. Direct Imaging of circumstellar disks in the Orion Nebula. Astrophysical Journal 111:1977. 14. Hazen R. 2005. Gen-e-sis: The Scientific Quest for Life's Origins. Joseph Henry Press. 15. Freeman S and Herron JC. 2007. Evolutionary Analysis, 4th ed. Benjamin Cummings. 16. Schlesinger G and Miller SL. 1983. Prebiotic synthesis in atmospheres containing CH4, CO, and CO2. I. Amino acids. Journal of Molecular Evolution 19:376-382. 17. Stribling R and Miller SL. 1987. Energy yields for hydrogen cyanide and formaldehyde syntheses: the HCN and amino acid concentrations in the primitive ocean. Origins of Life and Evolution of the Biosphere 17:261-273. 18. Miller SL. 1953. A production of amino acids under possible primitive earth conditions. Science 117:528-529. 19. Miller SL and Urey HC. 1959. Organic compound synthes on the primitive earth: several question about the origin of life have been answered, but much remains to be studied. Science 130:245-251. 20. Lee DH, Granja JR, Martinez JA, Severin K, and Ghadri MR. 1996. A self-replicating peptide. Nature 382:525-528. 21. Jeffares DC, Poole AM and Penny D. 1998. Relics from the RNA world. Journal of Molecular Evolution 46:18-36. 22. Ouelleth H. 1993. Bicknell's thrush: taxonomic status and distribution. The Wilson Bulletin. 105(4):545-572. 23. Wayne RK. 1993. Molecular evolution of the dog family. Trends in Genetics 9(6):218-224. 24. Polly PD, Wesley-Hunt GD, Heinricht RE, Davis G, and Houde P. 2006. Earliest known carnivoran auditory bulla and support for a recent origin of crown-group Canivora (Eutheria, Mammalia). Palaeontology 49(5):1019-1027. 25. Gould SJ. 1980. The Panda's Thumb. W. W. Norton.
Views: 1411 ExtantD0d0
Jens Ludwig: "Machine Learning in the Criminal Justice System" | Talks at Google
 
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Jens Ludwig, Director of the University of Chicago Crime Lab, talks about applying machine learning to reducing crime in Chicago and other public policy areas. In 2008, Ludwig helped found the Crime Lab to carry out data-driven methods to prevent crime and violence, and reduce the harms associated with the criminal justice system. Crime Lab’s work on gun policy, reducing crime, and education intervention studies have led to new policy initiatives in a number of US cities. The Crime Lab been has received coverage on major news outlets such as The New York Times and The Wall Street Journal. It is also the recent recipient of a $10 million donation from billionaire philanthropist Ken Griffin. Ludwig is the McCormick Foundation Professor of Social Service Administration, Law, and Public Policy at the University of Chicago. He is also a non-resident senior fellow in economic studies at the Brookings Institution, a research associate at the National Bureau of Economic Research (NBER), and co-director of the NBER's Working Group on the Economics of Crime. Ludwig is an economist by training and in 2012 was elected to the Institute of Medicine of the National Academies.
Views: 2678 Talks at Google
[SIGGRAPH Asia 2015] Generalized Cylinder Decomposition
 
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Decomposing a complex shape into geometrically simple primitives is a fundamental problem in geometry processing. We are interested in a shape decomposition problem where the simple primitives sought are generalized cylinders, which are ubiquitous in both organic forms and man-made artifacts. We introduce a quantitative measure of cylindricity for a shape part and develop a cylindricitydriven optimization algorithm, with a global objective function, for generalized cylinder decomposition. As a measure of geometric simplicity and following the minimum description length principle, cylindricity is defined as the cost of representing a cylinder through skeletal and cross-section profile curves. Our decomposition algorithm progressively builds local to non-local cylinders, which form over-complete covers of the input shape. The over-completeness of the cylinder covers ensures a conservative buildup of the cylindrical parts, leaving the final decision on decomposition to global optimization. We solve the global optimization by finding an exact cover, which optimizes the global objective function. We demonstrate results of our optimal decomposition algorithm on numerous examples and compare with other alternatives. For more details, please visit our project page: http://vcc.siat.ac.cn/index/getInfo?title_id=453&id=710&to_path=project
Views: 1170 Yang Zhou
What is META LEARNING? What does META LEARNING mean? META LEARNING meaning & explanation
 
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What is META LEARNING? What does META LEARNING mean? META LEARNING meaning - META LEARNING definition - META LEARNING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Meta learning is the study of disciplines.Meta learning is originally described by Donald B. Maudsley (1979) as "the process by which learners become aware of and increasingly in control of habits of perception, inquiry, learning, and growth that they have internalized". Maudsely sets the conceptual basis of his theory as synthesized under headings of assumptions, structures, change process, and facilitation. Five principles were enunciated to facilitate meta-learning. Learners must: (a) have a theory, however primitive; (b) work in a safe supportive social and physical environment; (c) discover their rules and assumptions; (d) reconnect with reality-information from the environment; and (e) reorganize themselves by changing their rules/assumptions. The idea of meta learning was later used by John Biggs (1985) to describe the state of "being aware of and taking control of one’s own learning". You can define meta learning as an awareness and understanding of the phenomenon of learning itself as opposed to subject knowledge. Implicit in this definition is the learner’s perception of the learning context, which includes knowing what the expectations of the discipline are and, more narrowly, the demands of a given learning task. Within this context, meta learning depends on the learner’s conceptions of learning, epistemological beliefs, learning processes and academic skills, summarized here as a learning approach. A student who has a high level of meta learning awareness is able to assess the effectiveness of her/his learning approach and regulate it according to the demands of the learning task. Conversely, a student who is low in meta learning awareness will not be able to reflect on her/his learning approach or the nature of the learning task set. In consequence, s/he will be unable to adapt successfully when studying becomes more difficult and demanding. Marcial Losada and other researchers have attempted to create a meta learning model to analyze teams and relationships. A 2013 paper provided a strong critique of this attempt, arguing that it was based on misapplication of complex mathematical modelling. This led to its abandonment by at least one former proponent. The meta learning model proposed by Losada is identical to the Lorenz system, which was originally proposed as a simplified mathematical model for atmospheric convection. It comprises one control parameter and three state variables, which in this case have been mapped to "connectivity," "inquiry-advocacy," "positivity-negativity," and "other-self" (external-internal focus) respectively. The state variables are linked by a set of nonlinear differential equations. This has been criticized as a poorly defined, poorly justified, and invalid application of differential equations. Losada and colleagues claim to have arrived at the meta-learning model from thousands of time series data generated at two human interaction laboratories in Ann Arbor, Michigan, and Cambridge, Massachusetts, although the details of the collection of this data, and the connection between the time series data and the model is unclear. These time series portrayed the interaction dynamics of business teams doing typical business tasks such as strategic planning. These teams were classified into three performing categories: high, medium and low. Performance was evaluated by the profitability of the teams, the level of satisfaction of their clients, and 360-degree evaluations. One proposed result of this theory is that there is a ratio of positivity-to-negativity of at least 2.9 (called the Losada line), which separates high from low performance teams as well as flourishing from languishing in individuals and relationships. Brown and colleagues pointed out that even if the proposed meta-learning model were valid, this ratio results from a completely arbitrary choice of model parameters—carried over from the literature on modeling atmospheric convection by Lorenz and others, without any justification.
Views: 1661 The Audiopedia
Time Series
 
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Learn how to analyze time-ordered historical data to forecast future behavior using BigML Time Series.
Views: 1202 bigmlcom
Evaluating Improvements to the Shapelet Transform
 
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Author: Aaron Bostrom, University of East Anglia Abstract: The Shapelet tree algorithm was proposed in 2009 as a novel way to find phase independent subsequences which could be used for time series classification. The shapelet discovery algorithm is O(n2m4), where n is the number of cases, and m is the length of the series. Several methods have sought to increase the speed of finding shapelets. The ShapeletTransform reduces the finding to a single pass, and FastShapelets smooths and reduces the series lengths through PAA and SAX. However neither of these techniques can enumerate all shapelets on the largest of the datasets present in the UCR repository. We first evaluate whether the FastShapelet algorithm is better as a transform, and secondly provide a contract classifier for the shapelet transform, by calculating the number of fundamental operations we can estimate the run time of the algorithm, and sample the data to fulfil this contract. We found that whilst the FastShapeletTransform does drastically reduce the operation count of finding shapelets it is not significantly better than FastShapelets, nor can it compete with the ShapeletTransform. The factory method for sampling the data is competitive with the ShapeletTransform and in some cases we see minor improvements despite being much faster. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 162 KDD2016 video
Differential Privacy : A comparative study
 
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Studied and compared two different techniques of preserving privacy in machine learning.
Views: 298 Inderjot Ratol
Hive Complex Data Types Tutorial | Hive Complex Data Types Example | Hive Complex Queries
 
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Hive Complex Data Types Tutorial | Hive Complex Data Types Example | Hive Complex Queries https://acadgild.com/big-data/big-data-development-training-certification?utm_source=youtube&utm_medium=organic&utm_campaign=Yotube_Gmccni11k5w-hive-complex-data-types_20180507 Hello and welcome back to Hadoop tutorials powered by Acadgild. In this video, you will be able to learn the Hive Complex Data Types. Hive Complex Data Types: • Complex Types can be built up from primitive types and other composite types • Data type of the fields in the collection are specified using an angled bracket notation • Currently, Hive supports four complex data types ARRAY: • ARRAY (data..type) • An ordered sequence of similar type elements that are indexable using • Zero-based integers • It is similar to arrays in Java. • Example – array (‘sivs’,’bala’,’praveen’); • Second element is accessed with array[1] MAP • MAP (primitive..type, data..type) • Collection of key value pairs • Fields are accessed using array notation of keys (e.g., [‘key’] STRUCT • STRUCT (col..name : data..type [COMMENT col..comment],…) • It is similar to STRUCT in C language. • It is a record type which encapsulates a set of named fields that can be any primitive data types. • Elements in STRUCT type are accessed using the DOT (.) notation • Example- For a column c of type STRUCT {a INT; b INT} the a field is accessed by the expression c.a UNIONTYPE • UNIONTYPE(data..type, data..type,…) • It is similar to Unions in C • At any point of time, an Union Type can hold anyone (exactly one) data type from its specified data types Kindly go through the Demo and please subscribe and stay tuned for more such videos. #Hive #types, #hivetable, #Hivetutorial, For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 315 ACADGILD
Hawk: The Blockchain Model of Cryptography and Privacy-Preserving Smart Contracts
 
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Hawk: The Blockchain Model of Cryptography and Privacy-Preserving Smart Contracts Andrew Miller (University of Maryland) Presented at the 2016 IEEE Symposium on Security & Privacy May 23–25, 2016 San Jose, CA http://www.ieee-security.org/TC/SP2016/ ABSTRACT Emerging smart contract systems over decentralized cryptocurrencies allow mutually distrustful parties to transact safely without trusted third parties. In the event of contractual breaches or aborts, the decentralized blockchain ensures that honest parties obtain commensurate compensation. Existing systems, however, lack transactional privacy. All transactions, including flow of money between pseudonyms and amount transacted, are exposed on the blockchain. We present Hawk, a decentralized smart contract system that does not store financial transactions in the clear on the blockchain, thus retaining transactional privacy from the public's view. A Hawk programmer can write a private smart contract in an intuitive manner without having to implement cryptography, and our compiler automatically generates an efficient cryptographic protocol where contractual parties interact with the blockchain, using cryptographic primitives such as zero-knowledge proofs. To formally define and reason about the security of our protocols, we are the first to formalize the blockchain model of cryptography. The formal modeling is of independent interest. We advocate the community to adopt such a formal model when designing applications atop decentralized blockchains.
5. How Variables Work in Java
 
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In this video tutorial, I show you how to declare, assign, and initialize variables in Java. LESSON NOTES: https://therevisionist.org/software-engineering/java/tutorials/variables/ 。◕‿◕。 ├┬┴┬┤ (ノ◕ヮ◕)ノ Java TUTORIAL PLAYLIST: https://youtu.be/ibEG9XqvEyg?list=PLOK2VRNQNad8TYxUeEBk59OVAUPRj1tbR --- In the Java programming language, the words field and variable are both one and the same thing. Variables are devices that are used to store data, such as a number, or a string of character data. Variables in Java programming Java is considered as a strongly typed programming language. Thus all variables in the Java programming language ought to have a particular data type. This is either declared or inferred and the Java language only allows programs to run if they adhere to type constraints. If you present a numeric type with data that is not numeric, say textual content, then such declarations would violate Java’s type system. This gives Java the ability of type safety. Java checks if an expression or data is encountered with an incorrect type or none at all. It then automatically flags this occurrence as an error at compile time. Most type-related errors are caught by the Java compiler, hence making a program more secure and safe once compiled completely and successfully. Some languages (such as C) define an interpretation of such a statement and use that interpretation without any warning; others (such as PL/I) define a conversion for almost all such statements and perform the conversion to complete the assignment. Some type errors can still occur at runtime because Java supports a cast operation which is a way of changing the type of one expression to another. However, Java performs run time type checking when doing such casts, so an incorrect type cast will cause a runtime exception rather than succeeding silently and allowing data corruption. On the other hand, Java is also known as a hybrid language. While supporting object oriented programming (OOP), Java is not a pure OO language like Smalltalk or Ruby. Instead, Java offers both object types and primitive types. Primitive types are used for boolean, character, and numeric values and operations. This allows relatively good performance when manipulating numeric data, at the expense of flexibility. For example, you cannot subclass the primitive types and add new operations to them. Instance variables: These are variables that are used to store the state of an object (for example, id). Every object created from a class definition would have its own copy of the variable. It is valid for and occupies storage for as long as the corresponding object is in memory. Class variables: These variables are explicitly defined within the class-level scope with a static modifier (for example, isClassUsed). No other variables can have a static modifier attached to them. Because these variables are defined with the static modifier, there would always be a single copy of these variables no matter how many times the class has been instantiated. They live as long as the class is loaded in memory. Parameters or Arguments: These are variables passed into a method signature (for example, parameter). Recall the usage of the args variable in the main method. They are not attached to modifiers (i.e. public, private, protected or static) and they can be used everywhere in the method. They are in memory during the execution of the method and can't be used after the method returns. Local variables: These variables are defined and used specifically within the method-level scope (for example, currentValue) but not in the method signature. They do not have any modifiers attached to it. They no longer exist after the method has returned. Variables and all the information they store are kept in the computer's memory for access. Think of a computer's memory as a table of data — where each cell corresponds to a variable. Upon creating a variable, we basically create a new address space and give it a unique name. Java goes one step further and lets you define what you can place within the variable — in Java parlance you call this a data type. So, you essentially have to do two things in order to create a variable: Create a variable by giving it a unique name; and, Define a data type for the variable. The following code demonstrates how a simple variable can be created. This process is known as variable declaration.
Views: 190 Raqib Zaman
Mini Lecture: Profit-driven Data Analytics: Classification Performance Measurement
 
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In this mini lecture, Thomas Verbraken talks about his research dealing with profit-driven classification performance measurement metrics. Acknowledgements ---------------------------- * Thomas obtained his PhD at the Research Center for Management Informatics, KU Leuven (University of Leuven), Belgium; under guidance of Prof. dr. Bart Baesens * For more information about our research, see http://www.dataminingapps.com and https://www.econ.kuleuven.be/liris
Views: 591 Bart Baesens
DMQL- Episode One
 
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Alright, I’m starting a new series. D: Draw M: My Q: Quoted L: Life Basically I take a quote that describes me and I illustrate something to go with it. This is an attempt to get y’all to know me better :) Most of these quotes will probably be depressing, but I’ll be completely honest. I’ll make sure they describe my life. On top of another series I’m gonna be starting soon *mwhahaha* It includes voice acting X) K no more spoilers. Alright yeah... hope y’all enjoy this series. It may or may not be daily idk.... Rip. K I don’t feel like typing my media’s so lel. Go on another video ya lazy skank! DONT BE OFFENDED IM KIDDING! I’m so mean ^_^ K uhhh I don’t own this music and do not plan to make profit off of it If you’re ready the desc like a fellow nerd then uh comment below sayinnnnnn: “Quotesssssss.”
Views: 135 Yochie
Data Lineage in Malicious Environments | Java IEEE 2016-2017
 
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Data Lineage in Malicious Environments | Java IEEE 2016-2017 To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Intentional or unintentional leakage of confidential data is undoubtedly one of the most severe security threats that organizations face in the digital era. The threat now extends to our personal lives: a plethora of personal information is available to social networks and smartphone providers and is indirectly transferred to untrustworthy third party and fourth party applications. In this work, we present a generic data lineage framework LIME for data flow across multiple entities that take two characteristic, principal roles (i.e., owner and consumer). We define the exact security guarantees required by such a data lineage mechanism toward identification of a guilty entity, and identify the simplifying non-repudiation and honesty assumptions. We then develop and analyze a novel accountable data transfer protocol between two entities within a malicious environment by building upon oblivious transfer, robust watermarking, and signature primitives. Finally, we perform an experimental evaluation to demonstrate the practicality of our protocol and apply our framework to the important data leakage scenarios of data outsourcing and social networks. In general, we consider LIME , our lineage framework for data transfer, to be an key step towards achieving accountability by design.
Views: 1600 JPINFOTECH PROJECTS
CONFidence2015 - How to Steal Bitcoins (Daniel Shearer, Nick Zeeb)
 
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How to Steal Bitcoins For everything that has value there are those that want to steal it. The continuing rise of bitcoin has seen a corresponding increase in creative individuals who want to acquire bitcoins without the trouble of buying or mining them. As with any complex system, there are many points that can be attacked. In this talk we show how bitcoin works and explain specific attacks that have been carried out from social engineering to the exploitation of cryptographic primitives. http://www.slideshare.net/proidea_conferences/daniel-shearer-nick-zeeb-how-to-steal-bitcoins
Views: 13052 PROIDEA Events
Geometric Range Search on Encrypted Spatial Data
 
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Geometric Range Search on Encrypted Spatial Data To get this project in Online or through training sessions Contact: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landline: (0413) - 4300535 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Geometric range search is a fundamental primitive for spatial data analysis in SQL and NoSQL databases. It has extensive applications in location-based services, computer aided design, and computational geometry. Due to the dramatic increase in data size, it is necessary for companies and organizations to outsource their spatial data sets to third-party cloud services (e.g., Amazon) in order to reduce storage and query processing costs, but, meanwhile, with the promise of no privacy leakage to the third party. Searchable encryption is a technique to perform meaningful queries on encrypted data without revealing privacy. However, geometric range search on spatial data has not been fully investigated nor supported by existing searchable encryption schemes. In this paper, we design a symmetric-key searchable encryption scheme that can support geometric range queries on encrypted spatial data. One of our major contributions is that our design is a general approach, which can support different types of geometric range queries. In other words, our design on encrypted data is independent from the shapes of geometric range queries. Moreover, we further extend our scheme with the additional use of tree structures to achieve search complexity that is faster than linear. We formally define and prove the security of our scheme with indistinguish ability under selective chosen-plaintext attacks, and demonstrate the performance of our scheme with experiments
Views: 664 jpinfotechprojects

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