Home
Search results “Multi relational data mining ppt presentation”
Data Mining  Association Rule - Basic Concepts
 
06:53
short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Business Intelligence: Multidimensional Analysis
 
24:15
An introduction to multidimensional business intelligence and OnLine Analytical Processing (OLAP) suitable for both a technical and non-technical audience. Covers dimensions, attributes, measures, Key Performance Indicators (KPIs), aggregates, hierarchies, and data cubes. Downloadable slides available from SlideShare at http://goo.gl/4tIjVI
Views: 54032 Michael Lamont
The Evolution and Future of BigData Ecosystem | DataEngConf SF '18
 
35:20
Don’t miss the next DataEngConf in Barcelona: https://dataeng.co/2O0ZUq7 Download Slides: https://dataeng.co/2sfhYni ABOUT THE TALK: Over the past ten years the Big Data infrastructure has evolved from flat files lying down in a distributed file system to a more efficient ecosystem and is turning into a fully deconstructed database. With Hadoop, we started from a system that was good at looking for a needle in a haystack using snowplows. We had a lot of horsepower and scalability but lacked the subtlety and efficiency of relational databases. Since Hadoop provided the ultimate flexibility compared to the more constrained and rigid RDBMSes we didn’t mind and plowed through with confidence. Machine Learning, Recommendations, Matching, Abuse detection and in general data driven products require a more flexible infrastructure. Over time we started applying everything that had been known to the Database world for decades to this new environment. They told us loud enough how Hadoop was a huge step backwards. And they were right in some way. The key difference was the flexibility of it all. There are many highly integrated components in a relational database and decoupling them took some time. Today we see the emergence of key components (optimizer, columnar storage, in-memory representation, table abstraction, batch and streaming execution) as standards that provide the glue between the options available to process, analyze and learn from our data. We’ve been deconstructing the tightly integrated Relational database into flexible reusable open source components. Storage, compute, multi-tenancy, batch or streaming execution are all decoupled and can be modified independently to fit every use case. This talk will go over key open source components of the Big Data ecosystem (including Apache Calcite, Parquet, Arrow, Avro, Kafka, Batch and Streaming systems) and will describe how they all relate to each other and make our Big Data ecosystem more of a database and less of a file system. Parquet is the columnar data layout to optimize data at rest for querying. Arrow is the in-memory representation for maximum throughput execution and overhead-free data exchange. Calcite is the optimizer to make the most of our infrastructure capabilities. We’ll discuss the emerging components that are still missing or haven’t become standard yet to fully materialize the transformation to an extremely flexible database that lets you innovate with your data. ABOUT THE SPEAKER: Julien Le Dem is the coauthor of Apache Parquet and the PMC chair of the project. He is also a committer and PMC Member on Apache Pig, Apache Arrow and a few others. Julien is a Principal Engineer at WeWork and was previously Architect at Dremio and tech lead for Twitter’s data processing tools, where he also obtained a two-character Twitter handle (@J_). Prior to Twitter, Julien was a principal engineer and tech lead working on content platforms at Yahoo, where he received his Hadoop initiation. His French accent makes his talks particularly attractive. Follow DataEngConf on: Twitter: https://twitter.com/dataengconf LinkedIn: https://www.linkedin.com/company/hakkalabs Facebook: https://web.facebook.com/hakkalabs
Views: 630 Hakka Labs
k -Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data
 
07:04
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://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 156 myproject bazaar
An Empirical Performance Evaluation of Relational Keyword Search Systems
 
02:46
Title: An Empirical Performance Evaluation of Relational Keyword Search Systems Domain: Data Mining Abstract: In the past decade, extending the keyword search paradigm to relational data has been an active area of research within the database and information retrieval (IR) community. A large number of approaches have been proposed and implemented, but despite numerous publications, there remains a severe lack of standardization for system evaluations. This lack of standardization has resulted in contradictory results from different evaluations, and the numerous discrepancies muddle what advantages are proffered by different approaches. In this paper, we present a thorough empirical performance evaluation of relational keyword search systems. Our results indicate that many existing search techniques do not provide acceptable performance for realistic retrieval tasks. In particular, memory consumption precludes many search techniques from scaling beyond small datasets with tens of thousands of vertices. We also explore the relationship between execution time and factors varied in previous evaluations; our analysis indicates that these factors have relatively little impact on performance. In summary, our work confirms previous claims regarding the unacceptable performance of these systems and underscores the need for standardization—as exemplified by the IR community—when evaluating these retrieval systems. Key Features: 1. The success of keyword search stems from what it does not require—namely, a specialized query language or knowledge of the underlying structure of the data. Internet users increasingly demand keyword search interfaces for accessing information, and it is natural to extend this paradigm to relational data. This extension has been an active area of research throughout the past decade. However, we are not aware of any research projects that have transitioned from proof-of-concept implementations to deployed systems. 2. We conduct an independent, empirical performance evaluation of 7 relational keyword search techniques, which doubles the number of comparisons as previous work. 3. Our results do not substantiate previous claims regarding the scalability and performance of relational keyword search techniques. Existing search techniques perform poorly for datasets exceeding tens of thousands of vertices. 4. We show that the parameters varied in existing evaluations are at best loosely related to performance, which is likely due to experiments not using representative datasets or query workloads. 5. Our work is the first to combine performance and search effectiveness in the evaluation of such a large number of systems. Considering these two issues in conjunction provides better understanding of these two critical tradeoffs among competing system designs. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique
Views: 1638 InnovationAdsOfIndia
Multidimensional analysis
 
20:00
http://www.ITLearnMore.com
Views: 659 IT LearnMore
SPSS for questionnaire analysis:  Correlation analysis
 
20:01
Basic introduction to correlation - how to interpret correlation coefficient, and how to chose the right type of correlation measure for your situation. 0:00 Introduction to bivariate correlation 2:20 Why does SPSS provide more than one measure for correlation? 3:26 Example 1: Pearson correlation 7:54 Example 2: Spearman (rhp), Kendall's tau-b 15:26 Example 3: correlation matrix I could make this video real quick and just show you Pearson's correlation coefficient, which is commonly taught in a introductory stats course. However, the Pearson's correlation IS NOT always applicable as it depends on whether your data satisfies certain conditions. So to do correlation analysis, it's better I bring together all the types of measures of correlation given in SPSS in one presentation. Watch correlation and regression: https://youtu.be/tDxeR6JT6nM ------------------------- Correlation of 2 rodinal variables, non monotonic This question has been asked a few times, so I will make a video on it. But to answer your question, monotonic means in one direction. I suggest you plot the 2 variables and you'll see whether or not there is a monotonic relationship there. If there is a little non-monotonic relationship then Spearman is still fine. Remember we are measuring the TENDENCY for the 2 variables to move up-up/down-down/up-down together. If you have strong non-monotonic shape in the plot ie. a curve then you could abandon correlation and do a chi-square test of association - this is the "correlation" for qualitative variables. And since your 2 variables are ordinal, they are qualitative. Good luck
Views: 495260 Phil Chan
Data warehouse Features Lecture in Hindi - DWDM Lectures in Hindi, English
 
16:30
Data warehouse Features Lecture in Hindi - DWDM Lectures in Hindi, English Data warehouse Features – Subject Oriented, Integrated, Time Variant, Non-Volatile Data, Data Granularity Data Warehouse and Data Mining Lectures in Hindi
Hey Relational Developer, Let's Go Crazy (Patrick McFadin, DataStax) | Cassandra Summit 2016
 
42:29
Slides: https://www.slideshare.net/DataStax/hey-relational-developer-lets-go-crazy-patrick-mcfadin-datastax-cassandra-summit-2016 | You've made a good career developing applications using a relational database. You know learning how to be a Cassandra developer is going to be a great skill to add. Now it's time to bridge those two things into reality. I was in your shoes and I can help. How do you work without ACID transactions? The data model looks similar but is so different! What are some of the bad things I should avoid? What are some of the traps I can fall into moving from a relational database? I hear these questions all the time. Let's spend some time to walk through each one and get you on track. Before you know it, you'll be going crazy on your next Cassandra based application! About the Speaker Patrick McFadin Chief Evangelist, DataStax Patrick McFadin is one of the leading experts of Apache Cassandra and data modeling techniques. As the Chief Evangelist for Apache Cassandra and consultant for DataStax, he has helped build some of the largest and exciting deployments in production. Previous to DataStax, he was Chief Architect at Hobsons and an Oracle DBA/Developer for over 15 years.
Views: 1460 DataStax
Data Integration and Data Exchange
 
53:29
Google TechTalks March 24, 2006 Alan Nash ABSTRACT I will discuss two fundamental problems in information integration: (1) how to answer a query over a public interface which combines data from several sources and (2) how to create a single database conforming to the public interface which combines data from several sources. I consider the case where the sources are relational databases, where the public interface is a public schema (a specification of the format of a database), and where the sources are related to the public schema by a mapping that is specified by constraints.
Views: 2659 Google
Lise Getoor, Professor, Computer Science, UC Santa Cruz @ MLconf SF
 
22:42
One of the challenges in big data analytics lies in being able to reason collectively about extremely large, heterogeneous, incomplete, noisy interlinked data. We need data science techniques which an represent and reason effectively with this form of rich and multi-relational graph data. In this presentation, I will describe some common collective inference patterns needed for graph data including: collective classification (predicting missing labels for nodes in a network), link prediction (predicting potential edges), and entity resolution (determining when two nodes refer to the same underlying entity). I will describe three key capabilities required: relational feature construction, collective inference, and scaling. Finally, I briefly describe some of the cutting edge analytic tools being developed within the machine learning, AI, and database communities to address these challenges.
Views: 581 MLconf
Lecture - 36 Object Oriented Databases
 
57:50
Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 49392 nptelhrd
Lecture - 3 Relational Model
 
51:35
Lecture Series on Database Management System by Dr.S.Srinath, IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 128551 nptelhrd
More Data Mining with Weka (5.5: ARFF and XRFF)
 
06:30
More Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 5: ARFF and XRFF http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/rDuMqu https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3795 WekaMOOC
Lecture - 30 Introduction to Data Warehousing and OLAP
 
57:50
Lecture Series on Database Management System by Dr.S.Srinath, IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 210167 nptelhrd
Collective Spammer Detection in Evolving Multi-Relational Social Networks
 
19:07
Authors: Shobeir Fakhraei, James Foulds, Madhusudana Shashanka, Lise Getoor Abstract: Detecting unsolicited content and the spammers who create it is a long-standing challenge that affects all of us on a daily basis. The recent growth of richly-structured social networks has provided new challenges and opportunities in the spam detection landscape. Motivated by the Tagged.com social network, we develop methods to identify spammers in evolving multi-relational social networks. We model a social network as a time-stamped multi-relational graph where vertices represent users, and edges represent different activities between them. To identify spammer accounts, our approach makes use of structural features, sequence modelling, and collective reasoning. We leverage relational sequence information using k-gram features and probabilistic modelling with a mixture of Markov models. Furthermore, in order to perform collective reasoning and improve the predictive power of a noisy abuse reporting system, we develop a statistical relational model using hinge-loss Markov random fields (HL-MRFs), a class of probabilistic graphical models which are highly scalable. We use Graphlab Create and Probabilistic Soft Logic (PSL) to prototype and experimentally evaluate our solutions on internet-scale data from Tagged.com. Our experiments demonstrate the effectiveness of our approach, and show that models which incorporate the multi-relational nature of the social network significantly gain predictive performance over those that do not. ACM DL: http://dl.acm.org/citation.cfm?id=2788606 DOI: http://dx.doi.org/10.1145/2783258.2788606
Lecture - 34 Data Mining and Knowledge Discovery
 
54:46
Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 133268 nptelhrd
Lecture - 35 Data Mining and Knowledge Discovery Part II
 
58:00
Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 43108 nptelhrd
Webinar: How to Prevent Bank Fraud & Monitor Risk in Real Time
 
57:35
Slides: http://www.slideshare.net/DataStax/webinar-how-to-prevent-bank-fraud-monitor-risk-in-real-time Financial Services Institutions (FSIs) are in fierce competition to earn customer trust and loyalty. Paramount in this effort is the war to combat the spread of money-laundering and payments fraud. FSIs must now transform their business to meet the demands of an ever changing market. Learn how Accenture and DataStax can help your organization implement these changes for a competitive edge. Join Accenture and DataStax for a webinar on March 2, 2016 to learn about the latest trends and use cases from top Financial Services companies and how to leverage your database to build state-of-the-art applications at scale with high availability and low latency. In this webinar, you will learn: - Best practices in tackling money-laundering and payments fraud - How to apply DataStax technologies to build real time anti-fraud solutions - Why existing technologies like relational databases cannot meet modern application demands and how DataStax Enterprise (DSE) can help you overcome these challenges
Views: 1266 DataStax
Lecture - 4 Relational Model
 
54:09
Lecture Series on Database Management System by Dr.S.Srinath, Department of Computer Science & Engineering ,IIIT Banglore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 76695 nptelhrd
Google I/O 2009 - Transactions Across Datacenters..
 
59:38
Google I/O 2009 - Transactions Across Datacenters (and Other Weekend Projects) Ryan Barrett -- Contents -- 0:55 - Background quotes 2:30 - Introduction: multihoming for read/write structured storage 5:12 - Three types of consistency: weak, eventual, strong 10:00 - Transactions: definition, background 12:22 - Why multihome? Why try do anything across multiple datacenters? 15:30 - Why *not* multihome? 17:45 - Three kinds of multihoming: none, some, full 27:35 - Multihoming techniques and how to evaluate them 28:30 - Technique #1: Backups 31:39 - Technique #2: Master/slave replication 35:42 - Technique #3: Multi-master replication 39:30 - Technique #4: Two phase commit 43:53 - Technique #5: Paxos 49:35 - Conclusion: no silver bullet. Embrace the tradeoffs! 52:15 - Questions -- End -- If you work on distributed systems, you try to design your system to keep running if any single machine fails. If you're ambitious, you might extend this to entire racks, or even more inconvenient sets of machines. However, what if your entire datacenter falls off the face of the earth? This talk will examine how current large scale storage systems handle fault tolerance and consistency, with a particular focus on the App Engine datastore. We'll cover techniques such as replication, sharding, two phase commit, and consensus protocols (e.g. Paxos), then explore how they can be applied across datacenters. For presentation slides and all I/O sessions, please go to: code.google.com/events/io/sessions.html
Views: 28031 Google Developers
Decentralized Access Control with Anonymous Authentication of Data Stored in Clouds
 
20:36
JAVA PROJECT In this project we are providing the decentralized access of data along with authentication here the authentication is not only for authenticated users but also for anonymous data by generating keys. For this project we are providing cloud execution also.
Views: 2552 Ramu Maloth
Deploying large scale Spark ML models for near real time scoring
 
55:17
slides - https://docs.google.com/presentation/d/1iM2MQgoGXLmCihAnyhAlfvbIlNCgGjjBrZDFB0l92m4/
Views: 167 subhojit banerjee
Process Structured AND Unstructured Big Data with an "ALL DATA MANAGEMENT" System
 
36:01
Gensonix® stores structured/unstructured data in Relational, Hierarchical, Network, and Column formats and is ideal for business, scientific, medical, etc., environments, as well as raw data analysis applications. Gensonix® utilizes the NSQL™© language; is native to low level languages such as C; supports many intrinsic functions; and performs easy recursive database/computing operations. Supporting multi-dimensional array processing, Gensonix® is capable of very efficient analysis of vast amounts of structured and unstructured data at ultrahigh speeds. Gensonix® also runs on Large Data Warehouse Appliance configurations and scales to large numbers of multiprocessor nodes.
Views: 186 DATAVERSITY
Lecture - 25 Basic 2-Phase and 3-phase commit protocol
 
57:12
Lecture Series on Database Management System by Prof. D. Janakiram, Department of Computer Science and Engineering,IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 57442 nptelhrd
Lecture - 8 Functional Dependencies and Normal Form
 
57:52
Lecture Series on Database Management System by Dr.S.Srinath, IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 169296 nptelhrd
Final Year Projects | Evaluation of a perturbation-based technique for privacy preservation
 
06:45
Including Packages ======================= * 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: 153 Clickmyproject
Spark 1.0 and Beyond - Patrick Wendell
 
01:15:37
Slides: http://files.meetup.com/3138542/Spark%201.0%20Meetup.ppt Abstract: This talk will cover the new features in the Spark 1.0 release, which is due out early May. I'll be talking as a Spark Committer and the 1.0 release manager. Spark 1.0 adds several new features and improved usability and performance. The talk will introduce SparkSQL, a relational execution engine that is tightly integrated with the core Spark API. It will also cover Spark 1.0's support for Java 8 lambdas, new improvements to Spark's machine learning library, support for Hadoop security, and several other features. I'll close by talking about the schedule for future releases and the Spark roadmap post 1.0
Views: 9909 Spark Summit
Final Year Projects | On the Use of Side Information for Mining
 
06:31
Including Packages ===================== * 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: 299 Clickmyproject
Database Vs Data Warehouse
 
17:59
Explains the difference between a database & Data warehouse in its simplest & understandable form.
Views: 5739 IT SHIKSHAK
FOSDEM 2013 - FluxGraph: A Time Machine for your Graphs
 
28:20
Slides: http://www.slideshare.net/datablend/flux-graphgraphdevroom-16377492 A retrospective cohort study is a medical research study in which the patient records of a group of similar individuals are compared for a particular outcome. For instance, a study can try to assess the impact of smoking behavior with respect to getting lung cancer in a group of 40-year old construction workers who also have been exposed to asbestos. As retrospective case studies are historical in nature, researchers require accurate representations of patient records over time in order to correctly assess the importance of particular time-dependent patient characteristics. During this presentation, we will show how state-of-the-art Graph Databases can be extended with a set of temporal primitives that effectively aid researchers at gathering the required insights from a set of longitudinal medical records. Graph Databases are the ideal platform to model and store the multi-dimensional data points of the individual patient records and the cohorts to which they are belonging. By introducing a temporal notion within Graph Databases, physicians are given the power to query beyond time boundaries and get historical access to individual patient characteristics or combinations thereof. Patterns for individual patients can be compared and evaluated against the patterns for the cohort. In order to validate our proposed approach, we have implemented FluxGraph, a proof-of-concept Temporal Graph Database. Being Blueprints-compatible, it should be straightforward to integrate the proposed API changes within mature Graph Database products. The explicit notion of time, combined with the flexible modelling offered by Graph Databases, provides users with an expressive and powerful data store and analysis platform which is difficult or even impossible to implement with traditional relational database technologies. Davy Suvee (Big Data Architect at Janssen Pharmaceutica / Datablend) Davy Suvee is currently working as an IT Lead/Software Architect in the Research and Development IT division of Janssen Pharmaceutica (Johnson & Johnson). Required to work with big and unstructured scientific data sets, Davy gathered hands-on expertise and insights in the best practices on Big Data and NOSQL. He is also the founder of Datablend and frequently blogs about the practical application of various NOSQL technologies.
Views: 355 Leonhard Euler
Lecture - 19 Foundation for Concurrency Control
 
57:45
Lecture Series on Database Management System by Prof. D. Janakiram, Department of Computer Science and Engineering,IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 35771 nptelhrd
Trust-but-Verify: Verifying Result Correctness of Outsourced Frequent Itemset Mining in Data-mining
 
29:25
Trust-but-Verify: Verifying Result Correctness of Outsourced Frequent Itemset Mining in Data-mining-as-a-service Paradigm
Final Year Projects | Finding Top-k Answers in Keyword Search over Relational Databases Using Tup
 
08:49
Final Year Projects | Finding Top-k Answers in Keyword Search over Relational Databases Using Tuple Units More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * 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: 168 Clickmyproject
Relational vs  Dimensional Modeling
 
01:02
This video explains the differences between the Relational data modeling and Dimensional data modeling concepts used in Data mining
Views: 1748 Tutorials_888
Database Lesson #4 of 8 - Data Modeling and the ER Model
 
58:16
Dr. Soper gives a lecture on data modeling and the entity-relationship (ER) model. Topics include the components of ER models, depicting entities, attributes, relationships, and cardinalities in ER models, one-to-one, one-to-many, and many-to-many relationships, strong and weak entities, identifying and non-identifying relationships, supertypes and subtypes, and recursive relationships in data models.
Views: 203537 Dr. Daniel Soper
CMU Advanced Database Systems - 19 Parallel Hash Join Algorithms (Spring 2018)
 
01:21:13
Slides PDF: http://15721.courses.cs.cmu.edu/spring2018/slides/19-hashjoins.pdf Reading List: http://15721.courses.cs.cmu.edu/spring2018/schedule.html#apr-04-2018 Andy Pavlo (http://www.cs.cmu.edu/~pavlo/) 15-721 Advanced Database Systems (Spring 2018) Carnegie Mellon University
Views: 726 CMU Database Group
Scott Sanderson: Developing an Expression Language for Quantitative Financial Modeling
 
34:32
PyData NYC 2015 We discuss the design and implementation of zipline.modeling, a new component of the backtesting engine that powers Quantopian. Drawing inspiration from libraries such as SQLAlchemy, Dask, and Theano, we develop a declarative API for concisely describing efficient computations on financial data. Along the way, we consider some of the unique challenges of computing in the financial domain. This talk details the challenges addressed during the development of Zipline's new Modeling API, which provides a high-level expression language allowing users to describe computations on rolling windows of continuously-adjusted financial data. We discuss the notion of "perspectival" time-series data, arguing that this concept provides a useful framework for formally reasoning about financial data in the face of domain oddities like stock splits, dividends, and restatements. We also consider the architectural and performance benefits of developing an API focused on symbolic computation, drawing comparisons to several recent developments in the Python numerical ecosystem. Slides available here: http://www.slideshare.net/ScottSanderson5/developing-an-expression-language-for-quantitative-financial-modeling. They're also available on SpeakerDeck here: https://speakerdeck.com/ssanderson/developing-an-expression-language-for-quantitative-financial-modeling The notebooks and underlying assets are on GitHub here: https://github.com/ssanderson/pydata-nyc-2015
Views: 839 PyData
How To Install R Studio For R In Windows Tamil
 
04:16
How To Install R Program In Windows In Tamil How To Install R Studio For R In Windows Tamil Single and Multi Line Comment Line In R Program In Tamil Arithmetic Calculation In R Language In Tamil How To List and Remove variable In R Tamil Maths Function In R Program Tamil How To Use Help For R Program In R Studio Relational Operator In R Program For Free source code and Free Project Please visit : http://www.tutorjoes.com/ http://www.facebook.com/tutorjoes http://www.youtube.com/tutorjoes
Views: 698 Tutor-Joes Stanley
Secure Mining of Association Rules in Horizontally Distributed Databases
 
00:46
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: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Secure Mining of Association Rules in Horizontally Distributed Databases We propose a protocol for secure mining of association rules in horizontally distributed databases. The current leading protocol is that of Kantarcioglu and Clifton. Our protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm of Cheung et al. which is an unsecured distributed version of the Apriori algorithm. The main ingredients in our protocol are two novel secure multi-party algorithms — one that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset held by another. Our protocol offers enhanced privacy with respect to the protocol. In addition, it is simpler and is significantly more efficient in terms of communication rounds, communication cost and computational cost.
Views: 222 jpinfotechprojects
Relevance Feature Discovery for Text Mining | Final Year Projects 2016
 
09:02
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 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 189 Clickmyproject
SAXually Explicit Images: Data Mining Large Shape Databases
 
51:52
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: 1480 GoogleTechTalks
Generalizing Random Forests Principles to other methods
 
06:40
The full-length 42-minute video in HD 720p can be downloaded from http://www.crm.ugent.be/youtube1 (the slides are available for download from this same location). This video lecture by Anita Prinzie (The University of Manchester, UK) and Dirk Van den Poel (Ghent University, Belgium) discusses our research about generalizing Random Forests (Leo Breiman, 2001) to other methods (both in classification and regression for data mining). We generalize our new method to Random Multinomial Logit and Random Naive Bayes. When applied to a case study in customer intelligence (cross-selling), we achieve a significant and substantial improvement in predictive performance over other multi-class classification methods in machine learning such as random forests and SVM (support vector machines).
Views: 3649 dirkvandenpoel
Lecture - 10 Storage Structures
 
54:56
Lecture Series on Database Management System by Dr.S.Srinath, IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 52334 nptelhrd
A Supermodularity-Based Differential Privacy Preserving Algorithm for Data Anonymization
 
16:11
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: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com A Supermodularity-Based Differential Privacy Preserving Algorithm for Data Anonymization in java Maximizing data usage and minimizing privacy risk are two conflicting goals. Organizations always apply a set of transformations on their data before releasing it. While determining the best set of transformations has been the focus of extensive work in the database community, most of this work suffered from one or both of the following major problems: scalability and privacy guarantee. Differential Privacy provides a theoretical formulation for privacy that ensures that the system essentially behaves the same way regardless of whether any individual is included in the database. In this paper, we address both scalability and privacy risk of data anonymization. We propose a scalable algorithm that meets differential privacy when applying a specific random sampling. The contribution of the paper is two-fold: 1) we propose a personalized anonymization technique based on an aggregate formulation and prove that it can be implemented in polynomial time; and 2) we show that combining the proposed aggregate formulation with specific sampling gives an anonymization algorithm that satisfies differential privacy. Our results rely heavily on exploring the supermodularity properties of the risk function, which allow us to employ techniques from convex optimization. Through experimental studies we compare our proposed algorithm with other anonymization schemes in terms of both time and privacy risk.
Views: 435 jpinfotechprojects
Privacy-Preserving Utility Verification of the Data Published by Non-interactive
 
14:07
Privacy-Preserving Utility Verification of the Data Published by Non-interactive Differentially Private Mechanisms 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 In the problem of privacy-preserving collaborative data publishing (PPCDP), a central data publisher is responsible for aggregating sensitive data from multiple parties and then anonymizing it before publishing for data mining. In such scenarios, the data users may have a strong demand to measure the utility of the published data since most anonymization techniques have side effects on data utility. Nevertheless, this task is non-trivial because the utility measuring usually requires the aggregated raw data, which is not revealed to the data users due to privacy concerns. What’s worse, the data publishers may even cheat in the raw data since no one including the individual providers knows the full dataset. In this paper, we first propose a privacy-preserving utility verification mechanism based upon cryptographic technique for DiffPart – a differentially private scheme designed for set-valued data. This proposal can measure the data utility based upon the encrypted frequencies of the aggregated raw data instead of the plain values, which thus prevents privacy breach. Moreover, it is enabled to privately check the correctness of the encrypted frequencies provided by the publisher, which helps detect dishonest publishers. We also extend this mechanism to DiffGen – another differentially private publishing scheme designed for relational data. Our theoretical and experimental evaluations demonstrate the security and efficiency of the proposed mechanism.
Views: 321 jpinfotechprojects
Final Year Projects | A Scalable Two-Phase Top-Down Specialization Approach for Data
 
09:34
Including Packages ======================= * 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: 812 Clickmyproject
RANWAR: Rank-Based Weighted Association Rule Mining from Gene Expression and Methylation Data
 
07:01
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: 157 Clickmyproject
Data Import and Modelling with Neo4j
 
01:01:02
Michael Hunger, Neo4j
Views: 1179 Neo4j

School admission cover letter example
Frimley park run newsletter formats
Custom writing service you
Theoreme milliman application letters
Computer service technician cover letter