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Search results “Multi relational data mining ppt slides”
Data Mining  Association Rule - Basic Concepts
 
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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.
Relational Database Concepts
 
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Basic Concepts on how relational databases work. Explains the concepts of tables, key IDs, and relations at an introductory level. For more info on Crow's Feet Notation: http://prescottcomputerguy.com/tmp/crows-foot.png
Views: 572559 Prescott Computer Guy
Business Intelligence: Multidimensional Analysis
 
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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: 57982 Michael Lamont
Aggregation | Database Management System
 
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This lecture explains the concept of Aggregation in enhanced ER model. To ask your doubts on this topic and much more, click on this Direct Link: http://www.techtud.com/video-lecture/lecture-aggregation IMPORTANT LINKS: 1) Official Website: http://www.techtud.com/ 2) Virtual GATE(for 'All India Test Series for GATE-2016'): http://virtualgate.in/login/index.php Both of the above mentioned platforms are COMPLETELY FREE, so feel free to Explore, Learn, Practice & Share! Our Social Media Links: Facebook Page: https://www.facebook.com/techtuduniversity Facebook Group: https://www.facebook.com/groups/virtualgate/ Google+ Page: https://plus.google.com/+techtud/posts Last but not the least, SUBSCRIBE our YouTube channel to stay updated about our regularly uploaded new videos.
Views: 62597 Techtud
Lecture - 36 Object Oriented Databases
 
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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: 50764 nptelhrd
Data warehouse Features Lecture in Hindi - DWDM Lectures in Hindi, English
 
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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
An Empirical Performance Evaluation of Relational Keyword Search Systems
 
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Title: An empirical performance evaluation of relational keyword search systems. Domain: Java Description: 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. Buy Whole Project Kit with 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. Contact for more details: Ph:044-43548566 Mob:8110081181 Mail id:[email protected]
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: 134228 nptelhrd
Feed Forward Network In Artificial Neural Network Explained In Hindi
 
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Views: 10172 5 Minutes Engineering
Lecture - 30 Introduction to Data Warehousing and OLAP
 
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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: 211479 nptelhrd
An Empirical Performance Evaluation of Relational Keyword Search Systems
 
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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: 1644 InnovationAdsOfIndia
Six types of data driven business models
 
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Part of the series "Big data for business", a course by Clement Levallois at EMLYON Business School.
Data Warehousing: Final Presentation
 
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By Matt Fonagy
Views: 17 Matthew Fonagy
Lecture - 8 Functional Dependencies and Normal Form
 
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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: 169921 nptelhrd
Relational vs  Dimensional Modeling
 
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This video explains the differences between the Relational data modeling and Dimensional data modeling concepts used in Data mining
Views: 2067 Tutorials_888
Secure Mining of Association Rules in Horizontally Distributed Databases
 
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We propose a protocol for secure mining of association rules in horizontally distributed databases. Our protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm 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.
Lecture - 3 Relational Model
 
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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: 129655 nptelhrd
Principal Components Analysis - SPSS (part 1)
 
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I demonstrate how to perform a principal components analysis based on some real data that correspond to the percentage discount/premium associated with nine listed investment companies. Based on the results of the PCA, the listed investment companies could be segmented into two largely orthogonal components.
Views: 194172 how2stats
Decentralized Access Control with Anonymous Authentication of Data Stored in Clouds
 
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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: 2698 Ramu Maloth
7 - How exactly Hyperledger Fabric works. Basic workflow of transaction endorsement
 
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On the video, you will see the technical details about the Hyperledger working process. You will understand: why you have to send a transactional proposal. Why you have to verify cryptomaterials and why Read-Write set is complex.
Views: 61609 Иван Ванков
Data Warehouse
 
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This video is about Data Warehouse. If you want this ppt the link is given below: click here for ppt: - https://goo.gl/HreJ55 if you want another ppt on different then comment Thanks for watching
Views: 15 Champ Talk
Representational similarity analysis of visual-object population codes
 
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A presentation given at Dartmouth College by Nikolaus Kriegeskorte (Cambridge CBU)
Views: 2518 Dartmouth
Secure Mining of Association Rules in Horizontally Distributed Databases
 
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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: 226 jpinfotechprojects
Keyword Query Routing
 
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Title: Keyword Query Routing Domain: Data Mining Abstract: Keyword search is an intuitive paradigm for searching linked data sources on the web. We propose to route keywords only to relevant sources to reduce the high cost of processing keyword search queries over all sources. We propose a novel method for computing top-k routing plans based on their potentials to contain results for a given keyword query. We employ a keyword-element relationship summary that compactly represents relationships between keywords and the data elements mentioning them. A multilevel scoring mechanism is proposed for computing the relevance of routing plans based on scores at the level of keywords, data elements, element sets, and subgraphs that connect these elements. Experiments carried out using 150 publicly available sources on the web showed that valid plans ([email protected] of 0.92) that are highly relevant (mean reciprocal rank of 0.89) can be computed in 1 second on average on a single PC. Further, we show routing greatly helps to improve the performance of keyword search, without compromising its result quality. Key Features: 1. We propose to investigate the problem of keyword query routing for keyword search over a large number of structured and Linked Data sources. Routing keywords only to relevant sources can reduce the high cost of searching for structured results that span multiple sources. To the best of our knowledge, the work presented in this paper represents the first attempt to address this problem. 2. Existing work uses keyword relationships (KR) collected individually for single databases .We represent relationships between keywords as well as those between data elements. They are\constructed for the entire collection of linked sources, and then grouped as elements of a compact summary called the set-level keyword-element relationship graph (KERG). Summarizing relationships is essential for addressing the scalability requirement of the Linked Data web scenario. 3. IR-style ranking has been proposed to incorporate relevance at the level of keywords. To cope with the increased keyword ambiguity in the web setting, we employ a multilevel relevance model, where elements to be considered are keywords, entities mentioning these keywords, corresponding sets of entities, relationships between elements of the same level, and inter-relationships between elements of different levels. 4. We implemented the approach and evaluated it in a real-world setting using more than 150 publicly available data sets. The results show the applicability of this approach: valid plans ([email protected] ¼ 0.92) that are highly relevant to the user information need (mean reciprocal rank (RR) ¼ 0.86) can be computed in 1 second on average using a commodity PC. Further, we show that when routing is applied to an existing keyword search system to prune sources, substantial performance gain can be achieved. 5. The web is no longer only a collection of textual documents but also a web of interlinked data sources (e.g., Linked Data). One prominent project that largely contributes to this development is Linking Open Data. 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
Views: 3491 InnovationAdsOfIndia
Different Types of OLAP Tools
 
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informatica training by 9 years experience trainer, Register now for free live interactive demo http://goo.gl/2QXuZA this video covers about types of olap tools like rolap tools (relational olap), molap tools (multidimensional olap), holap tools (hybrid olap tools)
Views: 6604 Informaticahub
Spark 1.0 and Beyond - Patrick Wendell
 
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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: 9934 Spark Summit
Learn Sql server in hindi/urdu part-24 (one to many relationship with practical example)
 
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http://learnfromsalman.blogspot.com/2017/12/learn-sql-server-in-hindiurdu-part-24.html In systems analysis, a one-to-many relationship is a type of cardinality that refers to the relationship between two entities (see also entity–relationship model) A and B in which an element of A may be linked to many elements of B, but a member of B is linked to only one element of A. For instance, think of A as mothers, and B as children. A mother can have several children, but a child can have only one biological mother. In a relational database, a one-to-many relationship exists when one row in table A may be linked with many rows in table B, but one row in table B is linked to only one row in table A. It is important to note that a one-to-many relationship is not a property of the data, but rather of the relationship itself. A list of authors and their books may happen to describe books with only one author, in which case one row of the books table will refer to only one row of the authors table, but the relationship itself is not one-to-many, because books may have more than one author, forming a many-to-many relationship.
Lecture - 14 Query Processing and Optimization
 
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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: 108416 nptelhrd
Context-Based Diversification for Keyword Queries Over XML Data
 
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Context-Based Diversification for Keyword Queries Over XML Data 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 ABSTRACT: While keyword query empowers ordinary users to search vast amount of data, the ambiguity of keyword query makes it difficult to effectively answer keyword queries, especially for short and vague keyword queries. To address this challenging problem, in this paper we propose an approach that automatically diversifies XML keyword search based on its different contexts in the XML data. Given a short and vague keyword query and XML data to be searched, we first derive keyword search candidates of the query by a simple feature selection model. And then, we design an effective XML keyword search diversification model to measure the quality of each candidate. After that, two efficient algorithms are proposed to incrementally compute top-k qualified query candidates as the diversified search intentions. Two selection criteria are targeted: the k selected query candidates are most relevant to the given query while they have to cover maximal number of distinct results. At last, a comprehensive evaluation on real and synthetic data sets demonstrates the effectiveness of our proposed diversification model and the efficiency of our algorithms.
Views: 384 jpinfotechprojects
Cryptoleq: A Heterogeneous Abstract Machine for Encrypted and Unencrypted Computation
 
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Cryptoleq: A Heterogeneous Abstract Machine for Encrypted and Unencrypted Computation-IEEE PROJECTS 2016-2017 MICANS INFOTECH offers Projects in CSE ,IT, EEE, ECE, MECH , MCA. MPHIL , BSC, in various domains JAVA ,PHP, DOT NET , ANDROID , MATLAB , NS2 , EMBEDDED , VLSI , APPLICATION PROJECTS , IEEE PROJECTS. CALL : +91 90036 28940 +91 94435 11725 [email protected] WWW.MICANSINFOTECH.COM COMPANY PROJECTS, INTERNSHIP TRAINING, MECHANICAL PROJECTS, ANSYS PROJECTS, CAD PROJECTS, CAE PROJECTS, DESIGN PROJECTS, CIVIL PROJECTS, IEEE MCA PROJECTS, IEEE M.TECH PROJECTS, IEEE PROJECTS, IEEE PROJECTS IN PONDY, IEEE PROJECTS, EMBEDDED PROJECTS, ECE PROJECTS PONDICHERRY, DIPLOMA PROJECTS, FABRICATION PROJECTS, IEEE PROJECTS CSE, IEEE PROJECTS CHENNAI, IEEE PROJECTS CUDDALORE, IEEEPROJECTSINPONDICHERRY, PROJECTDEVELOPMENTCENTRE
Provable Multicopy Dynamic Data Possession in Cloud Computing Systems
 
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2015 IEEE Transaction on Cloud Computing For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com Bangalore - Karnataka
Views: 276 manju nath
Lecture - 13 Constraints and Triggers
 
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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: 49382 nptelhrd
Privacy-Preserving Utility Verification of the Data Published by Non-Interactive Differentially
 
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Privacy-Preserving Utility Verification of the Data Published by Non-Interactive Differentially Private Mechanisms In the problem of privacy-preserving collaborative data publishing, 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. Furthermore, the data publishers may even cheat in the raw data, since no one, including the individual providers, knows the full data set. 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. SIMILAR VIDEOS: https://www.youtube.com/watch?v=AZI6oHAEtU8 https://www.youtube.com/watch?v=o0mT99zKAqA https://www.youtube.com/watch?v=X7jZtTq74WU https://www.youtube.com/watch?v=EO1rgFk07kQ https://www.youtube.com/watch?v=ACtU9aaoh_8 https://www.youtube.com/watch?v=cbZFKV4A0X8 https://www.youtube.com/watch?v=AWcD3pIGJjI https://www.youtube.com/watch?v=0y5w5CbMips https://www.youtube.com/watch?v=rhCtDFPNHCE https://www.youtube.com/watch?v=t41nfgBy8pY https://www.youtube.com/watch?v=LLUlzVlIJOw https://www.youtube.com/watch?v=mSjS4IGyrW0 https://www.youtube.com/watch?v=1TnAqAkxuws https://www.youtube.com/watch?v=nxoUUe8rrtQ https://www.youtube.com/watch?v=XBzwg1EY2SI https://www.youtube.com/watch?v=RRVWWUd9NLk https://www.youtube.com/watch?v=Es0eHDHksiM https://www.youtube.com/watch?v=x5CAAPGuo3g https://www.youtube.com/watch?v=sQKIpfEpQmo https://www.youtube.com/watch?v=hcmrJkwn1T4 https://www.youtube.com/watch?v=cNw3u68a424 https://www.youtube.com/watch?v=6sKfA1vFZBA https://www.youtube.com/watch?v=cFsryGMYxIE For More Videos - https://www.youtube.com/channel/UCR5lsF-lDQu6rVYVJPqNn6Q SOCIAL HANDLES: SCOOP IT- http://www.scoop.it/u/1croreprojects FACEBOOK - https://www.facebook.com/1Croreprojectsieeeprojects/ TWITTER - https://twitter.com/1crore_projects LINKEDIN - https://www.linkedin.com/in/1-crore-projects-ba982a118/ GOOGLE+ - https://plus.google.com/u/0/105783610929019156122 PINTEREST - https://in.pinterest.com/onecroreproject/ BLOG - 1croreprojectz.blogspot.com DOMAIN PROJECTS DOTNET - http://www.1croreprojects.com/dotnet-ieee-project-centers-in-chennai.php JAVA - http://www.1croreprojects.com/java-ieee-projects-chennai.php EMBEDDED - http://www.1croreprojects.com/embedded-systems-ieee-projects-chennai.php MATLAB - http://www.1croreprojects.com/matlab-ieee-projects-chennai.php NS2 - http://www.1croreprojects.com/ns2-ieee-projects-chennai.php VLSI -http://www.1croreprojects.com/vlsi-ieee-projects-chennai.php FOR PROJECTS - http://www.1croreprojects.com/ BUSINESS CONTACT: Email - [email protected] We are always open for all business prospects. You can get in touch which us, using the above mentioned e-mail id and contact number. ABOUT 1CROREPROJECTS: 1Crore Projects is company providing outstanding, cost-effective, effective result authorized on solutions. Our objective is to create solutions that enhance company process and increase come back in most possible time. We started truly to provide solutions to the customers all over the world. We have been effectively in providing solutions for different challenges across a wide range of market and customers propagate across the globe.
Views: 354 1 Crore Projects
What is DEDUCTIVE DATABASE? What does DEDUCTIVE DATABASE mean? DEDUCTIVE DATABASE meaning
 
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What is DEDUCTIVE DATABASE? What does DEDUCTIVE DATABASE mean? DEDUCTIVE DATABASE meaning - DEDUCTIVE DATABASE definition - DEDUCTIVE DATABASE 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 A deductive database is a database system that can make deductions (i.e., conclude additional facts) based on rules and facts stored in the (deductive) database. Datalog is the language typically used to specify facts, rules and queries in deductive databases. Deductive databases have grown out of the desire to combine logic programming with relational databases to construct systems that support a powerful formalism and are still fast and able to deal with very large datasets. Deductive databases are more expressive than relational databases but less expressive than logic programming systems. In recent years, deductive databases such as Datalog have found new application in data integration, information extraction, networking, program analysis, security, and cloud computing. Deductive databases and logic programming: Deductive databases reuse a large number of concepts from logic programming; rules and facts specified in the deductive database language Datalog look very similar to those in Prolog. However important differences between deductive databases and logic programming: Order sensitivity and procedurality: In Prolog, program execution depends on the order of rules in the program and on the order of parts of rules; these properties are used by programmers to build efficient programs. In database languages (like SQL or Datalog), however, program execution is independent of the order of rules and facts. Special predicates: In Prolog, programmers can directly influence the procedural evaluation of the program with special predicates such as the cut, this has no correspondence in deductive databases. Function symbols: Logic Programming languages allow function symbols to build up complex symbols. This is not allowed in deductive databases. Tuple-oriented processing: Deductive databases use set-oriented processing while logic programming languages concentrate on one tuple at a time.
Views: 1460 The Audiopedia
Big Data Vs Data Science Vs Data Analytics | Data Science vs Machine Learning | Intellipaat
 
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In this Intellipaat big data vs data science vs data analytics video you will understand the basic difference between these technologies. A lot of people generally gets confused between these and often believes they are all one and the same. Watch this video to find out the difference in them. Also subscribe to Intellipaat channel to get regular updates on them: https://goo.gl/hhsGWb Intellipaat Big Data, Data Science Masters Course Training: https://goo.gl/iYrwgC Are you interested to learn big data, data science or data analytics to get high paying jobs? Enroll in our Intellipaat course & become a certified Professional (https://goo.gl/iYrwgC). All Intellipaat trainings are provided by Industry experts and is completely aligned with industry standards and certification bodies. If you’ve enjoyed this data science vs machine learning or big data analytics or data analytics video, Like us and Subscribe to our channel for more informative tutorials. Got any questions? Ask us in the comment section below. ---------------------------- Intellipaat Edge 1. 24*7 Life time Access & Support 2. Flexible Class Schedule 3. Job Assistance 4. Mentors with +14 yrs 5. Industry Oriented Course ware 6. Life time free Course Upgrade #BigDatavsDataSciencevsDataAnalytics #DataSciencevsMachineLearning #BigDatavsDataAnalytics ------------------------------ For more Information: Please write us to [email protected], or call us at: +91- 7847955955 Website: https://goo.gl/iYrwgC Facebook: https://www.facebook.com/intellipaatonline LinkedIn: https://www.linkedin.com/in/intellipaat/ Twitter: https://twitter.com/Intellipaat
Views: 6129 Intellipaat
IEEE 2013 JAVA Automatic Semantic Content Extraction in Videos Using a Fuzzy Ontology
 
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PG Embedded Systems #197 B, Surandai Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2013 ieee projects, 2013 ieee java projects, 2013 ieee dotnet projects, 2013 ieee android projects, 2013 ieee matlab projects, 2013 ieee embedded projects, 2013 ieee robotics projects, 2013 IEEE EEE PROJECTS, 2013 IEEE POWER ELECTRONICS PROJECTS, ieee 2013 android projects, ieee 2013 java projects, ieee 2013 dotnet projects, 2013 ieee mtech projects, 2013 ieee btech projects, 2013 ieee be projects, ieee 2013 projects for cse, 2013 ieee cse projects, 2013 ieee it projects, 2013 ieee ece projects, 2013 ieee mca projects, 2013 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2013 mtech projects, 2013 mphil projects, 2013 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2013 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2013 ieee omnet++ projects, ieee 2013 oment++ project, innovative ieee projects, latest ieee projects, 2013 latest ieee projects, ieee cloud computing projects, 2013 ieee cloud computing projects, 2013 ieee networking projects, ieee networking projects, 2013 ieee data mining projects, ieee data mining projects, 2013 ieee network security projects, ieee network security projects, 2013 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2013 wireless networking projects ieee, 2013 ieee web service projects, 2013 ieee soa projects, ieee 2013 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2013 IEEE java projects,2013 ieee Project Titles, 2013 IEEE cse Project Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2013 - 2013 ... Image Processing. IEEE 2013 - 2013 Projects | IEEE Latest Projects 2013 - 2013 | IEEE ECE Projects2013 - 2013, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2013 IEEE C#, C Sharp Project Titles, 2013 IEEE EmbeddedProject Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE Android Project Titles. 2013 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2013, IEEE 2013 PROJECT TITLES, M.TECH. PROJECTS 2013, IEEE 2013 ME PROJECTS.
Views: 762 PG Embedded Systems
O'Reilly Webcast: Who Killed My Battery - Analyzing Mobile Browser Energy Consumption
 
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Despite the explosive growth of smartphones and growing popularity of mobile web browsing, the energy consumed by a phone browser while surfing the web is poorly understood. While web pages are often optimized for speed and beauty, little attention is given to the amount of energy needed to download and render the page. In this webcast presentation, we present an infrastructure for measuring the precise energy used by a mobile browser to render web pages. We then measure the energy needed to render financial, e-commerce, email, blogging, news and social networking sites. Our tools are sufficiently precise to measure the energy needed to render individual web elements, such as cascade style sheets (CSS), Javascript, images, and plug-in objects. Our results show that for popular sites, downloading and parsing cascade style sheets and Javascript consumes a significant fraction of the total energy needed to render the page. Using the data we collected we make concrete recommendations on how to design web pages so as to minimize the energy needed to render the page. We conclude by estimating the point at which offloading browser computations to a remote proxy can save energy on the phone. Our approach gives another dimension for evaluating mobile web sites and helps web developers build more energy efficient sites and improve user experience. About Dr. Angela Nicoara: Dr. Angela Nicoara is a Senior Research Scientist at Deutsche Telekom Innovation Laboratories, Silicon Valley Innovation Center, USA since 2008. She received a PhD in Computer Science from ETH Zurich in 2007, where she was the leader, designer and builder of the PROSE open source system. Dr. Nicoara joined ETH Zurich, working on adaptive software architectures. Dr. Nicoara joined Google Inc in Mountain View, California for the summer of 2004. Her current research activities include the development of open and programmable mobile platforms (e.g., Android) and novel information technology services to shape the emerging trends in fixed and mobile infrastructure and services sectors. Dr. Nicoara's work has been published in numerous leading scientific conferences, workshops and symposia proceedings, and is a regular speaker and panelist at major international scientific and industry conferences. Dr. Nicoara received several prestigious awards and honors for her research and technical contributions, the most recent ones include the Best Paper Award from IEEE RTAS 2012 and Best Student Paper Award from ACM WWW 2012 . Her work has been quoted by the press and media, as well as she chaired and served as a technical program committee (TPC) member of multiple industry and academic conferences. Dr. Nicoara is a member of ACM and IEEE societies. Produced by: Yasmina Greco
Views: 875 O'Reilly
Final Year Projects | Ranking Spatial Data by Quality Preferences
 
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Final Year Projects | Ranking Spatial Data by Quality Preferences 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: 1027 Clickmyproject
Spatial and non-spatial data
 
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Spatial and non-spatial data Thursday, September 29 2016 Wildfire Management with WebWorldWind  Patrick Hogan 4:51 An INSPIREd application to design landslide risk environmental impacts  Carlo Cipolloni 24:49 GeoAvalanche: data harmonisation on Natural Risk Zones for snow avalanche information  Francesco Bartoli 52:33 Wildfire risk in large urban–forest systems and the multi - emergency management solutions  Marc Castellnou 1:13:22 Strengthening Resilience to Water Emergencies through Citizen Participation  Neil Ireson 1:30:15
Views: 609 Inspire EU
Web Image Re Ranking Using Query Specific Semantic Signatures
 
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Title: Web Image Re-Ranking Using Query-Specific Semantic Signatures Domain: Data Mining Abstract: Image re-ranking, as an effective way to improve the results of web-based image search, has been adopted by current commercial search engines. Given a query keyword, pools of images are first retrieved by the search engine based on textual information. By asking the user to select a query image from the pool, the remaining images are re-ranked based on their visual similarities with the query image. A major challenge is that the similarities of visual features do not well correlate with images’ semantic meanings which interpret users’ search intention. On the other hand, learning a universal visual semantic space to characterize highly diverse images from the web is difficult and inefficient. Key Features: 1. We propose a novel image re-ranking framework, which automatically offline learns different visual semantic spaces for different query keywords through keyword expansions. The visual features of images are projected into their related visual semantic spaces to get semantic signatures. At the online stage, images are re-ranked by comparing their semantic signatures obtained from the visual semantic space specified by the query keyword. The new approach significantly improves both the accuracy and efficiency of image re-ranking. 2. Web-scale image search engines mostly use keywords as queries and rely on surrounding text to search images. It is well known that they suffer from the ambiguity of query keywords. For example, using “apple” as query, the retrieved images belong to different categories, such as “red apple”, “apple logo”, and “apple laptop”. 3. By asking a user to select a query image, which reflects the user’s search intention, from the pool, the remaining images in the pool are re-ranked based on their visual similarities with the query image. The visual features of images are pre-computed offline and stored by the search engine. The main online computational cost of image re-ranking is on comparing visual features. In order to achieve high efficiency, the visual feature vectors need to be short and their matching needs to be fast. 4. Another major challenge is that the similarities of low level visual features may not well correlate with images’ high-level semantic meanings which interpret users’ search intention. To narrow down this semantic gap, for offline image recognition and retrieval, there have been a number of studies to map visual features to a set of predefined concepts or attributes as semantic signature. 5. In this paper, a novel framework is proposed for web image re-ranking. Instead of constructing a universal concept dictionary, it learns different visual semantic spaces for different query keywords individually and automatically. We believe that the semantic space related to the images to be re-ranked can be significantly narrowed down by the query keyword provided by the user. 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
Views: 3008 InnovationAdsOfIndia
Lecture -1a Conceptual Designs
 
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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: 214462 nptelhrd
What are some of the benefits and limitations of data mining for business intelligence Use Bank Fina
 
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Need Answer Sheet of this Question paper Contact us at [email protected] M: 7019944355 Management Information Systems 1. Do you agree with the argument made by Nick Carr to support his position that IT no longer gives companies a competitive advantage? Why or why not? 2. Do you agree with the argument made by the business leaders in this case in support of the competitive advantage that IT can provide to a business? Why or why not? 3. What are several ways that IT could provide a competitive advantage to a business? Use some of the companies mentioned in this case as examples. Visit their websites to gather more information to help you answer. 1. What is the business of wireless technologies in the chemicals and automotive manufacturing industries? What other manufacturing applications might benefit from wireless technologies? Why? 2. What are some of the business benefits of wireless technologies in finance and investments? What other applications would you recommend? Why? Check the website of Fidelity.com to help you answer. 3. What are some of the business benefits and challenges of using wireless technologies in retailing? What are some other applications that might be beneficial to consumers, as well as retailers? Why? 1. What is the business value of AI technologies in business today? Use several examples from the case to illustrate your answer. 2. What are some of the benefits and limitations of data mining for business intelligence? Use Bank Financial’s experience to illustrate your answer. 3. Why have banks and other financial institutions been leading users of AI technologies like neural networks? What are the benefits and limitations of this technology 1. What are the benefits and limitations of the Rowe Companies’ ROI methods for IT project planning? 2. What is the business value of the ROI methodology required for project planning by Merrill Lynch? 3. Do you agree with the IT investment decisions being made by the Rowe Companies in response to changing economic conditions? Why or why not? 1. What security measures should companies, business professionals, and consumers take to protect their systems from being damaged by computer worms and viruses? 2. What is the ethical responsibility of Microsoft in helping to prevent the spread of computer viruses? Have they met this responsibility? Why or why not? 3. What are several possible reasons why some companies (like GM) were seriously affected by computer viruses, while others (like Verizon) were not? Need Answer Sheet of this Question paper Contact us at [email protected] M: 7019944355
Views: 33 Answer Sheet
Access:  How to create a query
 
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How to create a simple query in Access that helps you sort the records. For a full list of our videos organized by category - visit www.techywarrior.com
Views: 688 drlindadavis
toward secure multi keyword top-k retrivel  over  encrypted cloud data -IEEE 2013
 
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S3 technologies, 43, North Masi street, ( Near Krishnan Kovil) Simmakkal, Madurai Phone: 0452-4373398 Visit: www.s3techindia.com Mail: [email protected] visit:s3techindia com visit:s3studentproject.blogspot.in
Views: 85 S3 TECHNOLOGIES
Privacy-Preserving Patient-Centric Clinical Decision  Support System
 
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Privacy-Preserving Patient-Centric Clinical Decision Support System on Na¨ıve Bayesian Classification -IEEE PROJECTS 2016-2017 MICANS INFOTECH offers Projects in CSE ,IT, EEE, ECE, MECH , MCA. MPHIL , BSC, in various domains JAVA ,PHP, DOT NET , ANDROID , MATLAB , NS2 , EMBEDDED , VLSI , APPLICATION PROJECTS , IEEE PROJECTS. CALL : +91 90036 28940 +91 94435 11725 [email protected] WWW.MICANSINFOTECH.COM COMPANY PROJECTS, INTERNSHIP TRAINING, MECHANICAL PROJECTS, ANSYS PROJECTS, CAD PROJECTS, CAE PROJECTS, DESIGN PROJECTS, CIVIL PROJECTS, IEEE MCA PROJECTS, IEEE M.TECH PROJECTS, IEEE PROJECTS, IEEE PROJECTS IN PONDY, IEEE PROJECTS, EMBEDDED PROJECTS, ECE PROJECTS PONDICHERRY, DIPLOMA PROJECTS, FABRICATION PROJECTS, IEEE PROJECTS CSE, IEEE PROJECTS CHENNAI, IEEE PROJECTS CUDDALORE, IEEEPROJECTSINPONDICHERRY, PROJECTDEVELOPMENTCENTRE
Ontology implementation in biomedical informatics
 
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Yazhong Hou & Yan Wang
Views: 170 Yazhong Hou
NetSpam A Network Based Spam Detection Framework for Reviews in Online Social Media
 
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2017 IEEE Transaction on Information Forensics and Security For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2017 and 2018 IEEE [email protected] TMKS Infotech,Bangalore
Views: 893 manju nath
Lecture - 33 Case Study ORACLE and Microsoft Access
 
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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: 27277 nptelhrd
Slicing A New Approach for Privacy Preserving Data Publishing 2012 IEEE DOTNET PROJECT
 
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Slicing A New Approach for Privacy Preserving Data Publishing 2012 IEEE DOTNET PROJECT 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. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the '-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.
Views: 2334 jpinfotechprojects
Inference Attack on Browsing History of Twitter Users using Public Click Analytics and Twitter
 
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Inference Attack on Browsing History of Twitter Users using Public Click Analytics and Twitter Metadata 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 Twitter is a popular online social network service for sharing short messages (tweets) among friends. Its users frequently use URL shortening services that provide (i) a short alias of a long URL for sharing it via tweets and (ii) public click analytics of shortened URLs. The public click analytics is provided in an aggregated form to preserve the privacy of individual users. In this paper, we propose practical attack techniques inferring who clicks which shortened URLs on Twitter using the combination of public information: Twitter metadata and public click analytics. Unlike the conventional browser history stealing attacks, our attacks only demand publicly available information provided by Twitter and URL shortening services. Evaluation results show that our attack can compromise Twitter users’ privacy with high accuracy.
Views: 714 jpinfotechprojects