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What is Data Mining?
 
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NJIT School of Management professor Stephan P Kudyba describes what data mining is and how it is being used in the business world.
Views: 396974 YouTube NJIT
Introduction to data mining and architecture  in hindi
 
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Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 188544 Last moment tuitions
How data mining works
 
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In this video we describe data mining, in the context of knowledge discovery in databases. More videos on classification algorithms can be found at https://www.youtube.com/playlist?list=PLXMKI02h3_qjYoX-f8uKrcGqYmaqdAtq5 Please subscribe to my channel, and share this video with your peers!
Views: 217754 Thales Sehn Körting
Temporal Database in Hindi
 
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A temporal database is a database with built-in support for handling data involving time, being related to the slowly changing dimension concept, for example a temporal data model and a temporal version of Structured Query Language (SQL). More specifically the temporal aspects usually include valid time and transaction time. These attributes can be combined to form bitemporal data. Valid time is the time period during which a fact is true in the real world. Transaction time is the time period during which a fact stored in the database was known. Bitemporal data combines both Valid and Transaction Time. It is possible to have timelines other than Valid Time and Transaction Time, such as Decision Time, in the database. In that case the database is called a multitemporal database as opposed to a bitemporal database. However, this approach introduces additional complexities such as dealing with the validity of (foreign) keys. Temporal databases are in contrast to current databases (at term that doesn't mean, currently available databases, some do have temporal features, see also below), which store only facts which are believed to be true at the current time. Temporal databases supports System-maintained transaction time. With the development of SQL and its attendant use in real-life applications, database users realized that when they added date columns to key fields, some issues arose. For example, if a table has a primary key and some attributes, adding a date to the primary key to track historical changes can lead to creation of more rows than intended. Deletes must also be handled differently when rows are tracked in this way. In 1992, this issue was recognized but standard database theory was not yet up to resolving this issue, and neither was the then-newly formalized SQL-92 standard. Richard Snodgrass proposed in 1992 that temporal extensions to SQL be developed by the temporal database community. In response to this proposal, a committee was formed to design extensions to the 1992 edition of the SQL standard (ANSI X3.135.-1992 and ISO/IEC 9075:1992); those extensions, known as TSQL2, were developed during 1993 by this committee.[3] In late 1993, Snodgrass presented this work to the group responsible for the American National Standard for Database Language SQL, ANSI Technical Committee X3H2 (now known as NCITS H2). The preliminary language specification appeared in the March 1994 ACM SIGMOD Record. Based on responses to that specification, changes were made to the language, and the definitive version of the TSQL2 Language Specification was published in September, 1994[4] An attempt was made to incorporate parts of TSQL2 into the new SQL standard SQL:1999, called SQL3. Parts of TSQL2 were included in a new substandard of SQL3, ISO/IEC 9075-7, called SQL/Temporal.[3] The TSQL2 approach was heavily criticized by Chris Date and Hugh Darwen.[5] The ISO project responsible for temporal support was canceled near the end of 2001. As of December 2011, ISO/IEC 9075, Database Language SQL:2011 Part 2: SQL/Foundation included clauses in table definitions to define "application-time period tables" (valid time tables), "system-versioned tables" (transaction time tables) and "system-versioned application-time period tables" (bitemporal tables). A substantive difference between the TSQL2 proposal and what was adopted in SQL:2011 is that there are no hidden columns in the SQL:2011 treatment, nor does it have a new data type for intervals; instead two date or timestamp columns can be bound together using a PERIOD FOR declaration. Another difference is replacement of the controversial (prefix) statement modifiers from TSQL2 with a set of temporal predicates. For illustration, consider the following short biography of a fictional man, John Doe: John Doe was born on April 3, 1975 in the Kids Hospital of Medicine County, as son of Jack Doe and Jane Doe who lived in Smallville. Jack Doe proudly registered the birth of his first-born on April 4, 1975 at the Smallville City Hall. John grew up as a joyful boy, turned out to be a brilliant student and graduated with honors in 1993. After graduation he went to live on his own in Bigtown. Although he moved out on August 26, 1994, he forgot to register the change of address officially. It was only at the turn of the seasons that his mother reminded him that he had to register, which he did a few days later on December 27, 1994. Although John had a promising future, his story ends tragically. John Doe was accidentally hit by a truck on April 1, 2001. The coroner reported his date of death on the very same day.
Views: 11016 Introtuts
Mining Association Rules in Large Database
 
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Basic concepts of Association Rules and Stretagies
Views: 1303 Dr.Anamika Bhargava
Types of databases
 
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This video is telling about different types of databases. Next video will be about different types of attributes. #datamining #database #types
Views: 679 yaachana bhawsar
Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
 
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Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 50417 Well Academy
iSAX 2.0: Indexing and Mining One Billion Time Series; Database Cracking
 
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iSAX 2.0: Indexing and Mining One Billion Time Series abstract -------- There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to index and mine very large collections of time series. Examples of such applications come from astronomy, biology, the web, and other domains. It is not unusual for these applications to involve numbers of time series in the order of hundreds of millions to billions. In this paper, we describe iSAX 2.0, a data structure designed for indexing and mining truly massive collections of time series. We show that the main bottleneck in mining such massive datasets is the time taken to build the index, and we thus introduce a novel bulk loading mechanism, the first of this kind specifically tailored to a time series index. We show how our method allows mining on datasets that would otherwise be completely untenable, including the first published experiments to index one billion time series, and experiments in mining massive data from domains as diverse as entomology, DNA and web-scale image collections. Database Cracking and the Path Towards Auto-tuning Database Kernels ABSTRACT: Database cracking targets dynamic and exploratory environments where there is no sufficient workload knowledge and idle time to invest in physical design preparations and tuning. With DB cracking indexes are built incrementally, adaptively and on demand; each query is seen as an advice on how data should be stored. With each incoming query, data is reorganized on-the-fly as part of the query operators, while future queries exploit and continuously enhance this knowledge. Autonomously, adaptively and without any external human administration, the system quickly adapts to a new workload and reaches optimal performance when the workload stabilizes. We will talk about the basics of DB cracking including selection cracking, partial and sideways cracking and updates. We will also talk about important open and on going research issues such as disk based cracking, concurrency control and integration of cracking with offline and online index analysis.
Views: 375 Microsoft Research
KDD ( knowledge data discovery )  in data mining in hindi
 
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Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 65276 Last moment tuitions
E-Commerce 2014 | Big Data, Screen Scraping, Database Protection and Data Mining
 
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On Monday, June 16, 2014 the Stanford Program in Law, Science and Technology, it's Center for ECommerce, and the San Francisco Bay Area Chapter of the Association of Corporate Counsel(ACC) hosted the 11th Annual Stanford E-Commerce Best Practices Conference. The panel "Big Data, Screen Scraping, Database Protection and Data Mining" was moderated by Orrick, Herrington and Sutcliffe LLP's Eulonda Skyles and featured McAfee's Michelle Dennedy, Macy's Michael McCullough and Arthur Cox Solicitors Colin Rooney.
Views: 1204 stanfordlawschool
Database VS Data Warehouse
 
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Whats the difference between a Database and a Data Warehouse? I had a attendee ask this question at one of our workshops. In this short video I explain the distinction. Here's the link mentioned in the video:https://intricity.attach.io/r1x~TiWdz Talk with an Intricity Specialist: https://www.intricity.com/intricity101/
Views: 3276 Intricity101
Database and Data Mining Introduction Video
 
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Database and Data Mining Introduction Video
Views: 130 GaryBoetticher
Data Mining   KDD Process
 
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KDD - knowledge discovery in Database. short introduction on Data cleaning,Data integration, Data selection,Data mining,pattern evaluation and knowledge representation.
DATA MINING   1 Data Visualization   4 1 3  Database Visualization Part 1
 
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https://www.coursera.org/learn/datavisualization
Views: 56 Ryo Eng
SQL Database Fundamentals Tutorial
 
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Aiodex’s Referral Program  will give you 20% -80% commission from their transaction fee for 7 years. The value will be calculated starting from the date the member you invite sign up. ☞ https://aiodex.com/?ref=5b45a599c7165734d36bb3fc Learn to Code ☞ https://codequs.com CodeGeek's Discuss ☞ https://discord.gg/KAe3AnN Playlists Video Tutorial ☞ http://bit.ly/2IQdTwR Get Free 15 Geek ☞ https://my.geekcash.io/ref/5b3c4924d38b6158ce04633f or http://geekcash.org/ The Ultimate MySQL Bootcamp: Go from SQL Beginner to Expert ☞ http://wp.me/p8HH5D-17D Retrieving Data from Oracle Database with SQL ☞ http://edusavecoupon.net/?p=14285 SQL, SSAS & Data Mining Query Languages - T-SQL MDX DAX DMX ☞ https://wp.me/p8iOGF-N6 Would you like to learn the basics of relational databases? Join us for this look at SQL Database fundamentals, along with those of database management systems and database components. Get an in-depth introduction to the terminology, concepts, and skills you need to understand database objects, administration, security, and management tools. Plus, explore T-SQL scripts, database queries, and data types. Start with a look at creating tables, inserting data, and querying data in tables. Then, learn about data manipulation, optimize database performance, and work with non-relational data. Get practical help on basic database administration, including installation and configuration, backup and restore, security, monitoring, and maintenance. Take this SQL Database tutorial to prepare for additional online courses for database administrators (DBAs), developers, data scientists, and big data specialists. Check it out! 1 | Introduction to Databases View a course introduction, and get started with databases. 2 | Getting Started with Tables Get an introduction to concepts and techniques for creating tables, inserting data, and querying data in tables. 3 | Working with Data in Tables Learn about data manipulation using Transact-SQL (T-SQL), including INSERT, UPDATE, and DELETE. Explore wrapper objects, such as views and stored procedures. 4 | Optimizing Database Performance Get an introduction to terminology and concepts for optimizing database performance by using indexes. 5 | Working with Non-Relational Data Explore additional types of data that can be used in modern databases, including XML and JSON. 6 | Basic Database Administration Learn about terminology and concepts for basic database administration, including installation and configuration, backup and restore, security, monitoring, and maintenance. Video source via: MVA ---------------------------------------------------- Website: https://goo.gl/RBymXD Playlist: https://goo.gl/hnwbLS Fanpage: https://goo.gl/4C2pj9 Wordpress: https://goo.gl/znpKQ2 Twitter: https://goo.gl/6XgzWJ
Views: 19349 coderschool
Data Warehousing and Data Mining
 
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This course aims to introduce advanced database concepts such as data warehousing, data mining techniques, clustering, classifications and its real time applications. SlideTalk video created by SlideTalk at http://slidetalk.net, the online solution to convert powerpoint to video with automatic voice over.
Views: 3792 SlideTalk
Accelerating large-scale data mining using in-database analytics (KDD 2011)
 
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Accelerating large-scale data mining using in-database analytics KDD 2011 Mario E. Inchiosa In more and more industries, competitive advantage hinges on exploiting the largest quantity of data in the shortest possible time - and doing so cost-effectively. Data volumes are growing exponentially, while businesses are striving to deploy sophisticated and computationally intensive predictive analytics. Often, massive data is stored in a data warehouse running on dedicated parallel hardware, but advanced analytics is performed on a separate compute platform. Moving data from the data warehouse to the compute environment can constitute a significant bottleneck. Organizations resort to considering only a fraction of their data or refreshing their analyses infrequently. To address the data movement bottleneck and take full advantage of parallel data warehouse platforms, vendors are offering new in-database analytics capabilities. They are opening up their platforms, allowing users to run their own user-defined functions and statistical models as well as vendor- and partner-supplied advanced analytics on the database platform, close to the data, in parallel, without transporting the data through a host node or corporate network. In this talk, we will present the need for in-database analytics and discuss a number of the new solutions available, highlighting case studies where solution times have been reduced from hours to minutes or seconds.
Database Mining
 
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Views: 166 Sean Patrick Altea
Generalization | Database Management System
 
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This lecture describes the concept of Generalization as an enhanced feature of ER model. To ask your doubts on this topic and much more, click on this Direct Link: http://www.techtud.com/video-lecture/lecture-generalization 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: 51832 Techtud
Distributed Database Introduction | Features | Advantages and Disadvantages
 
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Distributed Database Introduction | Features | Advantages and Disadvantages Like Us on Facebook - https://www.facebook.com/Easy-Engineering-Classes-346838485669475/ DBMS Hindi Classes Database Management System Tutorial for Beginners in Hindi Database Management System Study Notes DBMS Notes Database Management System Notes
Part 1.1| DATA INFORMATION DATA BASE DATA BASE MANAGEMENT SYSTEM definition difference what
 
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Views: 45004 KNOWLEDGE GATE
DATABASE TUNING-INTRODUCTION
 
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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: 559947 Prescott Computer Guy
Database and Big Data
 
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This course introduces important database concepts, including data modeling, database design, and data extraction. Students will also learn data analysis skills they need to transform raw data into useful business information and knowledge for decision-making and problem solving. Students explore relational design, data warehousing, data mining, data visualization, data search, knowledge management, business intelligence, data querying, basic analytics, and reporting.
Views: 767 [email protected]
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.
Apriori Algorithm Video, KDD Knowledge Discovery in Database
 
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This is a video demonstration of finding representative rules and sets using the Apriori algorithm.
Views: 29861 Laurel Powell
Fabian Yamaguchi – Mining for Bugs with Graph Database Queries
 
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https://www.hacktivity.com While graph databases are primarily known as the backbone of the modern dating world, this nerd has found a much more interesting application for them: program analysis. This talk aims to demonstrate that graph databases and the typical program representations developed in compiler construction are a match made in heaven, allowing large code bases to be mined for vulnerabilities using complex bug descriptions encoded in simple, and not so simple graph database queries. This talk will bring together two well known but previously unrelated topics: static program analysis and graph databases. After briefly covering the "emerging graph landscape" and why it may be interesting for hackers, a graph representation of programs exposing syntax, control-flow, data-dependencies and type information is presented, designed specifically with bug hunting in mind. Our open-source program analysis platform Joern (http://mlsec.org/joern/) is then introduced, which implements these ideas and has been successfully used to uncover various vulnerabilities in the Linux kernel. Capabilities and limitations of the system will then be demonstrated live as we craft queries for buffer overflows, memory disclosure bugs and integer-related vulnerabilities.
Views: 1293 hacktivity
Scientists Create an Asteroid Mining Database - ASTERANK
 
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Hello and welcome to What Da Math! In this video, we will talk about Asterank - a database that describes and displays various asteroids and their profit margins if we were to mine them. Check it out here: http://www.asterank.com Patreon page: https://www.patreon.com/user?u=2318196&ty=h Enjoy and please subscribe. Other videos here: https://www.youtube.com/playlist?list=PL9hNFus3sjE7jgrGJYkZeTpR7lnyVAk-x Twitter: https://twitter.com/WhatDaMath Facebook: https://www.facebook.com/whatdamath Twitch: http://www.twitch.tv/whatdamath
Views: 2301 Anton Petrov
MINING HIGH UTILITY ITEM SETS IN TRANSACTIONAL DATABASE
 
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Data mining is the process of revealing nontrivial,previously unknown and potentially useful information from large databases. Discovering useful patterns hidden in the database plays an essential role in several data mining tasks,such as frequent pattern mining, weighted pattern mining and high utility pattern mining. This Project aims at mining the different combination of itemsets with high utility like profits from the transactional database. Utility based data mining is a new research area interested in all types of utility factors in data mining processes and targeted at incorporating utility considerations in data mining tasks. The UMining algorithm is used to find all high utility itemsets within the given utility constraint threshold. This algorithm has a pruning strategy of its own. Fast Utility Mining is a novel algorithm which is faster and simpler than the original UMining algorithm for generating high utility itemsets. The experimental evaluation on artificial datasets show that this algorithm executes faster than UMining algorithm. Another algorithm, Fast Utility Frequent Mining, is a more precise and very recent algorithm. It takes both the utility and the support measure into consideration.
Views: 751 Deepika Starz
▶ What is Data, Information, Database and Data Warehouse in Data Mining | Data Mining Tutorial
 
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See Full #Data_Mining Video Series Here: https://youtu.be/t8lSMGW5eT0 In This Video You are gonna learn What is Data? What is Information? What is Database? What is Data Warehouse? »See Full #Data_Mining Video Series Here: https://youtu.be/t8lSMGW5eT0 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on #Data_Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 ডেটা শব্দটি ল্যাটিন শব্দ ডেটাম এর বহুবচন। যার বাংলায় অর্থ দাঁড়ায় উপাত্ব। নাম্বার, লেটার, সিম্বল সবকিছুই ডেটা। ডেটাকে প্রসেস করলে যা পাওয়া যায় তা-ই মুলত ইনফরমেশন। ডেটা যেখানে রাখা হয় তাকেই ডেটাবেজ বলে। সাধারণত ডেটাবেজে টেবিল, কলাম , রো এর সাহায্যে অরগেনাইজড অবস্থায় ডেটা রাখা হয় । কয়েকটি ছোট ছোট ডেটাবেজ মিলে একটি ডেটা ওয়্যারহাউজ তৈরি করা হয়।
Views: 1746 BookBd
In-Database Data Mining for Retail Market Basket Analysis Using Oracle Advanced Analytics
 
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Market Basket Analysis presentation and demo using Oracle Advanced Analytics
Views: 10394 Charles Berger
@HashClocks, GPU Mining Hashrates Database! Including RX570/580
 
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Today i show you a database that has been added to http://www.buriedone.com/gpuhashrates.html that shows you an average of what you can expect from a gpu that you want to buy to give you a guideline on what this card is capable on mining a specific coin! Make sure to check out the rest of the website! Or check out all cards at: http://www.buriedone.com/hardware.html to support the channel while buying your hardware on Amazon! Feel free to talk in the BuriedONE Blockchain Community (BBC.. Lol?) Discord: https://discord.gg/6PwuaTq
The information that underpins the national coal mining database
 
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The Coal Authority holds a large quantity of data, including historic information, relating to coal mining in the United Kingdom.
Views: 385 The Coal Authority
Mining, Oil & Gas DataBase
 
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Amawealth.com has created a database of over 600 mining, oil and gas companies listed on the London, New York, Canadian and Australian stock exchanges. Database provides technical market data, operational information and fundamental analysis for the discerning investor, analyst and researcher.
Views: 159 Amawealth1
An Introduction to Temporal Databases
 
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Check out http://www.pgconf.us/2015/event/83/ for the full talk details. In the past manipulating temporal data was rather ad hoc and in the form of simple solutions. Today organizations strongly feel the need to support temporal data in a coherent way. Consequently, there is an increasing interest in temporal data and major database vendors recently provide tools for storing and manipulating temporal data. However, these tools are far from being complete in addressing the main issues in handling temporal data. The presentation uses the relational data model in addressing the subtle issues in managing temporal data: comparing database states at two different time points, capturing the periods for concurrent events and accessing to times beyond these periods, sequential semantics, handling multi-valued attributes, temporal grouping and coalescing, temporal integrity constraints, rolling the database to a past state and restructuring temporal data, etc. It also lays the foundation in managing temporal data in NoSQL databases as well. Having ranges as a data type PostgresSQL has a solid base in implementing a temporal database that can address many of these issues successfully. About the Speaker Abdullah Uz Tansel is professor of Computer Information Systems at the Zicklin School of Business at Baruch College and Computer Science PhD program at the Graduate Center. His research interests are database management systems, temporal databases, data mining, and semantic web. Dr. Tansel published many articles in the conferences and journals of ACM and IEEE. Dr. Tansel has a pending patent application on semantic web. Currently, he is researching temporality in RDF and OWL, which are semantic web languages. Dr. Tansel served in program committees of many conferences and headed the editorial board that published the first book on temporal databases in 1993. He is also one the editors of the forth coming book titled Recommendation and Search in Social Networks to be published by Springer. He received BS, MS and PhD degrees from the Middle East Technical University, Ankara Turkey. He also completed his MBA degree in the University of Southern California. Dr. Tansel is a member of ACM and IEEE Computer Society.
Views: 4604 Postgres Conference
Hadoop Vs Traditional Database Systems | Hadoop Data Warehouse | Hadoop and ETL | Hadoop Data Mining
 
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http://www.edureka.co/hadoop Email Us: [email protected],phone : +91-8880862004 This short video explains the problems with existing database systems and Data Warehouse solutions, and how Hadoop based solutions solves these problems. Let's Get Going on our Hadoop Journey and Join our 'Big Data and Hadoop' course. - - - - - - - - - - - - - - How it Works? 1. This is a 10-Module Instructor led Online Course. 2. We have a 3-hour Live and Interactive Sessions every Sunday. 3. We have 4 hours of Practical Work involving Lab Assignments, Case Studies and Projects every week which can be done at your own pace. We can also provide you Remote Access to Our Hadoop Cluster for doing Practicals. 4. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 5. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, MapReduce, Advance MapReduce, PIG, HIVE, HBase, Zookeeper, SQOOP, Hadoop 2.0 , YARN etc. will be covered in the course. - - - - - - - - - - - - - - Course Objectives After the completion of the Hadoop Course at Edureka, you should be able to: Master the concepts of Hadoop Distributed File System. Understand Cluster Setup and Installation. Understand MapReduce and Functional programming. Understand How Pig is tightly coupled with Map-Reduce. Learn how to use Hive, How you can load data into HIVE and query data from Hive. Implement HBase, MapReduce Integration, Advanced Usage and Advanced Indexing. Have a good understanding of ZooKeeper service and Sqoop, Hadoop 2.0, YARN, etc. Develop a working Hadoop Architecture. - - - - - - - - - - - - - - Who should go for this course? This course is designed for developers with some programming experience (preferably Java) who are looking forward to acquire a solid foundation of Hadoop Architecture. Existing knowledge of Hadoop is not required for this course. - - - - - - - - - - - - - - Why Learn Hadoop? BiG Data! A Worldwide Problem? According to Wikipedia, "Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, It is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success! The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop, which is not less than a panacea for all those companies working with BIG DATA in a variety of applications and has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data. - - - - - - - - - - - - - - Some of the top companies using Hadoop: The importance of Hadoop is evident from the fact that there are many global MNCs that are using Hadoop and consider it as an integral part of their functioning, such as companies like Yahoo and Facebook! On February 19, 2008, Yahoo! Inc. established the world's largest Hadoop production application. The Yahoo! Search Webmap is a Hadoop application that runs on over 10,000 core Linux cluster and generates data that is now widely used in every Yahoo! Web search query. Opportunities for Hadoopers! Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself! Happy Hadooping! Please write back to us at [email protected] or call us at +91-8880862004 for more information. http://www.edureka.co/big-data-and-hadoop
Views: 14274 edureka!
ABCs of Database Mining
 
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Staying in touch with your database is HUGE for maintaining your referral business. Learn this database mining tip that's as easy as A-B-C.
Views: 22 Lyndsey Coates
What is partition and why use it? Creating a Partition, Partitioning method
 
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What is partition and why use it? Creating a Partition, Partitioning method - ETIT 427 - ADBA - IP University Syllabus For Students of B.Tech, B.E, MCA, BCA, B.Sc., M.Sc., Courses - As Per IP University Syllabus and Other Engineering Courses
Mining Influencers from your Email Database
 
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Challenge: Mining Influencers from your Email database. Hypothesis: Email IDs that have a unique domain name are more likely to be from a brand/influencer. Solution: Find all domain names that occur only once in the Email database. GoogleSheet: https://docs.google.com/spreadsheets/d/198PBObHFxW0N50RpW67qTbcXntpmuncdsevJiYHPXeo/edit?usp=sharing
Views: 18 Amit Sarda
DATABASE LECTURE 9(DATA WAREHOUSE,DATA MINING,)
 
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DATABASE LECTURE 9(DATA WAREHOUSE,DATA MINING,)
Views: 324 LectureDekho.com
Database Clustering Tutorial 1 - Intro to Database Clustering
 
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Read the Blog: https://www.calebcurry.com/blogs/database-clustering/intro-to-database-clustering Get ClusterControl: http://bit.ly/ClusterControl In this video we are going to be discussing database clustering and how to manage database clusters with ClusterControl. Database clustering is when you have multiple computers working together that are all used to store your data. There are four primary reasons you should consider clustering. Data redundancy, Load balancing (scalability) High availability. Monitoring and Automation That is an intro to a few of the reasons having a cluster is a good idea. Obviously, not everyone needs a cluster. A cluster can be overkill. But the best way to know is to learn more about them, so I’ll see you in the next video! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://bit.ly/JoinCCNewsletter Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 20170 Caleb Curry
HEURISTICS RULES BASED MINING HIGH UTILITY ITEMSETS FROM TRANSACTIONAL DATABASE
 
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Mining frequent itemsets is an active area in data mining that aims at searching interesting relationships between items in databases. It can be used to address to a wide variety of problems such as discovering association rules, sequential patterns, correlations and much more. A transactional database is a data set of transactions, each composed of a set of items, called an itemset (frequently occurring in a database). Existing methods often generate a huge set of potential high utility item sets and their mining performance is degraded consequently. There is a lacking of mining performance with these huge number of potential high utility itemsets; higher processing Time too. Two novel algorithms as well as a compact data structure for efficiently discovering high utility itemsets are proposed. High utility itemsets is maintained in a tree-based data structure named UP-Tree (Utility Pattern Tree). Implementing mining process through Discarding Local Unpromising Items and Decreasing Local Node Utilities strategies. An experimental result predicts that not only reduces the number of candidates effectively but also outperforms other algorithms DIVYA BHARATHY.M (VMC 791) Department of Master of Computer Applications Veltech Multi Tech Engg College.
Views: 276 Divya Bharathy
Introduction to Data warehouse  and difference between Database and Data warehouse
 
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Introduction to Data warehouse and difference between Database and Data warehouse more videos In computer science, ACID (Atomicity, Consistency, Isolation, Durability) is a set of properties that guarantee that database transactions are processed reliably. In the context of databases, a single logical operation on the data is called a transaction. http://atozknowledge.com/ Technology in Tamil
Views: 18826 atoz knowledge
Selective Database Projections Based Approach for Mining High Utility Itemsets- IEEE PROJECTS 2018
 
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