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Introduction to Datawarehouse in hindi | Data warehouse and data mining Lectures
 
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#datawarehouse #datamining #lastmomenttuitions 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://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [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: 298088 Last moment tuitions
What is Data Warehousing & Data Mining ? Urdu / Hindi
 
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This Video Give The Basic Concept & Basic Logic's of What is Data Warehousing & Data Mining ? Urdu / Hindi ZPZ Education Channel Link: www.youtube.com/channel/UCwFzeQDf9cGm_ZeTXV_t5SA
Views: 865 ZPZ Education
Introduction to data mining and architecture  in hindi
 
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#datamining #datawarehouse #lastmomenttuitions 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://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [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: 227369 Last moment tuitions
1 - Introduction to Data warehouse and Data warehousing
 
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Short Introduction Video to understand, What is Data warehouse and Data warehousing? How it is different from Database? It also talks about properties of Data warehouse which are Subject Oriented, Integrated, Time Variant, Non Volatile ETL Tools: Talend Open Studio, Jaspersoft ETL, Ab initio, Informatica, Datastage, Clover ETL, Pentaho ETL, Kettle. #datawarehouse #ETL #DWH Business Intelligence tools: Oracle BI, Microsoft BI suite, Tableau, Qlik, Jaspersoft BI, Pentabo BI, Miscrostrategy, Tibco For more details visit: http://www.vikramtakkar.com/2015/08/what-is-datawarehouse-and.html Datawarehouse Playlist: https://www.youtube.com/playlist?list=PLJ4bGndMaa8FV7nrvKXeHCLRMmIXVCyOG
Views: 117289 Vikram Takkar
What is DATA WAREHOUSE? What does DATA WAREHOUSE mean? DATA WAREHOUSE meaning & explanation
 
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What is DATA WAREHOUSE? What does DATA WAREHOUSE mean? DATA WAREHOUSE meaning - DATA WAREHOUSE definition - DATA WAREHOUSE 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 In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place and are used for creating analytical reports for knowledge workers throughout the enterprise. The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the DW for reporting. The typical Extract, transform, load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data. The main source of the data is cleansed, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata. A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to: Integrate data from multiple sources into a single database and data model. Mere congregation of data to single database so a single query engine can be used to present data is an ODS. Mitigate the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, long running, analysis queries in transaction processing databases. Maintain data history, even if the source transaction systems do not. Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger. Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data. Present the organization's information consistently. Provide a single common data model for all data of interest regardless of the data's source. Restructure the data so that it makes sense to the business users. Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems. Add value to operational business applications, notably customer relationship management (CRM) systems. Make decision–support queries easier to write. Optimized data warehouse architectures allow data scientists to organize and disambiguate repetitive data. The environment for data warehouses and marts includes the following: Source systems that provide data to the warehouse or mart; Data integration technology and processes that are needed to prepare the data for use; Different architectures for storing data in an organization's data warehouse or data marts; Different tools and applications for the variety of users; Metadata, data quality, and governance processes must be in place to ensure that the warehouse or mart meets its purposes. In regards to source systems listed above, Rainer states, "A common source for the data in data warehouses is the company's operational databases, which can be relational databases"....
Views: 1582 The Audiopedia
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
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Warehousing | Edureka
 
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** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ** This Data Warehouse Tutorial For Beginners will give you an introduction to data warehousing and business intelligence. You will be able to understand basic data warehouse concepts with examples. The following topics have been covered in this tutorial: 1. What Is The Need For BI? 2. What Is Data Warehousing? 3. Key Terminologies Related To DWH Architecture: a. OLTP Vs OLAP b. ETL c. Data Mart d. Metadata 4. DWH Architecture 5. Demo: Creating A DWH - - - - - - - - - - - - - - Check our complete Data Warehousing & Business Intelligence playlist here: https://goo.gl/DZEuZt. #DataWarehousing #DataWarehouseTutorial #DataWarehouseTraining Subscribe to our channel to get video updates. Hit the subscribe button above. - - - - - - - - - - - - - - How it Works? 1. This is a 5 Week Instructor led Online Course, 25 hours of assignment and 10 hours of project work 2. 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. 3. 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: Edureka's Data Warehousing and Business Intelligence Course, will introduce participants to create and work with leading ETL & BI tools like: 1. Talend 5.x to create, execute, monitor and schedule ETL processes. It will cover concepts around Data Replication, Migration and Integration Operations 2. Tableau 9.x for data visualization to see how easy and reliable data visualization can become for representation with dashboards 3. Data Modeling tool ERwin r9 to create a Data Warehouse or Data Mart - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Data warehousing enthusiasts 2. Analytics Managers 3. Data Modelers 4. ETL Developers and BI Developers - - - - - - - - - - - - - - Why learn Data Warehousing and Business Intelligence? All the successful companies have been investing large sums of money in business intelligence and data warehousing tools and technologies. Up-to-date, accurate and integrated information about their supply chain, products and customers are critical for their success. With the advent of Mobile, Social and Cloud platform, today's business intelligence tools have evolved and can be categorized into five areas, including databases, extraction transformation and load (ETL) tools, data quality tools, reporting tools and statistical analysis tools. This course will provide a strong foundation around Data Warehousing and Business Intelligence fundamentals and sophisticated tools like Talend, Tableau and ERwin. - - - - - - - - - - - - - - For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka - - - - - - - - - - - - - - Customer Review: Kanishk says, "Underwent Mastering in DW-BI Course. The training material and trainer are up to the mark to get yourself acquainted to the new technology. Very helpful support service from Edureka."
Views: 254117 edureka!
Data Warehousing and Data Mining
 
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Research Project Presentation
Views: 16128 Thanos Caras
Last Minute Tutorials | Data mining | Introduction | Examples
 
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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 48907 Last Minute Tutorials
Introduction to Data Warehousing and Data Mining
 
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The term "Data Warehousing" is now commonly used in industry. It refers to a kind of heterogeneous information system -- one in which the focus is on gathering together the data from the different operational databases within an organization, and making it available for decision making purposes. This unit explains the differences between the type of information one can obtain from a data warehouse compared with a traditional database. We looked at the problems and steps involved in building a data warehouse, and examine some of the techniques that have been proposed for constructing data warehouses. (Chapter 15)
Views: 45355 vcilt14
Data Mining
 
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Views: 27497 Kiki Zachary
Data Mining Functionalities
 
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data warehousing and data mining || data mining funtionalities
Views: 7392 naga mounika Reddy
Data Warehousing and Data Mining
 
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A short video to describe data warehousing and data mining
Views: 4695 Mcdev777
What is OLAP?
 
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This video explores some of OLAP's history, and where this solution might be applicable. We also look at situations where OLAP might not be a fit. Additionally, we investigate an alternative/complement called a Relational Dimensional Model. To Talk with a Specialist go to: http://www.intricity.com/intricity101/
Views: 375619 Intricity101
INTRODUCTION TO DATA MINING
 
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INTRODUCTION TO DATA MINING
Views: 18093 LearnEveryone
Data Warehousing - An Overview
 
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This video aims to give an overview of data warehousing. It does not delve into the detail - that is for later videos. Here, you will meet Bill Inmon and Ralph Kimball who created the concept and the commercialised it respectively. You then get a quick tour of the basic concepts used in data warehousing.
Views: 351065 Andy Wicks
L1: Data Warehousing and Data Mining |Introduction to Warehousing| What is Mining| Tutorial in Hindi
 
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Join My official Whatsapp group by following link https://chat.whatsapp.com/F9XFi6QYFYOGA9JGw4gc1o L1: Data Warehousing and Data Mining | What is Warehousing| What is Mining| Tutorial in Hindi Namaskar, In the Today's lecture i will cover Introduction to Data Warehousing and Data Mining of subject Data Warehousing and Data Mining which is one of the important subject of computer science and engineering Syllabus Unit1: Data Warehousing: Overview, Definition, Data Warehousing Components, Building a Data Warehouse, Warehouse Database, Mapping the Data Warehouse to a Multiprocessor Architecture, Difference between Database System and Data Warehouse, Multi Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept. Unit 2: Data Warehouse Process and Technology: Warehousing Strategy, Warehouse /management and Support Processes, Warehouse Planning and Implementation, Hardware and Operating Systems for Data Warehousing, Client/Server Computing Model & Data Warehousing. Parallel Processors & Cluster Systems, Distributed DBMS implementations, Warehousing Software, Warehouse Schema Design. Unit 3: Data Mining: Overview, Motivation, Definition & Functionalities, Data Processing, Form of Data Pre-processing, Data Cleaning: Missing Values, Noisy Data, (Binning, Clustering, Regression, Computer and Human inspection), Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Discretization and Concept hierarchy generation, Decision Tree. Unit 4: Classification: Definition, Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining Class comparisons, Statistical measures in large Databases, Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree-Based Algorithms. Clustering: Introduction, Similarity and Distance Measures, Hierarchical and Partitional Algorithms. Hierarchical Clustering- CURE and Chameleon. Density Based Methods-DBSCAN, OPTICS. Grid Based Methods- STING, CLIQUE. Model Based Method –Statistical Approach, Association rules: Introduction, Large Item sets, Basic Algorithms, Parallel and Distributed Algorithms, Neural Network approach. Unit 5: Data Visualization and Overall Perspective: Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAP Servers, ROLAP, MOLAP, HOLAP, Data Mining interface, Security, Backup and Recovery, Tuning Data Warehouse, Testing Data Warehouse. Warehousing applications and Recent Trends: Types of Warehousing Applications, Web Mining, Spatial Mining and Temporal Mining I am Sandeep Vishwakarma (www.universityacademy.in) from Raj Kumar Goel Institute of Technology Ghaziabad. I have started a YouTube Channel Namely “University Academy” Teaching Training and Informative. On This channel am providing following services. 1 . Teaching: Video Lecture of B.Tech./ M.Tech. Technical Subject who provide you deep knowledge of particular subject. Compiler Design: https://www.youtube.com/playlist?list=PL-JvKqQx2Ate5DWhppx-MUOtGNA4S3spT Principle of Programming Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdIkEFDrqsHyKWzb5PWniI1 Theory of Automata and Formal Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdhlS7j6jFoEnxmUEEsH9KH 2. Training: Video Playlist of Some software course like Android, Hadoop, Big Data, IoT, R programming, Python, C programming, Java etc. Android App Development: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdBj8aS-3WCVgfQ3LJFiqIr 3. Informative: On this Section we provide video on deep knowledge of upcoming technology, Innovation, tech news and other informative. Tech News: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdFG5kMueyK5DZvGzG615ks Other: https://www.youtube.com/playlist?list=PL-JvKqQx2AtfQWfBddeH_zVp2fK4V5orf Download You Can Download All Video Lecture, Lecture Notes, Lab Manuals and Many More from my Website: http://www.universityacademy.in/ Regards University Academy UniversityAcademy Website: http://www.universityacademy.in/ YouTube: https://www.youtube.com/c/UniversityAcademy Facebook: https://www.facebook.com/UniversityAcademyOfficial Twitter https://twitter.com/UniAcadofficial Instagram https://www.instagram.com/universityacademyofficial Google+: https://plus.google.com/+UniversityAcademy
Views: 1351 University Academy
Data Warehouse Architecture In Data Mining And Warehousing Explained In Hindi
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 21638 5 Minutes Engineering
Data Ware House & Mining 1 what is data ware house ? |introduction| lecture|tutorial|sanjaypathakjec
 
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This video describe what is data ware house? or introduction to data warehouse Data ware house was first coined by bill inmon in 1990 According to him data warehouse is subject oriented, integrated , time variant and non volatile collection of data. data ware house data helps analysts to take informed decisions in an organization. data warehouse provides generalized and combined data in multidimensional view. data warehouse also provides us online analytical processing (olap) , helps in interactive and effective analysis. A data warehouse is kept seprate from organization operational database. in data warehouse there is no frequent updating of data in warehouse. data warehouse helps executives to use data to take strategic decisions this video complete describe what is data warehouse or data warehouse introduction, this is data warehouse lecture, data warehouse tutorial in hindi
Views: 41339 Sanjay Pathak
Data Warehouse in Telugu
 
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ETL and SSIS : https://youtu.be/kWxv9E7g-JM Business Intelligence is a technology based on customer and profit-oriented models that reduce operating costs and provide increased profitability by improving productivity, sales, service and helps to make decision-making capabilities at no time. Business Intelligence Models are based on multidimensional analysis and key performance indicators (KPI) of an enterprise "A data warehouse is a subject oriented, integrated, time variant, a nonvolatile collection of data in support of management's decision-making process". In addition to a relational/multidimensional database, a data warehouse environment often consists of an ETL solution, an OLAP engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users. Data Mart - Datamart is a subset of data warehouse and it supports a particular region, business unit or business function. ETL (Extract, Transform and Load) is a process in data warehousing responsible for pulling data out of the source systems and placing it into a data warehouse. ETL and SSIS : https://youtu.be/kWxv9E7g-JM The Online Analytical Processing is designed to answer multi-dimensional queries, whereas the Online Transaction Processing is designed to facilitate and manage the usual business applications. While OLAP is customer-oriented, OLTP is market-oriented. Both OLTP and OLAP are two of the common systems for the management of data. The OLTP is a category of systems that manage transaction processing. OLAP is a compilation of ways to query multi-dimensional databases Tools for Data warehouse: Amazon Redshift Oracle 12c MSBI Informatica Data Validation. QuerySurge. ICEDQ. Datagaps ETL Validator. QualiDI. Talend Open Studio for Data Integration. Codoid's ETL Testing Services. Data Centric Testing.
Views: 6129 Learners Page
Need for DWH | Data Warehouse Tutorial | Data Warehousing Concepts | Mr.Vijay Kumar
 
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Need for DWH | Data Warehouse Tutorial | Data Warehouse Concepts | Mr.Vijay Kumar ►For Registration : https://goo.gl/r6kJbB ►Call: +91-8179191999 ► Visit Our Website: http://nareshit.in/ http://nareshit.com/ ► About NareshIT: "Naresh IT is the Best Software Training Institute for Hadoop, Salesforce, AWS, DevOps, Sprak, Data Science, Python, Tableau, RPA ,Java, C#.NET, ASP.NET, Oracle, Testing Tools, Silver light, Linq, SQL Server, Selenium, Android, iPhone, C Language, C++, PHP and Digital Marketing in Hyderabad, Chennai and Vijayawada, India which provides online and classroom training classes" ►For Registration : https://goo.gl/r6kJbB ►Call: India- 8179191999, USA- 404-232-9879 Email: [email protected] ►Our Online Training Features: 1.Training with Real-Time Experts 2.Industry Specific Scenario’s 3.Flexible Timings 4.Soft Copy of Material 5.Share Video's of each and every session. Check The Below Links: ►For Course Reg : https://goo.gl/r6kJbB ► Subscribe to Our Channel: https://goo.gl/q9ozyG ► Circle us on G+: https://plus.google.com/NareshIT ► Like us on Facebook: https://www.facebook.com/NareshIT ► Follow us on Twitter: https://twitter.com/nareshitech ► Visit Our Website: http://nareshit.in/ http://nareshit.com/
Views: 31604 Naresh i Technologies
Data mining
 
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Introduction to data mining and its process
Views: 3605 Devi Priya
Why should data warehouse be denormalized
 
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https://www.udemy.com/t-sql-training/?couponCode=TSQL_10 Hello Friends I have launched a course on T-SQL .Name of the course is T-SQL Training with Real World Scenarios:Tricks of the Trade I believe it has a lot of good contents which will help a lot to a Microsoft SQL Developer . Please check out the following link to check the course contents and if you need this course then the following link also has discounted coupon . https://www.udemy.com/t-sql-training/?couponCode=TSQL_10
Views: 1939 Ellarr Admin
DATA WAREHOUSING Basics
 
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The first video in the series on data warehousing.
Views: 157255 eDewcate
What is Data Warehouse (Hindi)
 
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Introduction to Data Warehouse
2 - Data warehouse Architecture  Overview
 
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A quick video to understand standard Datawarehouse architecture. It consists of following layers 1. Data Source layer 2. ETL 3. Staging Area 4. Datawarehouse - Metadata, Summary and Raw Data 5. OLAP, Reporting and Data Mining Data warehouse is populated from multiple sources for an organisation. All these source system comes under Data Source layer. Some of the source systems are listed below: 1. Operations Systems -- such as Sales, HR, Inventory relational database. 2. ERP (SAP) and CRM (SalesForce.com) Systems. 3. Web server logs and Internal market research data. 4. Third-party data - such as census data, demographics data, or survey data. ETL Tools: Talend Open Studio, Jaspersoft ETL, Ab initio, Informatica, Datastage, Clover ETL, Pentaho ETL, Kettle For more details visit http://www.vikramtakkar.com/2015/09/data-warehouse-architecture-overview.html Datawarehouse Playlist: https://www.youtube.com/playlist?list=PLJ4bGndMaa8FV7nrvKXeHCLRMmIXVCyOG
Views: 91678 Vikram Takkar
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: 5267 SlideTalk
Introduction to Data Mining-:Data Warehouse Schema
 
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You can find the entire course here: https://goo.gl/rM2W1E You can find all the courses by Hashleen Kaur here: https://goo.gl/SPmZoX Introduction to Data Mining| | Lesson- Data Cube & its Operations In this lesson, Hashleen K has discussed about Data Warehouse Schema. The video includes the meaning of schema and it's tables. Download the Unacademy Learning App from the Google Play Store here:- https://goo.gl/02OhYI Download the Unacademy Educator app from the Google Play Store here: https://goo.gl/H4LGHE Visit Our Facebook Group on Engineering Curriculum here: https://goo.gl/5EqfqS
Datamarts in DWH | Data Warehouse Tutorials | Data Warehousing Concepts | Mr.Vijay Kumar
 
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Datamarts in DWH | Data Warehouse Tutorial | Data Warehousing Concepts | Mr.Vijay Kumar | Understanding Data Mart ►For Registration : https://goo.gl/r6kJbB ►Call: +91-8179191999 ► Visit Our Website: http://nareshit.in/ http://nareshit.com/ ► About NareshIT: "Naresh IT is the Best Software Training Institute for Hadoop, Salesforce, AWS, DevOps, Sprak, Data Science, Python, Tableau, RPA ,Java, C#.NET, ASP.NET, Oracle, Testing Tools, Silver light, Linq, SQL Server, Selenium, Android, iPhone, C Language, C++, PHP and Digital Marketing in Hyderabad, Chennai and Vijayawada, India which provides online and classroom training classes" ►For Registration : https://goo.gl/r6kJbB ►Call: India- 8179191999, USA- 404-232-9879 Email: [email protected] ►Our Online Training Features: 1.Training with Real-Time Experts 2.Industry Specific Scenario’s 3.Flexible Timings 4.Soft Copy of Material 5.Share Video's of each and every session. Check The Below Links: ►For Course Reg : https://goo.gl/r6kJbB ► Subscribe to Our Channel: https://goo.gl/q9ozyG ► Circle us on G+: https://plus.google.com/NareshIT ► Like us on Facebook: https://www.facebook.com/NareshIT ► Follow us on Twitter: https://twitter.com/nareshitech ► Visit Our Website: http://nareshit.in/ http://nareshit.com/
Views: 20315 Naresh i Technologies
L2: Data Warehousing and Data Mining |Enterprise data Warehousing|Data mart|Warehousing Terminology
 
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Join My official Whatsapp group by following link https://chat.whatsapp.com/F9XFi6QYFYOGA9JGw4gc1o L2: Data Warehousing and Data Mining |Enterprise data Warehousing|Data mart|Warehousing Terminology Namaskar, In the Today's lecture i will cover Introduction to Data Warehousing and Data Mining of subject Data Warehousing and Data Mining which is one of the important subject of computer science and engineering Syllabus Unit1: Data Warehousing: Overview, Definition, Data Warehousing Components, Building a Data Warehouse, Warehouse Database, Mapping the Data Warehouse to a Multiprocessor Architecture, Difference between Database System and Data Warehouse, Multi Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept. Unit 2: Data Warehouse Process and Technology: Warehousing Strategy, Warehouse /management and Support Processes, Warehouse Planning and Implementation, Hardware and Operating Systems for Data Warehousing, Client/Server Computing Model & Data Warehousing. Parallel Processors & Cluster Systems, Distributed DBMS implementations, Warehousing Software, Warehouse Schema Design. Unit 3: Data Mining: Overview, Motivation, Definition & Functionalities, Data Processing, Form of Data Pre-processing, Data Cleaning: Missing Values, Noisy Data, (Binning, Clustering, Regression, Computer and Human inspection), Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Discretization and Concept hierarchy generation, Decision Tree. Unit 4: Classification: Definition, Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining Class comparisons, Statistical measures in large Databases, Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree-Based Algorithms. Clustering: Introduction, Similarity and Distance Measures, Hierarchical and Partitional Algorithms. Hierarchical Clustering- CURE and Chameleon. Density Based Methods-DBSCAN, OPTICS. Grid Based Methods- STING, CLIQUE. Model Based Method –Statistical Approach, Association rules: Introduction, Large Item sets, Basic Algorithms, Parallel and Distributed Algorithms, Neural Network approach. Unit 5: Data Visualization and Overall Perspective: Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAP Servers, ROLAP, MOLAP, HOLAP, Data Mining interface, Security, Backup and Recovery, Tuning Data Warehouse, Testing Data Warehouse. Warehousing applications and Recent Trends: Types of Warehousing Applications, Web Mining, Spatial Mining and Temporal Mining I am Sandeep Vishwakarma (www.universityacademy.in) from Raj Kumar Goel Institute of Technology Ghaziabad. I have started a YouTube Channel Namely “University Academy” Teaching Training and Informative. On This channel am providing following services. 1 . Teaching: Video Lecture of B.Tech./ M.Tech. Technical Subject who provide you deep knowledge of particular subject. Compiler Design: https://www.youtube.com/playlist?list=PL-JvKqQx2Ate5DWhppx-MUOtGNA4S3spT Principle of Programming Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdIkEFDrqsHyKWzb5PWniI1 Theory of Automata and Formal Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdhlS7j6jFoEnxmUEEsH9KH 2. Training: Video Playlist of Some software course like Android, Hadoop, Big Data, IoT, R programming, Python, C programming, Java etc. Android App Development: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdBj8aS-3WCVgfQ3LJFiqIr 3. Informative: On this Section we provide video on deep knowledge of upcoming technology, Innovation, tech news and other informative. Tech News: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdFG5kMueyK5DZvGzG615ks Other: https://www.youtube.com/playlist?list=PL-JvKqQx2AtfQWfBddeH_zVp2fK4V5orf Download You Can Download All Video Lecture, Lecture Notes, Lab Manuals and Many More from my Website: http://www.universityacademy.in/ Regards University Academy UniversityAcademy Website: http://www.universityacademy.in/ YouTube: https://www.youtube.com/c/UniversityAcademy Facebook: https://www.facebook.com/UniversityAcademyOfficial Twitter https://twitter.com/UniAcadofficial Instagram https://www.instagram.com/universityacademyofficial Google+: https://plus.google.com/+UniversityAcademy
Views: 700 University Academy
KDD ( knowledge data discovery )  in data mining in hindi
 
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#kdd #datawarehouse #datamining #lastmomenttuitions 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://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [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: 76043 Last moment tuitions
L3: Data Warehousing and Data Mining |Characteristics | Advantage | Evolution of Database Technology
 
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Join My official Whatsapp group by following link https://chat.whatsapp.com/F9XFi6QYFYOGA9JGw4gc1o L2: Data Warehousing and Data Mining |Characteristics | Advantage |Evolution of Database Technology Namaskar, In Today's lecture, i will cover Characteristics, Advantage, Evaluation of Database Technology of subject Data Warehousing and Data Mining which is one of the important subjects of computer science and engineering I am Sandeep Vishwakarma (www.universityacademy.in) from Raj Kumar Goel Institute of Technology Ghaziabad. I have started a YouTube Channel Namely “University Academy” Teaching Training and Informative. On This channel am providing following services. 1 . Teaching: Video Lecture of B.Tech./ M.Tech. Technical Subject who provide you deep knowledge of particular subject. Compiler Design: https://www.youtube.com/playlist?list=PL-JvKqQx2Ate5DWhppx-MUOtGNA4S3spT Principle of Programming Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdIkEFDrqsHyKWzb5PWniI1 Theory of Automata and Formal Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdhlS7j6jFoEnxmUEEsH9KH 2. Training: Video Playlist of Some software course like Android, Hadoop, Big Data, IoT, R programming, Python, C programming, Java etc. Android App Development: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdBj8aS-3WCVgfQ3LJFiqIr 3. Informative: On this Section we provide video on deep knowledge of upcoming technology, Innovation, tech news and other informative. Tech News: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdFG5kMueyK5DZvGzG615ks Other: https://www.youtube.com/playlist?list=PL-JvKqQx2AtfQWfBddeH_zVp2fK4V5orf Download You Can Download All Video Lecture, Lecture Notes, Lab Manuals and Many More from my Website: http://www.universityacademy.in/ Regards University Academy UniversityAcademy Website: http://www.universityacademy.in/ YouTube: https://www.youtube.com/c/UniversityAcademy Facebook: https://www.facebook.com/UniversityAcademyOfficial Twitter https://twitter.com/UniAcadofficial Instagram https://www.instagram.com/universityacademyofficial Google+: https://plus.google.com/+UniversityAcademy
Views: 291 University Academy
Part 1.3 | Olap vs Oltp in hindi | online analytical processing online transaction processing
 
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• Counselling Guruji is our latest product & a well-structured program that answers all your queries related to Career/GATE/NET/PSU’s/Private Sector etc. You can register for the program at: https://goo.gl/forms/ZmLB2XwoCIKppDh92 You can check out the brochure at: https://www.google.com/url?q=http://www.knowledgegate.in/guruji/counselling_guruji_brochure.pdf&sa=D&ust=1553069285684000&usg=AFQjCNFaTk4Pnid0XYyZoDTlAtDPUGcxNA • Link for the complete playlist of DBMS is: https://www.youtube.com/playlist?list=PLmXKhU9FNesR1rSES7oLdJaNFgmuj0SYV • Links for the books that we recommend for DBMS are: 1.Database System Concepts (Writer: Avi Silberschatz · Henry F.Korth · S. Sudarshan) (Publisher: McGraw Hill Education) https://amzn.to/2HoR6ta 2.Fundamentals of database systems (Writer:Ramez Elmsari,Shamkant B.Navathe) https://amzn.to/2EYEUh2 3.Database Management Systems (Writer: Raghu Ramkrishnan, JohannesGehrke) https://amzn.to/2EZGYph 4.Introduction to Database Management (Writer: Mark L. Gillenson, Paulraj Ponniah, Alex Kriegel, Boris M. Trukhnov, Allen G. Taylor, and Gavin Powell with Frank Miller.(Publisher: Wiley Pathways) https://amzn.to/2F0e20w • Check out our website http://www.knowledgegate.in/ • Please spare some time and fill this form so that we can know about you and what you think about us: https://goo.gl/forms/b5ffxRyEAsaoUatx2 • Your review/recommendation and some words can help validating our quality of content and work so Please do the following: - 1) Give us a 5-star review with comment on Google https://goo.gl/maps/sLgzMX5oUZ82 2) Follow our Facebook page and give us a 5-star review with comments https://www.facebook.com/pg/knowledgegate.in/reviews 3) Follow us on Instagram https://www.instagram.com/mail.knowledgegate/ 4) Follow us on Quora https://www.quora.com/profile/Sanchit-Jain-307 • Links for Hindi playlists of other Subjects are: TOC: https://www.youtube.com/playlist?list=PLmXKhU9FNesSdCsn6YQqu9DmXRMsYdZ2T OS: https://www.youtube.com/playlist?list=PLmXKhU9FNesSFvj6gASuWmQd23Ul5omtD Digital Electronics: https://www.youtube.com/playlist?list=PLmXKhU9FNesSfX1PVt4VGm-wbIKfemUWK Discrete Mathematics: Relations:https://www.youtube.com/playlist?list=PLmXKhU9FNesTpQNP_OpXN7WaPwGx7NWsq Graph Theory: https://www.youtube.com/playlist?list=PLmXKhU9FNesS7GpOddHDX3ZCl86_cwcIn Group Theory: https://www.youtube.com/playlist?list=PLmXKhU9FNesQrSgLxm6zx3XxH_M_8n3LA Proposition:https://www.youtube.com/playlist?list=PLmXKhU9FNesQxcibunbD82NTQMBKVUO1S Set Theory: https://www.youtube.com/playlist?list=PLmXKhU9FNesTSqP8hWDncxpCj8a4uzmu7 Data Structure: https://www.youtube.com/playlist?list=PLmXKhU9FNesRRy20Hjr2GuQ7Y6wevfsc5 Computer Networks: https://www.youtube.com/playlist?list=PLmXKhU9FNesSjFbXSZGF8JF_4LVwwofCd Algorithm: https://www.youtube.com/playlist?list=PLmXKhU9FNesQJ3rpOAFE6RTm-2u2diwKn • About this video: This video discuss two types of database OLTP and OLAP. What is online transaction processing and what is online analytical processing. Properties of OLTP, Properties on OLAP, type of data in olap, type of data in oltp, what is historical data, where OLTP is used, where OLAP is used, Why we need OLTP and OLAP, Difference between OLTP and OLAP in dbms is discussed. OLAP features: i)stores historical data ii)It is subject oriented iii) It is useful in decision making iv)Used by CEO’s, General managers, high officials of company OLTP features: i)stores current data ii) It is application oriented iii)It is useful for day to day operations iv)Used by clerks, managers and employees of company database tutorial in hindi, definition of data in dbms, components of dbms in hindi,difference between oltp and olap, types of data in dbms dbms tutorials for gate, dbms for beginners in hindi, 3-tier architecture of dbms in hindi,dbms for net,knowledge gate dbms,advantage of dbms, disadvantage of file in dbms, DBMS blueprint, DataBase Management system,database,DBMS, RDBMS, Relations, Table, Query, Normalization, Normal forms,Database design,Relational Model,Instance,Schema,Data Definition Language, SQL queries, ER Diagrams, Entity Relationship Model,Constraints,Entity,Attributes,Weak entity, Types of entity,DataBase design, database architecture, Degree of relation,Cardinality ratio,One to many relationship,Many to many relationships,Relational Algebra,Relational Calculus, Tuples, Natural Join, Join operations,Database Architecture,database Schema, Keys in DBMS, Primary keys, Candidate keys, Foreign keys,Data redundancy, Duplicacy in data, Data Inconsistency, Normalization, First Normal Form,Second Normal Form, third normal forms, Boye codd's normal form,1NF,2NF,3NF,BCNF, Normalization rules, Decomposition of relation, Functional Dependency,Partial Dependency, Multivalued dependency,Indexing,Hashing, B tree,B+ tree,Ordered Indexing,Select operation,Join operations, Natural joins, SQL commands,File structure in DBMS,Primary Indexing,Clustered Indexing,Concurrency control protocols,
Views: 77583 KNOWLEDGE GATE
OLAP vs OLTP in hindi
 
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#olap #oltp #datawarehouse #datamining #lastmomenttuitions 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://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [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: 115328 Last moment tuitions
Data warehouse Components – 3 Layer Architecture of Data Warehouse with Diagram(Hindi)
 
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Data warehouse Components – 3 Layer Architecture of Data Warehouse with Diagram(Hindi) Data Warehouse and Data Mining Lectures in Hindi
Enterprise Data Warehouse Whiteboard Explainer Video
 
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The video is an introduction to the function and organizational structure of the St. Michael's Hospital Enterprise Data Warehouse.
Views: 1111 LKS-CHART
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 113407 LearnEveryone
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 Cube and it's Operations || Data Warehousing ||
 
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This video is by Yash Bhardwaj.
Views: 5842 CS Nerds
3 tier architecture of Data warehouse
 
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Arshdeep Kaur ( Department of Computer Applications )
Views: 5756 Techbytes ACET 2018
What is Data Mining
 
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Small introduction on Data Mining - What is Data Mining Data Mining is a tool to Extract Hidden data.
▶ 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: 2093 BookBd
DW03 : Architecture of data warehouse ||  Data Mining and  Warehouse  || in Hindi
 
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Welcome to our new series i.e. the new subject Data mining And Data warehousing . In this video I will tell you about the Architecture of data warehouse #learnwithsahuji #dwhmwithsahuji
Views: 1075 Learn With Sahuji
Data Warehouse & Mining 16 Meta data
 
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data warehouse and mining tutorial meta data : meta data is data about data we can say that meta data is the summarized data that lead us to detailed data in data warehouse , meta data repository lies in the bottom tier of the data warehouse architecture
Views: 5833 Sanjay Pathak
Data Mining - Clustering
 
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What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 35061 Red Apple Tutorials
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: 12350 Introtuts
ETL ( Extract Transform Load )   process fully explained  in hindi | Datawarehouse
 
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#etl #datawarehouse #datamining #lastmomenttuitions 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://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [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: 64138 Last moment tuitions

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