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Data Integration
 
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In this video I will discuss about Data Integration in data mining. Data integration means combining data from multiple sources into a one single store. There are number of issues to consider during data integration like schema integration , object matching and entity identification problem.
What is DATA INTEGRATION? What does DATA INTEGRATION mean? DATA INTEGRATION meaning & explanation
 
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What is DATA INTEGRATION? What does DATA INTEGRATION mean? DATA INTEGRATION meaning - DATA INTEGRATION definition - DATA INTEGRATION 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 Data integration involves combining data residing in different sources and providing users with a unified view of them. This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data integration appears with increasing frequency as the volume and the need to share existing data explodes. It has become the focus of extensive theoretical work, and numerous open problems remain unsolved. Consider a web application where a user can query a variety of information about cities (such as crime statistics, weather, hotels, demographics, etc.). Traditionally, the information must be stored in a single database with a single schema. But any single enterprise would find information of this breadth somewhat difficult and expensive to collect. Even if the resources exist to gather the data, it would likely duplicate data in existing crime databases, weather websites, and census data. A data-integration solution may address this problem by considering these external resources as materialized views over a virtual mediated schema, resulting in "virtual data integration". This means application-developers construct a virtual schema—the mediated schema—to best model the kinds of answers their users want. Next, they design "wrappers" or adapters for each data source, such as the crime database and weather website. These adapters simply transform the local query results (those returned by the respective websites or databases) into an easily processed form for the data integration solution (see figure 2). When an application-user queries the mediated schema, the data-integration solution transforms this query into appropriate queries over the respective data sources. Finally, the virtual database combines the results of these queries into the answer to the user's query. This solution offers the convenience of adding new sources by simply constructing an adapter or an application software blade for them. It contrasts with ETL systems or with a single database solution, which require manual integration of entire new dataset into the system. The virtual ETL solutions leverage virtual mediated schema to implement data harmonization; whereby the data are copied from the designated "master" source to the defined targets, field by field. Advanced data virtualization is also built on the concept of object-oriented modeling in order to construct virtual mediated schema or virtual metadata repository, using hub and spoke architecture. Each data source is disparate and as such is not designed to support reliable joins between data sources. Therefore, data virtualization as well as data federation depends upon accidental data commonality to support combining data and information from disparate data sets. Because of this lack of data value commonality across data sources, the return set may be inaccurate, incomplete, and impossible to validate. One solution is to recast disparate databases to integrate these databases without the need for ETL. The recast databases support commonality constraints where referential integrity may be enforced between databases. The recast databases provide designed data access paths with data value commonality across databases. ....
Views: 2794 The Audiopedia
Data Mining & Business Intelligence | Tutorial #12 | Data Integration Process
 
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Order my books at 👉 http://www.tek97.com/ Lets see what is Data Integration and its issues in various spheres of Data Mining. Watch now ! لنرى ما هو التكامل البيانات وقضاياها في مختلف مجالات البيانات التنقيب. شاهد الآن ! Давайте посмотрим, что такое Интеграция данных и ее проблемы в различных областях интеллектуального анализа данных. Смотри ! Voyons ce qu'est l'intégration de données et ses problèmes dans diverses sphères de l'exploration de données. Regarde maintenant ! Sehen wir uns an, was Data Integration und ihre Probleme in verschiedenen Bereichen des Data Mining sind. Schau jetzt ! Veamos qué es la integración de datos y sus problemas en varias esferas de la minería de datos. Ver ahora ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 304 Ranji Raj
KDD ( knowledge data discovery )  in data mining in hindi
 
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Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 45686 Last moment tuitions
Data Mining & Business Intelligence | Tutorial #10 | Data Cleaning Process
 
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Order my books at 👉 http://www.tek97.com/ Handing Missing values and reducing the noisy data are the two techniques by which data cleaning initiates in data mining towards knowledge extraction. Watch now ! إن تسليم القيم المفقودة وتقليل البيانات المزعجة هما الأسلوبان اللذان يبدأ بهما تنظيف البيانات في استخراج البيانات نحو استخراج المعرفة. شاهد الآن ! La gestion des valeurs manquantes et la réduction des données bruitées sont les deux techniques par lesquelles le nettoyage des données débute dans l'extraction de données vers l'extraction des connaissances. Regarde maintenant ! La entrega de valores perdidos y la reducción de datos ruidosos son las dos técnicas mediante las cuales se inicia la limpieza de datos en la extracción de datos hacia la extracción de conocimiento. Ver ahora ! Übergeben Fehlende Werte und das Reduzieren der verrauschten Daten sind die beiden Techniken, mit denen die Datenbereinigung beim Data Mining zur Wissensextraktion initiiert wird. Schau jetzt ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 459 Ranji Raj
ETL ( Extract Transform Load )   process fully explained  in hindi | Datawarehouse
 
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Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 40927 Last moment tuitions
Data Mining & Business Intelligence | Tutorial #18 | Data Reduction - Numerosity Reduction
 
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Order my books at 👉 http://www.tek97.com/ Watch this video to understand what is Numerosity Reduction as part of Data Reduction in Data Mining. Watch now ! شاهد هذا الفيديو لفهم ما هو تخفيض العداد كجزء من الحد من البيانات في استخراج البيانات. شاهد الآن ! Sehen Sie sich dieses Video an, um zu verstehen, was Numerositätsreduktion als Teil der Datenreduktion im Data Mining darstellt. Schau jetzt ! Regardez cette vidéo pour comprendre ce qu'est la réduction de la numération dans le cadre de la réduction des données dans l'exploration de données. Regarde maintenant ! Посмотрите это видео, чтобы понять, что такое Numerosity Reduction как часть сокращения данных в интеллектуальном анализе данных. Смотри ! Mire este video para comprender qué es Numerosity Reduction como parte de la reducción de datos en Data Mining. Ver ahora ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 180 Ranji Raj
Using Pentaho Schema Workbench   Part 2  Using the Schema Editor
 
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Pentaho is a powerful Business Intelligence Suite offering many features: reporting, OLAP pivot tables, dashboarding and more. Report Designer: An advanced report creation tool. If you want to build a complex data-driven report, this is the right tool to use. Report Designer offers far more flexibility and functionality than the ad hoc reporting capabilities of the Pentaho User Console. • Design Studio: An Eclipse-based tool that enables you to hand-edit a report or analysis view xaction file. Generally, people use Design Studio to add modifications to an existing report that cannot be added with Report Designer. • Aggregation Designer: A graphical tool that helps improve Mondrian cube efficiency. • Metadata Editor: Enables you to add a custom metadata layer to an existing data source. Usually you would do this for a data source that you intend to use for reporting; it's not required, but it makes it easier for business users to parse the database when building a query. • Pentaho Data Integration: The Kettle extract, transform, and load (ETL) tool, which enables you to access and prepare data sources for analysis, data mining, or reporting. This is generally where you will start if you want to prepare data for analysis. • Schema Workbench: A graphical tool that helps you create ROLAP schemas for analysis. This is a required step in preparing data for analysis.
Data Mining & Business Intelligence | Tutorial #2 | Architecture Of Data Mining System
 
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Order my books at 👉 http://www.tek97.com/ Get familiar with the architecture possessed by Data Mining System. 1. Knowledge Base 2. Data Mining Engine 3. Pattern Evaluation Module 4. User Interface Machen Sie sich mit der Architektur von Data Mining System vertraut. 1. Wissensdatenbank 2. Data Mining-Engine 3. Musterbewertungsmodul 4. Benutzeroberfläche تعرف على الهندسة المعمارية التي يمتلكها نظام استخراج البيانات. 1. قاعدة المعرفة 2. محرك تعدين البيانات 3. وحدة نمط التقييم 4. واجهة المستخدم Familiarícese con la arquitectura que posee Data Mining System. 1. Base de conocimiento 2. Motor de minería de datos 3. Módulo de evaluación de patrones 4. Interfaz de usuario ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 420 Ranji Raj
DM7 Data Integration تكامل البيانات
 
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أ.محمود رفيق الفرا مختصر مساق التنقيب عن البيانات Data Mining
Views: 1278 MahmoudRFarra
Data Mining & Business Intelligence | Tutorial #16 | Data Reduction - Attribute Subset Selection
 
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Order my books at 👉 http://www.tek97.com/ Confused about Attribute Subset Selection in Data Mining well this video can help you . Watch now ! ارتباك حول اختيار مجموعة فرعية سمة في استخراج البيانات بشكل جيد هذا الفيديو يمكن أن تساعدك. شاهد الآن ! Confundido acerca de la selección de subconjuntos de atributos en Data Mining, este video puede ayudarlo. Ver ahora ! Смутно о выборе подмножества атрибутов в Data Mining, это видео может вам помочь. Смотри ! Verwirrt über Attribut-Subset-Auswahl in Data Mining gut dieses Video kann Ihnen helfen. Schau jetzt ! Confus à propos de la sélection de sous-ensemble d'attributs dans Data Mining, cette vidéo peut vous aider. Regarde maintenant ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 322 Ranji Raj
Data Mining Functionalities
 
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data warehousing and data mining || data mining funtionalities
Views: 102 naga mounika Reddy
Data Mining & Business Intelligence | Tutorial #4 | Forms Of Data Preprocessing
 
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Order my books at 👉 http://www.tek97.com/ Its important to preprocess the data before processing. Have a look at different forms of data preprocessing in data mining. Watch now ! Il est important de prétraiter les données avant le traitement. Jetez un oeil à différentes formes de prétraitement des données dans l'exploration de données. Regarde maintenant ! Es ist wichtig, die Daten vor der Verarbeitung vorzuverarbeiten. Sehen Sie sich verschiedene Formen der Datenvorverarbeitung im Data Mining an. Schau jetzt ! Es importante preprocesar los datos antes del procesamiento. Eche un vistazo a las diferentes formas de preprocesamiento de datos en la minería de datos. Ver ahora ! من المهم أن preprocess البيانات قبل المعالجة. إلقاء نظرة على أشكال مختلفة من معالجة البيانات في تعدين البيانات. شاهد الآن ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 327 Ranji Raj
حصريا - تطبيق كامل-  لـ Data Warehouse - مستودعات البيانات - من الألف إلى الياء-
 
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SQl Server 2012 Data Warehouse - Star schema – Dimensions - Fact Table ETL – Integration Service – SSIS – Data Flow Task - Slowly Changing Dimension – Integration Service Catalog – SSISDB Olap – Cube – Service analysis – SSAS Business intelligence – BI – SSRS – Report Builder – matrix - Pivot الدرس الثاني شرح : ماهي Data Warehouse - YouTube شرح Building Data Warehouse بالعربي - YouTube مستودع البيانات - ويكيبيديا، الموسوعة الحرة مستودعات البيانات Data warehousesو استخراج البيانات Data Mining ... مستودعات البيانات data warehouse - الرئيسية مستودعات البيانات Data Warehousing | php Tricks مستودعات البيانات / أحمد عبد الله - Cybrarians Journal data warehouse, data mining, OLAP, OLTP - قواعد بيانات Microsoft .. الملف الأول سؤال وإجابة: ما هو الفرق بين قواعد البيانات ومخازن البيانات ... Search Results سؤال وإجابة: ما هو الفرق بين قواعد البيانات ومخازن البيانات ... اريد معرفه معانى بعض المصطلحات الهامة في الداتابيز - منتدى ... Data Warehousing Concepts - Oracle Help Center استخدام تقنيات مستودعات البيانات في دعم القرارات ... ما هي أنواع الـindexes في data warehouse مع شرح بسيط لكل نوع مستودعات البيانات pdf داتا ويرهاوس
Views: 5870 Ahmed Elsayed
SSAS Star Schema to Cube in 14 Minutes
 
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Introduction to SQL Server Analysis Service using Visual Studio 2017 with AdventureWorks data warehouse. I will demonstrate how to deploy a cube and browse with SSDT browser and Excel. Prerequisites: SQL Server 2016, AdventureWorks DW 2016 (data warehouse), SQL Server Data Tools for Visual Studio 2017 (or SSDT 2017), Visual Studio 2017.
Views: 363 Benjamin Yu
Data Mining & Business Intelligence | Tutorial #15 | Data Reduction - Data Cube Aggregation
 
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Order my books at 👉 http://www.tek97.com/ See the detailed explanation about Data Cube Aggregation in Data Mining as part of Data Reduction step. Watch now ! انظر الشرح التفصيلي حول تجميع بيانات المكعب في Data Mining كجزء من خطوة تقليل البيانات. شاهد الآن ! Weitere Informationen zur Datenwürfelaggregation im Data Mining finden Sie im Abschnitt Datenreduktion. Schau jetzt ! Consultez l'explication détaillée sur l'agrégation de cubes de données dans l'exploration de données dans le cadre de l'étape de réduction des données. Regarde maintenant ! Подробное описание агрегации Data Cube в Data Mining см. В разделе шага Data Reduction. Смотри ! Consulte la explicación detallada sobre la agregación de cubo de datos en la minería de datos como parte del paso de reducción de datos. Ver ahora ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 377 Ranji Raj
Data Mining & Business Intelligence | Tutorial #20 | Data Reduction - Concept Hierarchy Generation
 
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Order my books at 👉 http://www.tek97.com/ Confused about what is Concept Hierarchy Generation in Data Mining in the context of Data Mining well this video is for you. Watch Now! ارتباك حول ما هو الجيل الهرمي مفهوم في "التعدين البيانات" في سياق "التنقيب عن البيانات" جيدا هذا الفيديو لك. شاهد الآن! Confus au sujet de ce qu'est la génération de hiérarchie de concept dans l'exploration de données dans le contexte de l'exploration de données bien cette vidéo est pour vous. Regarde maintenant! Verwirrt darüber, was ist Konzept-Hierarchie-Generierung in Data Mining im Zusammenhang mit Data Mining gut dieses Video ist für Sie. Schau jetzt! Confundido sobre qué es la Generación de Jerarquía Conceptual en Minería de Datos en el contexto de la Minería de Datos, este video es para usted. ¡Ver ahora! Смутно о том, что представляет собой генерация концепции иерархии в интеллектуальном анализе данных в контексте Data Mining, это видео для вас. Смотри! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 176 Ranji Raj
Introduction to Datawarehouse in hindi
 
10:36
Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 191276 Last moment tuitions
Data Integration UAS --- Tutorial Pentaho & Data Mining
 
36:52
I Kadek Putrawan-----13101167 Agus Putra Utama Yasa-----13101292 Ekky Agustana-----13101325
Views: 283 deks putrawan
Data Mining & Business Intelligence | Tutorial #11 | Smoothing by Binning (Solved Problem)
 
17:35
Order my books at 👉 http://www.tek97.com/ Worried about how to smooth data by binning with means, medians and mode well this video will solve your queries. Watch now ! قلق حول كيفية بسلاسة البيانات عن طريق binning بالوسائل ، والوسائط والوضع بشكل جيد هذا الفيديو سوف يحل استفساراتك. شاهد الآن ! Besorgt darüber, wie Daten durch Binning mit Mitteln, Medianen und Modus geglättet werden können, wird dieses Video Ihre Fragen lösen. Schau jetzt ! Preocupado acerca de cómo suavizar los datos al agrupar los medios, las medianas y el modo, este video resolverá sus dudas. Ver ahora ! Inquiet sur la façon de lisser les données en binning avec les moyens, les médianes et le mode bien cette vidéo va résoudre vos questions. Regarde maintenant ! Беспокоитесь о том, как сгладить данные путем биннинга средствами, медианами и режимами, это видео решает ваши запросы. Смотри ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 822 Ranji Raj
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Warehousing | Edureka
 
01:38:50
***** 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. - - - - - - - - - - - - - - Please write back to us at [email protected] or call us at +91 90660 20866 for more information. 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: 140769 edureka!
SQL Server 2008/R2 Analysis Services Data Mining: An Intro
 
12:20
This video is part of LearnItFirst's SQL Server 2008/R2 Analysis Services course. More information on this video and course is available here: http://www.learnitfirst.com/Course165 Now, Scott will talk about putting Analysis Services and data mining together. He explains that there are really two parts to Analysis Services: the multidimensional part, and the data mining part, although most people just work with the multidimensional part. You will see the many uses for data mining, and get more comfortable with the basics before learning some terms in videos later in the chapter. Highlights from this video: - What can you do with SSAS and data mining? - Do you need anything other than SSAS to do data mining? - The data mining language - Example data mining reports - How much work is data mining? and much more...
Views: 5214 LearnItFirst.com
The Model Driven Approach for Data Integration, by Stambia
 
07:24
This short video explains how Stambia can provide agility and flexibility through a Model Driven Approach of Data Integration.
Views: 319 Stambia
ODM 11g R2 - Creating ODM User & Repository
 
14:44
Oracle Data Miner 11g R2. This video shows you how to create data mining schemas and the repository in the database.
Views: 2194 Brendan Tierney
Datawarehousing : OLTP Vs OLAP
 
07:13
In this tutorial, you 'll learn what is the difference between OLAP and OLTP. 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. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons. The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc., shown in the figure to the right). The data may pass through an operational data store for additional operations 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.[4] This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, cataloged and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support.[5] 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.
Views: 32882 radhikaravikumar
Creating Cubes in Pentaho Schema Workbench
 
27:47
Pentaho is a powerful Business Intelligence Suite offering many features: reporting, OLAP pivot tables, dashboarding and more. Report Designer: An advanced report creation tool. If you want to build a complex data-driven report, this is the right tool to use. Report Designer offers far more flexibility and functionality than the ad hoc reporting capabilities of the Pentaho User Console. • Design Studio: An Eclipse-based tool that enables you to hand-edit a report or analysis view xaction file. Generally, people use Design Studio to add modifications to an existing report that cannot be added with Report Designer. • Aggregation Designer: A graphical tool that helps improve Mondrian cube efficiency. • Metadata Editor: Enables you to add a custom metadata layer to an existing data source. Usually you would do this for a data source that you intend to use for reporting; it's not required, but it makes it easier for business users to parse the database when building a query. • Pentaho Data Integration: The Kettle extract, transform, and load (ETL) tool, which enables you to access and prepare data sources for analysis, data mining, or reporting. This is generally where you will start if you want to prepare data for analysis. • Schema Workbench: A graphical tool that helps you create ROLAP schemas for analysis. This is a required step in preparing data for analysis.
Data Mining   KDD Process
 
03:08
KDD - knowledge discovery in Database. short introduction on Data cleaning,Data integration, Data selection,Data mining,pattern evaluation and knowledge representation.
Microsoft Visio Data Mining  Add in for Excel  Demonstration
 
09:03
The Visio Data Mining Add In for Excel provides a very flexible template to visualize & format data mining models produced in the Excel Data Mining Addin. This demonstration covers 4 topics - 1) what is the Microsoft Visio Data Mining Add in? 2) Why use it? 3) How to produce a data mining model in Visio using the Visio data mining wizard 4) additional 4) additional sources for information on data mining and analytics. For more videos on Data Mining using the Excel Data Mining Add In, and business intelligence Visit http://www.analyticsinaction.com/category/modeling/ I also have a comprehensive 60 minute T-SQL course available at Udemy : https://www.udemy.com/t-sql-for-data-analysts/?couponCode=ANALYTICS50%25OFF
Views: 3719 Steve Fox
DWH: Snowflake Vs Star Schema
 
03:50
In this tutorial, you 'll learn what is the difference between snowflake and star schema? 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. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons. The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc., shown in the figure to the right). The data may pass through an operational data store for additional operations 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.[4] This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, cataloged and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support.[5] 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.
Views: 12287 radhikaravikumar
Data Warehouse Interview Questions And Answers | Data Warehouse Tutorial | Edureka
 
01:22:36
***** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** This Data Warehouse Interview Questions And Answers tutorial will help you prepare for Data Warehouse interviews. Watch the entire video to get an idea of the 30 most frequently asked questions in Data Warehouse interviews. - - - - - - - - - - - - - - Check our complete Data Warehousing & Business Inelligence playlist here: https://goo.gl/DZEuZt. #DataWarehouseInterviewQuestions #DataWarehouseConcepts #DataWarehouseTutorial 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. - - - - - - - - - - - - - - Please write back to us at [email protected] or call us at +91 90660 20866 for more information. 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: 59020 edureka!
Data Integration Concepts
 
11:01
Task Workflow Mapping ETL Transformation
Views: 1640 hire thefire
Data Integration Made Easy: Automate Data Aggregation from Websites and Portals
 
01:25
Partners, suppliers, and portals are often managed manually, which is complex and labor-intensive. Quickly and easily automate manual data aggregation and business processes, enhance data-driven decisions, and free staff to focus on higher-value tasks, without relying completely on IT.
Views: 398 Kofax, Inc.
Data Preprocessing in Hindi |DMBI | Data Warehouse and Data mining Tutorials
 
05:28
Sample Notes : https://drive.google.com/file/d/19xmu... for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho : https://goo.gl/85HQGm for full notes please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made notes of data warehouse and data mining its only 200rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 5798 Last moment tuitions
Video Tutorial - Star Schema vs Starflake / Using Aggregate Tables
 
05:11
In this brief video tutorial, Doug Terbush from our customer education team describes the benefits of leveraging aggregate tables with MicroStrategy.
Introduction to Data Mining: Types of Sampling
 
05:15
In part four of data preprocessing, we discuss the different types of sampling such as random sampling, stratified sampling, sampling without and with replacement. And go into the issues of sample size. -- At Data Science Dojo, we're extremely passionate about data science. Our in-person data science training has been attended by more than 3200+ employees from over 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2mKLNu1 See what our past attendees are saying here: http://bit.ly/2ozVo4j -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 3641 Data Science Dojo
Using Pentaho Schema Workbench   Part 1  Establishing a Connection
 
01:40
Pentaho is a powerful Business Intelligence Suite offering many features: reporting, OLAP pivot tables, dashboarding and more. Report Designer: An advanced report creation tool. If you want to build a complex data-driven report, this is the right tool to use. Report Designer offers far more flexibility and functionality than the ad hoc reporting capabilities of the Pentaho User Console. • Design Studio: An Eclipse-based tool that enables you to hand-edit a report or analysis view xaction file. Generally, people use Design Studio to add modifications to an existing report that cannot be added with Report Designer. • Aggregation Designer: A graphical tool that helps improve Mondrian cube efficiency. • Metadata Editor: Enables you to add a custom metadata layer to an existing data source. Usually you would do this for a data source that you intend to use for reporting; it's not required, but it makes it easier for business users to parse the database when building a query. • Pentaho Data Integration: The Kettle extract, transform, and load (ETL) tool, which enables you to access and prepare data sources for analysis, data mining, or reporting. This is generally where you will start if you want to prepare data for analysis. • Schema Workbench: A graphical tool that helps you create ROLAP schemas for analysis. This is a required step in preparing data for analysis.
Pentaho 7 Tips | How To Access Microsoft SQL Server Database Using SQLJDBC Library
 
08:19
Pentaho 7.0 Tips: This video will show you on How To Access Microsoft SQL Server Database Using SQLJDBC Library. Tutorial Pentaho 7, Pentaho Tutorial, Pentaho Tutorial 7, Pentaho Data source, Create pentaho data source, Sqljdbc driver, Sql Server Connection, pentaholic, pentaho data integration, pentaho data integration tutorial, pentaho kettle, pentatonix, pentagon, pentaho report designer tutorial, pentaho tutorial, pentaho kettle tutorial for beginners, pentaho etl, pentaho, pentaho tutorial for beginners, pentaho installation on windows, pentaho reporting, pentaho report designer, pentaho architecture, pentaho analytics, pentaho aggregation designer, pentaho analysis report, pentaho business analytics, pentaho business analytics tutorial, pentaho rest api, pentaho bi, pentaho bi server, pentaho bi server installation, pentaho big data, pentaho business intelligence, pentaho bi tutorial, pentaho bi tutorial for beginners, pentaho ba server, pentaho cde, pentaho ce, pentaho community edition, pentaho cde dashboard, pentaho ctools, pentaho csv, pentaho carte, pentaho community, pentaho cube, pentaho community edition 7, pentaho dashboard, pentaho data integration examples, pentaho demo, pentaho data, pentaho data integration installation, pentaho data integration basics, pentaho dashboard tutorial, pentaho data mining, pentaho etl tutorial, pentaho etl tutorial for beginners, pentaho excel, pentaho edureka, pentaho metadata editor, pentaho community edition installation, etl pentaho data integration, tutorial pentaho metadata editor, pentaho formula, pentaho filter rows, pentaho for beginners, pentaho fuzzy match, pentaho francais, pentaho tutorial francais, pentaho hadoop, pentaho installation, pentaho installation on linux, pentaho introduction, pentaho interactive reporting, pentaho interview questions, pentaho intro, pentaho integration, pentaho job, pentaho java, pentaho kettle tutorial, pentaho kettle transformation, pentaho kettle installation, pentaho kitchen, kettle pentaho tutorial español, pentaho lookup, pentaho database lookup, learn pentaho, pentaho metadata injection, pentaho mapreduce, pentaho mondrian, pentaho mondrian tutorial, pentaho mysql, pentaho mongodb, pentaho mapping, pentaho mongo, pentaho overview, pentaho oracle, pentaho olap, pentaho oracle connection, install pentaho on windows, cubo olap pentaho, como instalar o pentaho, pentaho con oracle, openerp pentaho, pentaho pdi, pentaho postgres, pentaho portugues, que es pentaho, pentaho reporting tutorial, pentaho rest client, pentaho report designer installation, pentaho reporting java, pentaho report designer 5.0, pentaho report designer parameters, pentaho spoon, pentaho schema workbench tutorial, pentaho salesforce, pentaho spoon tutorial, pentaho server, pentaho spark, pentaho scheduler, pentaho sql server, pentaho schema workbench, pentaho sql, pentaho transformation, pentaho training, pentaho transformation tutorial, kettle pentaho tutorial, pentaho cde tutorial, pentaho user console, pentaho ubuntu, como usar pentaho, instalar pentaho en ubuntu, pentaho vs informatica, pentaho variables, pentaho videos, talend vs pentaho, pentaho weka, pentaho web service, pentaho world, pentaho data warehouse, instalar pentaho en windows, pentaho xml, pentaho y mysql, pentaho 2015, pentaho 5.4, pentaho 5, pentaho 6.1
Views: 3365 Yadishare Tutorial
Pentaho Data Integration short demo
 
09:28
Pentaho is a powerful Business Intelligence Suite offering many features: reporting, OLAP pivot tables, dashboarding and more. Report Designer: An advanced report creation tool. If you want to build a complex data-driven report, this is the right tool to use. Report Designer offers far more flexibility and functionality than the ad hoc reporting capabilities of the Pentaho User Console. • Design Studio: An Eclipse-based tool that enables you to hand-edit a report or analysis view xaction file. Generally, people use Design Studio to add modifications to an existing report that cannot be added with Report Designer. • Aggregation Designer: A graphical tool that helps improve Mondrian cube efficiency. • Metadata Editor: Enables you to add a custom metadata layer to an existing data source. Usually you would do this for a data source that you intend to use for reporting; it's not required, but it makes it easier for business users to parse the database when building a query. • Pentaho Data Integration: The Kettle extract, transform, and load (ETL) tool, which enables you to access and prepare data sources for analysis, data mining, or reporting. This is generally where you will start if you want to prepare data for analysis. • Schema Workbench: A graphical tool that helps you create ROLAP schemas for analysis. This is a required step in preparing data for analysis.
Data Warehouse tutorial. Creating an ETL.
 
30:25
This Data Warehouse video tutorial demonstrates how to create ETL (Extract, Load, Transform) package. See more lessons http://www.learn-with-video-tutorials.com/data-warehouse-tutorial-video
Creating a  Cube in SSAS
 
08:09
SQL Server Analysis Services (SSAS) is the technology from the Microsoft Business Intelligence stack, to develop Online Analytical Processing (OLAP) solutions. In simple terms, you can use SSAS to create cubes using data from data marts / data warehouse for deeper and faster data analysis. Cubes (Analysis Services) A cube is a set of related measures and dimensions that is used to analyze data. A measure is a fact, which is a transactional value or measurement that a user may want to aggregate. Measures are sourced from columns in one or more source tables, and are grouped into measure groups. A Data source is a connection that represents a simple connection to a data store; it includes all tables and views in the data store. A data source has project scope, which means that a data source created in an Integration Services project is available to all the packages in the project. A data source can be defined and then referenced by connection managers in multiple packages. This makes it easy to update all connection managers that use that data source. A project can have multiple data sources, just as it can have multiple connection managers A Data source view contains the logical model of the schema used by Analysis Services multidimensional database objects—namely cubes, dimensions, and mining structures. A data source view is the metadata definition, stored in an XML format, of these schema elements used by the Unified Dimensional Model (UDM) and by the mining structures
Why Twitter is blocking the government from using a data-mining tool (CNET Update)
 
02:24
Watch more CNET Upate: http://bit.ly/1M6Q5xn The social network is banning US spy agencies from accessing an analytics service used by news agencies. Meanwhile, Facebook wins a trademark battle in China. Sorry, you won't be able to taste "Face Book" the drink. Subscribe to CNET: http://bit.ly/17qqqCs Watch more CNET videos: http://www.cnet.com/video Follow CNET on Twitter: http://twitter.com/CNET Follow CNET on Facebook: http://www.facebook.com/cnet
Views: 10524 CNET
Using Pentaho Schema Workbench   Part 6  Using the MDX Query Tool
 
01:00
Pentaho is a powerful Business Intelligence Suite offering many features: reporting, OLAP pivot tables, dashboarding and more. Report Designer: An advanced report creation tool. If you want to build a complex data-driven report, this is the right tool to use. Report Designer offers far more flexibility and functionality than the ad hoc reporting capabilities of the Pentaho User Console. • Design Studio: An Eclipse-based tool that enables you to hand-edit a report or analysis view xaction file. Generally, people use Design Studio to add modifications to an existing report that cannot be added with Report Designer. • Aggregation Designer: A graphical tool that helps improve Mondrian cube efficiency. • Metadata Editor: Enables you to add a custom metadata layer to an existing data source. Usually you would do this for a data source that you intend to use for reporting; it's not required, but it makes it easier for business users to parse the database when building a query. • Pentaho Data Integration: The Kettle extract, transform, and load (ETL) tool, which enables you to access and prepare data sources for analysis, data mining, or reporting. This is generally where you will start if you want to prepare data for analysis. • Schema Workbench: A graphical tool that helps you create ROLAP schemas for analysis. This is a required step in preparing data for analysis.
Analysis Services tutorial. Creating OLAP cube. Introduction to data warehouse
 
14:17
Analysis Services is a collection of OLAP supplied in Microsoft SQL Server. See more lessons http://www.learn-with-video-tutorials.com/analysis-services-video-tutorial
Tutorial Schema Workbench, Pentaho Mondrian Tut  1
 
11:32
Pentaho is a powerful Business Intelligence Suite offering many features: reporting, OLAP pivot tables, dashboarding and more. Report Designer: An advanced report creation tool. If you want to build a complex data-driven report, this is the right tool to use. Report Designer offers far more flexibility and functionality than the ad hoc reporting capabilities of the Pentaho User Console. • Design Studio: An Eclipse-based tool that enables you to hand-edit a report or analysis view xaction file. Generally, people use Design Studio to add modifications to an existing report that cannot be added with Report Designer. • Aggregation Designer: A graphical tool that helps improve Mondrian cube efficiency. • Metadata Editor: Enables you to add a custom metadata layer to an existing data source. Usually you would do this for a data source that you intend to use for reporting; it's not required, but it makes it easier for business users to parse the database when building a query. • Pentaho Data Integration: The Kettle extract, transform, and load (ETL) tool, which enables you to access and prepare data sources for analysis, data mining, or reporting. This is generally where you will start if you want to prepare data for analysis. • Schema Workbench: A graphical tool that helps you create ROLAP schemas for analysis. This is a required step in preparing data for analysis.
Challenges and Issues in various types of Data Mining
 
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Challenges and Issues in various types of Data Mining
Data Preprocessing
 
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Data Preprocessing using Rapidminer The steps undertaken are : 1. Handling missing values 2. Binning 3. Sampling 4. Normalization 5. Correlation Determination
JSON Integration with KNIME
 
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There is a full new category in KNIME 2.11 about JSON Processing. In particular, there are nodes to read and extract content from JSON structures, validate JSON schema, ascertain differences between 2 JSON objects, transform and convert JSON structures to other structures like XML. This video is part of the recording of the "What is new in KNIME 2.11" webinar held on Dec 11 2014 and available on Youtube at: http://youtu.be/9RkRHI32Dy8 For more infos about updates in KNIME 2.11 check http://tech.knime.org/whats-new-in-knime-211
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