Search results “Text mining query languages”
Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 39550 Well Academy
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 366929 sentdex
Natural Language Processing with Graphs
William Lyon, Developer Relations Enginner, Neo4j:During this webinar, we’ll provide an overview of graph databases, followed by a survey of the role for graph databases in natural language processing tasks, including: modeling text as a graph, mining word associations from a text corpus using a graph data model, and mining opinions from a corpus of product reviews. We'll conclude with a demonstration of how graphs can enable content recommendation based on keyword extraction.
Views: 28802 Neo4j
Extract Structured Data from unstructured Text (Text Mining Using R)
A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 9282 Stat Pharm
Data Mining-Query optimization using Document Frequency
Query optimization using Document Frequency
Views: 472 John Paul
#9. Working with Text. Building query text
Natural Language Interface to Database
Natural Language Interface to Database To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: www.jpinfotech.org, Blog: www.jpinfotech.blogspot.com The need for natural language interfaces to database has become increasingly acute as more and more people access information from web browsers. Yet NLI (Natural Language Interface) is only usable if they map natural language questions to SQL queries correctly. Natural Language processing is becoming one of the most active areas in Human-Computer Interaction. It is a branch of AI (Artificial Intelligence) which includes in information retrieval, machine translation and Language analysis. The goal of NLP (Natural Language Processing) is to enable communication between people and computers without requiring to memorization of complex commands and procedures. In other words NLP (Natural Language Processing) is techniques which can make the computer understand the languages naturally used by humans. The main purpose of natural Language Query Processing is for an English sentence to be interpreted by the computer and appropriate action taken. Asking questions to databases in natural language is a very convenient and easy method of data access, especially for casual users who do not understand complicated database query languages such as SQL.
Views: 1466 jpinfotechprojects
MDX Query Basics (Analysis Services 2012)
This video is part of LearnItFirst's SQL Server 2012: A Comprehensive Introduction course. More information on this video and course is available here: http://www.learnitfirst.com/Course170 In this video, we walk through the basics of the MDX Query language. It is a very logical language, however, is somewhat large in syntax. If you enjoy writing Transact-SQL, you will really enjoy the MDX language. The AdventureWorks2012 multidimensional models need to be installed on your SSAS Multidimensional mode instance from the CodePlex web site. Highlights from this video: - The basics of an MDX query - What is the basic format of the MDX query language? - Is it necessary to have a WHERE clause in an MDX query? - How to signal the end of a statement in the MDX query language - Using the Internet Order Count and much more...
Views: 96404 LearnItFirst.com
What is the difference between keyword search and text mining?
This is a brief introduction to the difference between keyword search and text mining. Find out more from the leader in natural language based text mining solutions, by visiting our blog and website: http://www.linguamatics.com/blog Following us on social media: Twitter: www.twitter.com/linguamatics LinkedIn: www.linkedin.com/company/linguamatics Facebook: www.facebook.com/Linguamatics YouTube: https://www.youtube.com/user/Linguama... You can contact us with questions at enquiries @ linguamatics.com
Views: 490 Linguamatics
Text analytics extract key phrases using Power BI and Microsoft Cognitive Services
Download the PDF to keep as reference http://theexcelclub.com/extract-key-phrases-from-text/ FREE Power BI course - Power BI - The Ultimate Orientation http://theexcelclub.com/free-excel-training/ Or on Udemy https://www.udemy.com/power-bi-the-ultimate-orientation Or on Android App https://play.google.com/store/apps/details?id=com.PBI.trainigapp Carry out a text analytics like the big brand...only for free with Power BI and Microsoft Cognitive Services. this video will cover Obtain a Text Analytics API Key from Microsoft Cognitive Services Power BI – Setting up the Text Data Setting up the Parameter in Power BI Setting up the Custom function Query(with code to copy) Grouping the text Running the Key Phrase Extraction by calling the custom function. Extracting the key phrases from the returned Json file. Sign up to our newsletter http://theexcelclub.com/newsletter/ Watch more Power BI videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiEsQ-68y0tdnaU9hCqjJ5Dh Watch Excel Videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiFFpjWeK7CE3AEXy_IRZp4y Join the online Excel and PowerBI community https://plus.google.com/u/0/communities/110804786414261269900
Views: 3663 Paula Guilfoyle
Advanced Analytics with R and SQL
R is the lingua franca of Analytics. SQL is the world’s most popular database language. What magic can you make happen by combining the power of R and SQL for Data Science and Advanced Analytics? Imagine the power of exploring, transforming, modeling, and scoring data at scale from the comfort of your favorite R environment. Now, imagine operationalizing the models you create directly in SQL Server, allowing your applications to use them from T-SQL, executed right where your data resides. Come learn how to build and deploy intelligent applications that combine the power of R, SQL Server, thousands of open source R extension packages, and high-performance implementations of the most popular machine learning algorithms at scale.
Query Languages for Document Stores by Jan Steemann
NoSQL matters Conference in Cologne, Germany 2013 - Query Languages for Document Stores by Jan Steemann - http://2013.nosql-matters.org/cgn/ SQL is the standard and established way to query relational databases. As the name "NoSQL" suggests, NoSQL databases have gone some other way, coming up with several approaches of querying, e.g. access by key, map/reduce, and even own full-featured query languages. We surely don't want the super-fast key/value store require us to use a full-blown query language and slow us down -- but for several other cases querying using a language can still be convenient. This is especially the case in document stores that have a wide range of use cases and allow us to look at different aspects of the same data. As there isn't yet an established standard for querying document databases, the talk will showcase some of the existing implementations such as UNQL, AQL, and jsoniq. Additionally, related topics such as graph query languages will be covered. Slides are available: http://2013.nosql-matters.org/cgn/wp-content/uploads/2013/05/querylanguages.pdf
Analyzing Text Data with Google Sheets and Cloud Natural Language (Next Rewind '18)
Some of the most valuable insights for businesses come from free-form user feedback, but text data can be difficult to process and summarize in a scalable way. This session will show how to use Cloud Natural Language to open up opportunities for analyzing qualitative feedback alongside quantitative data. I’ll show you how to use Google Forms to collect feedback, Google Sheets and Cloud Natural Language to analyze it, and Data Studio to visualize the insights; a powerful yet lightweight solution! Watch the full session here → http://bit.ly/2NQGQKV Watch more recaps here → http://bit.ly/2PBswWO Watch more Collaboration & Productivity sessions here → http://bit.ly/2LldTsw Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Don’t forget to subscribe → http://bit.ly/G-Suite1
Views: 2552 G Suite
Text Analysis in Power BI with Cognitive services with Leila Etaati
Abstract: Data that we collected always is not about numbers and structured data. In any organization, there is a need to analyze the text data such as customer comments, extract the primary purpose of a call from its scripts, detect the language of customer feedback and translate it and so forth. To address this issue, Microsoft Cognitive Services provides a set of APIs, SDKs, and services available to developers to do text analysis without writing R or Python codes. In this session, I will explain what is text analysis such as sentiment analysis, key phrase extraction, Language detection and so forth. Next, the process of text analysis in Power BI using cognitive services will be demonstrated. Follow us on Twitter - https://twitter.com/mspowerbi More questions? Try asking the Power BI Community @ https://community.powerbi.com/
Views: 6086 Microsoft Power BI
Natural Language Query
Ability to simply ask questions about data in Salesforce
Views: 283 NuikuInc
Jiwon Seo - SociaLite Python integrated Query Lamguage for Big Data Analysis
PyData SV 2014 SociaLite is a Python-integrated query language for big data analysis. It makes big data analysis simple, yet achieves fast performance with its compiler optimizations, often more than three orders of magnitude faster than Hadoop MapReduce programs. For example, PageRank algorithm can be implemented in just 2 lines of SociaLite query, which runs nearly as fast as an optimal C implementation. High-level abstractions in SociaLite help implement distributed data analysis algorithms. For example, its distributed in-memory tables allow large data to be stored across multiple machines, and with minimal user annotations, fast distributed join operations can be performed. Moreover, its Python integration makes SociaLite very powerful. We support embedding and extending, where embedding supports using SociaLite queries directly in Python code, and extending supports using Python functions in SociaLite queries. To support embedding, we apply source code rewriting that transforms SociaLite queries to invoke SociaLite runtime interfaces. For extending, we keep track of functions defined in Python interpreter and make them accesible from SociaLite. The integration makes it easy to implement various data mining algorithms in SociaLite and Python. I will demonstrate in the talk a few well-known algorithms implemented in SociaLite, including PageRank, k-means, and logistic regression. The high-level queries can achieve fast performance with various optimizations. The queries are compiled into Java bytecode with compiler optimizations applied, such as prioritizations or pipelined evaluation. Also, the runtime system gives its best effort to achieve full utilizations of multi-core processors as well as network bandwidths. With the compiler optimizations and the runtime system we achieve very fast performance that is often close to optimal C implementations.
Views: 404 PyData
Excel Magic Trick 1336: Power Query: Import Big Data Text Files: Connection Only or Data Model?
Download File: http://people.highline.edu/mgirvin/excelisfun.htm See how to use Import 10 Text Files and Append (combine) then into a single Proper Data Set before making a PivotTable Report. Compare and Contrast whether we should use Connection Only or Data Model to store the data. 1. (00:18) Introduction & Look at Text Files that Contain 7 Million Transactional Records 2. (01:43) Power Query (Get & Transform) Import From Folder to append (combine) 10 Text Files that contain 7 Millions transactional records. 3. (05:07) Load Data as Connection Only and Make PivotTable 4. (08:17) Load Data into Data Model and Make PivotTable. 5. (10:46) Summary
Views: 21482 ExcelIsFun
FastText Tutorial - How to Classify Text with FastText
Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com FastText is an open-source, Natural Processing Language (NLP) library created by Facebook AI Research that allows users to efficiently learn word representations and sentence classification. In this FastText Tutorial, we discuss how FastText enables text classification through supervised learning. Watch this video to learn: - How text classification models are built and evaluated using FastText - Tricks used in FastText that improve the time complexity of model building - How FastText can be used to identify spam in a sample inbox
Views: 9155 Fullstack Academy
Academic Research: Unifying Semi-Structured Query Languages with SQL++ – Couchbase Connect 2014
SQL++ Query Language for Semi-structured data A lack of a unifying language across NoSQL, newSQL and Big Data technologies presents many problems to practitioners building next generation applications. So what are the key requirements behind a next generation query language for multi-structured data? SQL ++ introduces a unifying language integrating these new technologies while being fully backwards compatible with the massively adopted SQL. Get this 30-page, cutting edge research paper to learn about – SQL++ syntax and semantics – A data model and query language expressiveness benchmark across NoSQL, NewSQL and Hadoop – A detailed classification of N1QL and other Big Data languages across 15 feature categories Speaker: Yannis Papakonstantinou, Professor of Computer Science and Engineering, UCSD Slideshare: http://www.slideshare.net/Couchbase/yannis-papakonstantinou-sql-query-language-for-semistructured-data Visit our website for more information: https://www.couchbase.com/
Views: 124 Couchbase
Introduction to Data Mining  (1/3)
http://www.creativecommit.com. This video gives a brief demo of the various data mining techniques. The demo mainly uses Microsoft SQL server 2008, BIDS 2008 and Excel for data mining
Views: 149520 creativecommIT
Overview of Recent and Ongoing Work 2016
In this talk, I give an overview of my recent and ongoing work, mostly on query optimization but also on text mining, adiabatic quantum annealing, and natural language query interfaces.
Views: 981 Immanuel Trummer
Basic Excel Business Analytics #30: Excel 2016 Power Query: Data Ribbon Tab, Get and Transform
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm Learn how to import multiple Text Files (.txt) from a folder into Excel using the new Excel 2016 Power Query: Data Ribbon Tab, Get and Transform group. Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
Views: 63725 ExcelIsFun
Lecture - 5 Structured Query Language
Lecture Series on Database Management System by Dr.S.Srinath, IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 123080 nptelhrd
Working With AQL on Text - Chapter 3
Text Mining and Analytics Working With AQL on Text - Chapter 3 This video tutorials cover major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications. analytics | analytics tools | analytics software | data analysis programs | data mining tools | data mining | text analytics | strucutred data | unstructured data |text mining | what is text mining | text mining techniques | AQL | Annotation Query Language More Articles, Scripts and How-To Papers on http://www.aodba.com
Views: 36 AO DBA
Knowledge Graphs Webinar
Petra Selmer, Neo4j Engineer and openCypher member (Cypher Language Group)
Views: 4321 Neo4j
Neo4j Online Meetup #38: Text Analytics With Neo4j Graph Database
Every project has thousands of decisions that go into creating an outcome. Every building has thousands of building information models associated with it. Every mining operation or oil and gas well has countless activities and events that have occurred on the site. These decisions, drawings, models and events tell a story about the work that was done, the people who were involved and the outcomes that were created. Right now, it is very difficult for organizations to access this rich history because it is spread across the many different systems, databases and filestores organizations must use to run their operations. Menome Technologies will discuss how the combination of multi-agent systems, probabilistic topic modelling and neo4j make it possible harvest and link an organization’s data together to create a knowledge graph that makes it easy for people to understand the work they do, the place it was done and the value it produced.
Views: 821 Neo4j
Big Data - An Introduction to Hive and HQL
http://www.ibm.com/software/data/bigdata/ Rajesh Kartha from the BigInsights Enablement team introduces Hive including the Hive data model and HQL, Hive's SQL like query language. Video produced, directed and edited by Gary Robinson, contact robinsg at us.ibm.com Music Track title: Clouds, composer: Dmitriy Lukyanov, publisher:Shockwave-Sound.Com Royalty Free
Views: 34158 IBM Analytics
Use the Query Editor
Learn how to use the query editor in the Power BI Desktop to clean and transform your data. Learn more at https://support.powerbi.com/knowledgebase/articles/471648-common-query-tasks-in-power-bi-desktop
Views: 146031 Microsoft Power BI
Excel 2013 Statistical Analysis #11: Power Query Import Multiple Text Files, Grade Histogram by Year
Download files: http://people.highline.edu/mgirvin/excelisfun.htm Topics in this video: 1. (00:16) Over View of File Import and Histogram Creation 2. (00:56) Look at Zipped Folder from class download then unzip it with Right-click, “Extract All” 3. (01:15) Text Files for communication between databases and data analysis programs like Excel 4. (02:06) Use Power Query to Import Multiple Files 5. (02:10) Get External Data Tab in Power Query, From File Button, From Folder Button 6. (02:33) We only need to keep “Content” Column, so right-click “Content” Field Name and point to “Remove Other Columns” 7. (02:51) To reveal data in imported tables, click the button with the Two Downward Point Arrows. 8. (02:58) Filter out Field Name. 9. (04:10) Name Query 10. (04:17) Close and Load To a cell in our worksheet (this brings table of data from the Power Query editor window into our worksheet) 11. (04:51) Build Frequency Distribution with a PivotTable 12. (05:28) Use Find and Replace feature to create non-ambiguous labels in a Grouped Decimal Number PivotTable. 13. (06:31) Add a Slicer for the Year Variable to the PivotTable 14. (07:26) Create Histogram
Views: 12992 ExcelIsFun
How do queries work in Cloud Firestore? | Get to Know Cloud Firestore #2
How do queries work in Cloud Firestore? What kinds of queries can you run, and what kind can't you run? And what are composite indexes, anyway? Find out the answers to all of these questions and more on this episode of Get to Know Cloud Firestore. Subscribe to the Firebase channel for more content like this and let us know what you think in the comments below! Watch Get to Know Cloud Firestore #1 → http://bit.ly/2ssnX7i Subscribe to the Firebase channel → http://bit.ly/firebase2
Views: 22285 Firebase
Basic Excel Business Analytics #28: Power Query: Import Multiple Text Files & Build Grade Dashboard
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm Learn about: 1) (00:04) Notes about downloading files 2) (00:36) Look at Finished Grade Dashboard 3) (01:10) Look at Text files that we need to import from a folder 4) (02:07) Power Query to Import Text Files (extension “.txt”) from a Folder 5) (06:14) What is a Dashboard? 6) (08:02) Build PivotTable for Class Mean and Standard Deviation 7) (09:52) Build Column Chart from Categorical data (Class Name) 8) (10:43) Add Slicer to PivotTable for Academic Year 9) (11:07) What a Slicer does. 10) (11:18) Create Dynamic Chart Title for Chart that changes when Slicer Changes. 11) (12:37) Build PivotTable for Grade Frequency Distribution based on the filter of Class Name 12) (13:46) Create Dynamic Chart Title for Chart that changes when Filter AND Slicer Changes. 13) (14:03) Build Histogram For Grade Distribution 14) (15:56) Connect Second PivotTable and Histogram to Slicer for First PivotTable 15) (16:31) Video is Paused and third PivotTable and Histogram is created. 16) (16:35) Finishing Touches on Dashboard. Sizing Charts and arranging Slicers. Learn about Alt keyboard to line charts up against cells. 17) (18:58) Drop new Text Files in original folder and see that the entire: 1) Power Query Import and Data Transformation, 2) PivotTables and 3) Charts in our Dashboards updates when we refresh with the keyboard Ctrl + Alt + F5. 18) (21:14) Summary and Conclusion Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
Views: 19268 ExcelIsFun
Firebase Database Querying 101 - The Firebase Database For SQL Developers #3
Learn more about qureying in the official documentation: https://goo.gl/iLDAvS Welcome to the third video in the Firebase Database for SQL Developers series! Querying may be less powerful in NoSQL databases than compared to SQL databases, but there's still a lot you can do with the Firebase Database. Watch more videos from this series: https://goo.gl/ZDcO0a Subscribe to the Firebase Channel: https://goo.gl/9giPHG
Views: 83480 Firebase
Force Power Query to read as text file (or other format)
Let's force a file or source to be read in Power Query as something else. Pre-order the book: http://www.amazon.com/Is-Data-Monkey-Guide-Language/dp/1615470344 Ken Pul's blog post: http://www.excelguru.ca/blog/2014/12/03/force-power-query-to-import-as-a-text-file/ Version of Power Query used: November 2014
Views: 2266 The Power User
Query with Couchbase Analytics in a Node.js Application
Learn how to query massive amounts of NoSQL documents in Couchbase using the Analytics service, Node.js, and SQL++. A written version of this tutorial can be found at https://blog.couchbase.com/using-couchbase-analytics-node-js-javascript/
Views: 37 Couchbase
Webtrekk Analytics Bot / AskBy.ai
AskBy.ai translates natural language into query language.
Views: 474 Sajagan Thirugnanam
Import Data and Analyze with Python
Python programming language allows sophisticated data analysis and visualization. This tutorial is a basic step-by-step introduction on how to import a text file (CSV), perform simple data analysis, export the results as a text file, and generate a trend. See https://youtu.be/pQv6zMlYJ0A for updated video for Python 3.
Views: 183084 APMonitor.com
Amazing Things NLP Can Do!
In this video I want to highlight a few of the awesome things that we can do with Natural Language Processing or NLP. NLP basically means getting a computer to understand text and help you with analysis. Some of the major tasks that are a part of NLP include: · Automatic summarization · Coreference resolution · Discourse analysis · Machine translation · Morphological segmentation · Named entity recognition (NER) · Natural language generation · Natural language understanding · Optical character recognition (OCR) · Part-of-speech tagging · Parsing · Question answering · Relationship extraction · Sentence breaking (also known as sentence boundary disambiguation) · Sentiment analysis · Speech recognition · Speech segmentation · Topic segmentation and recognition · Word segmentation · Word sense disambiguation · Lemmatization · Native-language identification · Stemming · Text simplification · Text-to-speech · Text-proofing · Natural language search · Query expansion · Automated essay scoring · Truecasing Let’s discuss some of the cool things NLP helps us with in life 1. Spam Filters – nobody wants to receive spam emails, NLP is here to help fight span and reduce the number of spam emails you receive. No it is not yet perfect and I’m sure we still all still receive some spam emails but imagine how many you’d get without NLP! 2. Bridging Language Barriers – when you come across a phrase or even an entire website in another language, NLP is there to help you translate it into something you can understand. 3. Investment Decisions – NLP has the power to help you make decisions for financial investing. It can read large amounts of text (such as news articles, press releases, etc) and can pull in the key data that will help make buy/hold/sell decisions. For example, it can let you know if there is an acquisition that is planned or has happened – which has large implications on the value of your investment 4. Insights – humans simply can’t read everything that is available to us. NLP helps us summarize the data we have and pull out meaningful information. An example of this is a computer reading through thousands of customer reviews to identify issues or conduct sentiment analysis. I’ve personally used NLP for getting insights from data. At work, we conducted an in depth interview which included several open ended response type questions. As a result we received thousands of paragraphs of data to analyze. It is very time consuming to read through every single answer so I created an algorithm that will categorize the responses into one of 6 categories using key terms for each category. This is a great time saver and turned out to be very accurate. Please subscribe to the YouTube channel to be notified of future content! Thanks! https://en.wikipedia.org/wiki/Natural_language_processing https://www.lifewire.com/applications-of-natural-language-processing-technology-2495544
Views: 4894 Story by Data
Twitter data analysis with IBM Watson Explorer: Unstructured Text Analysis
This video provides an overview of IBM Watson Explorer used to analyze and gain insights from unstructured data, such as Twitter data. For more information about Watson Explorer, please visit: https://www.ibm.com/us-en/marketplace/content-analytics
Views: 2697 IBM Analytics
Don’t Optimize my queries, Optimize my data | DataEngConf NYC '17
Don’t miss the next DataEngConf in Barcelona: https://dataeng.co/2O0ZUq7 Download slides for this talk: https://goo.gl/FZJZrn Your queries won't run fast if your data is not organized right. Apache Calcite optimizes queries, but can we evolve it so that it can optimize data? We had to solve several challenges. Users are too busy to tell us the structure of their database, and the query load changes daily, so Calcite has to learn and adapt. We talk about new algorithms we developed for gathering statistics on massive database, and how we infer and evolve the data model based on the queries, suggesting materialized views that will make your queries run faster without you changing them. About the speaker: Julian Hyde is an expert in query optimization, in-memory analytics, and streaming. He is the original developer of Apache Calcite, the query planning framework behind Apache Hive, Drill, Kylin and Phoenix, and was also the original developer of the open source Mondrian OLAP engine. He is an architect at Hortonworks. Follow DataEngConf on: Twitter: https://twitter.com/dataengconf LinkedIn: https://www.linkedin.com/company/hakkalabs/ Facebook: https://web.facebook.com/hakkalabs
Views: 557 Hakka Labs
Natural Language Search in Solr, Tommaso Teofili, Sourcesense, Eurocon 2011
Natural Language Search in Solr Presented by Tommaso Teofili, Sourcesense This presentation aims to showcase how to build and implement a search engine which is able to understand a query written in a way much nearer to spoken language than to keyword-based search using Apache Lucene/Solr and Apache UIMA. A system which can recognize semantics in natural language can be very handy for non expert users, e-learning systems, customer care systems, etc. With such a system it's possible to submit queries such as "hotels near Rome" or "people working at Google" without having to manually transform a user entered natural language query to a Lucene/Solr query. The Solr - UIMA integration (since Solr 3.1.0) can help on building such intelligent systems using NLP / Text mining algorithms on documents being indexed and on queries written by the user. This module gives Solr the ability of calling UIMA pipelines when documents are indexed to trigger automatic extraction of metadata (i.e. named entities like people, places, organizations, etc.) using existing and custom algorithms as UIMA analysis engines. The talk will cover: The Solr - UIMA integration Introducing UIMA to Lucene's analysis phase Running existing open source NLP algorithms in Lucene/Solr Orchestrating blocks to build a sample system able to understand natural language queries We'll introduce these points using examples (architectures & code) and a sample demo system.
Views: 3026 LuceneSolrRevolution
Create Hierarchies in Power Query
In today's video I will show you how to create hierarchies in Power Query. I showed you in a previous video how to create hierarchies using DAX: https://www.youtube.com/watch?v=EzfLJFEKV8I but this time we will use Power Query. Link to PowerBI file: http://gofile.me/2kEOD/kr5z9Do2H SUBSCRIBE to learn more about Power and Excel BI! https://www.youtube.com/channel/UCJ7UhloHSA4wAqPzyi6TOkw?sub_confirmation=1 Our PLAYLISTS: - Join our DAX Fridays! Series: https://goo.gl/FtUWUX - Power BI dashboards for beginners: https://goo.gl/9YzyDP - Power BI Tips & Tricks: https://goo.gl/H6kUbP - Power Bi and Google Analytics: https://goo.gl/ZNsY8l ABOUT CURBAL: Website: http://www.curbal.com Contact us: http://www.curbal.com/contact ▼▼▼▼▼▼▼▼▼▼ If you feel that any of the videos, downloads, blog posts that I have created have been useful to you and you want to help me keep on going, here you can do a small donation to support my work and keep the channel running: https://curbal.com/product/sponsor-me Many thanks in advance! ▲▲▲▲▲▲▲▲▲▲ QUESTIONS? COMMENTS? SUGGESTIONS? You’ll find me here: ► Twitter: @curbalen, @ruthpozuelo ► Google +: https://goo.gl/rvIBDP ► Facebook: https://goo.gl/bME2sB #MAGICMONDAYS #CURBAL #POWERQUERY #POWERBI ► Linkedin: https://goo.gl/3VW6Ky
Views: 4388 Curbal
Basics #7. Query Language #3. Make it look beautiful: Format!
https://sheetswithmaxmakhrov.wordpress.com/2017/12/18/10-query-samples/ You can make awesome reports and use `format` option to make data output look properly.
Basic Excel Business Analytics #32: Power Query Import Multiple Excel Files with Multiple Sheets
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm Learn how to import multiple Excel workbooks (each with the store name in the file name) with multiple sheets in each workbook (each sheet contains the Sales Rep name) and import the sales data into a proper data set, including a column for the sales rep name (data from the sheet tab names) and a column for the store name (data from file name): 1) (00:04) Download File Information 2) (00:23) Look at Excel Workbooks that need to be imported, including the names of each Sales Rep on each sheet tab. 3) (01:20) Power Query, From File, From Folder, to import files from a folder 4) (02:07) Remove Other Columns, being sure to keep file with File name, which contains the store name. 5) (02:24) Add Column and use Power Query Function called Excel.Workbook, so that we can extract the data from the Excel workbook. 6) (03:13) Use Replace Values feature in Power Query to extract the store name from the file name. 7) (03:47) Remove Content Column 8) (03:54) Expand data Column which will expose the Data Column (Data in Excel Workbook), Item Column (contains sheet name data), Kind Column (contains object information such as: Sheet, Table and Defined Names). 9) (04:12) Filter the Kind Column to remove Tables and Defined Names and keep Sheets only. 10) (04:39) Filter Item column to remove sheets that were not properly named (have default names such as Sheet1, Sheet2). We use the “Does Not Contain” Filter to keep sheets that do not contain the text “sheet”; another way to think about it is: “Filter out sheet tabs that have the text ‘sheet’ in them” 11) (05:15) Amazing results: data from sheet tabs and file names is retained for proper data set. 12) (05:27) Remove columns that are not File Name (Store name), Item (Sales Rep Name) and Data. 13) (05:37) Expand data from Excel Workbooks. 14) (05:42) “Use First Rows As Headers” to promote the Field Names from the first sheet in the first workbook to Field Names. 15) (05:53) Filter out Field names from other Sheet tabs. 16) (06:48) Rename Columns 17) (07:15) Add correct Data Types before importing 18) (07:37) Close and Load To a Table on Existing Sheet 19) (08:11)Summary and Conclusion Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
Views: 8255 ExcelIsFun
Perform a Lookup with Power Query
Check out my Blog: http://exceltraining101.blogspot.com This video show how to use Power Query to perform lookups. For small data sets you are better off with Excel's other lookup function, but if you are dealing with large data sets (over half a million) you may be better off using Power Query. This method also would make it easier if you do this on a recurring basis (daily, weekly, etc) and didn't/couldn't write macros. P.S. Feel free to provide a comment or share it with a friend! #exceltips #exceltipsandtricks #exceltutorial #doughexcel
Views: 48043 Doug H
What is an Ontology
Description of an ontology and its benefits. Please contact [email protected] for more information.
Views: 133415 SpryKnowledge
Filtering and Consolidation - Chapter 5
Text Mining and Analytics Filtering and Consolidation - Chapter 5 This video tutorials cover major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications. analytics | analytics tools | analytics software | data analysis programs | data mining tools | data mining | text analytics | strucutred data | unstructured data |text mining | what is text mining | text mining techniques | AQL | Annotation Query Language More Articles, Scripts and How-To Papers on http://www.aodba.com
Views: 14 AO DBA
Combine Tables from Multiple Sheets in the Same Workbook with Power Query
Check out my Blog: http://exceltraining101.blogspot.com This video shows how to combine tables from different tabs in the same file using the Append capability in the Excel with Power Query. P.S. Feel free to provide a comment or share it with a friend! #exceltips #exceltipsandtricks #exceltutorial #doughexcel
Views: 147782 Doug H