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Language Technologies for Geomatics: From Intelligence to Agility
1) The document discusses how language technologies can be integrated with geomatics applications. It covers topics like business intelligence, data mining, natural language processing, and geo-analytics. 2) Language technologies use techniques from linguistics, artificial intelligence, and machine learning to analyze text. This includes tasks like classification, information extraction, and question answering. 3) Integrating language technologies with geomatics could enhance decision-making by extracting insights from large amounts of textual data and applying geographic context. This could help organizations become more responsive to changes.
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Language Technologies for Geomatics: From Intelligence to Agility
- 1. Language Technologies for Geomatics: From Intelligence to Agility Vision Géomatique - 2014-11-12 Stéphane Gagnon, Ph.D. Professeur, DSA, UQO
- 2. Outline 1. BusinessIntelligence 2 Stéphane Gagnon 2014-11-12 2. Language Technologies 3. Geomatics Applications 4. Big Data and Geo-Agility
- 3. Abstract 3 StéphaneGagnon 2014-11-12 Language Technologies are used for automated text analytics, and rely on a blend of Linguistics, Artificial Intelligence (AI), and Decision Sciences. They include such applications as content management, document indexing and search, text classification, automated translation, geographic and contextual localization, semantic web, real-time text stream processing, event patterns analysis, and others. We present a brief discussion of how Language Technologies may be integrated with geomatics applications, not simply to enhance business and decisional intelligence, but with the aim of making organizations more agile and resilient in the face of risk and uncertainty.
- 4. Sources 4 StéphaneGagnon 2014-11-12 Baccalauréat en administration - Systèmes d'information de gestion SIG1003 - Systèmes d'information pour gestionnaires Efraim Turban, Linda Volonino, Gregory Wood, et Janice Sipior, (2013), Information technology for management: Advancing sustainable, profitable business growth, 9th edition, New York, Wiley, 480 pages, ISBN: 9781118547861 SIG1043 - Intelligence d’affaires Ramesh Sharda, Dursun Delen, Efraim Turban, (2013), Business Intelligence: A Managerial Perspective on Analytics, CourseSmart eTextbook, 3rd edition, New York, Pearson Higher Education, 330 pages, ISBN: 9780133051070
- 5. 5 2014-11-12 StéphaneGagnon 1. Business Intelligence Stéphane Gagnon
- 6. Goals of BI 6 Stéphane Gagnon 2014-11-12
- 7. BI Evolution 7Stéphane Gagnon 2014-11-12
- 8. 8 Stéphane Gagnon2014-11-12 Modern BI Dashboard
- 9. 9 Stéphane Gagnon2014-11-12 BI Project Lifecycle
- 10. 10 Stéphane Gagnon2014-11-12 Typical BI Architecture Data Data Warehouse Environment ü Organizing Warehouse BPM strategy ü Summarizing ü Standardizing Technical staff Data Sources Business Analytics Environment Performance and Strategy Business users Managers / executives Built the data warehouse Access Manipulation Results Future component intelligent systems User Interface - browser - portal - dashboard
- 11. 11 Stéphane Gagnon2014-11-12 BI Data Management
- 12. 12 2014-11-12 StéphaneGagnon 2. Language Technologies Stéphane Gagnon
- 13. 13 Stéphane Gagnon2014-11-12 Data Mining (DM) Statistics Artificial Intelligence Pattern Recognition Machine Learning DATA MINING Databases Mathematical Modeling Management Science & Information Systems
- 14. DM Tasks 14Stéphane Gagnon 2014-11-12 Data Mining Prediction Classification Regression Association Link analysis Sequence analysis Clustering Learning Method Popular Algorithms Supervised Supervised Supervised Unsupervised Unsupervised Unsupervised Unsupervised Classification and Regression Trees, ANN, SVM, Genetic Algorithms Decision trees, ANN/MLP, SVM, Rough sets, Genetic Algorithms Linear/Nonlinear Regression, Regression trees, ANN/MLP, SVM Apriory, OneR, ZeroR, Eclat Expectation Maximization, Apriory Algorithm, Graph-based Matching Apriory Algorithm, FP-Growth technique K-means, ANN/SOM Outlier analysis Unsupervised K-means, Expectation Maximization (EM)
- 15. 15 Stéphane Gagnon2014-11-12 Language Technologies Statistical Methods Analyze documents as bags of words Semantic Methods Analyze documents using tags from ontologies describing relationships
- 16. 16 Stéphane Gagnon2014-11-12 Statistical Methods Information retrieval/search Topic/keyword tracking Geo-language recognition Categorization/classification Clustering/recommendation Concept linking/association rules
- 17. Semantic Methods NaturalLanguage Processing (NLP) Part-of-speech tagging Text segmentation Word sense disambiguation Syntax ambiguity Imperfect or irregular input Speech acts 17 Stéphane Gagnon 2014-11-12
- 18. NLP Tasks Information extraction Named-entity recognition Question answering Automatic summarization Natural language generation & understanding Machine translation Foreign language reading & writing Speech recognition Text proofing, optical character recognition Sentiment analysis 18 Stéphane Gagnon 2014-11-12
- 19. 19 Stéphane Gagnon2014-11-12 Text Mining (TM) Process Task 1 Task 2 Task 3 Establish the Corpus: Collect & Organize the Domain Specific Unstructured Data Create the Term- Document Matrix: Introduce Structure to the Corpus Extract Knowledge: Discover Novel Patterns from the T-D Matrix The inputs to the process includes a variety of relevant unstructured (and semi-structured) data sources such as text, XML, HTML, etc. The output of the Task 1 is a collection of documents in some digitized format for computer processing The output of the Task 2 is a flat file called term-document matrix where the cells are populated with the term frequencies The output of Task 3 is a number of problem specific classification, association, clustering models and visualizations Feedback Feedback
- 20. 20 Stéphane Gagnon2014-11-12 Enterprise Index/Search
- 21. Web Mining 21Stéphane Gagnon 2014-11-12 Web Analytics Voice of Customer Customer Experience Management Customer Interaction on theWeb Analysis of Interactions Knowledge about the Holistic View of the Customer
- 22. IBM Watson QA 22 Stéphane Gagnon 2014-11-12 Trained models Question analysis Answer sources Hypothesis generation Query decomposition Soft filtering Evidence sources Hypothesis and evidence scoring Synthesis Final merging and ranking Answer and confidence Hypothesis generation Soft filtering Hypothesis and evidence scoring ... ... ... Primary search Candidate answer generation Support evidence retrieval Deep evidence Question scoring 1 2 3 4 5
- 23. TM for Lies 23 Stéphane Gagnon 2014-11-12 Statements Transcribed for Processing Text Processing Software Identified Cues in Statements Statements Labeled as Truthful or Deceptive By Law Enforcement Text Processing Software Generated Quantified Cues Classification Models Trained and Tested on Quantified Cues Cues Extracted & Selected
- 24. 24 2014-11-12 StéphaneGagnon 3. Geomatics Applications Stéphane Gagnon
- 25. Geo-Analytics 25 StéphaneGagnon 2014-11-12
- 26. 26 Stéphane Gagnon2014-11-12 Geo-Textual Contextualization Geo-Information Sensors Software/hardware limitations Privacy issues Linguistic limitations Extract knowledge from available data sources A0 Unstructured data (text) Structured data (databases) Context-specific knowledge Geo-Localized Contents Domain expertise Tools and techniques Geographic Information Geo-Intelligence Models
- 27. 27 Stéphane Gagnon2014-11-12 Geo-Social Network Analysis
- 28. 28 Stéphane Gagnon2014-11-12 Geo-Analytics of Voter Talk INPUT: Data Sources § Census data Population specifics, age, race, sex, income, etc. § Election Databases Party affiliations, previous election outcomes, trends and distributions § Market research Polls, recent trends and movements § Social media Facebook, Twitter, LinkedIn, Newsgroups, Blogs, etc. § Web (in general) Web pages, posts and replies, search trends, etc. · Other data sources OUTPUT: Goals § Raise money contributions § Increase number of volunteers § Organize movements § Mobilize voters to get out and vote § Other goals and objectives § ... Big Data & Analytics (Data Mining, Web Mining, Text Mining, Multi-media Mining) § Predicting outcomes and trends § Identifying associations between events and outcomes § Assessing and measuring the sentiments § Profiling (clustering) groups with similar behavioral patterns § Other knowledge nuggets
- 29. 29 Stéphane Gagnon2014-11-12 Geo-Contextualized Text and Voice Messages
- 30. 30 Stéphane Gagnon2014-11-12 Geo-Analytics of Test Reports
- 31. 31 2014-11-12 StéphaneGagnon 4. Big Data and Geo-Agility Stéphane Gagnon
- 32. 32 Stéphane Gagnon2014-11-12 Competitive Advantage
- 33. 33 Stéphane Gagnon2014-11-12 Pressures for Agility
- 34. BI and Agility 34 Stéphane Gagnon 2014-11-12 Process efficiency and cost reduction Brand management Revenue maximization, cross-selling/up-selling Enhanced customer experience Churn identification, customer recruiting Improved customer service Identifying new products and market opportunities Risk management Regulatory compliance Enhanced security capabilities
- 35. Big Data -Definition Big Data means different things to people with different backgrounds and interests Traditionally, “Big Data” = Giga and Tera E.g., volume of data at CERN, NASA, Google, … The Vs that define Big Data Volume Variety Velocity Veracity Variability Value 35 Stéphane Gagnon 2014-11-12
- 36. 36 2014-11-12 StéphaneGagnon Big Data Examples Data Sources Web text documents Multimedia annotations Web logs RFID GPS systems Sensor networks Social networks Internet search indexes Detail call records Application Domains Financial markets Broadcasting Biology and life sciences Healthcare informatics Transportation Security and defense Atmospheric science Genomics and research Energy and SCADA
- 37. 37 Stéphane Gagnon2014-11-12 Big Data Architecture Marketing Applications Business Intelligence Data Mining Math and Stats Languages ANALYTIC Customers Partners Business Analysts Data Scientists TOOLS & APPS USERS UNIFIED DATA ARCHITECTURE MOVE MANAGE ACCESS INTEGRATED DATA WAREHOUSE DISCOVERY PLATFORM DATA PLATFORM System Conceptual View Marketing Executives Operational Systems Frontline Workers Engineers EVENT PROCESSING ERP ERP SCM CRM Images Audio and Video Machine Logs Text Web and Social BIG DATA SOURCES
- 38. 38 Stéphane Gagnon2014-11-12 Big Data Requirements A Clear business need Keys to Success with Big Data Analytics Strong, committed sponsorship Alignment between the business and IT strategy A fact-based decision-making culture Personnel with advanced analytical skills A strong data infrastructure The right analytics tools
- 39. 39 Stéphane Gagnon2014-11-12 Conclusion: Toward Geo-Agility People-Centric: Track geo-information from key individuals and assets across/around the enterprise Contextualize: Add geo-info to unstructured contents, use DM and TM with geo-analytics Exploration: Link contextualized geo-info to real-time decision requirements Open: Leverage open and mobile sources Big Data: Make real-time streaming capabilities Event-Driven: Develop organization agility and resilience, capability to automate adaptation Emergent Strategies: Adapt business strategy along with evidence-based decision-making
- 40. Merci! Stéphane Gagnon,Ph.D. Professeur agrégé Département des sciences administratives Université du Québec en Outaouais Pavillon Lucien-Brault 101, rue St-Jean-Bosco, Local A2228 C.P. 1250, succursale Hull Gatineau (Québec) J8X 3X7 Téléphone: 819-595-3900, poste 1942 Télécopieur: 819-773-1747 Courriel: stephane.gagnon@uqo.ca Web: http://www.gagnontech.org Skype: stephanegagnon 40 Stéphane Gagnon 2014-11-12 Crédits des photos: SJ: http://www.lgt.ws, AT et LB: http://www.flickr.com/photos/uqo/