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e.bis Research Focus

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e.bis Research Focus

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  1. Enterprise and Business Intelligence Systems (e.bis.business.utah.edu)Research Lab, UA -> UUDirectorOlivia R. Liu Sheng, Ph.D.Emma Eccles Jones Presidential Chair of BusinessSchool of Accounting and Information SystemsDavid Eccles School of BusinessUniversity of Utah801-585-9071, olivia.sheng@business.utah.edu

  2. e.bis Research Focus • Enterprise Systems • E-procurement technology • Web content caching and storage mgmt • Enterprise application integration • Process modeling and re-use • System security and risk management • Portal design and management • Business Intelligence Systems • Decision support systems • Data/web mining • Knowledge management • Knowledge refreshing • Personalization

  3. e.bis Research Output • Models • Methods • Technology • Analyses Fueled by Applications!

  4. Faculty Olivia R. Liu Sheng, Ph.D. UU Paul Hu, Ph.D. UU Ph.D. students and Post Docs Xiao Fang, 5th-yr Ph.D. student UA Lin Lin, 3rd-yr Ph.D. student UA Wei Gao, 3rd-yr Ph.D. student UA Hua Su, post-doc UA Xiaoyun Sun, 1st-yr Ph.D. student UA Zhongmin Ma, 1st-yr Ph.D. student UU 6 to 10 Master and UG students per yr International and industrial collaborators

  5. Web Mining for Knowledge Management

  6. What is Data Mining? • The automated process of discovering relationships and patterns in data • Related terms: knowledge discovery in database (KDD), machine learning • A step in the knowledge discovery process consisting of particular algorithms (methods) that under some acceptable objective, produces a particular enumeration of patterns (models) over the data. • An iterative process within which progress is defined by “discovery”, through either automatic or manual methods • The application of statistical and artificial intelligence techniques (algorithms) for discovering patterns and regularities in large volumes of data.

  7. Why Data Mining • Type of knowledge (more abstract) and the level of sophistication in required computation, e.g., • Data Visualization Needs • Going beyond business charts (e.g., pie, line, bar charts) • Maps, trees, 2-D, and 3-D • Which buyers are likely to be late on future payments? • Which sellers are likely to be late on future deliveries? • If a seller increases product-in-week by x units, how much % of sales increase can be expected. • Which buyers are similar in their buying powers and product and contract preferences? • Frequency in discovering and applying the knowledge is met with bottlenecks in human processing • Decision support for buyers, sellers and market hosts at each transaction decision point

  8. Taxonomies of Data Mining • By Tasks • By Data

  9. Data Mining Tasks • Association/Sequential Patterns • The discovery of co-occurrence correlations among a set of items. • Time-series Analysis • Analyzing large set of time-series data to find certain regularities and interesting characteristics. • Clustering • Identifying clusters embedded in the data, where a cluster is a collection of data objects that are “similar” to one another. • Classification • Analyzing a set of training data and constructing a model for each class based on the features in the data. • Class Description • Providing a concise and succinct summarization of a collection of data.

  10. Market Basket (Association Rule) Analysis • A market basket is a collection of items purchased by a customer • in an individual customer transaction, which is a well-defined • business activity • Ex: • a customer’s visit a grocery store • an online purchase from a virtual store such as ‘Amazon.com’

  11. Market Basket (Association Rule) Analysis • Market basket analysisis a common analysis run against • a transaction database to find sets of items, or itemsets, • that appear together in many transactions. Each pattern extracted • through the analysis consists of an itemset and the number of • transactions that contain it. • Applications: • improve the placement of items in a store • the layout of mail-order catalog pages • the layout of Web pages • others?

  12. Clustering Clustering distributes data into several groups so that similar objects fall into the same group. For example, we can cluster customers based on their purchase behavior. Applications: customer, web content, document and gene segmentation

  13. Classification Classification classifies data into pre-defined outcome classes Example:

  14. Classification Age <25 Car Type in {sports} High Low High Applications: customer profiling, shopping prediction Diagnostic decision support

  15. By Data • Structured alphanumeric data • Buyer, supplier, product, order, bank acct • Image data • Satellite, patient, document, handwriting, facial, etc. • Spatial data • Map, traffic, geological, CAD, graphics, etc.

  16. By Data, Cont’d • Temporal data • Time series, population, stock, inventory, sales, etc. • Spatial and temporal data – trajectory • Text – documents, web pages, etc. • Video/audio – surveillance video, voice, music, etc.

  17. Web (Data) Mining • Web data – generated or used by the Web • Web content - static or dynamic • Web structure – hyperlinks • Web usage – web access log

  18. Why is Web Mining Important? • Rich data gathering and access medium • A variety of important applications • Information retrieval • Ecommerce – CRM, SCM, etc. • Knowledge management • Interesting challenges • Scalability – global, multi-lingual, growth • Agility of knowledge

  19. What is “knowledge”? • Relationships and patterns in data • Organized, analyzed and understandable • Truths, beliefs, perspectives, concepts, procedures, judgments, expectations, methodologies, heuristics, restrictions, know-how • Applicable to problem solving and decision making • DBs, documents, policies and procedures as well as the un-captured, tacit expertise and experience Actionable, at the right place and right time!!!

  20. What is Knowledge Management? • Views: • Process (KM activities) • Goal (Operational efficiency and innovations) • Methodology (formalization, control and technology) • Delphi Group: “Leveraging collective wisdom to increase responsiveness and innovation.”

  21. What is a KM program? • Processes • Organizational structure and policies • Management theories and methodologies • Information assurance • Technologies and resources • Implementation, training and change management • Measurement, maintenance and evolution A multi-disciplinary effort!!! • Managerial and cultural • Technological and engineering • esources, support and technology for • Creation, acquisition, organization, storage, retrieval, visualization and sharing of knowledge

  22. Identify Collect Organize Represent Store Locate Retrieve Extract Discover Visualize Interpret Share Transfer Adapt Apply Monitor Evaluate Create KM Process

  23. Data Mining & KM • Data mining  discover knowledge • Data mining  support management of KM infrastructure • (Personalized) content management • Security management • Workflow management • Scalable performance

  24. Web Mining & KM • Web mining  discover knowledge • Web mining  support management of web KM portal • R&D • Intranet • Consulting • B2B, B2C, e-government, e-financing, e-risk management

  25. Web Mining & Knowledge Refreshing

  26. Step 5: Interpretation & Evaluation Step 4: Data Mining Discovered Knowledge Step 3: Transformation Step 2: Cleaning & Preprocessing Patterns Step 1: Selection Transformed Data Preprocessed Data Target Data The KDD Process Data

  27. Step 5: Interpretation & Evaluation Step 4: Data Mining Discovered Knowledge Step 3: Transformation Step 2: Cleaning & Preprocessing Patterns Step 1: Selection Transformed Data Preprocessed Data Target Data Types of Domain Knowledge DBA Knowledge Data Domain Expert Knowledge Data Mining Expert Knowledge

  28. Fundamental Problems • The size of the database is significantly large • The number of rules resulting from mining activity is also large • The knowledge derived from a database reflects only the current state of the database • 

  29. Step 5: Interpretation & Evaluation Step 4: Data Mining Discovered Knowledge Step 3: Transformation Step 2: Cleaning & Preprocessing Patterns Transformed Data Preprocessed Data Target Data Issues in the KDD Process Agility Scalability Data

  30. Knowledge Refreshing • The process to efficiently update discovered knowledge as data and domain knowledge change. • Goals • Up-to-date knowledge (Agility) • Knowledge Re-use (Scalability)

  31. Discovered Knowledge Patterns Transformed Data Preprocessed Data Target Data Type of Changes NEW NEW NEW NEW NEW NEW DBA Knowledge Data Domain Expert Knowledge Data Mining Expert Knowledge NEW

  32. Knowledge Refreshing • Needs assessment • Monitoring vs. analytic approaches • Monitoring/estimate changes in knowledge to determine if and when to re-mine • Incremental data mining (learning) • How to leverage knowledge previously discovered from data mining to improve computational efficiency and quality of knowledge

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