1. 10/20021 Enterprise and Business Intelligence Systems (e.bis.business.utah.edu) Research Lab, UA -> UU Director Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential Chair of Business School of Accounting and Information Systems David Eccles School of Business University of Utah 801-585-9071, olivia.sheng@business.utah.edu

2. 10/20022 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. 10/20023 e.bis Research Output Models Methods Technology Analyses Fueled by Applications!

4. 10/20024 Faculty Olivia R. Liu Sheng, Ph.D.UU Paul Hu, Ph.D.UU Ph.D. students and Post Docs Xiao Fang, 5 th -yr Ph.D. studentUA Lin Lin, 3 rd -yr Ph.D. studentUA Wei Gao, 3 rd -yr Ph.D. studentUA Hua Su, post-docUA Xiaoyun Sun, 1 st -yr Ph.D. studentUA Zhongmin Ma, 1 st -yr Ph.D. studentUU 6 to 10 Master and UG students per yr International and industrial collaborators

5. Web Mining for Knowledge Management

6. 10/20026 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. What is Data Mining?

7. 10/20027 Why Data Mining Data Visualization Needs Going beyond business charts (e.g., pie, line, bar charts) Maps, trees, 2-D, and 3-D Type of knowledge (more abstract) and the level of sophistication in required computation, e.g., 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. 10/20028 Taxonomies of Data Mining By Tasks By Data

9. 10/20029 Data Mining Tasks Time-series Analysis Analyzing large set of time-series data to find certain regularities and interesting characteristics. Association/Sequential Patterns The discovery of co-occurrence correlations among a set of items. 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. 10/200210 Market Basket (Association Rule) Analysis market basket 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. 10/200211 Market Basket (Association Rule) Analysis Market basket analysis Market basket analysis is 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. 10/200212 Clustering 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. 10/200213 Classification Example: Classification classifies data into pre-defined outcome classes

14. 10/200214 Classification Car Type in {sports} High Low Age <25 High Applications: customer profiling, shopping prediction Diagnostic decision support

15. 10/200215 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. 10/200216 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. 10/200217 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. 10/200218 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. 10/200219 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. 10/200220 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. 10/200221 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. 10/200222 KM Process Identify Collect Organize Represent Store Locate Retrieve Extract Discover Visualize Interpret Share Transfer Adapt Apply Monitor Evaluate Create

23. 10/200223 Data Mining & KM Data mining  discover knowledge Data mining  support management of KM infrastructure (Personalized) content management Security management Workflow management Scalable performance

24. 10/200224 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. 10/200226 Data Step 1: Selection Step 2: Cleaning & Preprocessing Step 3: Transformation Step 4: Data Mining Step 5: Interpretation & Evaluation Target Data Preprocessed Data Transformed Data Patterns Discovered Knowledge The KDD Process

27. 10/200227 Data Step 1: Selection Step 2: Cleaning & Preprocessing Step 3: Transformation Step 4: Data Mining Step 5: Interpretation & Evaluation Target Data Preprocessed Data Transformed Data Patterns Discovered Knowledge Types of Domain Knowledge DBA Knowledge Domain Expert Knowledge Data Mining Expert Knowledge

28. 10/200228 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. 10/200229 Issues in the KDD Process Data Step 2: Cleaning & Preprocessing Step 3: Transformation Step 4: Data Mining Step 5: Interpretation & Evaluation Target Data Preprocessed Data Transformed Data Patterns Scalability Discovered Knowledge Agility

30. 10/200230 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. 10/200231 Data Target Data Preprocessed Data Transformed Data Patterns Discovered Knowledge Type of Changes DBA Knowledge Domain Expert Knowledge Data Mining Expert Knowledge NEW

32. 10/200232 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