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 ,

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

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

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

Web Mining for Knowledge Management

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?

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

10/20028 Taxonomies of Data Mining By Tasks By Data

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/ 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’

10/ 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?

10/ 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

10/ Classification Example: Classification classifies data into pre-defined outcome classes

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

10/ 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.

10/ 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.

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

10/ 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

10/ 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!!!

10/ 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.”

10/ 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

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

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

10/ 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

Web Mining & Knowledge Refreshing

10/ 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

10/ 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

10/ 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 

10/ 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

10/ 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)

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

10/ 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