1、10/2002,1,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, ol
2、ivia.shengbusiness.utah.edu,10/2002,2,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 De
3、cision support systems Data/web mining Knowledge management Knowledge refreshing Personalization,10/2002,3,e.bis Research Output,Models Methods Technology Analyses,Fueled by Applications!,10/2002,4,Faculty Olivia R. Liu Sheng, Ph.D. UU Paul Hu, Ph.D. UUPh.D. students and Post Docs Xiao Fang, 5th-yr
4、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 UU6 to 10 Master and UG students per yrInternational and industrial collaborators,Web Mining for Knowledge Management,10/2002,
5、6,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 enumera
6、tion 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 d
7、ata.,What is Data Mining?,10/2002,7,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 futu
8、re 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 knowl
9、edge is met with bottlenecks in human processing Decision support for buyers, sellers and market hosts at each transaction decision point,10/2002,8,Taxonomies of Data Mining,By Tasks By Data,10/2002,9,Data Mining Tasks,Time-series Analysis Analyzing large set of time-series data to find certain regu
10、larities 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 Analyzin
11、g 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.,10/2002,10,Market Basket (Association Rule) Analysis,A market basket is a collection of items purchased by a c
12、ustomerin an individual customer transaction, which is a well-defined business activity Ex: a customers visit a grocery store an online purchase from a virtual store such as A,10/2002,11,Market Basket (Association Rule) Analysis,Market basket analysis is a common analysis run against a transaction d
13、atabase 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
14、 layout of Web pages others?,10/2002,12,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,10/2002,13,
15、Classification,Example:,Classification classifies data into pre-defined outcome classes,10/2002,14,Classification,Car Type in sports,High,Low,Age 25,High,Applications: customer profiling, shopping prediction Diagnostic decision support,10/2002,15,By Data,Structured alphanumeric data Buyer, supplier,
16、 product, order, bank acct Image data Satellite, patient, document, handwriting, facial, etc.Spatial data Map, traffic, geological, CAD, graphics, etc.,10/2002,16,By Data, Contd,Temporal data Time series, population, stock, inventory, sales, etc. Spatial and temporal data trajectory Text documents,
17、web pages, etc. Video/audio surveillance video, voice, music, etc.,10/2002,17,Web (Data) Mining,Web data generated or used by the Web Web content - static or dynamic Web structure hyperlinks Web usage web access log,10/2002,18,Why is Web Mining Important?,Rich data gathering and access medium A vari
18、ety of important applications Information retrieval Ecommerce CRM, SCM, etc. Knowledge management Interesting challenges Scalability global, multi-lingual, growth Agility of knowledge,10/2002,19,What is “knowledge”?,Relationships and patterns in data Organized, analyzed and understandable Truths, be
19、liefs, 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 an
20、d right time!,10/2002,20,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.”,10/2002,21,What is a KM progr
21、am?,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 engi
22、neering,esources, support and technology for Creation, acquisition, organization, storage, retrieval, visualization and sharing of knowledge,10/2002,22,KM Process,Identify Collect Organize Represent Store Locate Retrieve Extract Discover,Visualize Interpret Share Transfer Adapt Apply Monitor Evaluat
23、e Create,10/2002,23,Data Mining & KM,Data mining discover knowledge Data mining support management of KM infrastructure (Personalized) content management Security management Workflow management Scalable performance,10/2002,24,Web Mining & KM,Web mining discover knowledge Web mining support managemen
24、t of web KM portal R&D Intranet Consulting B2B, B2C, e-government, e-financing, e-risk management,Web Mining & Knowledge Refreshing,10/2002,26,Data,The KDD Process,10/2002,27,Data,Types of Domain Knowledge,DBA Knowledge,Domain Expert Knowledge,Data Mining Expert Knowledge,10/2002,28,Fundamental Prob
25、lems,The size of the database is significantly largeThe number of rules resulting from mining activity is also largeThe knowledge derived from a database reflects only the current state of the database,10/2002,29,Issues in the KDD Process,Data,Scalability,Agility,10/2002,30,Knowledge Refreshing,The
26、process to efficiently update discovered knowledge as data and domain knowledge change.Goals Up-to-date knowledge (Agility)Knowledge Re-use (Scalability),10/2002,31,Data,Type of Changes,DBA Knowledge,Domain Expert Knowledge,Data Mining Expert Knowledge,NEW,NEW,NEW,NEW,NEW,NEW,NEW,10/2002,32,Knowledg
27、e 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,
