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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

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Title: 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


1
Enterprise and Business Intelligence Systems
(e.bis.business.utah.edu)Research Lab, UA -gt
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_at_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.,
  • 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
  • Data Visualization Needs
  • Going beyond business charts (e.g., pie, line,
    bar charts)
  • Maps, trees, 2-D, and 3-D

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.
  • 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.
  • Time-series Analysis
  • Analyzing large set of time-series data to find
    certain regularities and interesting
    characteristics.

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 customers visit a grocery store
  • an online purchase from a virtual store such as
    Amazon.com

11
Market Basket (Association Rule) 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
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 lt25
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, Contd
  • 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
KM Process
  • Identify
  • Collect
  • Organize
  • Represent
  • Store
  • Locate
  • Retrieve
  • Extract
  • Discover
  • Visualize
  • Interpret
  • Share
  • Transfer
  • Adapt
  • Apply
  • Monitor
  • Evaluate
  • Create

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
  • RD
  • Intranet
  • Consulting
  • B2B, B2C, e-government, e-financing, e-risk
    management

25
Web Mining Knowledge Refreshing
26
The KDD Process
Data
27
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
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
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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