Chapter 6 Date Warehousing

1
??? ?????????

• 6.1 ?????????
• 6.2 ????????????
• 6.3 ??????????????

2
Chapter 6 Date Warehousing Data Mining

• 6.1 Definitions of data warehousing data mining
• 6.2 Approach and technologies of data mining
• 6.3 Application of data mining in CRM

3
6.1 ?????????

???????

• E????????
• E????,?????????13?
• ?7????????????
• ?????????????????????,????????????
• ????
• ??????/??/??/????
• ??????/????/????
• ?????????????????????????????????/????????

4
6.1 Definitions of Data Warehousing Data Mining

Background of Business Intelligence (BI)

• Information features in the e-century
• In E era, the amount of electrical data grows 1
  to 3 times per year.
• only 7 of existing data are properly analyzed
  and applied.
• Demand for information application data -gt
  information -gt knowledge -gt wisdom.
• Goals
• To assist organization in obtaining /
  generalizing / explaining /analyzing data.
• Profit analysis / Relationship marketing /
  Customer management
• Application domain customer contribution
  analysis / market segmentation / risk management
  / cross-sells analysis / product profile analysis
  / investment portfolio analysis

5
6.1 ?????????

????(BI)??

• ???????????????????,?????????????????
• ??
• ????????????????
• ?????????????
• ?????????????????
• Provide the Right Information to the Right
  Persons at the Right Time.

BI????????????????????????????????,?????????
6
6.1 Definitions of Data Warehousing Data Mining
Definition of Business Intelligence (BI)

• Definition The ability to rapidly analyze and
  synthesize enterprise data in order to improve
  its decision quality and business performance.
• Features
• Analyze business development trend.
• Decision supports.
• Provide the Right Information to the Right
  Persons at the Right Time.

BI techniques can provide statistical analysis,
data mining and analysis, and transform great
quantity of data into meaningful knowledge to
support the decision making.
7
6.1 ?????????

???????(1)

• ???????,??Bottom-Up??????,???????,??????(Row
  Data)?
• ??????,???????????????,????????????(Organized
  Information)?
• ??????????(domain know-how),????????(Knowledge)?
• ???????????,???????,????(Wisdom)?

BI????Bottom-Up??
8
6.1 Definitions of Data Warehousing Data Mining

Evolution of BI (1)

• In the organization, using the Bottom-Up process
  to collect and save the record that forms the Raw
  Data.
• On the basis of making a decision, selecting,
  reading, handling, analyzing the raw data the
  that obtain the Organized Information.
• Merging with organized information and domain
  know-how of the company that transforms the
  Knowledge.
• Added the experience of the decision-makers,
  fully utilized the knowledge that produce Wisdom.

The approach to constitute BI is Bottom-Up
process
9
6.1 ?????????

???????(2)

• Data?????(?????)
• Information ?????Data(?????)
• Knowledge Information????(?????)
• Wisdom ???????(???????)

10
6.1 Definitions of Data Warehousing Data Mining
Evolution of BI (2)

• Dataraw data (ex experiment data)
• Information collection and arrangement of data
• (ex experiment result)
• Knowledge Integration of information and
  experience
• (ex experiment conclusion)
• Wisdom Heuristic knowledge
• (ex application of experiment conclusion)

11
6.1 ?????????

???????

• ?????????????????????
• ???????????/???????????,???????????
• ????????????????,?????????????
• ???????????????(Bottom-Up)???????
• E?????????(1)????/???(2)????/??/??????

BI????Top-Down??
12
6.1 Definitions of Data Warehousing Data Mining

Application of BI (1)

• The high-level executives often need deeper
  knowledge to make crucial decisions.
• The application of e-technology can be used to
  improve (1) information extraction/understanding
  and (2)decision making /transaction.

The applications of BI are Top-Down processes
13
6.1 ?????????

??????????

• ????????????????(DW?DM)
• ??????IT?????????????????(KM)
• ???????????????/????(GIS)
• ??????????????,??????????(KM?VR)
• ??????????????????
• ?????????????????????????????(ERP?PM)
• ??????????????????????(KM?PM)
• ??/???????????????(CRM)
• ?????????????????(CRM)

14
6.1 Definitions of Data Warehousing Data Mining
Application of BI (2)

• Data analysis Discover the trend and model of
  the great quantity data(DW?DM)
• Intelligence disposition Apply IT solution for
  commerce analysis and knowledge extraction.(KM)
• Geographical (spatial) analysis Integrate with
  business data and geographic or population
  information. (GIS)
• Data Visualization Use of the GUI to view data
  and to support the optimal business decisions.
  (KM?VR)

15
6.1 Definitions of Data Warehousing Data Mining

• BSC (Balance Score Card) Inspect the performance
  of the managing of enterprise correctly.
• Project management Fully grasp information in
  order to make decision of resource allocation and
  project choosing (ERP?PM).
• Collaborative intelligence accumulation Use the
  staffs experience (KM?PM).
• Business / marketing analysisFully control the
  sales data and trends (CRM).
• Customer information analysisUnderstand
  customers behavior and preferences (CRM).

Application of BI (3)

16
6.1 ?????????

CRM????????

• ??1????????,??????????,????????????
• ?????
• ????????
• ???????
• ??????
• ??2??????????????????????,??????,?????????,??????
  ????????
• ?????????????????????????????????????????????????
  ????

17
6.1 Definitions of Data Warehousing Data Mining
Definition of CRM

• Definition 1 Through effective communication and
  understanding customer's behavior, enterprises
  can reach the connection and expand the customer
  source goal.
• Increase new customer.
• Take precautions against the existing customer
  lost.
• Improve customer's loyalty and the profit of the
  company.
• Definition 2It is a kind of repeated course that
  changes customers information into customers
  positive relation. Using of information
  technology, strengthen the practicability and
  speed of administrative decision.
• ConclusionCRM can offer more relevant
  information to CSR, MIS, sales, and executive
  about relation BI.

18
6.1 ?????????

CRM????

• CRM???????????????????????
• CRM? ??????????????(????????),????????????????????
  ??????
• ???????????,???????????????????????
• ??????????????????????????,??????????????
• Note?????????
• ????????????????

?????BI??????,????????????????,?????????????
19
6.1 Definitions of Data Warehousing Data Mining

Topics of CRM

• CRM should integrate all of the operations,
  members , transactions of the company.
• Customer relationship of CRM is not only trade
  (products or the service), but expect customers
  purchase continually or other valued behaviors.
• Communication must be two-way, integration, have
  records, management that make customer
  relationship is really exist.
• Contact Center is the first step of CRM.

Integrate the concepts and technologies of BI
collection, extraction, analyze services
information effectively, support decision making
of the organization.
20
6.1 ?????????

BI?CRM?? (1)

• ???????????????????,??/????,??????????????????
• ????????????????,??????
• ??E?????????(Transaction)??????,??????????????????
  ???
• ??BI???????,???????????,???????
• ?CRM????????????????????????(BI)???????

21
6.1 Definitions of Data Warehousing Data Mining

Relationship between BI and CRM (1)

• Digital information includes various kinds of
  data that enterprises have accumulated for many
  years. Internal and external information and
  various types of accumulated experiences with
  customers or other enterprises
• Most of enterprise information systems only deal
  with transactional data.
• BI data mining helps better understand enterprise
  operational models and customers characteristics.
  
• CRM integrates marketing and customer information
  systems and BI decision analysis systems.

22
6.1 ?????????

?????CRM??(2)
Source http//bi.fast.com.tw/newpages/bi01.htm
Source http//www.bitech.com.tw/
23
6.1 Definitions of Data Warehousing Data Mining

Relationship between BI and CRM (2)
Source http//bi.fast.com.tw/newpages/bi01.htm
Source http//www.bitech.com.tw/
24
?????CRM??(3)
Data

Information
Knowledge
??BI??????????????????Bottom-Up????,????Top-Down??
????
Wisdom
25
Relationship between BI and CRM (3)
Data

Information
Knowledge
Button-Up collect related marketing, services
and customers data Top-Down strategies and
applications.
Wisdom
26
6.1 ?????????

????????????
Database Design
27
6.1 Definitions of Data Warehousing Data Mining

Steps of building and analyzing data of contact
center
Database Design
28
6.1 ?????????

?????????

• ??????????????????????,??????????,????????????
• ??????????????????,??????????????
• ?????????????????????????????,?????????????

29
6.1 Definitions of Data Warehousing Data Mining

Database system application

• Database is an IT application that collects and
  marshals various data, and then saving them by
  effective and organizational approach.
• Database is widely used in various areas, i.e.,
  library management.

30
6.1 ?????????

?????????

• File(??)???????????????????????????????????,?????
  ??????
• Record(??)
• Field(??)
• Character(??)
• Entity(??)
• Attribute(????????)
• ????????,?Database???Record???Record?Field???,??Fi
  eld?????????,??Data Dictionary???????

31
6.1 Definitions of Data Warehousing Data Mining
Database system (DBMS) related vocabularies

• File
• Record
• Field
• Character
• Entity
• Attribute
• A database system includes various Records that
  are form by Field. Field can be any data type,
  and using Data Dictionary to class the context.

32
6.1 ?????????

???????????

• ????????
• ????????
• ????????
• ??????
• ???????????,????

33
6.1 Definitions of Data Warehousing Data Mining

Bottleneck of file systems for data storage

• Lack security of the data
• Lack commonality of the data
• Lack flexibility of the data
• Repeat records
• Hard to maintain the system because of tight
  linkages between data and programs.

34
6.1 ?????????

????????????

• ????????????????????,????????????????????????????
• ?????????????????,??????????
• ???????????????????,????????????????????
• ????????,????????????????????????????????

35
6.1 Definitions of Data Warehousing Data Mining
Advantages of database system for data storage

• Storage, search, indexing capabilities
• Extraction of information for different commerce
  applications. Generate the necessary reports.
• Internet access.
• Flexible search functions. Efficiently manage
  large amount of data.

36
6.1 ?????????

?????(1)

• Selection????????????????(Row)?
• Projection????????????(Column)?
• Product????Table??,?Table 1?N Rows?I
  ColumnsTable 2?M Rows?J Columns,????????????NM
  Rows?IJ Columns??Table?
• ??Table???N Rows?M Rows,?Union????NM
  Rows(????Table?Schema??,?????)?

37
6.1 ?????????

?????(2)

• Set Difference?????Relation???,?????Relation???,?R
  elation??????A-(A-B)???
• Join???????????Field???Tuple??,??Tuple????????Tupl
  e???????????,??Selection?Product???,??????????????
  ???????????????View??,???Row??????

38
6.1 Definitions of Data Warehousing Data Mining
Operations of database

• Selection is choosing the pass muster Row from
  table.
• Projection is moving out specific Column from
  table.
• Product is the multiplication of two tables. If
  Table 1 has N rows, I columns, and Table 2 has M
  rows, J columns, then the product is a new table
  that has NM rows, IJ columns.
• If two table is N rows, M rows respectively, then
  Union result is N M Rows .
• Set Difference is to find that data exists in
  one Relation and not exists in another. The
  interaction of two Relations is A-(A-B).
• Join is to combine with two mutual Fields.

39
6.1 ?????????

SQL (Structured Query Language)

• SQL????????Relational Algebra?????????
• ?????????????????????
• ?IBM?1970???,????ANSI???
• ???RDBMS???,SQL?????Table Schema??????????????
• ?Create Table???????????Delete???Row?Insert
  Into???Row??Table?)?

40
6.1 Definitions of Data Warehousing Data Mining
SQL (Structured Query Language)

• SQL standards for Structured Query Language and
  is developed according to Relational Algebra.
• SQL is developed for allowing high level language
  to access the database.
• SQL is developed by IBM in 1970 and is an ANSI
  standard language.
• In addition to query the data of RDBMS, SQL can
  define the meaning of Table Schema, data built up
  and translation.
• Ex Creating Table can set up data scheme and
  attribute, Delete can delete a row, Insert
  Into is added a row to a table.

41
6.1 ?????????

RDBMS vs OODBMS

• ?RDBMS?,??????????????????(Object-Oriented
  Database Management SystemOODBMS)?
• ?OODBMS???,????????????,?????Object ID????
• ??????????Object ID?Object Link??(??Join??)?
• OODBMS??????Schema Querying(Run-Time Schema
  Querying),???????????Meta-Data(?)???
• ????Hybrid OO-Relational???????,????RDBMS?????????
  ?,?OODBMS?????

42
6.1 Definitions of Data Warehousing Data Mining

RDBMS vs OODBMS

• Except RDBMS, DBM gradually develops
  OODBMS(Object-Oriented Database Management
  System) way.
• In the OODBMS environment, all objects save in
  one space and tracking by Object ID and Object
  Link.
• OODBMS provides really time Schema
  Querying(Run-Time Schema Querying) to query
  Meta-Data of application object.
• Using Hybrid OO-Relational database systems now
  is more, it offers the expression method with
  objects of RDBMS, pure OODBMS is comparatively
  used few.

43
6.2 ????????????

?????????

• ???????????????????????
• ??????????????-?????,?Entity-Relation
  Model(??ER Model)
• ????????????????????????????,???????????????????
  
• ???????????????????????DBMS?????,???????????????
  ??
• ??????(Normalization)?????????????????,?????????
• ??(Primary Key)????????(Key),??????(Primary
  Key)????????????

44
6.2 Approach and technologies of data mining

Database design of the contact center

• First Step The demand specification of DB
  should be clear out.
• Second Step concept designbuild up
  Entity-Relation Model (ER-Model)
• Third Step logic desgntransform into decided
  DBMS. (Ex the logic data model of RDBMS)
• Fourth Step reality design transform the logic
  data model into hardware type of DBMS to decide
  data storage structure and search route.
• Normalizationseek relation, and set up
  structural format.
• Decide Primary KeyPrimary Key represents the
  characteristic of this table uniquly.

45
6.2 ????????????
ER Model??

46
6.2 Approach and technologies of data mining
ER Model example

47
6.2 ????????????

?????????(1)

• ???????????,??????????????????,???????????
• ????????????????,??????????(Extraction?Transformat
  ion?Loading, ETL)?????,??????????
• ???????????
• ???(?????????)
• ???(?????????)
• ????
• ?????(??????)??????????

48
6.2 Approach and technologies of data mining

Data Warehousing of a contact center (1)

• Data Warehousing is to bring together
  information from multiple operation systems as to
  provide a consistent database source for decision
  support.
• Collection of data by CSR (Customer Service
  representative) that is deal with extraction,
  transformation, loading, ETL and saving in the
  data base.
• Data is the database should be
• correctness
• completeness
• Integration
• Classification according to translation (Ex
  customers, products).

49
6.2 ????????????

?????????(2)

• ??????????????,???????????
• ??(Multi-Dimension)???????
• ????(Client/Server Architecture)
• ????(Middleware)
• ???????(GUI)
• ????(Replication)
• ????(Parallel Processing)

50
6.2 Approach and technologies of data mining

Data Warehousing of a contact center (2)

• Data Warehouse is the base of Decision Support
  System (DSS) and the technologies are
  multi-dimension and complicated
• Multi-Dimension Database Management System (DBMS)
• Client / Server Architecture
• Middleware softwre
• GUI (Graphical user interface)
• Replication
• Parallel Processing

51
6.2 ????????????

?????????(3)

• ????
• ????????
• ??????
• ?????????????
• ??????????
• ?????????????????

52
6.2 Approach and technologies of data mining

Data Warehousing of a contact center (3)

• Fundamental attributes
• Classification according to translation
• Integration of multiple data
• Users can not change data without authorization
• Data changes with time constantly
• To help enterprise makes faster and better
  decisions.

53
6.2 ????????????

?????????(4)

• ???????????????????,???????????
• ????????,?????????
• ???????????,?????????????
• ??????????????,???????
• ??????????,?????????????????
• ??????????????
• ??????????????

54
6.2 Approach and technologies of data mining

Data Warehousing of a contact center (4)

• Whether construction the data warehousing project
  is succeeded and obtained its benefit, there are
  some essential factors that should have high
  dependence as following
• Set up the support department and support the
  projects to carry out.
• Define the clear goal and demand range and let
  the data warehousing platform accord with the
  requirement.
• It should have the high-level executive support,
  a good trans-departmental communicative channel,
  and setting up the corporate accountability.
• The data warehousing platform must possesses
  opening, expanding, stable, attributes, keeping
  the same interface specification of the front
  system .

55
6.2 ????????????

????????
56
6.2 Approach and technologies of data mining

Data Warehousing Process
57
6.2 ????????????

????????

• ????????????????????????
• ????????????????????????????????
• ??????????????
• ???????????????Data Mining?OLAP????????
• ???????????????

58
6.2 Approach and technologies of data mining

Benefits of data warehousing

• Promote the operation ability and analysis
  efficiency of the system for users.
• Train enterprises the ability of obtaining
  information rapidly, shorten the reflect time of
  executive .
• Strengthen enterprise's information
  centralization and integration ability.
• Offer a new approach of analysis to enterprises,
  support analysis tasks , such as Data Mining ,
  OLAP ,etc..
• Improve enterprises to analyze the business trend.

59
6.2 ????????????
??????

• ?????????,???????????????????????????,????????????
  ???
• ????????(???????),???????????(?????????
• ???????????????????????????,
• ?????????????????????????????,????????????????????
  ?????,?????????????????
• ????,??????????,??????????????????,???????????????
  ????,??????????????

60
6.2 Approach and technologies of data mining
Future trend of data warehousing

• The data warehousing technology overcomes the
  bottleneck of the information technology
  gradually, i.e., portability of cross-platform,
  analysis efficiency, and the huge amount data
  storage.
• The subject applied to CRM at present will be
  developed towards the depth value of information
  analysis gradually.
• Using the data warehousing technology, that can
  transform a great quantity, unorderly data into
  BI, and then help enterprises to analysis the
  customers and assistance in the product
  planning, increasing the competitiveness of
  enterprises and profit-making chance.

61
6.2 ????????????

????

• ????(Data Mining)?????????????????????????,???????
  ???????
• ????????????????????
• ???????????????????????????????????
• ??????????????????????????????????????????????????
  ?????
• ????????????????????????????????????(Baysian
  Network)?Nearest Neighbor?Attributed-Oriented
  Induction?Binary/Quantitative Association Rules??

62
6.2 Approach and technologies of data mining
Data Mining

• Data Mining discusses how to explore hiding
  useful information and trend among a large amount
  of data, in order to offer decision supports.
• Data Mining process can be viewed as a KDD
  process, including data selecting,
  pre-processing, data translation, data mining,
  explanation, and estimation.
• Related research areas with the data mining
  include DB technology, AI, expert system, data
  visualization, statistics.
• The popular models and technologies at present
  include decision tree, neural network, Baysian
  Network, Nearest Neighbor, Attributed-Oriented
  Induction, Binary/Quantitative Association Rules.

63
6.3 ??????????????

?????????(1)

• ????
• ??????????????,???????,?????(Class)
• ?????????????????????,??????????
• ????,??????????????????????,??????????????????????
  
• ???????????????????????????
• ?????????????????????,???????????(Segmentation)

64
6.3 Application of data mining to CRM

Data Mining of the contact center (1)

• Data Mining Functionalities
• Classification Classification is subdivided by
  assigning each element or record to a predefined
  class on the basis of a model developed through
  training on pre-classified examples.
• Estimation Estimation deals with continuously
  valued outcomes and given some related input data
  to come up with a value for unknown continuous
  variable.

65
6.3 Application of data mining to CRM
Data Mining of the contact center(2)

• Data Mining Functionalities
• Prediction Prediction is similar classification
  and estimation except that the records are
  classified according to some predicted future
  behavior or estimated future value according to
  observation value of the past.
• Affiliation Grouping Affinity grouping is to
  determine which things go together.
• Clustering Clustering is to segment a
  heterogeneous population into a number of more
  homogeneous subgroups or clusters.

66
6.3 ??????????????

??????
67
6.3 Application of data mining to CRM

Data Mining Approach
68
6.3 ??????????????

??????

• ???????????????????????
• ????????????????????????
• ???????????????????,???????????
• ?????????OLAP????????????
• ????????????????????,????????????,????????
• ?????????????????????????????
• ???????????????????,??????????????????,??????????
  ??????

69
6.3 Application of data mining to CRM

Data Mining Plan

• Confirm problemConfirm the potential problems
  that are wanted to deal with.
• Decide data sourceDetermine where the basic data
  comes from .
• Decide requirementDecide interview and seek the
  relevant problem data.
• Establish ModelOLAP or neurual network.
• Data arrangementTransform the data because of
  different data models and data demand.
• Apply data warehousingData mining process must
  be supported with data warehousing.
• Software applicationSet up model through the
  specialized statistical analysis software.

70
6.3 ??????????????

??????
71
6.3 Application of data mining to CRM

Data Mining Model
72
6.3 ??????????????

?????????(1)

• ???????????,??????????????????,???????????
• ????????????????,??????????(Extraction?Transformat
  ion?Loading, ETL)?????,??????????
• ???????????
• ???(?????????)
• ???(?????????)
• ????
• ?????(??????)??????????

73
6.3 Application of data mining to CRM
Data Warehousing of the contact center(1)

• Data mining A huge database using structural
  ways to store data that was been related from
  different operation systems.
• Extracting, transforming, loading, the data
  received from customer service personnels
  on-line process system. Then store it in the
  database.
• Must ensure the data in the warehouse is
• Correctness(There are not wrong materials mixed
  among them )
• Completeness(essential data are all stored )
• Combine each other
• Regard trade subject (such as customers,
  products) as its store categorized basis

74
6.3 ??????????????

?????????(2)

• ??????????????,???????????
• ?????????????????????????????,????????????????
• ?????????????(??????????????)????????????????????
  ?????,???????????
• ??????????????????????????,???????,???????,??????
  ???????
• ??????????????????,?????????????

75
6.3 Application of data mining to CRM
Data Mining of the contact center (2)

• All information collected is valuable assets, the
  importance of data mining is very high.
• Data mining is use automation or semi-automatic
  way to carry on trend analysis to a large amount
  of data, and to seek the meaningful relation or
  rule to enterprises.
• Customer segment Analyzing and clustering the
  customers according to various kinds of attribute
  of them(like gender, profession, income and
  Education degree). The same group of customers
  means that their entirety attribute is similar.
  According to this, we can proceed difference
  marketing.
• Customer segment by goals Record and investigate
  the unity consumption habits of all customers to
  segment them. And according to customers
  characteristics, using decision tree structure
  to build segment model.
• Characteristic (features) analysis Utilize the
  cluster analyze model to segment customers, and
  Step forward to analyze each groups
  characteristics.

76
6.3 ??????????????

?????????(3)

• ???????????????????????????????????????,?????????
  ?
• ?????????????,????????????
• ??????????????,?????????????
• ??????????????,????????????
• ??????????????????????????,????????
• ??????????????,?????????????????,???????????????
  ?
• ?????????????????????????,???????????,???????????
  ????
• ???????????????,????????????,????????????????????
  ??????

77
6.3 Application of data mining to CRM
Data Mining of the contact center (3)

• Contribution analysis It means to analyze
  customer's grade to enterprise's contribution
  degree. It is to analyze certain consumption
  attribute for all customers to segment and
  cluster customers.
• Period analysis According to the purchase
  recently to analyze every the shopper's
  characteristic during each period.
• Frequency analysis According to the customer
  buys frequency recently to analyze each shopping
  frequency of the customers characteristic.
• Amount analysis According to the customers
  shopping amount to analyze each amount of
  customers characteristic

78
6.3 Application of data mining to CRM
Data Mining of the contact center (4)

• Lifetime value analysis according to customers
  buying day, frequency and amount to generalize
  the value grades.
• Cross-sell rule analysis Analyzing the detail
  ledger of customer to know which goods they tends
  to buying at the same time while doing shopping,
  to recommend goods conform to customer's
  interests .
• Sequential-sell analysis Seek the priority
  relation that the customer does shopping in
  different shopping experience to make products
  marketing orientation more correct and to slash
  marketing and advertisement cost
• Forecast (prediction) analysis According to
  various kinds of attribute of the potential
  customers, through built customer segment model
  to accurately predict the customer of marketing
  in future will belong to what kinds customer
  types.

79
6.3 ??????????????

?????????(4)

• ????
• ???????????,???????
• ??????????????
• ????????????????????
• ?????????????????
• ??????????????
• ????????,?????????

80
6.3 Application of data mining to CRM
Data Mining of the contact center (5)

• Typical application
• According to the tendency of customer surf the
  webpage to infer their preferences.
• According to the sale data to Explore customer's
  consumption habits.
• Early warning credit card debt from customers
  consume and the data of paying.
• Product sales affiliation (co-relations) in a
  large amount of trade data.
• Discover the hot topics in a large amount of
  customer service data
• Look for the authorize rule of credit card
  according to the history verify data.

81
6.3 ??????????????

????????(1)

• ????
• ????????????????????,?????????????????????????????
  ??????????????,???????????,????????????????
• ?????
• ???????????,??????????,???????????????????,???????
  ???,?????????,?????????????????

82
6.3 ??????????????

????????(2)

• ???
• ??????(The Data Mining Group)???????????????(Predi
  ctive Model Markup Language, PMML)?PMML???XML??,??
  ????????,?????????????????????????????
• ???
• ?????????????????????????,????????????????????????
  ??,?????????????????????,?????????????????

83
6.3 Application of data mining to CRM
Future trend of data mining

• Elasticity improving Because data mining tools
  are high relation with sample, so the tool should
  have high expandable property, store more
  multiple higher-dimension data.
• Economy improving The conclusion of analysis can
  be implemented in industry's demand, offer the
  rate of returns of investment.
• StandardizationThe Data Mining Group start to
  develop PMML (Predictive Model Markup Language)
  that is a XML standard to describe the common
  prediction model for other data mining, BI
  application.
• IntegrationIn order to promote the operation
  efficiency of data mining model, some data mining
  functions integrate RDBMS to promote query and
  selecting efficacy.