Презентация на тему: 1 Data Management: Warehousing, Analyzing, Mining, and Visualization

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1 Data Management: Warehousing, Analyzing, Mining, and Visualization
Goals
Data М anagement
Data Life Cycle Process
Data Life Cycle Continued
Data Sources
Methods to collect Raw Data
Methods for managing data collection
Data Quality and Integrity
Document Management
Transactional vs. Analytical Data Processing
The Data Warehouse
The Data Warehouse Continued
The Data Warehouse Continued
The Data Mart
The Data Cube
Operational Data Stores
Business Intelligence
Business Intelligence Continued
Knowledge Discovery
Knowledge Discovery Continued
Queries
Online Analytical Processing
Data Mining
Data Mining Techniques
Data Mining Tasks
Data Visualization
Data Visualization Continued
Data Visualization Continued
Data Visualization Continued
Specialized Databases
Web-based Data Management Systems
Web-based Data Management Systems
Web-based Data Management Systems
1 Data Management: Warehousing, Analyzing, Mining, and Visualization
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Первый слайд презентации

1 Data Management: Warehousing, Analyzing, Mining, and Visualization

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Слайд 2: Goals

2 Goals Recognize the importance of data, their issues, and their life cycle. Describe the sources of data, their collection, and quality issues. Describe document management systems. Explain the operation of data warehousing and its role in decision support. Describe information and knowledge discovery and business intelligence. Understand the power and benefits of data mining. Describe data presentation methods and geoinfosystems and virtual reality as decision support tools. Discuss the role of marketing databases Recognize the role of the Web in data management

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Слайд 3: Data М anagement

3 Data М anagement The amount of data increases exponentially with time. Data are dispersed throughout different organizations. Data are collected by many individuals using several methods. External data needs to be considered in making organizational decisions. Data security, quality, and integrity are critical factors of data management procedures. IT applications cannot be done without using some kind of data Which are at the core of daily management and marketing operations. However, managing data is difficult for various reasons. Data become an asset, when it converted to information and knowledge, and give the firm an competitive advantage.

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Слайд 4: Data Life Cycle Process

4 Data Life Cycle Process New data collection occurs from various sources. It is temporarily stored in a database then preprocessed to fit the format of the organizations data warehouse or data marts Users then access the warehouse or data mart and take a copy of the needed data for analysis. Analysis (looking for patterns) is done with Data analysis tools Data mining tools Businesses run on data that have been processed to information and knowledge, which managers apply to businesses problems and opportunities. This transformation of data into knowledge and solutions is accomplished in several ways. The result of all these activities is the generating of decision support and new knowledge

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Слайд 5: Data Life Cycle Continued

5 Data Life Cycle Continued The result of data processing is to generate a solution

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Слайд 6: Data Sources

6 Data Sources Internal Data Sources are usually stored in the corporate database and are about people, products, services, and processes. Personal Data is documentation on the expertise of corporate employees usually maintained by the employee. It can take the form of: estimates of sales opinions about competitors business rules Procedures Etc. External Data Sources range from commercial databases to Government reports. Internet Databases and Commercial Database Services are accessible through the Internet. The data life cycle begins with the acquisition of data from data sources. These sources can be classified as internal, personal, and external.

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Слайд 7: Methods to collect Raw Data

7 Methods to collect Raw Data Collection can take place in the field from individuals via manually methods time studies Surveys Observations contributions from experts using instruments and sensors Transaction processing systems (TPS) via electronic transfer from a web site The task of data collection is fairly complex. Which can create data-quality problem requiring validation and cleansing of data.

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Слайд 8: Methods for managing data collection

8 Methods for managing data collection A Data Flow Manager consists of a decision support system a central data request processor a data integrity component links to external data suppliers the processes used by the external data suppliers. One way to improve data collection from multiple external sources is to use a data flow manager (DFM), which takes information from external sources and puts it where it is needed, when it is needed, in a usable form.

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Слайд 9: Data Quality and Integrity

9 Data Quality and Integrity Internal DQ: Accuracy, objectivity, believability, and reputation. Accessibility DQ: Accessibility and access security. Contextual DQ: Relevancy, value added, timeliness, completeness, amount of data. Representation DQ: Interpretability, ease of understanding, representation Data quality (DQ) is an extremely important factor since quality determines the data’s usefulness as well as the quality of the decisions based on the data analysis. Data integrity means that data must be accurate, accessible, and up-to-date. Data quality is the cornerstone of effective business intelligence.

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Слайд 10: Document Management

\ 10 Document Management Maintaining paper documents, requires that: Everyone have the current version An update schedule should be determined Security be provided for the document The documents be distributed to the appropriate individuals in a timely manner Document management is the automated control of electronic documents, page images, spreadsheets, word processing documents, and other complex documents through their entire life cycle within an organization, from initial creation to final deleting or archiving.

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Слайд 11: Transactional vs. Analytical Data Processing

11 Transactional vs. Analytical Data Processing Transactional processing takes place in systems at operational level (TPS) that provide the organization with the capability to perform business transactions and produce transaction reports. The data are organized mainly in a structured manner and are centrally processed. This is done primarily for fast and efficient processing of routine, repetitive data flows. A supplementary activity to transaction processing is called analytical processing, which involves the analysis of accumulated data. Analytical processing, sometimes referred to as business intelligence, includes data mining, decision support systems (DSS), querying, and other analysis activities. These analyses place strategic information in the hands of decision makers to enhance productivity and make better decisions, leading to greater competitive advantage.

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Слайд 12: The Data Warehouse

12 The Data Warehouse Benefits of a data warehouse are: The ability to reach data quickly, since they are located in one place The ability to reach data easily and frequently by end users with Web browsers. Characteristics of data warehousing are: Organization. Data are organized by subject Consistency. In the warehouse data will be coded in a consistent manner. A data warehouse is a repository of subject-oriented historical data that is organized to be accessible in a form readily acceptable for analytical processing activities ( such as data mining, decision support, querying, and other applications ).

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Слайд 13: The Data Warehouse Continued

13 The Data Warehouse Continued Characteristics of data warehousing: Time variant. The data are kept for many years so they can be used for trends, forecasting, and comparisons over time. Relational. Typically the data warehouse uses a relational structure. Client/server. The data warehouse uses the client/server architecture mainly to provide the end user an easy access to its data. Web-based. Data warehouses are designed to provide an efficient computing environment for Web-based applications

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Слайд 14: The Data Warehouse Continued

14 The Data Warehouse Continued

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Слайд 15: The Data Mart

15 The Data Mart There are two major types of data marts: Replicated (dependent) data marts are small subsets of the data warehouse. In such cases one replicates some subset of the data warehouse into smaller data marts, each of which is dedicated to a certain functional area. Stand-alone data marts. A company can have one or more independent data marts without having a data warehouse. Typical data marts are for marketing, finance, and engineering applications. A data mart is a small scaled-down version of a data warehouse designed for a strategic business unit (SBU) or a department. Since they contain less information than the data warehouse they provide more rapid response and are more easily navigated than enterprise-wide data warehouses.

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Слайд 16: The Data Cube

16 The Data Cube One intersection might be the quantities of a product sold by specific retail locations during certain time periods. Another matrix might be Sales volume by department, by day, by month, by year for a specific region Cubes provide faster the following opportunities for analysis : Queries Slices and D ices of the information Rollups Drill Downs Multidimensional databases (sometimes called OLAP) are specialized data stores that organize facts by dimensions, such as geographical region, product line, salesperson, time. The data in these databases are usually preprocessed and stored in data cubes.

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Слайд 17: Operational Data Stores

17 Operational Data Stores It is typically used for short-term decisions that require time sensitive data analysis It logically falls between the operational data in legacy systems and the data warehouse. It provides detail as opposed to summary data. It is optimized for frequent access It provides faster response times. Operational data store is a database for transaction processing systems that uses data warehouse concepts to provide clean data to the TPS. It brings the concepts and benefits of a data warehouse to the operational portions of the business.

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Слайд 18: Business Intelligence

18 Business Intelligence Business intelligence includes: outputs such as financial modeling and budgeting resource allocation coupons and sales promotions Seasonality trends Benchmarking (business performance) competitive intelligence. Business intelligence (BI) is a broad category of applications and techniques for gathering, storing, analyzing and providing access to data. It help’s enterprise users make better business and strategic decisions. Major applications include the activities of query and reporting, online analytical processing (OLAP), DSS, data mining, forecasting and statistical analysis. Business Intelligence tools starts with Knowledge Discovery

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Слайд 19: Business Intelligence Continued

19 Business Intelligence Continued How It Works

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Слайд 20: Knowledge Discovery

20 Knowledge Discovery KDD supported by three techniques : massive data collection powerful multiprocessor computing data mining and other algorithms processing. KDD primarily employs three tools for information discovery: Traditional query languages (SQL, …) OLAP Data mining Before information can be processed into BI it must be discovered or extracted from the data stores. The major objective of this procedure of knowledge discovery in databases (KDD) is to identify valid, novel, potentially useful, and understandable patterns in data. Discovering useful patterns

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Слайд 21: Knowledge Discovery Continued

21 Knowledge Discovery Continued Discovering useful patterns

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Слайд 22: Queries

22 Queries User requests are stated in a query language and the results are subsets of the relationship : Sales by department by customer type for specific period Weather conditions for specific date Sales by day of week … Queries allow users to request information from the computer that is not available in periodic reports. Query systems are often based on menus or if the data is stored in a database via a structured query language (SQL) or using a query-by-example (QBE) method.

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Слайд 23: Online Analytical Processing

23 Online Analytical Processing ROLAP (Relational OLAP) is an OLAP database implemented on top of an existing relational database. The multidimensional view is created each time for the user. MOLAP (Multidimensional OLAP) is a specialized multidimensional data store such as a Data Cube. The multidimensional view is physically stored in specialize data files. Online analytical processing (OLAP) is a set of tools that analyze and aggregate data to reflect business needs of the company. These business structures (multidimensional views of data) allow users to quickly answer business questions. OLAP is performed on Data Warehouses and Marts.

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Слайд 24: Data Mining

24 Data Mining Data mining technology can generate new business opportunities by providing: Automated prediction of trends and behaviors. Automated discovery of previously unknown or hidden patterns. Data mining tools can be combined with: Spreadsheets Other end-user software development tools Data mining creates a data cube then extracts data Data mining is a tool for analyzing large amounts of data. It derives its name from the similarities between searching for valuable business information in a large database, and mining a mountain for a valuable ore.

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Слайд 25: Data Mining Techniques

25 Data Mining Techniques Case-based reasoning. uses historical cases to recognize patterns Neural computing is a machine learning approach which examines historical data for patterns. Intelligent agents retrieving information from the Internet or from intranet-based databases. Association analysis uses a specialized set of algorithms that sort through large data sets and express statistical rules among items. Decision trees Genetic algorithms Nearest-neighbor method

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Слайд 26: Data Mining Tasks

26 Data Mining Tasks Classification. Infers the defining characteristics of a certain group. Clustering. Identifies groups of items that share a particular characteristic. Clustering differs from classification in that no predefining characteristic is given. Association. Identifies relationships between events that occur at one time. Sequencing. Identifies relationships that exist over a period of time. Forecasting. Estimates future values based on patterns within large sets of data. Regression. Maps a data item to a prediction variable. Time Series analysis examines a value as it varies over time.

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Слайд 27: Data Visualization

27 Data Visualization Multidimensional visualization means that modern data and information may have several dimensions. Dimensions: Products Salespeople Market segments Business units Geographical locations Distribution channels Countries Industries Data visualization refers to presentation of data by technologies such as digital images, geographical information systems, graphical user interfaces, multidimensional tables and graphs, virtual reality, three-dimensional presentations, videos and animation.

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Слайд 28: Data Visualization Continued

28 Data Visualization Continued Measures: Money Sales volume Head count Inventory profit Actual versus forecasted results. Time: Daily Weekly Monthly Quarterly Yearly. Multidimensionality Visualization:

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Слайд 29: Data Visualization Continued

29 Data Visualization Continued

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Слайд 30: Data Visualization Continued

30 Data Visualization Continued A geographical information system (GIS) is a computer-based system for capturing, storing, checking, integrating, manipulating, and displaying data using digitized maps. Every record or digital object has an identified geographical location. It employs spatially oriented databases. Visual interactive modeling (VIM) uses computer graphic displays to represent the impact of different management or operational decisions on objectives such as profit or market share. Virtual reality (VR) is interactive, computer-generated, three-dimensional graphics delivered to the user. These artificial sensory cues cause the user to “believe” that what they are doing is real.

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Слайд 31: Specialized Databases

31 Specialized Databases Marketing transaction database (MTD) combines many of the characteristics of the current databases and marketing data sources into a new database that allows marketers to engage in real-time personalization and target every interaction with customers Interactive capability an interactive transaction occurs with the customer exchanging information and updating the database in real time, as opposed to the periodic (weekly, monthly, or quarterly) updates of classical warehouses and marts. Data warehouses and data marts serve end users in all functional areas. Most current databases are static: They simply gather and store information. Today’s business environment also requires specialized databases.

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Слайд 32: Web-based Data Management Systems

32 Web-based Data Management Systems Enterprise BI suites and Corporate Portals integrate query, reporting, OLAP, and other tools Intelligent Data Warehouse Web-based Systems employ a search engine for specific applications which can improve the operation of a data warehouse Clickstream Data Warehouse occur inside the Web environment, when customers visit a Web site. Data management and business intelligence activities—from data acquisition to mining—are often performed with Web tools, or are interrelated with Web technologies and e-business. This is done through intranets, and for outsiders via extranets.

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Слайд 33: Web-based Data Management Systems

33 Web-based Data Management Systems Continued

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Слайд 34: Web-based Data Management Systems

34 Web-based Data Management Systems Continued

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Последний слайд презентации: 1 Data Management: Warehousing, Analyzing, Mining, and Visualization

35 Thank you ! Questions ?

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