Презентация Data Management: Warehousing, Analyzing, Mining, and Visualization онлайн
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- Тип файла:ppt / pptx (powerpoint)
- Всего слайдов:35 слайдов
- Для класса:1,2,3,4,5,6,7,8,9,10,11
- Размер файла:470.50 kB
- Просмотров:75
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- Автор:неизвестен
Слайды и текст к этой презентации:
№2 слайд
Содержание слайда: 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
№3 слайд
Содержание слайда: Data Мanagement
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.
№4 слайд
Содержание слайда: 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
№6 слайд
Содержание слайда: 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.
№7 слайд
Содержание слайда: 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
№9 слайд
Содержание слайда: 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
№12 слайд
Содержание слайда: 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.
№13 слайд
Содержание слайда: 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
№15 слайд
Содержание слайда: 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.
№16 слайд
Содержание слайда: 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 Dices of the information
Rollups
Drill Downs
№17 слайд
Содержание слайда: 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.
№20 слайд
Содержание слайда: 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
№23 слайд
Содержание слайда: 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.
№24 слайд
Содержание слайда: 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
№25 слайд
Содержание слайда: 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
№26 слайд
Содержание слайда: 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.
№30 слайд
Содержание слайда: 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.
№31 слайд
Содержание слайда: 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.
№32 слайд
Содержание слайда: 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.
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