Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Chapter 10 New Application

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Chapter Outline Data Mining Data Warehousing Knowledge Discovery in Databases (KDD) Goals of Data Mining and Knowledge Discovery Purpose of Data Warehousing Introduction, Definitions, and Terminology Comparison with Traditional Databases Characteristics of Data Warehouses Classification of Data Warehouses Multimedia Databases

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Definitions of Data Mining The discovery of new information in terms of patterns or rules from vast amounts of data. The process of finding interesting structure in data. The process of employing one or more computer learning techniques to automatically analyze and extract knowledge from data.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Data Warehousing The data warehouse is a historical database designed for decision support. Data mining can be applied to the data in a warehouse to help with certain types of decisions. Proper construction of a data warehouse is fundamental to the successful use of data mining.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Knowledge Discovery in Databases (KDD) Data mining is actually one step of a larger process known as knowledge discovery in databases (KDD). The KDD process model comprises six phases Data selection Data cleansing Enrichment Data transformation or encoding Data mining Reporting and displaying discovered knowledge

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Goals of Data Mining and Knowledge Discovery (PICO) Prediction: Determine how certain attributes will behave in the future. Identification: Identify the existence of an item, event, or activity. Classification: Partition data into classes or categories. Optimization: Optimize the use of limited resources.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Types of Discovered Knowledge Association Rules Classification Hierarchies Sequential Patterns Patterns Within Time Series Clustering

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Data Mining Applications Marketing Marketing strategies and consumer behavior Finance Fraud detection, creditworthiness and investment analysis Manufacturing Resource optimization Health Image analysis, side effects of drug, and treatment effectiveness

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Purpose of Data Warehousing Traditional databases are not optimized for data access only they have to balance the requirement of data access with the need to ensure integrity of data. Most of the times the data warehouse users need only read access but, need the access to be fast over a large volume of data. Most of the data required for data warehouse analysis comes from multiple databases and these analysis are recurrent and predictable to be able to design specific software to meet the requirements. There is a great need for tools that provide decision makers with information to make decisions quickly and reliably based on historical data. The above functionality is achieved by Data Warehousing and Online analytical processing (OLAP)

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Introduction, Definitions, and Terminology W. H Inmon characterized a data warehouse as: “A subject-oriented, integrated, nonvolatile, time-variant collection of data in support of management’s decisions.”

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Introduction, Definitions, and Terminology Data warehouses have the distinguishing characteristic that they are mainly intended for decision support applications. Traditional databases are transactional. Applications that data warehouse supports are: OLAP (Online Analytical Processing) is a term used to describe the analysis of complex data from the data warehouse. DSS (Decision Support Systems) also known as EIS (Executive Information Systems) supports organization’s leading decision makers for making complex and important decisions. Data Mining is used for knowledge discovery, the process of searching data for unanticipated new knowledge.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Conceptual Structure of Data Warehouse Data Warehouse processing involves Cleaning and reformatting of data OLAP Data Mining

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Comparison with Traditional Databases Data Warehouses are mainly optimized for appropriate data access. Traditional databases are transactional and are optimized for both access mechanisms and integrity assurance measures. Data warehouses emphasize more on historical data as their main purpose is to support time-series and trend analysis. Compared with transactional databases, data warehouses are nonvolatile. In transactional databases transaction is the mechanism change to the database. By contrast information in data warehouse is relatively coarse grained and refresh policy is carefully chosen, usually incremental.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Characteristics of Data Warehouses Multidimensional conceptual view Generic dimensionality Unlimited dimensions and aggregation levels Unrestricted cross-dimensional operations Dynamic sparse matrix handling Client-server architecture Multi-user support Accessibility Transparency Intuitive data manipulation Consistent reporting performance Flexible reporting

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Classification of Data Warehouses Generally, Data Warehouses are an order of magnitude larger than the source databases. The sheer volume of data is an issue, based on which Data Warehouses could be classified as follows. Enterprise-wide data warehouses They are huge projects requiring massive investment of time and resources. Virtual data warehouses They provide views of operational databases that are materialized for efficient access. Data marts These are generally targeted to a subset of organization, such as a department, and are more tightly focused.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Multimedia Databases In the years ahead multimedia information systems are expected to dominate our daily lives. Our houses will be wired for bandwidth to handle interactive multimedia applications. Our high-definition TV/computer workstations will have access to a large number of databases, including digital libraries, image and video databases that will distribute vast amounts of multisource multimedia content.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Multimedia Databases DBMSs have been constantly adding to the types of data they support. Today many types of multimedia data are available in current systems.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Multimedia Databases(2) Types of multimedia data are available in current systems Text: May be formatted or unformatted. For ease of parsing structured documents, standards like SGML and variations such as HTML are being used. Graphics: Examples include drawings and illustrations that are encoded using some descriptive standards (e.g. CGM, PICT, postscript).

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Multimedia Databases(3) Types of multimedia data are available in current systems (contd.) Images: Includes drawings, photographs, and so forth, encoded in standard formats such as bitmap, JPEG, and MPEG. Compression is built into JPEG and MPEG. These images are not subdivided into components. Hence querying them by content (e.g., find all images containing circles) is nontrivial. Animations: Temporal sequences of image or graphic data.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Multimedia Databases(4) Types of multimedia data are available in current systems (contd.) Video: A set of temporally sequenced photographic data for presentation at specified rates– for example, 30 frames per second. Structured audio: A sequence of audio components comprising note, tone, duration, and so forth.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Multimedia Databases(5) Types of multimedia data are available in current systems (contd.) Audio: Sample data generated from aural recordings in a string of bits in digitized form. Analog recordings are typically converted into digital form before storage.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Multimedia Databases(6) Types of multimedia data are available in current systems (contd.) Composite or mixed multimedia data: A combination of multimedia data types such as audio and video which may be physically mixed to yield a new storage format or logically mixed while retaining original types and formats. Composite data also contains additional control information describing how the information should be rendered.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Multimedia Databases(7) Nature of Multimedia Applications: Multimedia data may be stored, delivered, and utilized in many different ways. Applications may be categorized based on their data management characteristics.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Multimedia Databases(8) Characterization of applications based on their data management characteristics: Repository applications: A large amount of multimedia data as well as metadata is stored for retrieval purposes. Examples include repositories of satellite images, engineering drawings and designs, space photographs, and radiology scanned pictures.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Multimedia Databases(9) Characterization of applications based on their data management characteristics (contd.): Presentation applications: A large amount of applications involve delivery of multimedia data subject to temporal constraints; simple multimedia viewing of video data, for example, requires a system to simulate VCR-like functionality. Complex and interactive multimedia presentations involve orchestration directions to control the retrieval order of components in a series or in parallel. Interactive environments must support capabilities such as real-time editing analysis or annotating of video and audio data.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Multimedia Databases(10) Characterization of applications based on their data management characteristics: Collaborative work using multimedia information: This is a new category of applications in which engineers may execute a complex design task by merging drawings, fitting subjects to design constraints, and generating new documentation, change notifications, and so forth. Intelligent healthcare networks as well as telemedicine will involve doctors collaborating among themselves, analyzing multimedia patient data and information in real time as it is generated.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Data Management Issues Multimedia applications dealing with thousands of images, documents, audio and video segments, and free text data depend critically on Appropriate modeling of the structure and content of data Designing appropriate database schemas for storing and retrieving multimedia information.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Data Management Issues(2) Multimedia information systems are very complex and embrace a large set of issues: Modeling Complex objects Design Conceptual, logical, and physical design of multimedia has not been addressed fully.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Data Management Issues(3) Multimedia information systems are very complex and embrace a large set of issues (contd.): Storage Multimedia data on standard disklike devices presents problems of representation, compression, mapping to device hierarchies, archiving, and buffering during the input/output operation. Queries and retrieval “Database” way of retrieving information is based on query languages and internal index structures.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Data Management Issues(4) Multimedia information systems are very complex and embrace a large set of issues (contd.): Performance Multimedia applications involving only documents and text, performance constraints are subjectively determined by the user. Applications involving video playback or audio-video synchronization, physical limitations dominate.

Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide Multimedia Database Applications Large-scale applications of multimedia databases can be expected encompasses a large number of disciplines and enhance existing capabilities. Documents and records management Knowledge dissemination Education and training Marketing, advertising, retailing, entertainment, and travel Real-time control and monitoring