Database Systems - Lec 13: Emerging Research Directions in DBs/ISs - Nguyen Thanh Tung

Nội dung text: Database Systems - Lec 13: Emerging Research Directions in DBs/ISs - Nguyen Thanh Tung

1. Outline ▪ Mobile Databases ▪ Multimedia Databases ▪ Geographic Information Systems ▪ Bioinformatics ▪ XML ▪ Data Mining ▪ Data Warehousing ▪ Introduction to ASIS Lab 2
2. Mobile Databases(2) ▪ In mobile computing, the problems are more difficult, mainly: • The limited and intermittent connectivity afforded by wireless communications. • The limited life of the power supply(battery). • The changing topology of the network. • In addition, mobile computing introduces new architectural possibilities and challenges. 4
3. Mobile Computing Architecture(2) ▪ It is distributed architecture where a number of computers, generally referred to as Fixed Hosts and Base Stations are interconnected through a high-speed wired network. • Fixed hosts are general purpose computers configured to manage mobile units. • Base stations function as gateways to the fixed network for the Mobile Units. 6
4. Data Management Issues(2) ▪ Data management issues as it is applied to mobile databases: • Data distribution and replication • Transactions models • Query processing • Recovery and fault tolerance • Mobile database design • Location-based service • Division of labor • Security ▪ M-Commerce 8
5. 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. 10
6. Multimedia Databases(3) ▪ 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). 12
7. Multimedia Databases(5) ▪ 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. 14
8. Multimedia Databases(7) ▪ 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. 16
9. Outline ▪ Mobile Databases ▪ Multimedia Databases ▪ Geographic Information Systems ▪ Bioinformatics ▪ XML ▪ Data Mining ▪ Data Warehousing ▪ Introduction to ASIS Lab ▪ Revision 18
10. Geographic Information Systems(2) ▪ The scope of GIS broadly encompasses two types of data: • Spatial data, originating from maps, digital images, administrative and political boundaries, roads, transportation networks, physical data, such as rivers, soil characteristics, climatic regions, land elevations, and • Non-spatial data, such as socio-economic data (like census counts), economic data, and sales or marketing information. GIS is a rapidly developing domain that offers highly innovative approaches to meet some challenging technical demands. 20
11. Spatial data 22
12. GIS Applications(2) GIS Applications Digital Terrain Modeling Geographic Objects Cartographic Applications Applications Irrigation Car navigation Earth systems science Crop yield Geographic analysis Civil engineering and market analysis Land military evaluation Evaluation Utility Soil Surveys Planning and distribution and Facilities Air and water consumption management pollution studies Consumer product Landscape and services – studies Flood Control economic analysis Traffic pattern Water resource analysis management 24
13. Data Management Requirements of GIS (2) Data Modeling and Representation ▪ GIS data can be broadly represented in two formats: • Vector data represents geometric objects such as points, lines, and polygons. 26
14. Data Management Requirements of GIS (4) Data Integration ▪ GISs must integrate both vector and raster data from a variety of sources. • Sometimes edges and regions are inferred from a raster image to form a vector model, or conversely, raster images such as aerial photographs are used to update vector models. • Several coordinate systems such as Universal Transverse Mercator (UTM), latitude/longitude, and local cadastral systems are used to identify locations. • Data originating from different coordinate systems requires appropriate transformations. 28
15. Specific GIS Data Operations(2) ▪ The functionality of a GIS database is also subject to other considerations: • Extensibility • Data quality control • Visualization ▪ Such requirements clearly illustrate that standard RDBMSs or ODBMSs do not meet the special needs of GIS. • Therefore it is necessary to design systems that support the vector and raster representations and the spatial functionality as well as the required DBMS features. 30
16. Bioinformatics ▪ Bioinformatics: The study of genetics can be divided into three branches: • Mendelian genetics is the study of the transmission of traits between generations • Molecular genetics is the study of the chemical structure and function of genes at the molecular level • Population genetics is the study of how genetic information varies across populations of organisms ▪ Bioinformatics addresses information management of genetic information with special emphasis on DNA sequence analysis ▪ Interdisciplinary research field 32
17. XML: Extensible Markup Language ▪ Although HTML is widely used for formatting and structuring Web documents, it is not suitable for specifying structured data that is extracted from databases. ▪ A new language—namely XML (eXtended Markup Language) has emerged as the standard for structuring and exchanging data over the Web. • XML can be used to provide more information about the structure and meaning of the data in the Web pages rather than just specifying how the Web pages are formatted for display on the screen. ▪ The formatting aspects are specified separately—for example, by using a formatting language such as XSL (eXtended Stylesheet Language). 34
18. XML (3) ▪ The basic object is XML is the XML document. ▪ There are two main structuring concepts that are used to construct an XML document: • Elements • Attributes ▪ Attributes in XML provide additional information that describe elements. 36
19. Outline ▪ Mobile Databases ▪ Multimedia Databases ▪ Geographic Information Systems ▪ Bioinformatics ▪ XML ▪ Data Mining ▪ Data Warehousing ▪ Introduction to ASIS Lab ▪ Revision 38
20. 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 40
21. 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. ▪ W. H Inmon characterized a data warehouse as: • “A subject-oriented, integrated, nonvolatile, time- variant collection of data in support of management’s decisions.” 42
22. Data Warehousing (3) ▪ 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. 44
23. 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. 46
24. Introduction to ASIS Lab ▪ Advances in Security & Information Systems Lab (www.cse.hcmut.edu.vn/~asis ) ▪ Research Directions (2006-2010) • Information Systems Security: →Database Security →Security Issues in E-/M-Commerce →Security and Privacy in Location-Based Applications →Security Issues in Outsourced Databases Services →DBs/ISs Security Visualization →E-Learning Systems Security →Digital Watermarking and Steganography →Privacy and Identity Management 48
25. Outline ▪ Mobile Databases ▪ Multimedia Databases ▪ Geographic Information Systems ▪ Bioinformatics ▪ XML ▪ Data Mining ▪ Data Warehousing ▪ Introduction to ASIS Lab 50