850 research outputs found
Cloud Classification in JPEG-compressed Remote Sensing Data (LANDSAT 7/ETM+)
No full textEnvironmental parameters required for geo-information modelling are subject to spatial and temporal dynamics. Remote sensing data can contribute to measure those parameters. For that purpose high-accuracy classifications of remote sensing data are required which can be very time-consuming due to the large data volumes involved. In many applications, however, the rapid provision of classified mass data is of higher priority than classification accuracy. One important focus on research and development efforts in the past years has been to optimise the automated interpretation of remote sensing data. Different investigators have shown that this interpretation can both be effective and efficient in JPEG compressed data with acceptable accuracy. This paper presents an operational processing chain for cloud detection in JPEG-compressed quick-look products of LANDSAT 7/ETM+-scenes (compression ratio is 10:1). Two well-developed conventional algorithms are applied to these datasets for cloud detection. Results show that the processing chain developed is stable and produces quality results with substantially compressed mass data
Mining Matrix Pattern from Mobile Users
No full textMobile user data mining is about extracting knowledge from raw data collected from mobile users. There have been a few approaches developed, such as frequency pattern (Goh & Taniar, 2004), group pattern (Lim, Wang, Ong, et al., 2003; Wang, Lim, & Hwang, 2003), parallel pattern (Goh & Taniar, 2005) and location dependent mobile user data mining (Goh & Taniar, 2004). Previously proposed methods share the common drawbacks of costly resources that have to be spent in identifying the location of the mobile node and constant updating of the location information. The proposed method aims to address this issue by using the location dependent approach for mobile user data mining. Matrix pattern looks at the mobile nodes from the point of view of a particular fixed location rather than constantly following the mobile node itself. This can be done by using sparse matrix to map the physical location and use the matrix itself for the rest of mining process, rather than identifying the real coordinates of the mobile users. This allows performance efficiency with slight sacrifice in accuracy. As the mobile nodes visit along the mapped physical area, the matrix will be marked and used to perform mobile user data mining. The proposed method further extends itself from a single layer matrix to a multi-layer matrix in order to accommodate mining in different contexts, such as mining the relationship between the theme of food and fashion within a geographical area, thus making it more robust and flexible. The performance and evaluation shows that the proposed method can be used for mobile user data mining.</p
Emerging perspectives in big data warehousing/ David Taniar and Wenny Rahayu [editors].
No full textIncludes bibliographical references and index."This book disseminates the latest research findings in the areas of data management and analyzation. It also focuses on the integration between the fields of data warehousing and data mining"--Chapter 1. A two-tiered segmentation approach for transaction data warehousing -- Chapter 2. Real-time big data warehousing -- Chapter 3. Deductive data warehouses: analyzing data warehouses with datalog (by example) -- Chapter 4. A trajectory ontology design pattern for semantic trajectory data warehouses: behavior analysis and animal tracking case studies -- Chapter 5. Personalized spatio-temporal OLAP queries suggestion based on user behavior and a new similarity measure -- Chapter 6. Building OLAP cubes from columnar NoSQL data warehouses -- Chapter 7. Commercial and open source business intelligence platforms for big data warehousing -- Chapter 8. Index structures for data warehousing and big data analytics -- Chapter 9. Multidimensional analysis of big data -- Chapter 10. Development of ETL processes using the domain-specific modeling approach -- Chapter 11. Introduction of item constraints to discover characteristic sequential patterns.1 online resource (xviii, 348 pages
Web-Powered Databases
No full textEdited by David Taniar, Johanna Wenny Rahayu.
Includes chapter by College at Brockport faculty member Anthony Scime: Web Mining to Create a Domain Specific Web Portal Database.
Providing a snapshot of current methods and development activities in the area of Internet databases, this book supplies answers to many questions that have been raised regarding database access through the web. Provided are a number of case studies of successful web database applications, including multiple-choice assessment through the web, an online pay claim, a product catalog, and content management and dynamic web pages. Also covered are querying and mining of web data and issues such as gaining physical/low-level access to web-powered databases and heterogeneous web databases.https://digitalcommons.brockport.edu/bookshelf/1249/thumbnail.jp
Selected Papers of the International Conference on Computational Science and Applications
No full textBoo
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