Conference Paper

Experiments on Remote Sensing image cube and its OLAP

Inst. of Remote Sensing & GIS, Peking Univ., Beijing
DOI: 10.1109/IGARSS.2004.1370124 Conference: Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International, Volume: 7
Source: IEEE Xplore


OLAP can answer questions such as 'what next' and 'what if'. OLAP, which always needs the support of data warehouse, is a complement to data mining. In the early steps of data mining progress, OLAP tools might be helpful in tasks such as exploring the data sets, locating more important variables and unwanted data, which can help users to understand the source data and quicken the data mining progress. In order to find data or information quickly in great volumes of Remote Sensing (RS) image data sets, for one thing, metadata bases need to be built; for another thing, research and development are required to deal with spatial OLAP application servers, which can manipulate spatial data warehouse containing RS images. Thematic images such as TM2, TM4, land cover, transportation, slope and the result image of ERDAS IMAGINE Expert Classifier, etc., have been inputted into MS Access tables. On grounds of these tables and the relational multi-dimensional data model, dimension tables and the fact table have been generated, and furthermore a RS image cube structure has been constructed. After the pre-computation and materialization, a RS image cube has been created. Experiments of OLAP on this cube have been carried out. Owning to the pre-computation and materialization, queries on the cube can be carried out with no delay. As the experiments show, if data warehouse and OLAP are adopted, not only different factors and their concept hierarchies can be used conveniently, but also queries speed up

5 Reads
  • Source
    • "Related studies [19] [13] partially deal with the image dimension issue in a traditional data cube way. However, there are open problems if considering the special properties of images. "
    [Show abstract] [Hide abstract]
    ABSTRACT: On-Line Analytical Processing (OLAP) has shown great success in many industry applications, including sales, marketing, management, financial data analysis, etc. In this paper, we propose Visual Cube and multi-dimensional OLAP of image collections, such as web images indexed in search engines (e.g., Google and Bing), product images (e.g. Amazon) and photos shared on social networks (e.g., Facebook and Flickr). It provides online responses to user requests with summarized statistics of image information and handles rich semantics related to image visual features. A clustering structure measure is proposed to help users freely navigate and explore images. Efficient algorithms are developed to construct Visual Cube. In addition, we introduce the new issue of Cell Overlapping in data cube and present efficient solutions for Visual Cube computation and OLAP operations. Extensive experiments are conducted and the results show good performance of our algorithms.
    Proceedings of the 19th ACM Conference on Information and Knowledge Management, CIKM 2010, Toronto, Ontario, Canada, October 26-30, 2010; 01/2010
  • Source

    Workshop Complex Data Mining in a GeoSpatial Context - AGILE International Conference on Geographic Information Science; 04/2012
  • [Show abstract] [Hide abstract]
    ABSTRACT: Recently new mobile devices such as cellular phones, smartphones, and digital cameras are popularly used to take photos. By virtue of these convenient instruments, we can take many photos easily, but we suffer from the difficulty of managing and searching photos due to their large volume. This paper develops a mobile application software, called Photo Cube, which automatically extracts various metadata for photos (e.g., date/time, address, place name, weather, personal event, etc.) by taking advantage of sensors and programming functions embedded in mobile smartphones like Android phones or iPhones. To avoid heavy network traffic and high processing overhead, it clusters photos into a set of clusters hierarchically by GPSs and it extracts the metadata for each centroid photo of clusters automatically. Then it constructs and stores the hierarchies of clusters based on the date/time, and address within the extracted metadata as well as the other metadata into photo database tables in the flash memory of smartphones. Furthermore, the system builds a multidimensional cube view for the photo database, which is popularly used in OLAP(On-Line Analytical Processing) applications and it facilitates the top-down browsing of photos over several dimensions such as date/time, address, etc. In addition to the hierarchical browsing, it provides users with keyword search function in order to find photos over every metadata of the photo database in a user-friendly manner. With these convenient features of the Photo Cube, therefore, users will be able to manage and search a large number of photos easily, without inputting any additional information but with clicking simply the shutter in a camera.
    Applied Mathematics & Information Sciences 01/2015; 9(3):1391-1406. DOI:10.12785/amis/090334 · 1.23 Impact Factor