@conference {17888,
	title = {Spatial indexing of distributed multidimensional datasets},
	booktitle = {IEEE International Symposium on Cluster Computing and the Grid, 2005. CCGrid 2005},
	volume = {2},
	year = {2005},
	month = {2005/05/09/12},
	pages = {743- 750 Vol. 2 - 743- 750 Vol. 2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {While declustering methods for distributed multidimensional indexing of large datasets have been researched widely in the past, replication techniques for multidimensional indexes have not been investigated deeply. In general, a centralized index server may become the performance bottleneck in a wide area network rather than the data servers, since the index is likely to be accessed more often than any of the datasets in the servers. In this paper, we present two different multidimensional indexing algorithms for a distributed environment - a centralized global index and a two-level hierarchical index. Our experimental results show that the centralized scheme does not scale well for either insertion or searching the index. In order to improve the scalability of the index server, we have employed a replication protocol for both the centralized and two-level index schemes that allows some inconsistency between replicas without affecting correctness. Our experiments show that the two-level hierarchical index scheme shows better scalability for both building and searching the index than the non-replicated centralized index, but replication can make the centralized index faster than the two-level hierarchical index for searching in some cases.},
	keywords = {centralized global index algorithm, centralized index server, Computer science, database indexing, distributed databases, distributed multidimensional dataset, Educational institutions, File servers, Indexing, Large-scale systems, Multidimensional systems, Network servers, replication protocol, replication techniques, scalability, Sensor systems, spatial data structures, spatial indexing, two-level hierarchical index algorithm, wide area networks},
	isbn = {0-7803-9074-1},
	doi = {10.1109/CCGRID.2005.1558637},
	author = {Nam,B. and Sussman, Alan}
}
@conference {17878,
	title = {Improving access to multi-dimensional self-describing scientific datasets},
	booktitle = {3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings. CCGrid 2003},
	year = {2003},
	month = {2003/05/12/15},
	pages = {172 - 179},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Applications that query into very large multidimensional datasets are becoming more common. Many self-describing scientific data file formats have also emerged, which have structural metadata to help navigate the multi-dimensional arrays that are stored in the files. The files may also contain application-specific semantic metadata. In this paper, we discuss efficient methods for performing searches for subsets of multi-dimensional data objects, using semantic information to build multidimensional indexes, and group data items into properly sized chunks to maximize disk I/O bandwidth. This work is the first step in the design and implementation of a generic indexing library that will work with various high-dimension scientific data file formats containing semantic information about the stored data. To validate the approach, we have implemented indexing structures for NASA remote sensing data stored in the HDF format with a specific schema (HDF-EOS), and show the performance improvements that are gained from indexing the datasets, compared to using the existing HDF library for accessing the data.},
	keywords = {Application software, application-specific semantic metadata, Bandwidth, Computer science, database indexing, disk I/O bandwidth, distributed databases, Educational institutions, Indexing, indexing structures, Libraries, meta data, Middleware, multidimensional arrays, multidimensional datasets, Multidimensional systems, NASA, NASA remote sensing data, Navigation, query formulation, self-describing scientific data file formats, structural metadata, very large databases},
	isbn = {0-7695-1919-9},
	doi = {10.1109/CCGRID.2003.1199366},
	author = {Nam,B. and Sussman, Alan}
}
@conference {17081,
	title = {Direct annotation: a drag-and-drop strategy for labeling photos},
	booktitle = {IEEE International Conference on Information Visualization, 2000. Proceedings},
	year = {2000},
	month = {2000///},
	pages = {88 - 95},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Annotating photographs is such a time-consuming, tedious and error-prone data entry task that it discourages most owners of personal photo libraries. By allowing the user to drag labels, such as personal names, from a scrolling list and drop them onto a photo, we believe we can make the task faster, easier and more appealing. Since the names are entered in a database, searching for all photos of a friend or family member is dramatically simplified. We describe the user interface design and the database schema to support direct annotation, as implemented in our PhotoFinder prototype},
	keywords = {Biomedical imaging, Cities and towns, Computer errors, Computer science, database indexing, database schema, Databases, digital libraries, direct annotation, direct manipulation, drag-and-drop strategy, Educational institutions, graphical user interface, Graphical user interfaces, History, hobby computing, image searching, label placement, Labeling, Laboratories, Libraries, personal information systems, personal names database, PhotoFinder prototype, photograph labelling, photographic libraries, Photography, scrolling list, user interface design, visual databases},
	isbn = {0-7695-0743-3},
	doi = {10.1109/IV.2000.859742},
	author = {Shneiderman, Ben and Kang,H.}
}
