@conference {15029,
	title = {Strategies for exploring large scale data},
	booktitle = {Parallel Architectures, Algorithms and Networks, 2004. Proceedings. 7th International Symposium on},
	year = {2004},
	month = {2004/05//},
	pages = {2 - 2},
	abstract = {We consider the problem of querying large scale multidimensional time series data to discover events of interest, test and validate hypotheses, or to associate temporal patterns with specific events. This type of data currently dominates most other types of available data, and will very likely become even more prevalent in the future given the current trends in collecting time series of business, scientific, demographic, and simulation data. The ability to explore such collections interactively, even at a coarse level, will be critical in discovering the information and knowledge embedded in such collections. We develop indexing techniques and search algorithms to efficiently handle temporal range value querying of multidimensional time series data. Our indexing uses linear space data structures that enable the handling of queries in I/O time that is essentially the same as that of handling a single time slice, assuming the availability of a logarithmic number of processors as a function of the temporal window. A data structure with provably almost optimal asymptotic bounds is also presented for the case when the number of multidimensional objects is relatively small. These techniques improve significantly over standard techniques for either serial or parallel processing, and are evaluated by extensive experimental results that confirm their superior performance.},
	keywords = {algorithms;, association;, asymptotic, bounds;, business, data, data;, database, databases;, demographic, discovery;, Indexing, indexing;, information, knowledge, large, linear, mining;, multidimensional, objects;, optimal, Parallel, pattern, processing;, query, querying;, range, scale, scientific, search, serial, series, series;, simulation, space, structure;, structures;, techniques;, temporal, TIME, value, very, window;},
	doi = {10.1109/ISPAN.2004.1300447},
	author = {JaJa, Joseph F.}
}
@conference {14961,
	title = {Efficient techniques for range search queries on earth science data},
	booktitle = {Scientific and Statistical Database Management, 2002. Proceedings. 14th International Conference on},
	year = {2002},
	month = {2002///},
	pages = {142 - 151},
	abstract = {We consider the problem of organizing large scale earth science raster data to efficiently handle queries for identifying regions whose parameters fall within certain range values specified by the queries. This problem seems to be critical to enabling basic data mining tasks such as determining associations between physical phenomena and spatial factors, detecting changes and trends, and content based retrieval. We assume that the input is too large to fit in internal memory and hence focus on data structures and algorithms that minimize the I/O bounds. A new data structure, called a tree-of-regions (ToR), is introduced and involves a combination of an R-tree and efficient representation of regions. It is shown that such a data structure enables the handling of range queries in an optimal I/O time, under certain reasonable assumptions. We also show that updates to the ToR can be handled efficiently. Experimental results for a variety of multi-valued earth science data illustrate the fast execution times of a wide range of queries, as predicted by our theoretical analysis.},
	keywords = {based, computing;, content, data, data;, databases;, Earth, factors;, large, mining, mining;, natural, processing;, queries;, query, range, raster, retrieval;, scale, Science, sciences, search, spatial, structures;, tasks;, temporal, tree, tree-of-regions;, visual},
	doi = {10.1109/SSDM.2002.1029714},
	author = {Shi,Qingmin and JaJa, Joseph F.}
}
