Title :
Lossy reduction for very high dimensional data
Author :
Jermaine, Christopher ; Omiecinski, Edward
Author_Institution :
Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA, USA
Abstract :
We consider the use of data reduction techniques for the problem of approximate query answering. We focus on applications for which accurate answers to selective queries are required, and for which the data are very high dimensional (having hundreds of attributes). We present a new data reduction method for this type of application, called the RS kernel. We demonstrate the effectiveness of this method for answering difficult, highly selective queries over high dimensional data using several real datasets
Keywords :
data reduction; query processing; very large databases; RS kernel; accurate answers; approximate query answering; data reduction techniques; lossy reduction; very high dimensional data; Clouds; Data engineering; Discrete cosine transforms; Discrete wavelet transforms; Educational institutions; Grid computing; Kernel; Multidimensional systems; Sampling methods; Statistical distributions;
Conference_Titel :
Data Engineering, 2002. Proceedings. 18th International Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
0-7695-1531-2
DOI :
10.1109/ICDE.2002.994783
