DocumentCode :
3324434
Title :
Designing Random Sample Synopses with Outliers
Author :
Rösch, Philipp ; Gemulla, Rainer ; Lehner, Wolfgang
Author_Institution :
Database Technol. Group, Tech. Univ. Dresden, Dresden
fYear :
2008
fDate :
7-12 April 2008
Firstpage :
1400
Lastpage :
1402
Abstract :
Random sampling is one of the most widely used means to build synopses of large datasets because random samples can be used for a wide range of analytical tasks. Unfortunately, the quality of the estimates derived from a sample is negatively affected by the presence of "outliers" in the data. In this paper, we show how to circumvent this shortcoming by constructing outlier-aware sample synopses. Our approach extends the well-known outlier indexing scheme to multiple aggregation columns.
Keywords :
database indexing; random processes; sampling methods; very large databases; large dataset synopses design; multiple aggregation column; outlier indexing scheme; outlier-aware sample synopses; random sampling; Aggregates; Computer science; Data analysis; Estimation error; Image databases; Indexing; Large-scale systems; Query processing; Sampling methods; Streaming media;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on
Conference_Location :
Cancun
Print_ISBN :
978-1-4244-1836-7
Electronic_ISBN :
978-1-4244-1837-4
Type :
conf
DOI :
10.1109/ICDE.2008.4497569
Filename :
4497569
Link To Document :
بازگشت