Name
Abstract | ||
|---|---|---|
Managing large amounts of information is one of the most expensive, time-consuming and non-trivial activities and it usually requires expert knowledge. In a wide range of application areas, such as data mining, histogram construction, approximate query evaluation, and software validation, handling exponentially growing databases has become a difficult challenge, and a subset of the data is generally preferred. As a solution to the current challenges in managing large amounts of data, database sampling from the operational data available has proved to be a powerful technique. However, none of the existing sampling approaches consider the dependencies between the data in a relational database. In this paper, we propose a novel approach towards constructing a realistic testing environment, by analyzing the distribution of data in the original database along these dependencies before sampling, so that the sample database is representative to the original database. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2448556.2448568 | ICUIMC |
Keywords | Field | DocType |
data mining,current challenge,approximate query evaluation,relational database,towards realistic sampling,sample database,original database,large amount,application area,existing sampling approach,operational data,test,measurement,algorithms | Data mining,Database tuning,Relational database,Database model,Information retrieval,Computer science,View,Database testing,Database design,Database schema,Database theory | Conference |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Teodora Sandra Buda | 1 | 26 | 7.50 |
| John Murphy | 2 | 75 | 10.07 |
| Morten Kristiansen | 3 | 11 | 2.67 |