Name
Abstract | ||
|---|---|---|
We discuss how the specifics of data granulation methodology can influence Infobright's database system performance. We put together our two previous research paths related to machine-generated data sets, namely, dynamic reorganization of data during load and efficient handling of alphanumeric columns with compound values. We emphasize the role of domain knowledge while tuning data granulation processes. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IFSA-NAFIPS.2013.6608377 | IFSA World Congress and NAFIPS Annual Meeting |
Keywords | Field | DocType |
data handling,granular computing,inference mechanisms,Infobright database system,alphanumeric columns,compound values,data granulation methodology,dynamic reorganization,granulation processes,intelligent granulation,machine generated data,Analytic Databases,Compound Values,Domain Knowledge,Outliers,Stream Clustering | Alphanumeric,Data mining,Data set,Domain knowledge,Computer science,Machine-generated data,Granular computing,Artificial intelligence,Granulation,Group method of data handling,Machine learning | Conference |
Citations | PageRank | References |
2 | 0.42 | 17 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dominik Ślęzak | 1 | 553 | 50.04 |
| Marcin Kowalski | 2 | 2 | 0.42 |