Name
Abstract | ||
|---|---|---|
'Big Data' techniques are often adopted in cross-organization scenarios for integrating multiple data sources to extract statistics or other latent information. Even if these techniques do not require the support of a schema for processing data, a common conceptual model is typically defined to address name resolution. This implies that each local source is tasked of applying a semantic lifting procedure for expressing the local data in term of the common model. Semantic heterogeneity is then potentially introduced in data. In this paper we illustrate a methodology designed to the implementation of consistent process mining algorithms in a `Big Data' context. In particular, we exploit two different procedures. The first one is aimed at computing the mismatch among the data sources to be integrated. The second uses mismatch values to extend data to be processed with a traditional map reduce algorithm. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/BigData.Congress.2013.17 | BigData Congress |
Keywords | Field | DocType |
multiple data source integration,consistent process mining algorithm,cross-organization scenarios,name resolution,data source,big data,local data,statistics,common conceptual model,semantic heterogeneity,semantic lifting procedure,big data techniques,statistics extraction,map reduce algorithm,big data triple stores,latent information extraction,local source,process mining,uses mismatch value,data mining,data integration,conceptual model,common model,consistent process mining,multiple data source | Data warehouse,Data mining,Data modeling,Data mapping,Computer science,Logical data model,IDEF1X,Semantic heterogeneity,Data model,Database,Semantic computing | Conference |
ISSN | ISBN | Citations |
2379-7703 | 978-0-7695-5006-0 | 9 |
PageRank | References | Authors |
0.82 | 13 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonia Azzini | 1 | 119 | 20.38 |
| Paolo Ceravolo | 2 | 252 | 44.89 |