Name
Abstract | ||
|---|---|---|
Meta-data plays a significant role in large modern enterprises, research experiments and digital libraries where it comes from many different sources and is distributed in a variety of digital formats. It is organized and managed by constantly evolving software using both relational and non-relational data sources. Even though we can apply an information retrieval approach to non-relational data sources, we can't do so for relational ones, where information is accessed via a pre-established set of data-services. Here we discuss a new data aggregation system which consumes, indexes and delivers information from different relational and non-relational data sources to answer cross data-service queries and explore meta-data associated with petabytes of experimental data. We combine the simplicity of keyword-based search with the precision of RDMS under the new system. The aggregated information is collected from various sources, allowing end-users to place dynamic queries, get precise answers and trigger information retrieval on demand. Based on the use cases of the CMS experiment, we have performed a set of detailed, large scale tests the results of which we present in this paper. (C) 2010 Published by Elsevier Ltd. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.procs.2010.04.172 | Procedia Computer Science |
Keywords | Field | DocType |
Meta-data,data aggregation,information discovery,HEP | Data mining,Metadata,Use case,Information retrieval,Experimental data,Petabyte,Computer science,Software,Digital library,Data aggregator,Information discovery | Journal |
Volume | Issue | ISSN |
1 | 1 | 1877-0509 |
Citations | PageRank | References |
1 | 0.45 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Valentin Kuznetsov | 1 | 14 | 1.79 |
| Dave Evans | 2 | 5 | 0.85 |
| Simon Metson | 3 | 5 | 0.85 |