Abstract
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.
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