`Indexing for scientific big data`

The amazing increase of data produced and stored in all kinds of scientific domains from trade, life sciences, social networks,… represents a major change in human activities. The management of big data is at the heart of the MASTODONS project call. Inevitably, computer based solutions are needed to organize, maintain and exploit this data, but the size of the data makes impractical current solutions. The design of novel computational approaches must take up the challenge of scalability, that is to remain highly efficient despite the increase of data. A key step towards this goal is how the data is stored and organized within the computer memory or disk, and how it is pre-processed to compute additional data structures that index this data. Once computed, the index is repeatedly used to query the data almost instantaneously and thus serves to mine novel information from huge and originally unstructured data sets.

This forum will give us the opportunity to survey state-of-the-art developments in indexing strategies and their applications for mining huge information sets of multimedia, like for instance texts, images, or genomes. Invited speakers will describe aspects of indexing related to ontologies, database management, scientific applications, machine learning, algorithms and data structures. Our goal is to point out and discuss the current challenges that computational sciences must take up, as well as future research directions.

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