Similarity Searching for the Big Data Challenges and Research Objectives

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Authors

ZEZULA Pavel

Year of publication 2015
Type Article in Periodical
Magazine / Source MOBILE NETWORKS & APPLICATIONS
MU Faculty or unit

Faculty of Informatics

Citation
Doi http://dx.doi.org/10.1007/s11036-014-0547-2
Field Informatics
Keywords Big data; Scalability; Information retrieval; Similarity search; Findability; Data outsourcing; Data privacy; Information extraction
Description Analysis of contemporary Big Data collections require an effective and efficient content-based access to data which is usually unstructured. This first implies a necessity to uncover descriptive knowledge of complex and heterogeneous objects to make them findable. Second, multimodal search structures are needed to efficiently execute complex similarity queries possibly in outsourced environments while preserving privacy. After explaining the impacts of Big Data on similarity searching and summarizing the state of the art in the search technology, four specific research objectives to tackle the challenges are outlined and discussed. It is believed that effective and efficient processing of raw data for object findability and developing hybrid similarity search structures for multi-modal and privacy-preserving searching are necessary to achieve a scalable similarity search technology able to operate on Big Data.
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