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One of the most prolific sources of big data is represented by search engines' services. In particular, the astonishing number of queries received each day from search engines represent a rich repository of data that is waiting to be turned into knowledge. The knowledge mined from query logs can be used either to enhance the effectiveness of the search services or to improve the efficiency in terms of response time and query throughput. Typical applications of results of query log mining are: Caching Search Results, Query Suggestion, User Profiling, User Behavior Modeling. With the proliferation of mobile devices it is important to study how the above problems change in the context of mobility.  

Given the increasing multimedia content produced by users in social media it is also important to study techniques  able to aggregate, annotate, analyze, and make searchable such data. Taming appropriately the enormous amount of data produced by real-world search engine services and social media require several heterogeneous expertise: statistics, mathematics, high-performance (cloud) computing, algorithmics, and data bases. Members of the SoBigData lab are experts in the fields above mentioned and have many years of research experience.


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  3. Amato G, Gennaro C, Savino P.  2012.  MI-File: using inverted files for scalable approximate similarity search. Multimedia Tools and Applications. :1–30.
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  6. Silvestri F, Venturini R.  2010.  VSEncoding: efficient coding and fast decoding of integer lists via dynamic programming. Proceedings of the 19th ACM international conference on Information and knowledge management.
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  9. Silvestri F.  2010.  Mining Query Logs: Turning Search Usage Data into Knowledge. Found. Trends Inf. Retr.. 4:1–174.
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  10. Ceccarelli D, Lucchese C, Orlando S, Perego R, Silvestri F.  2011.  Caching query-biased snippets for efficient retrieval. Proceedings of the 14th International Conference on Extending Database Technology.
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