Searchology

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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.

Publications

  1. Lucchese C, Orlando S, Perego R, Silvestri F, Tolomei G.  2013.  Discovering tasks from search engine query logs. ACM Trans. Inf. Syst.. 31:14:1–14:43.
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  2. Tonellotto N, Macdonald C, Ounis I.  2013.  Efficient and effective retrieval using selective pruning. Proceedings of the sixth ACM international conference on Web search and data mining.
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  3. Freire A, Macdonald C, Tonellotto N, Ounis I, Cacheda F.  2013.  Hybrid query scheduling for a replicated search engine. Proceedings of the 35th European conference on Advances in Information Retrieval.
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  4. Bonchi F, Perego R, Silvestri F, Vahabi H, Venturini R.  2012.  Efficient query recommendations in the long tail via center-piece subgraphs. Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval.
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  5. Jonassen S, B. Cambazoglu B, Silvestri F.  2012.  Prefetching query results and its impact on search engines. Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval.
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  6. Macdonald C, Tonellotto N, Ounis I.  2012.  Learning to predict response times for online query scheduling. Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval.
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  7. Morales GDe Francis, Gionis A, Lucchese C.  2012.  From chatter to headlines: harnessing the real-time web for personalized news recommendation. Proceedings of the fifth ACM international conference on Web search and data mining.
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  8. Blanco R, Ceccarelli D, Lucchese C, Perego R, Silvestri F.  2012.  You should read this! let me explain you why: explaining news recommendations to users. Proceedings of the 21st ACM international conference on Information and knowledge management.
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  10. Falchi F, Lucchese C, Orlando S, Perego R, Rabitti F.  2012.  Similarity caching in large-scale image retrieval. Information Processing & Management. 48:803–818.
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