Interactive Data-Driven Research: the place where databases and data mining research meet

Data-driven research, or the science of letting data tell us what we are looking for, is in many areas, the only viable approach to research. In some domains like adaptive clinical trials and emerging research areas such as social computing, useful results are highly dependent on the ability to observe and interactively explore large volumes of real datasets. Database management is the science of efficiently storing and retrieving data. Data mining is the science of discovering hidden correlations in data. Interactive data-driven research is a natural meeting point that presents a new research opportunity. The ability to conduct effective data-driven research requires to combine efficient indexing and querying from databases and pattern mining and classification from data mining to help analysts understand what lies behind large data volumes. In this paper, we explore key challenges and new opportunities in building robust systems for interactive data-driven research.

[1]  Cong Yu,et al.  Who Tags What? An Analysis Framework , 2012, Proc. VLDB Endow..

[2]  Mehrbakhsh Nilashi,et al.  Collaborative filtering recommender systems , 2013 .

[3]  Gerhard Weikum,et al.  Efficient top-k querying over social-tagging networks , 2008, SIGIR '08.

[4]  Ari Rappoport,et al.  Enhanced Sentiment Learning Using Twitter Hashtags and Smileys , 2010, COLING.

[5]  Hiroki Arimura,et al.  LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets , 2004, FIMI.

[6]  Anne-Marie Kermarrec,et al.  The Gossple Anonymous Social Network , 2010, Middleware.

[7]  ManolopoulosYannis,et al.  Collaborative recommender systems , 2008 .

[8]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[9]  Sihem Amer-Yahia,et al.  Efficient sentiment correlation for large-scale demographics , 2013, SIGMOD '13.

[10]  Bart Goethals,et al.  Randomly sampling maximal itemsets , 2013, IDEA@KDD.

[11]  Cong Yu,et al.  Leveraging Tagging to Model User Interests in del.icio.us , 2008, AAAI Spring Symposium: Social Information Processing.

[12]  Tom Brijs,et al.  Profiling high frequency accident locations using associations rules , 2002 .

[13]  Silviu Maniu,et al.  Taagle: efficient, personalized search in collaborative tagging networks , 2012, SIGMOD Conference.

[14]  Sihem Amer-Yahia,et al.  MAQSA: a system for social analytics on news , 2012, SIGMOD Conference.

[15]  Stefan Wrobel,et al.  One click mining: interactive local pattern discovery through implicit preference and performance learning , 2013, IDEA@KDD.

[16]  Andrei Z. Broder,et al.  Anatomy of the long tail: ordinary people with extraordinary tastes , 2010, WSDM '10.

[17]  Dorota Glowacka,et al.  Directing exploratory search: reinforcement learning from user interactions with keywords , 2013, IUI '13.

[18]  Cong Yu,et al.  Group Recommendation: Semantics and Efficiency , 2009, Proc. VLDB Endow..

[19]  Nicolas Pasquier,et al.  Discovering Frequent Closed Itemsets for Association Rules , 1999, ICDT.

[20]  Moni Naor,et al.  Optimal aggregation algorithms for middleware , 2001, PODS '01.

[21]  John Riedl,et al.  PolyLens: A recommender system for groups of user , 2001, ECSCW.

[22]  Cong Yu,et al.  Automatic construction of travel itineraries using social breadcrumbs , 2010, HT '10.