PICKT: A Solution for Big Data Analysis

Emerging information technologies and application patterns in modern information society, e.g., Internet, Internet of Things, Cloud Computing and Tri-network Convergence, are growing in an amazing speed which causes the advent of the era of Big Data. Big Data is often described by using five V’s: Volume, Velocity, Variety, Value and Veracity. Exploring efficient and effective data mining and knowledge discovery methods to handle Big Data with rich information has become an important research topic in the area of information science. This paper focuses on the introduction of our solution, PICKT, on big data analysis based on the theories of granular computing and rough sets, where P refers to parallel/cloud computing for the Volume, I refers to incremental learning for the Velocity, C refers to composite rough set model for the Variety, K refers to knowledge discovery for the Value and T refers to three-way decisions for the Veracity of Big Data.

[1]  Luis Martínez-López,et al.  Preface: Intelligent Techniques for Data Science , 2015, Int. J. Intell. Syst..

[2]  Tianrui Li,et al.  A PARALLEL APPROACH FOR COMPUTING APPROXIMATIONS OF DOMINANCE-BASED ROUGH SETS APPROACH , 2014 .

[3]  Da Ruan,et al.  Maintenance of approximations in incomplete ordered decision systems while attribute values coarsening or refining , 2012, Knowl. Based Syst..

[4]  Gregory Piatetsky-Shapiro,et al.  The KDD process for extracting useful knowledge from volumes of data , 1996, CACM.

[5]  Yiyu Yao,et al.  An Outline of a Theory of Three-Way Decisions , 2012, RSCTC.

[6]  Tianrui Li,et al.  Fast algorithms for computing rough approximations in set-valued decision systems while updating criteria values , 2015, Inf. Sci..

[7]  Shaojie Qiao,et al.  A rough set based dynamic maintenance approach for approximations in coarsening and refining attribute values , 2010 .

[8]  Yiyu Yao,et al.  Granular Computing: Past, Present, and Future , 2008, Rough Sets and Knowledge Technology.

[9]  Hongmei Chen,et al.  Dynamic maintenance of approximations in set-valued ordered decision systems under the attribute generalization , 2014, Inf. Sci..

[10]  Dun Liu,et al.  A fuzzy rough set approach for incremental feature selection on hybrid information systems , 2015, Fuzzy Sets Syst..

[11]  Dun Liu,et al.  Dynamic Maintenance of Approximations in Dominance‐Based Rough Set Approach under the Variation of the Object Set , 2013, Int. J. Intell. Syst..

[12]  Dun Liu,et al.  Incremental approaches for updating approximations in set-valued ordered information systems , 2013, Knowl. Based Syst..

[13]  Xindong Wu,et al.  Data mining with big data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[14]  Yi Pan,et al.  A Parallel Matrix-Based Method for Computing Approximations in Incomplete Information Systems , 2015, IEEE Transactions on Knowledge and Data Engineering.

[15]  Tianrui Li,et al.  Composite rough sets for dynamic data mining , 2014, Inf. Sci..

[16]  Guoyin Wang,et al.  A Decision-Theoretic Rough Set Approach for Dynamic Data Mining , 2015, IEEE Transactions on Fuzzy Systems.

[17]  Da Ruan,et al.  An Incremental Approach for Inducing Knowledge from Dynamic Information Systems , 2009, Fundam. Informaticae.

[18]  Tianrui Li,et al.  Dynamic Maintenance of Three-Way Decision Rules , 2014, RSKT.

[19]  Benjamin W. Wah,et al.  Significance and Challenges of Big Data Research , 2015, Big Data Res..

[20]  Tianrui Li,et al.  Incremental Maintenance of Rough Fuzzy Set Approximations under the Variation of Object Set , 2014, Fundam. Informaticae.

[21]  Trudie Lang,et al.  Advancing Global Health Research Through Digital Technology and Sharing Data , 2011, Science.

[22]  Salvatore Greco,et al.  Rough sets theory for multicriteria decision analysis , 2001, Eur. J. Oper. Res..

[23]  Da Ruan,et al.  A parallel method for computing rough set approximations , 2012, Inf. Sci..

[24]  Vijay V. Raghavan,et al.  Dynamic Data Mining , 2000, IEA/AIE.

[25]  Tianrui Li,et al.  Update of approximations in composite information systems , 2015, Knowl. Based Syst..

[26]  Andrzej Skowron,et al.  Rudiments of rough sets , 2007, Inf. Sci..

[27]  Da Ruan,et al.  Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems , 2012, Int. J. Approx. Reason..

[28]  Yi Pan,et al.  A Parallel Implementation of Computing Composite Rough Set Approximations on GPUs , 2013, RSKT.

[29]  Dun Liu,et al.  Incremental updating approximations in dominance-based rough sets approach under the variation of the attribute set , 2013, Knowl. Based Syst..

[30]  Yiyu Yao,et al.  Three-way decisions with probabilistic rough sets , 2010, Inf. Sci..

[31]  Chien-Chung Chan,et al.  A Rough Set Approach to Attribute Generalization in Data Mining , 1998, Inf. Sci..

[32]  Wojciech Ziarko,et al.  Variable Precision Rough Set Model , 1993, J. Comput. Syst. Sci..

[33]  Da Ruan,et al.  Incremental learning optimization on knowledge discovery in dynamic business intelligent systems , 2011, J. Glob. Optim..

[34]  A. Lyon.  Dealing with data , 1970 .

[35]  Jianhui Lin,et al.  A Rough-Set-Based Incremental Approach for Updating Approximations under Dynamic Maintenance Environments , 2013, IEEE Transactions on Knowledge and Data Engineering.

[36]  Da Ruan,et al.  Neighborhood rough sets for dynamic data mining , 2012, Int. J. Intell. Syst..

[37]  Geert Wets,et al.  A rough sets based characteristic relation approach for dynamic attribute generalization in data mining , 2007, Knowl. Based Syst..

[38]  Guoyin Wang,et al.  A Rough Set-Based Method for Updating Decision Rules on Attribute Values’ Coarsening and Refining , 2014, IEEE Transactions on Knowledge and Data Engineering.

[39]  Yi Pan,et al.  PLAR: Parallel Large-Scale Attribute Reduction on Cloud Systems , 2013, 2013 International Conference on Parallel and Distributed Computing, Applications and Technologies.

[40]  Tianrui Li,et al.  Incremental Three-Way Decisions with Incomplete Information , 2014, RSCTC.

[41]  Da Ruan,et al.  AN EXTENDED PROCESS MODEL OF KNOWLEDGE DISCOVERY IN DATABASE , 2004 .

[42]  Tianrui Li,et al.  Parallel computing of approximations in dominance-based rough sets approach , 2015, Knowl. Based Syst..

[43]  Yi Pan,et al.  International Journal of Approximate Reasoning a Comparison of Parallel Large-scale Knowledge Acquisition Using Rough Set Theory on Different Mapreduce Runtime Systems , 2022 .