A Framework for Uncertainty-Aware Visual Analytics in Big Data

Visual analytics has become an important tool for gaining insight on big data. Numerous statistical tools have been integrated with visualization to help analysts understand big data better and faster. However, data is inherently uncertain, due to sampling error, noise, latency, approximate measurement or unreliable sources. It is very important and vital to quantify and visualize uncertainties for analysts to improve the results of decision making process and gain valuable insights during analytic process on big data. In this paper, we propose a new framework to support uncertainty in the visual analytics process through a fuzzy self-organizing map algorithm running in MapReduce framework for parallel computations on massive amounts of data. This framework uses an interactive data mining module, uncertainty modeling and knowledge representation that supports insertion of the user’s experience and knowledge for uncertainty modeling and visualization in the big data.

[1]  Yon Dohn Chung,et al.  Parallel data processing with MapReduce: a survey , 2012, SGMD.

[2]  Daniel A. Keim,et al.  Integrated Spatial Uncertainty Visualization using Off-screen Aggregation , 2015, EuroVA@EuroVis.

[3]  Johannes Bendler,et al.  Taming Uncertainty in Big Data , 2014, Bus. Inf. Syst. Eng..

[4]  Maria Riveiro,et al.  Evaluation of uncertainty visualization techniques for information fusion , 2007, 2007 10th International Conference on Information Fusion.

[5]  Manel Guerrero Zapata,et al.  An ANFIS-based cache replacement method for mitigating cache pollution attacks in Named Data Networking , 2015, Comput. Networks.

[6]  Daniel A. Keim,et al.  Advanced visual analytics interfaces , 2010, AVI.

[7]  Hai Qian.  PivotalR: A Package for Machine Learning on Big Data , 2014 .

[8]  Daniel A. Keim,et al.  Visual Analytics: Scope and Challenges , 2008, Visual Data Mining.

[9]  Kwan-Liu Ma,et al.  A framework for uncertainty-aware visual analytics , 2009, 2009 IEEE Symposium on Visual Analytics Science and Technology.

[10]  Daniel A. Keim,et al.  Visual analytics for the big data era — A comparative review of state-of-the-art commercial systems , 2012, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST).

[11]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[12]  Paul R. Havig,et al.  VAST Challenge 2012: Visual analytics for big data , 2012, IEEE VAST.

[13]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[14]  Miriam A. M. Capretz,et al.  Challenges for MapReduce in Big Data , 2014, 2014 IEEE World Congress on Services.

[15]  Manel Guerrero Zapata,et al.  A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks , 2015, Neurocomputing.

[16]  Manel Guerrero Zapata,et al.  Mining and Visualizing Uncertain Data Objects and Named Data Networking Traffics by Fuzzy Self-Organizing Map , 2014, AIC.