@article{(Open Science Index):https://publications.waset.org/pdf/9997551,
	  title     = {REDUCER – An Architectural Design Pattern for Reducing Large and Noisy Data Sets},
	  author    = {Apkar Salatian},
	  country	= {},
	  institution	= {},
	  abstract     = {To relieve the burden of reasoning on a point to point basis, in many domains there is a need to reduce large and noisy data sets into trends for qualitative reasoning. In this paper we propose and describe a new architectural design pattern called REDUCER for reducing large and noisy data sets that can be tailored for particular situations. REDUCER consists of 2 consecutive processes: Filter which takes the original data and removes outliers, inconsistencies or noise; and Compression which takes the filtered data and derives trends in the data. In this seminal article we also show how REDUCER has successfully been applied to 3 different case studies.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {8},
	  number    = {2},
	  year      = {2014},
	  pages     = {214 - 216},
	  ee        = {https://publications.waset.org/pdf/9997551},
	  url   	= {https://publications.waset.org/vol/86},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 86, 2014},
	}