@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}, }