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REDUCER – An Architectural Design Pattern for Reducing Large and Noisy Data Sets

Authors: Apkar Salatian


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.

Keywords: Design Pattern, filtering, compression.

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[1] Nsang, A., Salatian, A. "Data Reduction of ICU Data using a Random Selection Approach”, International Journal of Advanced Science and Technology, Vol. 55, 2013, pp. 81-88,.
[2] Salatian, A., & Taylor, B., "ABSTRACTOR: An Agglomerative Approach to Interpreting Building Monitoring Data”, Journal of Information Technology in Construction, Vol. 13, May 2008, pages 193-211.
[3] Salatian, A. & Adepoju, F. "In Praise of Wavelets – 3 Disparate Case Studies”, The 3rd International Multi-Conference on Complexity, Informatics and Cybernetics: IMCIC 2012, Volume 1, pages 36 – 40. Orlando, Florida, USA, March 25th - 28th, 2012.
[4] Singhal A, Singh RP, Tenguria M, "Comparison of Different Wavelets for Watermarking of Colored Images”, 2011 IEEE 3rd International Conference on Electronics Computer Technology (ICECT 2011), Kanyakumari, India, pp. 187 – 191.
[5] Salatian, A. & Oborkhale, L., "Filtering of ICU Monitor Data to Reduce False Alarms and Enhance Clinical Decision Support”, International Journal of Bio-Science and Bio-Technology, Volume 3, Number 3, June 2011, pages 49-55.