Abstract
In real-time processing, stream has to be processed as soon as it’s generated. Data streams generated from IOT sensors are processed into a finite-size window. A sheer window is considered for processing of data streams in a particular time stamp. In these sheer windows, compare reduce aggregate (CRA) algorithm is applied for determining linear relation for multiple feature vectors. A real-time inference pattern is determine using hash-based classification. In this chapter, sheer window hash-based classification using binarised window analytic (SHCUBA) approach is proposed. This approach is beneficial for calculating linear relationship between sheer windows. SHCUBA approach is comprised of transformation and virtualisation. Here, time and space complexity for this approach is \(O(n)\) and \(O\left(1\right)\), respectively. This reduces latency and space requirements for various real-time use cases such as smart applications, sentiment analysis, IOT-based solutions, fraud detection and prevention, stock market prediction, etc. Data generated in smart societies can be correlated using SHCUBA approach for inferring useful decisions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Maheswar R, Kanagachidambaresan GR (2020) Sustainable development through Internet of Things. Wireless Netw 26(4):2305–2306
Artikis A et al (2014) Heterogeneous stream processing and crowdsourcing for urban traffic management. In: EDBT, vol 14
Carvalho O, Roloff E, Navaux POA (2017) A distributed stream processing based architecture for IoT smart grids monitoring. In: Companion proceedings of the10th international conference on utility and cloud computing
Abdullah N, Alwesabi OA, Abdullah R (2018) Iot-based smart waste management system in a smart city. In: International conference of reliable information and communication technology. Springer, Cham
Cortés R et al (2015) Stream processing of healthcare sensor data: studying user traces to identify challenges from a big data perspective. Procedia Comput Sci 52:1004–1009
Biem A et al (2010) Ibm infosphere streams for scalable, real-time, intelligent transportation services. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data
Dimitropoulos X, Hurley P, Kind A (2008) Probabilistic lossy counting: an efficient algorithm for finding heavy hitters. ACM SIGCOMM Comput Commun Rev 38(1):5–5
Cormode G, Muthukrishnan S (2005) An improved data stream summary: the count-min sketch and its applications. J Algorithms 55(1):58–75
Kumar A, Xu J, Wang J (2006) Space-code bloom filter for efficient per-flow traffic measurement. IEEE J Sel Areas Commun 24.12:2327–2339
Lal DK, Suman U (2020) SBASH stack based allocation of sheer window architecture for real time stream data processing. Int J Data Anal (IJDA) 1.1:1–21
Lal DK, Suman U (2021) An online approach to determine correlation between data streams. In: Companion of the ACM/SPEC international conference on performance engineering
Pang S, Ozawa S, Kasabov N (2005) Incremental linear discriminant analysis for classification of data streams. IEEE Trans Syst Man Cybern Part B (Cybernetics) 35.5: 905–914
Beringer J, Hüllermeier E (2006) Online clustering of parallel data streams. Data Knowl Eng 58(2):180–204
Mencagli G et al (2017) Harnessing sliding-window execution semantics for parallel stream processing. J Parallel Distrib Comput
Zhang P et al (2014) E-tree: an efficient indexing structure for ensemble models on data streams. IEEE Trans Knowl Data Eng 27.2:461–474
Masud M et al (2010) Classification and novel class detection in concept-drifting data streams under time constraints. IEEE Trans Knowl Data Eng 23.6:859–874
Mueen A, Nath S, Liu J (2010) Fast approximate correlation for massive time-series data. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data
Zhang T et al (2009) Adaptive correlation analysis in stream time series with sliding windows. Comput Math Appl 57.6:937–948
Cormode G, Muthukrishnan S (2005) Summarizing and mining skewed data streams. In: Proceedings of the 2005 SIAM international conference on data mining. Society for industrial and applied mathematics
Nelson J (2012) Sketching and streaming algorithms for processing massive data. XRDS Crossroads ACM Mag Stud 19.1:14–19
Yang T et al (2017) SF-sketch: a two-stage sketch for data streams. ar**v:1701.04148
Palma-Mendoza R-J et al (2019) Distributed correlation-based feature selection in spark. Inf Sci 496:287–299
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
Amen B, Grigoris A (2018) A theoretical study of anomaly detection in big data distributed static and stream analytics. In: 2018 IEEE 20th international conference on high performance computing and communications; IEEE 16th international conference on smart city; IEEE 4th international conference on data science and systems (HPCC/SmartCity/DSS). IEEE
Zhang Y et al (2017) Improved visual correlation analysis for multidimensional data. J Vis Lang Comput 41:121–132
Hauke J, Kossowski T (2011) Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. Quaest Geograph 30(2):87–93
Zar JH (2005) Spearman rank correlation. Encycl Biostat 7
Acknowledgements
I offer my most sincere gratitude towards Council of Scientific and Industrial Research (CSIR), Government of India, for providing me research grant in the form of Senior Research Fellowship.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Lal, D.K., Suman, U. (2022). An Analytical Approach Towards Data Stream Processing on Smart Society for Sustainable Development. In: Bali, V., Bhatnagar, V., Lu, J., Banerjee, K. (eds) Decision Analytics for Sustainable Development in Smart Society 5.0. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-19-1689-2_13
Download citation
DOI: https://doi.org/10.1007/978-981-19-1689-2_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-1688-5
Online ISBN: 978-981-19-1689-2
eBook Packages: Business and ManagementBusiness and Management (R0)