Abstract
In the big data era, the volume of streaming data produced by sensor networks is staggeringly large that enables business intelligence to make well-informed decisions on emerging modern applications. Performing the data analytics and query processing over the fast arriving data streams is a tedious process. The semantic annotation of the data stream provides a high-level description, and a semantic context supports intelligent querying and data analytics. This paper presents a framework called SEmantic Annotation over Summarized sensOr Data stReam (SEASOR) that includes summarization, semantic annotation, and query processing that facilitates sensor data stream analytics. The summarization merges these types of stream values to increase the query performance and decrease the memory space. The semantic annotation is scripted with the help of application-dependent base ontology that extends the Semantic Sensor Network (SSN) ontology. The annotation of the sensor stream provides detailed descriptions for the observation of sensors using the base ontology, and it divides the streaming sensor data into several subsets according to the sensing features. The domain model enables the query processor to access the relevant results via an annotated Resource Description Framework (RDF). The query processor uses the extended SPARQL (Cs-SPARQL) to access only the relatively small subset via an annotated RDF file and allows extending the query processing to support windows and the parallel processing of data streams. The experimental results prove that the proposed SEASOR provides timely answers to the user queries and achieves better performance in terms of result accuracy by 95%.
Similar content being viewed by others
Change history
05 January 2018
R. Sethukarasi was not listed among the authors. The original article has been corrected.
References
Rawat P, Singh KD, Chaouchi H, Bonnin JM (2014) Wireless sensor networks: a survey on recent developments and potential synergies. J Supercomput 68(1):1–48
Krempl G et al (2014) Open challenges for data stream mining research. ACM SIGKDD Explor Newsl 16(1):1–10
Sheth AP, Henson CA, Sahoo SS (2008) Semantic sensor web. IEEE Internet Comput 12:78–83
Rodrıguez A et al (2009) Semantic management of streaming data. In: Proceedings of semantic sensor networks, vol 80
Sheth AP, Thomas C, Mehra P (2010) Continuous semantics to analyze real-time data. IEEE Internet Comput 14(6):84–89
Henson C, Sheth A, Thirunarayan K (2012) Semantic perception: converting sensory observations to abstractions. IEEE Trans Internet Comput 16(2):26–34
Thirunarayan K, Sheth A (2013) Semantics-empowered approaches to big data processing for physical-cyber-social applications. In: Proceedings of AAAI Fall Symposium on Semantics for Big Data
Zhang X, Zhao Y, Liu W (2015) A method for mapping sensor data to the SSN ontology. Int J u- e-service Sci Technol 8(9):303–316
Boury-Brisset A-C (2013) Managing semantic big data for intelligence. In: STIDS, pp 41–47
Zhao J et al Extending semantic provenance into web of data. IEEE Internet Comput 15(1):40–48
Ha SW, Lee YK et al (2012) An environmental monitoring system for managing spatiotemporal sensor data over sensor networks. Sensors 12(4):3997–4015
Rocha OR et al (2015) Semantic annotation and classification in practice. IT Prof 17(2):33–39
Takis J et al (2015) Crowdsourced semantic annotation of scientific publications and tabular data in PDF. In: ACM Proceedings of the 11th International Conference on Semantic Systems, pp 1–8
Moraru A, Mladenić D (2012) A framework for semantic enrichment of sensor data. J Comput Inf Technol 20(3):167–173
Jabbar S, Ullah F, Khalid S, Khan M, Han K (2017) Semantic interoperability in heterogeneous IoT infrastructure for healthcare. Hindawi Wirel Commun Mob Comput 2017:1–10
Chen X, Chen H, Zhang N, Huang J, Zhang W (2015) Large-scale real-time semantic processing framework for internet of things. Hindawi Int J Distrib Sens Netw 2015:1–11
Wu Z et al (2016) Towards Semantic web of things: from manual to semi-automatic semantic annotation on web of things. In: International Conference on Big Data Computing and Communications, Springer International Publishing, pp 295–308
Vidyasankar (2017) On continuous queries in stream processing. In: 8th International Conference on Ambient Systems, Networks, and Technologies, Elsevier, pp 640–647
Xie Q et al (2016) Optimizing cost of continuous overlapping queries over data streams by filter adaption. IEEE Trans Knowl Data Eng 28(5):1258–1271
Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar R, Thota C (2017) A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting. Future Gener Comp Syst 80(5):1–10
Xiao G, Li K, Zhou X, Li K (2016) Queuing analysis of continuous queries for uncertain data streams over sliding windows. Int J Pattern Recognit Artif Intell 30(9):16
Mondal J, Deshpande A (2014) Stream querying and reasoning on social data. In: Encyclopedia of social network analysis and mining, Springer, pp 2063–2075
Bolles A, Grawunder M, Jacobi J (2008) Streaming SPARQL-extending SPARQL to process data streams. Springer, Berlin, pp 448–462
Wei Y, Son SH, Stankovic JA (2006) RTSTREAM: real-time query processing for data streams. In: Ninth IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing, ISORC
Manogaran G, Thota C, Kumar MV (2016) MetaCloudDataStorage architecture for big data security in cloud computing. Procedia Comput Sci 87:128–133
Manogaran G, Lopez D (2016) Health data analytics using scalable logistic regression with stochastic gradient descent. Int J Adv Intell Paradig 9:1–15
Manogaran G, Lopez D (2017) Disease surveillance system for big climate data processing and dengue transmission. Int J Ambient Comput Intell 8(2):1–25
Cichocki A (2014) Era of big data processing: a new approach via tensor networks and tensor decompositions. arXiv preprint arXiv:1403.2048
Zhou G, Zhao Q, Zhang Y, Adalı T, Xie S, Cichocki A (2016) Linked component analysis from matrices to high-order tensors: applications to biomedical data. Proc IEEE 104(2):310–331
Wang R, Zhang Y, Zhang L (2016) An adaptive neural network approach for operator functional state prediction using psychophysiological data. Integr Comput Aided Eng 23(1):81–97
Wang H, Zhang Y, Waytowich NR, Krusienski DJ, Zhou G, Jin J, Cichocki A (2016) Discriminative feature extraction via multivariate linear regression for SSVEP-based BCI. IEEE Trans Neural Syst Rehabil Eng 24(5):532–541
Thota C, Manogaran G, Lopez D, Vijayakumar V (2017) Big data security framework for distributed cloud data centers. In: Cybersecurity breaches and issues surrounding online threat protection, IGI Global, pp 288–310
Manogaran G, Thota C, Lopez D, Vijayakumar V, Abbas KM, Sundarsekar R (2017). Big data knowledge system in healthcare. In: Internet of things and big data technologies for next generation healthcare, Springer International Publishing, pp 133–157
Manogaran G, Lopez D (2017) Spatial cumulative sum algorithm with big data analytics for climate change detection. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2017.04.006
Manogaran G, Lopez D (2017) A Gaussian process based big data processing framework in cluster computing environment. Clust Comput 1–16. https://doi.org/10.1007/s10586-017-0982-5
Varatharajan R, Manogaran G, Priyan MK, Sundarasekar R (2017) Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Clust Comput 1–10. https://doi.org/10.1007/s11227-017-2169-5
Varatharajan R, Manogaran G, Priyan MK, Balaş VE, Barna C (2017) Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis. Multimed Tools Appl 1–21. https://doi.org/10.1007/s11042-017-4768-9
Manogaran G, Thota C, Lopez D (2018) Human-computer interaction with big data analytics. In: HCI challenges and privacy preservation in big data security, IGI Global, pp 1–22
Varatharajan R, Vasanth K, Gunasekaran M, Priyan M, Gao XZ (2017) An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2017.05.035
Thota C, Sundarasekar R, Manogaran G, Varatharajan R, Priyan MK (2018) Centralized fog computing security platform for IoT and cloud in healthcare system. In: Exploring the convergence of big data and the internet of things, IGI Global, pp 141–154
Author information
Authors and Affiliations
Corresponding author
Additional information
The original version of this article was revised: R. Sethukarasi was not listed among the authors.
Rights and permissions
About this article
Cite this article
Pacha, S., Murugan, S.R. & Sethukarasi, R. Semantic annotation of summarized sensor data stream for effective query processing. J Supercomput 76, 4017–4039 (2020). https://doi.org/10.1007/s11227-017-2183-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-017-2183-7