Abstract
We propose a heuristics-based social-sensor cloud service selection and composition model to reconstruct mosaic scenes. The proposed approach leverages crowdsourced social media images to create an image mosaic to reconstruct a scene at a designated location and an interval of time. The novel approach relies on the set of features defined on the bases of the image metadata to determine the relevance and composability of services. Novel heuristics are developed to filter out non-relevant services. Multiple machine learning strategies are employed to produce smooth service composition resulting in a mosaic of relevant images indexed by geolocation and time. The preliminary analytical results prove the feasibility of the proposed composition model.
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
References
Rosi, A., Mamei, M., Zambonelli, F., Dobson, S., Stevenson, G., Ye, J.: Social sensors and pervasive services: approaches and perspectives. In: Proceedings of PERCOM (2011)
Castillo, C.: Big Crisis Data: Social Media in Disasters and Time-Critical Situations. University Press, New York (2016)
Stieglitz, S., Mirbabaie, M., Ross, B., Neuberger, C.: Social media analytics - challenges in topic discovery, data collection, and data preparation. Int. J. Inf. Manag. 39, 156–168 (2018)
Aamir, T., Bouguettaya, A., Dong, H., Mistry, S., Erradi, A.: Social-sensor cloud service for scene reconstruction. In: Maximilien, M., Vallecillo, A., Wang, J., Oriol, M. (eds.) ICSOC 2017. LNCS, vol. 10601, pp. 37–52. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69035-3_3
Aamir, T., Dong, H., Bouguettaya, A.: Social-sensor composition for scene analysis. In: Pahl, C., Vukovic, M., Yin, J., Yu, Q. (eds.) ICSOC 2018. LNCS, vol. 11236, pp. 352–362. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03596-9_25
Neiat, A.G., Bouguettaya, A., Sellis, T., Dong, H.: Failure-proof spatio-temporal composition of sensor cloud services. In: Franch, X., Ghose, A.K., Lewis, G.A., Bhiri, S. (eds.) ICSOC 2014. LNCS, vol. 8831, pp. 368–377. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45391-9_26
Bouguettaya, A., et al.: A service computing manifesto: the next 10 years. Commun. ACM 60(4), 64–72 (2017)
Durgesh, K.S., Lekha, B.: Data classification using support vector machine. J. Theor. Appl. Inf. Technol. 12(1), 1–7 (2010)
Calvet, L., de Armas, J., Masip, D., Juan, A.A.: Learnheuristics: hybridising metaheuristics with machine learning for optimisation with dynamic inputs. Open Math. 15(1), 261–280 (2017)
Han, J., Kamber, M., Jian, P.: Classification: advanced methods. Data Min. Concepts Tech. (2012)
Lowe, D.G.: Distinctive image features from scale-invariant key points. Int. J. Comput. Vision 60(2), 91–110 (2004)
Aamir, T., Dong, H., Bouguettaya, A.: Social-sensor composition for tapestry scenes. In: IEEE Transactions on Services Computing (2020)
Aamir, T., Bouguettaya, A., Dong, H., Erradi, A., Hadjidj, R.: Social-sensor cloud service selection. In: Proceedings of ICWS (2017)
Acknowledgement
This research was partly made possible by DP160103595 and LE180100158 grants from the Australian Research Council. The statements made herein are solely the responsibility of the authors.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Aamir, T., Dong, H., Bouguettaya, A. (2020). Heuristics Based Mosaic of Social-Sensor Services for Scene Reconstruction. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12342. Springer, Cham. https://doi.org/10.1007/978-3-030-62005-9_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-62005-9_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62004-2
Online ISBN: 978-3-030-62005-9
eBook Packages: Computer ScienceComputer Science (R0)