An efficient multidimensional fusion algorithm for iot data based on partitioning

Tsinghua Science and Technology

Volumn 18, Issue 4, 2013, Pages 369-378

  Zhou, Jin ^a   Hu, Liang ^a   Wang, Feng ^a   Lu, Huimin ^b   Zhao, Kuo ^a  

^a JILIN UNIVERSITY   (China)

^b CHANGCHUN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Data fusion; Internet of Things; Multidimensional data; Partitioning; Rough set theory

Indexed keywords

BIG DATA; COMPUTATION THEORY; COMPUTATIONAL EFFICIENCY; DATA FUSION; EFFICIENCY; INTERNET OF THINGS; ROUGH SET THEORY; SET THEORY;

ADVANCED INTELLIGENCES; ATTRIBUTE REDUCTION; INTERNET OF THING (IOT); MULTIDIMENSIONAL DATA; PARTITIONING; PROCESS EFFICIENCY; RULE EXTRACTION METHOD; WORLD-WIDE NETWORKS;

DATA HANDLING;

EID: 84897724047     PISSN: 10070214     EISSN: None     Source Type: Journal    
DOI: 10.1109/TST.2013.6574675     Document Type: Article

References (21)

1. ITU Strategy and Policy Unit (SPU), ITU internet reports 2005: The internet of things. Geneva: International Telecommunication Union (ITU), 2005.
2. O. Vermesan, M. Harrison, H. Vogt, K. Kalaboukas, M. Tomasella, K. Wouters, S. Gusmeroli, and S. Haller, Internet of things strategic research roadmap. EPoSS: European Technology Platform on Smart Systems Integration, 2009.
3. P. Barnaghi,W.Wang, C. Henson, and K. Taylor, Semantics for the Internet of Things: Early progress and back to the future, International Journal on Semantic Web and Information Systems, vol. 8, no. 1, pp. 1-21, 2012.
4. C. C. Aggarwal, The Internet of Things: A survey and form the date-centric perspective, in Managing and Mining Sensor Data. New York, USA: Springer, 2013, pp. 383-428.
5. Z. Pawlak, Rough sets and intelligent data analysis, Information Sciences, vol. 147, no. 124, pp. 1-12, 2002. (Pubitemid 35349784)
6. Z. Pawlak, Rough sets theory and its applications, Journal of Communications and Information Technology, no. 3, pp. 7-10, Mar. 2002.
7. M. Krysikiewicz, Rough set approach to incomplete information system, Information Sciences, vol. 112, nos. 1-4, pp. 39-49, 1998.
8. L. Wald, Some terms of reference in data fusion, IEEE Transactions on Geosciences and Remote Sensing, vol. 37, no. 3, pp. 1190-1193, 1999.
9. E. F. Nakamura, A. A. F. Loureiro, and A. C. Frery, Information fusion for wireless sensor networks: Methods, models, and classifications, ACM Computing Surveys, vol. 39, no. 3, pp. 1-55, 2007.
10. M. Compton, P. Barnaghi, L. Bermudez, R. Garcła-Castro, O. Corcho, S. Cox, J. Graybeal, M. Hauswirth, C. Henson, A. Herzog, V. Huang, K. Janowicz, W. D. Kelsey, D. Le Phuoc, L. Lefort, M. Leggieri, H. Neuhaus, A. Nikolov, K. Page, A. Passant, A. Sheth, and K. Taylor, The SSN ontology of the W3C semantic sensor network incubator group, Journal of Web Semantics, vol. 17, pp. 25-32, 2012.
11. C. Henson, A. Sheth, and K. Thirunarayan, Semantic perception: Converting sensory observations to abstractions, IEEE Internet Computing, vol. 16, no. 2, pp. 26-34, 2012.
12. H. Patni, C. Henson, and A. Sheth, Linked sensor data, in Proc. 2010 International Symposium on Collaborative Technologies and Systems (CTS 2010), Chicago, USA, 2010, pp. 1-9.
13. M. Rinne, S. Torma, and E. Nuutila, SPARQL-based applications for RDF-encoded sensor data, in Proc. 5th InternationalWorkshop on Semantic Sensor Networks 2012 (SSN12), Boston, Massachusetts, USA, 2012, pp. 81-96.
14. A. K. Joshi, P. Jain, P. Hitzler, P. Z. Yeh, K. Verma, A. P. Sheth, and M. Damova, Alignment-based querying of linked open data, in Proc. on the Move to Meaningful Internet Systems: OTM 2012, Confederated International Conferences: CoopIS, DOA-SVI, and ODBASE 2012, Rome, Italy, 2012, pp. 1-18.
15. A. Rajaraman, J. Leskovec, and J. D. Ullman, Mining of Massive Datasets. Cambridge University Press, 2010.
16. N. Pham and R. Pagh, A near-linear time approximation algorithm for angle-based outlier detection in highdimensional Data, in Proc. 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'12), Beijing, China, 2012, pp. 877-885.
17. S. Kahramanli, M. Hacibeyoglu, and A. Arslan, Attribute reduction by partitioning the minimized discernibility function, International Journal of Innovative Computing, Information and Control, Computer Research and Development, vol. 7, no. 5, pp. 2167-2186, 2011.
18. E. Oren, S. Kotoulas, G. Anadiotis, R. Srebes, A. ten Teiji, and F. van Harmelen, Marvin: Distributed reasoning over large-scale semantic web data, Journal of Web Semantics, vol.7, no. 4, pp. 305-316, 2009.
19. K. Thangavel and A. Pethalakshmi, Dimensionality reduction based on rough set theory: A review, Applied Soft Computing, vol. 1, no. 9, pp. 1-12, 2009.
20. Y. Qian, J. Liang, W. Pedrycz, and C. Dang, Positive approximation: An accelerator for attribute reduction in rough set theory, Artificial Intelligence, vol. 174, nos. 9-10, pp. 597-618, 2010.
21. D. Cao, X. Qiao, G. Judith, X. Li, and L. Meng, Mining data correlation from multi-faceted sensor data in the Internet of Things, China Communications, vol. 8, no. 1, pp. 132-138, 2011.