Computing

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Cloud data processing using granular based weighted concept lattice and Hamming distance

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Abstract

In the last decade, much attention has been paid towards connection among mobile and cloud devices for providing the optimum computational time to process any query of globally distributed users. This mathematics provides a large number of generated queries at a given phase of time. It creates a major problem in selecting some of the user required (or interested) queries and their changes to process the task within stimulated time. To elicit this problem the current paper introduces a method for matrix representation of given query and its hierarchical ordering via calculus of applied lattice theory. The importance of each query is decided through their entropy based computed weight and the level of granulation for their selection. The properties of Huffman coding are utilized to measure the changes in each query based on their Hamming distance. In addition, each of the proposed method are illustrated with an example.

Keywords

Concept lattice Formal Concept Analysis Query Hamming Distance Mobile Cloud Computing 

Mathematics Subject Classification

06Axx 06Bxx 06Fxx 15Bxx 

Notes

Acknowledgements

Author thanks the anonymous reviewers and the editor for their valuable suggestions and insights to improve the quality of this paper.

Compliance with ethical standards

Conflict of interest

Author declares that there is no conflict of interest.

References

  1. 1.
    Gani A, Nayeem GM, Shiraz M, Sookhak M, Whaiduzzaman M, Khan S (2014) A review on interworking and mobility techniques for seamless connectivity in mobile cloud computing. J Netw Comput Appl 43:84–102CrossRefGoogle Scholar
  2. 2.
    Khan S, Shiraz M, Boroumand L, Gani A, Khan MK (2017) Towards port-knocking authentication methods for mobile cloud computing. J Netw Comput Appl 97:66–78CrossRefGoogle Scholar
  3. 3.
    Khan S, Gani A, Wahab AWA, Bagiwa MA, Shiraz M, Khan SU, Buyya RK, Zomaya AY (2016) Cloud log forensics: foundations, state of the art, and future firections. ACM Comput Surv 49(1): Article number 7.  https://doi.org/10.1145/2906149
  4. 4.
    Fang W, Yin X, An Y, Xiong N, Guo Q, Li J (2015) Optimal scheduling for data transmission between mobile devices and cloud. Inf Sci 301:169–180MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Shiraz M, Gani A, Khokhar RH, Buyya R (2013) A review on distributed application processing frameworks in smart mobile devices for mobile cloud computing. IEEE Commun Surv Tutor 15(3):1294–1313CrossRefGoogle Scholar
  6. 6.
    Sanaei Z, Abolfazli S, Gani A, Buyya R (2015) Heterogeneity in mobile cloud computing: taxonomy and open challenges. IEEE Commun Surv Tutor 16(1):369–392CrossRefGoogle Scholar
  7. 7.
    Singh PK (2017b) Complex vague set based concept lattice. Chaos Solitons Fractals 96:145–153.  https://doi.org/10.1016/j.chaos.2017.01.019 CrossRefMATHGoogle Scholar
  8. 8.
    Liu W, Nishio T, Shinkuma R, Takahashi T (2014) Adaptive resource discovery in mobile cloud computing. Comput Commun 50:119–129CrossRefGoogle Scholar
  9. 9.
    Duro FR, Blas JG, Higuero D, Perez O, Carretero J (2015) CoSMiC: a hierarchical cloudlet-based storage architecture for mobile clouds. Simul Model Pract Theory 50(2015):3–19CrossRefGoogle Scholar
  10. 10.
    Aminzadeh N, Sanaei Z, Hamid SHA (2015) Mobile storage augmentation in mobile cloud computing: taxonomy, approaches, and open issues. Simul Modell Pract Theory 50:96–108CrossRefGoogle Scholar
  11. 11.
    Todoran I, Glinz M (2014) Quest for requirements: scrutinizing advanced search queries for cloud services with fuzzy Galois lattices. In: Proceedings of international conference on IEEE 10th world congress on services, pp 234–241Google Scholar
  12. 12.
    Mezni H, Sellam M (2017) Multi-cloud service composition using formal concept analysis. J Syst Softw 134:138–152CrossRefGoogle Scholar
  13. 13.
    Sarnovsky M, Butka P, Pocsova J (2012) Cloud computing as a platform for distributed fuzzy FCA approach in data analysis. In Proceedings of IEEE 16th international conference on intelligent engineering systems INES 2012, Lisbon, Portugal, pp 291–296Google Scholar
  14. 14.
    Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I (ed) Ordered sets. Reidel Dordrect, Boston, pp 445–470CrossRefGoogle Scholar
  15. 15.
    Kumar CA, Singh PK (2014) Knowledge representation using formal concept analysis: a study on concept generation. In: Tripathy BK, Acharjya DP (eds) Global trends in knowledge representation and computational intelligence. IGI Global Publishers, Hershey, pp 306–336Google Scholar
  16. 16.
    Poelmans J, Kuznetsov SO, Ignatov DI, Dedene G (2013) Formal concept analysis in knowledge processing: a survey on applications. Expert Syst Appl 40(16):6538–6560CrossRefGoogle Scholar
  17. 17.
    Burusco A, Fuentes-Gonzales R (1994) The study of L-fuzzy concept lattice. Matheware Soft Comput 3:209–218MathSciNetMATHGoogle Scholar
  18. 18.
    Singh PK, Gani A (2015) Fuzzy concept lattice reduction using Shannon entropy and Huffman coding. J Appl Non Class Log 25(2):101–119.  https://doi.org/10.1080/11663081.2015.1039857 MathSciNetCrossRefGoogle Scholar
  19. 19.
    Singh PK (2018a) Interval-valued neutrosophic graph representation of concept lattice and its (\(\alpha, \beta, \gamma \))-decomposition. Arab J Sci Eng 43(2):723–740.  https://doi.org/10.1007/s13369-017-2718-5 CrossRefGoogle Scholar
  20. 20.
    Singh PK, Kumar CA (2014) Bipolar fuzzy graph representation of concept lattice. Inf Sci 288:437–448MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Singh PK (2017) Concept learning using vague concept lattice. Neural Process Lett.  https://doi.org/10.1007/s11063-017-9699-y Google Scholar
  22. 22.
    Singh PK (2018b) m-polar fuzzy graph representation of concept lattice. Eng Appl Artif Intell 67:52–64.  https://doi.org/10.1016/j.engappai.2017.09.011 CrossRefGoogle Scholar
  23. 23.
    Qin Z, Zhang J, Zhang X (2012) An effective partition approach for elastic application development on Mobile Cloud Computing. Lect Notes Comput Sci 7296:46–53CrossRefGoogle Scholar
  24. 24.
    Park JS, Lee EY (2013) Entropy-based grouping techniques for resource management in mobile cloud computing. Lect Notes Electr Eng.  https://doi.org/10.1007/978-94-007-5857-5-83 Google Scholar
  25. 25.
    Khan S, Gani A, Wahab AWA, Singh PK (2018) Feature selection of denial-of-service attacks using entropy and granular computing. Arab J Sci Eng 43(2):499–508.  https://doi.org/10.1007/s13369-017-2634-8 CrossRefGoogle Scholar
  26. 26.
    Otebolaku AM, Andrade MT (2014) Supporting context aware cloud based media recommendation smartphones. In: Proceedings of 2014 international conference of mobile cloud computing, services and engineering, pp 109–116Google Scholar
  27. 27.
    Yang CT, Shih WC, Huang CL, Jiang FC, Cheng-Chung Chu W (2016) On construction of a distributed data storage system in cloud. Computing 98(1):93–118MathSciNetCrossRefGoogle Scholar
  28. 28.
    Yao D, Yu C, Jin H, Zhou J (2013) Energy efficient task scheduling in mobile cloud computing. International federation for information processing 2013. LNCS 8147:344–355Google Scholar
  29. 29.
    Shiraz M, Gani A, Shamim A, Khan S, Ahmad RW (2015) Energy efficient computational offloading framework for mobile cloud computing. J Grid Comput 13:1–18CrossRefGoogle Scholar
  30. 30.
    Castellanos A, Cigarrán J, García-Serrano A (2017) Formal concept analysis for topic detection: a clustering quality experimental analysis. Inf. Syst. 66:24–42CrossRefGoogle Scholar
  31. 31.
    Kumar CA, Srinivas S (2010) Concept lattice reduction using fuzzy K-means clustering. Expert Syst Appl 37(3):2696–2704CrossRefGoogle Scholar
  32. 32.
    Kumar CA, Dias SM, Vieira NJ (2015) Knowledge reduction in formal contexts using non-negative matrix factorization. Math Comput Simul 109:46–63MathSciNetCrossRefGoogle Scholar
  33. 33.
    Lin X, Wang Y, Xie Q, Pedram M (2015) Task scheduling with dynamic voltage and frequency scaling for energy minimization in the Mobile Cloud Computing environment. IEEE Trans Serv Comput 8(2):175–186CrossRefGoogle Scholar
  34. 34.
    Verbelen T, Stevens T, Turck FD, Dhoedt B (2013) Graph partitioning algorithms for optimizing software deployment in mobile cloud computing. Future Gen Comput Syst 29:451–459CrossRefGoogle Scholar
  35. 35.
    Chen S, Wang G, Jia W (2015) k-Fuzzy trust: efficient trust computation for large-scale mobile social networks using a fuzzy implicit social graph. Inf. Sci. 318:123–143MathSciNetCrossRefGoogle Scholar
  36. 36.
    Chen S, Wang G, Jia W (2015) Cluster-group based trusted computing for mobile social networks using implicit social behavioral graph. Future Gen Comput Syst 55:391–400CrossRefGoogle Scholar
  37. 37.
    Wu W, Hu S, Yang X, Liu JK, Au MH (2015) Towards secure and cost-effective fuzzy access control in mobile cloud computing. Soft Comput.  https://doi.org/10.1007/s00500-015-1964-2 Google Scholar
  38. 38.
    Wang Y, Liu Z, Du Z, Huang Y (2013) Mobile cloud computing network attack and defense learning system based on fuzzy soft sets. Proc Comput Sci 17:214–221CrossRefGoogle Scholar
  39. 39.
    Feng F, Fujita H, Jun YB, Khan M (2014) Decomposition of fuzzy soft sets with finite value spaces. Sci World J.  https://doi.org/10.1155/2014/902687 Google Scholar
  40. 40.
    Papadopoulos A, Pallis G, Dikaiakos MD (2017) Weighted clustering of attributed multi-graphs. Computing 99(9):813–840.  https://doi.org/10.1007/s00607-016-0526-5 MathSciNetCrossRefMATHGoogle Scholar
  41. 41.
    Kang X, Maio D (2016) A study on information granularity in formal concept analysis based on concept-bases. Knowl Based Syst 105:147–159CrossRefGoogle Scholar
  42. 42.
    Zhi H, Li JH (2016) Granule description based on formal concept analysis. Knowl Based Syst 104:62–73CrossRefGoogle Scholar
  43. 43.
    Singh PK, Kumar CA, Gani A (2016) A comprehensive survey on formal concept analysis, its research trends and applications. Int J Appl Math Comput Sci 26(2):495–516MathSciNetCrossRefMATHGoogle Scholar
  44. 44.
    Singh PK, Kumar CA (2017) Concept lattice reduction using different subset of attributes as information granules. Granul Comput 2(3):159–173.  https://doi.org/10.1007/s41066-016-0036-z CrossRefGoogle Scholar
  45. 45.
    Li F, Liu B, Hong JJ (2017) An efficient signcryption for data access control in cloud computing. Computing 99(5):465–479MathSciNetCrossRefMATHGoogle Scholar
  46. 46.
    Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(379–423):623–656MathSciNetCrossRefMATHGoogle Scholar
  47. 47.
    Singh PK, Kumar CA, Li J (2017) Concepts reduction in formal concept analysis with fuzzy setting using Shannon entropy. Int J Mach Learn Cybern 8(1):179–189CrossRefGoogle Scholar
  48. 48.
    Babin MA, Kuznetsov SO (2012) Approximating concept stability. Lect. Notes Comput. Sci. 7278:7–15CrossRefMATHGoogle Scholar
  49. 49.
    Belohlavek R, Macko J (2011) Selecting important concepts using weights. Lect. Notes Comput. Sci. 6628:65–80CrossRefMATHGoogle Scholar
  50. 50.
    Belohlavek R, Trnecka M (2012) Basic level of concepts in formal concept analysis. Lect. Notes Comput. Sci. 7278:28–44CrossRefMATHGoogle Scholar
  51. 51.
    Dias SM, Viera NJ (2013) Applying the JBOS reduction method for relevant knowledge extraction. Experts Syst Appl 40(5):1880–1887CrossRefGoogle Scholar
  52. 52.
    Li C, Li J, He M (2014) Concept lattice compression in incomplete contexts based on K-medoids clustering. Int J Mach Learn Cybern 5(4):1–14.  https://doi.org/10.1007/s13042-014-02883 CrossRefGoogle Scholar
  53. 53.
    Kang X, Li D, Wang S, Qu K (2012) Formal concept analysis based on fuzzy granularity base for different granulation. Fuzzy Sets Syst 203:33–48MathSciNetCrossRefMATHGoogle Scholar
  54. 54.
    Wu WZ, Leung Y, Mi JS (2009) Granular computing and knowledge reduction in formal context. IEEE Trans Knowl Data Eng 21(10):1461–1474CrossRefGoogle Scholar
  55. 55.
    Hanko P (2015) Relation-based granules to represent relational data and patterns. Appl Soft Comput 37:467–478CrossRefGoogle Scholar
  56. 56.
    Ganter B, Wille R (1999) Formal concept analysis: mathematical foundation. Springer, BerlinCrossRefMATHGoogle Scholar
  57. 57.
    Zadeh L (1965) Fuzzy sets. Inf Control 8:338–353CrossRefMATHGoogle Scholar
  58. 58.
    Zadeh L (1996) Fuzzy logic=computing with words. IEEE Trans Fuzzy Syst 4(2):103–111CrossRefGoogle Scholar
  59. 59.
    Bhensle RC, Singh PK, Chandramoulli K (2017) A design of network protocol for IoT to optimize the power consumption using ARDUINO 1.6.0. In: Proceedings of the 4th international conference on computing for sustainable global development, 01st–03rd March, 2017. Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi (INDIA), pp 1951–1956Google Scholar
  60. 60.
    Li J, He Z, Zhu Q (2013) An entropy-based weighted concept lattice for merging multi-source geo-ontologies. Entropy 15(6):2303–2318MathSciNetCrossRefMATHGoogle Scholar
  61. 61.
    Singh PK (2017a) Three-way fuzzy concept lattice representation using neutrosophic set. Int J Mach Learn Cybern 8(1):69–79CrossRefGoogle Scholar
  62. 62.
    Singh PK, Kumar CA (2012) A method for decomposition of fuzzy formal context. Proc Int Conf Model Optim Comput Proc Eng 38:1852–1857Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Amity Institute of Information TechnologyAmity UniversityNoidaIndia

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