Main Article Content

Abstract

Abstract— as information is a vital asset, it is important to improve information quality and increment the adequacy of the information. Presently a day’s exercises and basic leadership in an association depends on information and data acquired from information examination, which gives different administrations to developing solid and exact process. In any case, recognizing different parts of information quality from definition, measurements, types, methodologies, strategies are basic to prepare techniques and procedures for enhancing information. The genuine reason for poor information quality can be ascribed to an absence of supporting business forms and inadequate investigation procedures. Superb information can expand open door for accomplishing top administrations in an association. In the meantime information quality appraisal is a precondition for advising the clients about the conceivable employments of the information. This paper audits the work done on information quality evaluation to enhance the nature of the information and considered multi layer information digging model for Internet of Things to address the issues in information accumulation. Give answer for the open difficulties in information gathering layer by embracing proposed information quality evaluation process with input system for information quality that has great expansibility and versatility and can address the issues of IoT information quality appraisal.

Article Details

Author Biographies

Seema S, M.S.Ramaiah Institute of Technology Bengaluru, India

Prof., Dept. of Computer Science and Engineering
M.S.Ramaiah Institute of Technology
Bengaluru, India

Sushma B, M.S.Ramaiah Institute of Technology Bengaluru, India

PhD Scholar Dept. of Computer Science and Engineering
M.S.Ramaiah Institute of Technology
Bengaluru, India

How to Cite
[1]
S. S and S. B, “Advanced data analysis and data mining model for Internet of Things in smart city: A Survey”, Ausjournal, vol. 1, no. 1, pp. 16-23, Feb. 2019.

References

[1] Cai, L and Zhu, Y 2015 The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14: 2, pp. 1-10, DOI: http://dx.doi.org/10.5334/dsj-2015-002.

[2] Alexander, J. E., & Tate, M. A. Web wisdom: How to evaluate and create information on the web, Mahwah, NJ: Erlbaum.

[3] Shanks, G., & Corbitt, B. (1999) Understanding data quality: Social and cultural aspects. Procedures of the 10th Australasian Conference on Information Systems, Wellington: MCB University Press Ltd., pp 785–797.

[4] Cooper J, James “A. Challenges for Database Management in the Internet of Things,” IETE Tech Rev. 2009. 26:320-9.

[5] P. Borek, A.Woodall. A classification of data quality assessment methods. international conference on information quality, 2011.

[6] Hector Gonzalez, Jiawei Han, Xiaolei Li. “FlowCube Constructuing RFID FlowCubes for Multi-Dimensional Analysis of Commodity Flows,” VLDB 2006: 834-845.

[7] Schahram Dustdar, Reinhard Pichler, Vadim Savenkov, and HongLinh Truong. Quality-aware service-oriented data integration: requirements, state of the art and open challenges. ACM SIG-MOD Record, 41(1):{11,19}, 2012.

[8] M. Heravizadeh, J. Mendling, M. Rosemann, "Dimensions of business processes quality (QoBP)," 2009, pp. 80-91.

[9] D. McGilvray, Executing data quality projects: Ten steps to quality data and trusted information: Morgan Kaufmann, 2008.

[10] Joshua Huang. (2009) “RFID Data Mining: Opportunities and Challenges,” [Online]. Available: http://homepage.fudan.edu.cn/~wdzhao/rfid.html.

[11] Batini, C., Cappiello, C., Francalanci, C. and Maurino, A. (2009). “Methodologies for data quality assessment and improvement”. ACM Computing Survey, Vol. 41, No. 3, pp1-52.

[12] Batini Carlo1, Barone Daniele1, Cabitza Federico1 and Grega Simone2 : A DATA QUALITY METHODOLOGY FOR HETEROGENEOUS DATA, International Journal of Database Management Systems ( IJDMS ),Vol.3, No.1, February 2011 DOI: 10.5121/ijdms.2011.3105 60

[13] Vivek Teegalapally1, Kiran Dhote2, Vamsi S. Krishna3, Shubham Rao4: Survey on Data Profiling and Data Quality Assessment for Business Intelligence . International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 11