Answering the Min-Cost Quality-Aware Query on Multi-sources in Sensor-Cloud Systems

  • Mohan Li
  • Yu Jiang
  • Yanbin Sun
  • Zhihong TianEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11342)


In sensor-cloud systems, a common scenario is that more than one sources can provide the data of the same object. Since the data quality of these sources might be different, when querying the observations, it is necessary to carefully select the sources to make sure that high quality data is accessed. A solution is to perform a quality evaluation in the cloud and select a set of high-quality, low-cost data sources (i.e. sensors or small sensor networks) that can answer queries. This paper studies the problem of min-cost quality-aware query which aims to find high quality results from multi-sources with the minimized cost. The measurement of the query results is provided, and two methods for answering min-cost quality-aware query are proposed. Experiments on real-life data verified that the proposed techniques are effective.


Sensor-based systems Sensor-cloud systems Data quality Quality-aware query Source quality 



The work is supported by the National Natural Science Foundation of China (No. 61871140, 61702220, 61702223, 61572153) and the National Key Research and Development Plan (Grant No. 2018YFB0803504).


  1. 1.
    Abiteboul, S., Kanellakis, P., Grahne, G.: On the representation and querying of sets of possible worlds. Theoret. Comput. Sci. 78(1), 159–187 (1991)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Alamri, A., Ansari, W.S., Hassan, M.M., Hossain, M.S., Alelaiwi, A., Hossain, M.A.: A survey on sensor-cloud: architecture, applications, and approaches. Int. J. Distrib. Sens. Netw. 9(2), 917923 (2013)CrossRefGoogle Scholar
  3. 3.
    Cao, Y., Fan, W., Yu, W.: Determining the relative accuracy of attributes. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 565–576. ACM (2013)Google Scholar
  4. 4.
    Chu, X., Ilyas, I.F., Papotti, P.: Holistic data cleaning: putting violations into context. In: The IEEE 29th International Conference on Data Engineering (ICDE), pp. 458–469 (2013)Google Scholar
  5. 5.
    Dong, X.L., Berti-Equille, L., Srivastava, D.: Integrating conflicting data: the role of source dependence. PVLDB 2(1), 550–561 (2009)Google Scholar
  6. 6.
    Dong, X.L., et al.: Knowledge-based trust: estimating the trustworthiness of web sources. Proc. VLDB Endow. 8(9), 938–949 (2015)CrossRefGoogle Scholar
  7. 7.
    Fan, W., Geerts, F.: Foundations of data quality management. Synth. Lect. Data Manag. 4(5), 1–217 (2012)CrossRefGoogle Scholar
  8. 8.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. WH Freeman and Co., San Francisco (1979)zbMATHGoogle Scholar
  9. 9.
    Ilyas, I.F., Chu, X., et al.: Trends in cleaning relational data: consistency and deduplication. Found. Trends® Databases 5(4), 281–393 (2015)CrossRefGoogle Scholar
  10. 10.
    Lazaridis, I., et al.: QUASAR: quality aware sensing architecture. ACM SIGMOD Rec. 33(1), 26–31 (2004)CrossRefGoogle Scholar
  11. 11.
    Rahm, E., Do, H.H.: Data cleaning: problems and current approaches. IEEE Data Eng. Bull. 23, 3–13 (2000)Google Scholar
  12. 12.
    Rammelaere, J., Geerts, F., Goethals, B.: Cleaning data with forbidden itemsets. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 897–908 (2017)Google Scholar
  13. 13.
    Rekatsinas, T., Joglekar, M., Garcia-Molina, H., Parameswaran, A., Ré, C.: SLiMFast: guaranteed results for data fusion and source reliability. In: Proceedings of the 2017 ACM International Conference on Management of Data, pp. 1399 –1414. ACM (2017)Google Scholar
  14. 14.
    Wu, H., Luo, Q., Li, J., Labrinidis, A.: Quality aware query scheduling in wireless sensor networks. In: Proceedings of the Sixth International Workshop on Data Management for Sensor Networks, p. 7. ACM (2009)Google Scholar
  15. 15.
    Yeganeh, N.K., Sadiq, S., Sharaf, M.A.: A framework for data quality aware query systems. Inf. Syst. 46, 24–44 (2014)CrossRefGoogle Scholar
  16. 16.
    Zou, Z., Gao, H., Li, J.: Discovering frequent subgraphs over uncertain graph databases under probabilistic semantics. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2010, pp. 633–642 (2010)Google Scholar
  17. 17.
    Zou, Z., Li, J., Gao, H., Zhang, S.: Frequent subgraph pattern mining on uncertain graph data. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management. CIKM 2009, pp. 583–592 (2009)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Cyberspace Institute of Advanced TechnologyGuangzhou UniversityGuangzhouChina

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