Annals of Computer Science and Information Systems, Volume 18

DOI: http://dx.doi.org/10.15439/2019F168

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 639–643 (2019)

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Abstract. This paper discusses data quality checking during business process execution by using runtime verification. While runtime verification verifies the correctness of business process execution, data quality checks assure that particular process did not negatively impact the stored data. Both, runtime verification and data quality checks run in parallel with the base processes affecting them insignificantly. The proposed idea allows verifying (a) if the process was ended correctly as well as (b) whether the results of the correct process did not negatively impact the stored data in result of its modification caused by the specific process. The desired result will be achieved by use of domain specific languages that would describe runtime verification and data quality checks at every stage of business process execution.

References

1. El Hadji Bassirou Toure, I. Fall, A. Bah, M. S. Camara, Megamodel-based Management of Dynamic Tool Integration in Complex Software Systems. In FedCSIS Position Papers, 2016, pp. 211-218, http://dx.doi.org/10.15439/2016F585.
2. I. Oditis, J. Bicevskis, Asynchronous Runtime Verification of Business Processes: Proof of Concept. International Journal of Simulation-Systems, Science & Technology, 2015, 16(6), 1-11, http://dx.doi.org/10.5013/IJSSST.a.16.06.06.
3. J. Bicevskis, Z. Bicevska, A. Nikiforova, I. Oditis, An Approach to Data Quality Evaluation. In 2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS), IEEE, 2018, pp. 196-201, http://dx.doi.org/10.1109/SNAMS.2018.8554915.
4. J. Bicevskis, Z. Bicevska, A. Nikiforova, I. Oditis, Data quality evaluation: a comparative analysis of company registers’ open data in four European countries. In Communication Papers of the Federated Conference on Computer Science and Information Systems (FedCSIS), 2018, pp. 197-204, http://dx.doi.org/10.15439/2018F92.
5. A. Coronato, A. Testa, Approaches of Wireless sensor network dependability assessment. In 2013 Federated Conference on Computer Science and Information Systems, IEEE, 2013, pp. 881-888.
6. A. Nikiforova, Open Data Quality Evaluation: A Comparative Analysis of Open Data in Latvia. Baltic Journal of Modern Computing, 2018, 6(4), 363-386, https://doi.org/10.22364/bjmc.2018.6.4.04.
7. A. Nikiforova, J. Bicevskis, An Extended Data Object-driven Approach to Data Quality Evaluation: Contextual Data Quality Analysis. In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS, 274-281, 2019, http://dx.doi.org/10.5220/0007838602740281.
8. A. Nikiforova, Analysis of Open Health Data Quality Using Data Object-Driven Approach to Data Quality Evaluation: Insights from a Latvian Context. In IADIS International Conference e-Health 2019, Part of the IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2019, IADIS
9. G. C. Deka, NoSQL: database for storage and retrieval of data in cloud, Ed. CRC Press, 2017, https://doi.org/10.1201/9781315155579.
10. C. Gröger, F. Niedermann, B. Mitschang. Data mining-driven manufacturing process optimization. In Proceedings of the world congress on engineering, 2012, Vol. 3, pp. 4-6.
11. S. Khurshid, C. S. Păsăreanu, W. Visser, Generalized symbolic execution for model checking and testing. In International Conference on Tools and Algorithms for the Construction and Analysis of Systems. Springer, Berlin, Heidelberg, 2003, pp. 553-568, https://doi.org/10.1007/3-540-36577-X_40.
12. E. Ziemba, T. Papaj, D. Descours, Assessing the quality of e-government portals-the Polish experience. In 2014 Federated Conference on Computer Science and Information Systems, IEEE, 2014, pp. 1259-1267, http://dx.doi.org/10.15439/2014F121.
13. A. Karabegovic, M. Ponjavic, Geoportal as decision support system with spatial data warehouse. In 2012 Federated Conference on Computer Science and Information Systems (FedCSIS), IEEE, 2012, pp. 915-918, http://dx.doi.org/10.13140/RG.2.2.26385.68963.