Dataset quality visualization in BEXIS2
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This is the presentation part of an M.Sc. thesis in Software Engineering at Friedrich Schiller University Jena entitled “Dataset quality visualization in BEXIS2”. In this thesis, a visual overview of data quality was prototypically implemented as a new feature for the BEXIS2 Data Management System to make studying dataset quality straightforward.
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- 1. Dataset Quality Visualization in BEXIS2 M A S T E R T H E S I S Nafiseh Navabpour March 2021 Supervisors Prof. Dr. Birgitta König-Ries, Roman Gerlach, Sirko Schindler
- 2. Outline 1. Problem statement 2. The path in solving the problem 3. A prototypical solution 4. Future perspective 2
- 3. Select a dataset Problem statement 3
- 7. Summary of problem statement • Data is continuously produced • Data is stored in different quality level in data portals • Data consumers search data portals to review dataset quality • Reviewing dataset quality is time consuming => Dataset quality overview Problem statement 7
- 8. • Interview with BEXIS2 users • Literature review • Looking at other data portals 8 The path in solving the problem The path in solving the problem
- 9. Interview with BEXIS2 users Upload data Quality check • Excel sheet • TXT • CSV • Image • Comprehensible metadata • Well-defined dataset • Clearly defined variables • Accurate metadata • Correct information • Complete data • Ready for analyzing • Ready for reuse The path in solving the problem 9
- 10. Literature review: Data quality Dimensions Intrinsic DQ Accuracy Objectivity Believability Reputation Contextual DQ Value- added Relevancy Timeliness Completeness amount of data Representational DQ Interpretability Ease of understanding Consistency Conciseness Accessibility DQ Accessibility Access security The path in solving the problem 10 Wang et al. 1996.
- 11. Literature review: Dataset summarization Data origin The way of specifying the time The way of specifying the location Dataset completion Anything else that make data clearer A short description Dataset format and size Data headers Data types and data values The path in solving the problem 11 Koesten et al. 2020.
- 12. Dataset quality components Quality components Quality attributes Description length Accuracy, Completeness Dataset format and size Accessibility A list of variables Relevancy, Completeness, Understandability data types, data distribution Completeness, Amount of data A list of files, file extensions Relevancy, Understandability, Accessibility Dataset contributors Believability Metadata/data completeness Completeness Dataset security level Accessibility, Security Shared elements Reputation Comparison Value-added The path in solving the problem 12
- 13. Literature review: Dataset quality visualization pipeline Data import Data preparation Mapping Data manipulation Rendering The path in solving the problem 13 Qin et al. 2020.
- 14. looking at other data portals The path in solving the problem https://data.cityofnewyork.us https://www.kaggle.com
- 15. Dataset quality overview a) General quality information b) Comparison c) Data quality 15 A prototypical solution
- 16. General quality information 16 A prototypical solution
- 17. General quality information 17 A prototypical solution
- 18. Dataset list 18 A prototypical solution
- 21. Compare dataset sizes 21 A prototypical solution
- 22. Compare dataset sizes 22 A prototypical solution
- 23. Tabular data quality 23 A prototypical solution
- 24. File data quality 24 A prototypical solution
- 25. Positive and negative points + Short text + Similar shapes + Color palette + Legend and tooltips + Interactive elements - Loading time 25 A prototypical solution
- 26. Future perspective • Shorten loading time • Review users’ feedback • Add new features • Data visualization 26 Future perspective
- 27. Thank you for your attention! Jena, 23.03.2021 Nafiseh Navabpour Nafiseh.Navabpour@uni-jena.de
- 28. References • Atto – amazon tall tower observatory, Feb. 2021. [Online]. Available: https://www.attoproject.org/. • Covid-19 world vaccination progress, Feb. 2021. [Online]. Available: https://www.kaggle.com/gpreda/covid-world-vaccination-progress. • L. Koesten, E. Simperl, T. Blount, E. Kacprzak, and J. Tennison, “Everything you always wanted to know about a dataset: Studies in data summarisation,” International Journal of Human-Computer Studies, vol. 135, p. 102 367, 2020. DOI: 10.1016/j.ijhcs.2019.10.004. • N. Navabpour, Dataset quality visualization in bexis2, version 1.0.0, Mar. 2021. DOI: 10.5281/zenodo.4485845. • Covid-19 free meals locations, Feb. 2021. [Online]. Available: https://data.cityofnewyork.us/Education/COVID-19-Free-Meals-Locations/sp4a-vevi. • X. Qin, Y. Luo, N. Tang, and G. Li, “Making data visualization more efficient and effective: A survey,” The VLDB Journal, vol. 29, no. 1, pp. 93–117, 2020. DOI: 10.1007/s00778-019-00588-3. • R. Y. Wang and D. M. Strong, “Beyond accuracy: What data quality means to data consumers,” Journal of management information systems, vol. 12, no. 4, pp. 5–33, 1996. DOI: 10.1080/07421222.1996.11518099. 28