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Knowledge Graph for Reusing Research Knowledge on Related Work in Data Analytics

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Advanced Information Systems Engineering Workshops (CAiSE 2024)

Abstract

Data analytics projects encompass a multitude of facets, including the types of analytics employed, algorithms utilized, and data sources scrutinized. Despite this wealth of information, there remains a challenge in effectively leveraging previous related work for future projects. Traditional approaches often lack mechanisms for preserving and repurposing the knowledge gained from the analysis of related works. In response, this paper introduces a novel method leveraging RDF triples to encapsulate attributes of analytics projects. These RDF triples are then integrated into a web-based knowledge graph, facilitating the exploration of related work within specific data analytics domains. By harnessing this method, researchers and practitioners can identify valuable resources, including data sources, tools, and algorithms, for future endeavors. To demonstrate its efficacy, we apply this method to the domain of real estate analytics, showcasing its potential to enhance project efficiency and innovation.

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References

  1. Balali, F., Nouri, J., Nasiri, A., Zhao, T.: Data analytics. In: Balali, F., Nouri, J., Nasiri, A., Zhao, T. (eds.) Data Intensive Industrial Asset Management: IoT-based Algorithms and Implementation, pp. 105–113. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-35930-0_7

    Chapter  Google Scholar 

  2. Duan, W., Chiang, Y.Y.: Building knowledge graph from public data for predictive analysis: a case study on predicting technology future in space and time. In: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, pp. 7–13 (2016)

    Google Scholar 

  3. Hasan, S.S., Rivera, D., Wu, X.C., Durbin, E.B., Christian, J.B., Tourassi, G.: Knowledge graph-enabled cancer data analytics. IEEE J. Biomed. Health Inform. 24(7), 1952–1967 (2020)

    Article  Google Scholar 

  4. Mehta, N., Pandit, A.: Concurrence of big data analytics and healthcare: a systematic review. Int. J. Med. Inform. 114, 57–65 (2018)

    Article  Google Scholar 

  5. Gidea, M., Katz, Y.: Topological data analysis of financial time series: landscapes of crashes. Physica A 491, 820–834 (2018)

    Article  MathSciNet  Google Scholar 

  6. Blazquez, D., Domenech, J.: Big Data sources and methods for social and economic analyses. Technol. Forecast. Soc. Chang. 130, 99–113 (2018)

    Article  Google Scholar 

  7. Huang, F., Teng, Z., Guo, Z., Catani, F., Huang, J.: Uncertainties of landslide susceptibility prediction: Influences of different spatial resolutions, machine learning models and proportions of training and testing dataset. Rock Mech. Bull. 2(1), 100028 (2023)

    Article  Google Scholar 

  8. IEEE Standard for Framework of Knowledge Graphs. In IEEE Std 2807-2022, pp. 1–52 (2022). https://doi.org/10.1109/IEEESTD.2022.10017167

  9. Graudone, J., Kirikova, M.: A weighted knowledge graph for representing the results of a systematic literature review. In: Ruiz, M., Soffer, P. (eds.) CAiSE 2023 LNBIP, vol. 482, pp. 125–131. Springer, Cham (2023)

    Google Scholar 

  10. Masoud, M., Pereira, B., McCrae, J., Buitelaar, P.: Automatic construction of knowledge graphs from text and structured data: a preliminary literature review. In: 3rd Conference on Language, Data and Knowledge (LDK 2021). Schloss Dagstuhl-Leibniz-Zentrum für Informatik (2021)

    Google Scholar 

  11. Vincent, N.: Automatic Creation of Knowledge Graphs from Scientific Literature. Kairntech. https://kairntech.com/blog/articles/automatic-creation-of-knowledge-graphs-from-scientific-literature/. Accessed 03 Mar 2024

  12. Sahlab, N., Kahoul, H., Jazdi, N., Weyrich, M.: A knowledge graph-based method for automating systematic literature reviews. Procedia Comput. Sci. 207, 2814–2822 (2022)

    Article  Google Scholar 

  13. Runkler, T.A.: Data Analytics. Springer Fachmedien Wiesbaden, Wiesbaden (2020)

    Google Scholar 

  14. Abrasaldo, P.M.B., Zarrouk, S.J., Kempa-Liehr, A.W.: A systematic review of data analytics applications in above-ground geothermal energy operations. Renew. Sustain. Energy Rev. 189, 113998 (2024)

    Article  Google Scholar 

  15. Rashid, S.M., et al.: The semantic data dictionary–an approach for describing and annotating data. Data Intell. 2(4), 443–486 (2020)

    Article  Google Scholar 

  16. Pichiyan, V., Muthulingam, S., Sathar, G., Nalajala, S., Ch, A., Das, M.N.: Web scraping using natural language processing: exploiting unstructured text for data extraction and analysis. Procedia Comput. Sci. 230, 193–202 (2023)

    Article  Google Scholar 

  17. Dong, X.L., Srivastava, D.: Big data integration. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 1245–1248. IEEE (2013)

    Google Scholar 

  18. Unpingco, J.: Python Programming for Data Analysis. Springer, Heidelberg (2021)

    Book  Google Scholar 

  19. Lehner, B., Czisch, G., Vassolo, S.: The impact of global change on the hydropower potential of Europe: a model-based analysis. Energy Policy 33(7), 839–855 (2005)

    Article  Google Scholar 

  20. Adadi, A.: A survey on data-efficient algorithms in big data era. J. Big Data 8(1), 24 (2021)

    Article  Google Scholar 

  21. Edwards, J.R., et al.: National Healthcare Safety Network (NHSN) report: data summary for 2006 through 2008, issued December 2009. Am. J. Infect. Control 37(10), 783–805 (2009)

    Article  Google Scholar 

  22. Streit, M., Gehlenborg, N.: Bar charts and box plots: creating a simple yet effective plot requires an understanding of data and tasks. Nat. Methods 11(2), 117–118 (2014)

    Article  Google Scholar 

  23. Hoelscher, J., Mortimer, A.: Using Tableau to visualize data and drive decision-making. J. Account. Educ. 44, 49–59 (2018)

    Article  Google Scholar 

  24. Shin, S.J., Woo, J., Rachuri, S.: Predictive analytics model for power consumption in manufacturing. Procedia CIRP 15, 153–158 (2014)

    Article  Google Scholar 

  25. Rodriguez, J.D., Perez, A., Lozano, J.A.: Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 569–575 (2009)

    Article  Google Scholar 

  26. Kumarasinghe, A., Kirikova, M.: Generic requirements template for data analytics. In: BIR 2023 Workshops and Doctoral Consortium, 22nd International Conference on Preceptive in Business Informatics Research (BIR 2023), Ascoli Piceno, Italy, 13–15 September 2023 (2023)

    Google Scholar 

  27. Kumarasinghe, A.: Knowledge Graph for Reusing Research Knowledge on Related Works in Data Analytics (Version 2.0.4) [Computer software] (2023). https://github.com/ArithaRTU/Knowledge-Graph-for-Reusing-Research-Knowledge-on-Related-Works-in-Data-Analytics.git

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Acknowledgment

The choice of real estate analytics to demonstrate the knowledge graph for the reuse of research knowledge on related work in data analytics was inspired by the cooperation with Ltd Lursoft IT and Ltd Hagberg, Latvia.

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Correspondence to Aritha Kumarasinghe .

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Kumarasinghe, A., Kirikova, M. (2024). Knowledge Graph for Reusing Research Knowledge on Related Work in Data Analytics. In: Almeida, J.P.A., Di Ciccio, C., Kalloniatis, C. (eds) Advanced Information Systems Engineering Workshops. CAiSE 2024. Lecture Notes in Business Information Processing, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-031-61003-5_17

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  • DOI: https://doi.org/10.1007/978-3-031-61003-5_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-61002-8

  • Online ISBN: 978-3-031-61003-5

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