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
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
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)
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)
Mehta, N., Pandit, A.: Concurrence of big data analytics and healthcare: a systematic review. Int. J. Med. Inform. 114, 57–65 (2018)
Gidea, M., Katz, Y.: Topological data analysis of financial time series: landscapes of crashes. Physica A 491, 820–834 (2018)
Blazquez, D., Domenech, J.: Big Data sources and methods for social and economic analyses. Technol. Forecast. Soc. Chang. 130, 99–113 (2018)
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)
IEEE Standard for Framework of Knowledge Graphs. In IEEE Std 2807-2022, pp. 1–52 (2022). https://doi.org/10.1109/IEEESTD.2022.10017167
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)
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)
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
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)
Runkler, T.A.: Data Analytics. Springer Fachmedien Wiesbaden, Wiesbaden (2020)
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)
Rashid, S.M., et al.: The semantic data dictionary–an approach for describing and annotating data. Data Intell. 2(4), 443–486 (2020)
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)
Dong, X.L., Srivastava, D.: Big data integration. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 1245–1248. IEEE (2013)
Unpingco, J.: Python Programming for Data Analysis. Springer, Heidelberg (2021)
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)
Adadi, A.: A survey on data-efficient algorithms in big data era. J. Big Data 8(1), 24 (2021)
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)
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)
Hoelscher, J., Mortimer, A.: Using Tableau to visualize data and drive decision-making. J. Account. Educ. 44, 49–59 (2018)
Shin, S.J., Woo, J., Rachuri, S.: Predictive analytics model for power consumption in manufacturing. Procedia CIRP 15, 153–158 (2014)
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)
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)
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
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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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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