Abstract
This paper introduces the concept of a knowledge graph for time series data, which allows for a structured management and propagation of characteristic time series information and the ability to support query-driven data analyses. We gradually link and enrich knowledge obtained by domain experts or previously performed analyses by representing globally and locally occurring time series insights as individual graph nodes. Supported by a utilization of techniques from automated knowledge discovery and machine learning, a recursive integration of analytical query results is exploited to generate a spectral representation of linked and successively condensed information. Besides a time series to graph mapping, we provide an ontology describing a classification of maintained knowledge and affiliated analysis methods for knowledge generation. After a discussion on gradual knowledge enrichment, we finally illustrate the concept of knowledge propagation based on an application of state-of-the-art methods for time series analysis.
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References
The RDF Data Cube Vocabulary (2014). https://www.w3.org/TR/vocab-data-cube/
Time Ontology in OWL (2022). https://www.w3.org/TR/owl-time/
Cheng, Z., et al.: Time2graph: revisiting time series modeling with dynamic shapelets. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3617–3624 (2020)
Dau, H.A., Keogh, E., Kamgar, K., et al.: The ucr time series classification archive, October 2018. https://www.cs.ucr.edu/eamonn/time_series_data_2018/
Dwivedi, S., Kasliwal, P., Soni, S.: Comprehensive study of data analytics tools (rapidminer, weka, r tool, knime). In: 2016 Symposium on Colossal Data Analysis and Networking (CDAN), pp. 1–8. IEEE (2016)
Hamilton, J.D.: Time Series Analysis. Princeton University Press, Princeton (2020)
Hogan, A., Blomqvist, E., Cochez, M., et al.: Knowledge graphs. ACM Comput. Surv. 54(4), 1–37 (2021). https://doi.org/10.1145/3447772, https://doi.org/10.1145%2F3447772
Jensen, S.K., Pedersen, T.B., Thomsen, C.: Time series management systems: a survey. IEEE Trans. Knowl. Data Eng. 29(11), 2581–2600 (2017)
Johnson, T., Lakshmanan, L.V., Ng, R.T.: The 3w model and algebra for unified data mining. In: VLDB, pp. 21–32 (2000)
Karmaker, S.K., Hassan, M.M., Smith, M.J., et al.: Automl to date and beyond: challenges and opportunities. ACM Comput. Surv. 54(8), 1–36 (2021)
Kietz, J.U., Serban, F., Bernstein, A., et al.: Designing KDD-workflows via HTN-planning for intelligent discovery assistance (2012)
Lu, Y., et al.: Grab: finding time series natural structures via a novel graph-based scheme. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 2267–2272. IEEE (2021)
Panov, P., Džeroski, S., Soldatova, L.: ONTODM: an ontology of data mining. In: 2008 IEEE International Conference on Data Mining Workshops, pp. 752–760. IEEE (2008)
Panov, P., Soldatova, L., Džeroski, S.: OntoDM-KDD: ontology for representing the knowledge discovery process. In: Fürnkranz, J., Hüllermeier, E., Higuchi, T. (eds.) DS 2013. LNCS (LNAI), vol. 8140, pp. 126–140. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40897-7_9
Publio, G.C., Esteves, D., Ławrynowicz, A., et al.: Ml-schema: exposing the semantics of machine learning with schemas and ontologies (2018)
Schubert, E., Sander, J., Ester, M., Kriegel, H.P., et al.: DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans. Database Syst. (TODS) 42(3), 1–21 (2017)
Truong, C., Oudre, L., Vayatis, N.: Ruptures: change point detection in python. arXiv preprint arXiv:1801.00826 (2018)
Von Rueden, L., Mayer, S., Beckh, K., et al.: Informed machine learning-a taxonomy and survey of integrating prior knowledge into learning systems. IEEE Trans. Knowl. Data Eng. 35(1), 614–633 (2021)
Yeh, C.C.M., Zhu, Y., Ulanova, L., et al.: Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In: 2016 IEEE 16th International Conference on Data Mining, pp. 1317–1322 (2016)
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Graß, A., Beecks, C., Chala, S.A., Lange, C., Decker, S. (2023). A Knowledge Graph for Query-Induced Analyses of Hierarchically Structured Time Series Information. In: Abelló, A., et al. New Trends in Database and Information Systems. ADBIS 2023. Communications in Computer and Information Science, vol 1850. Springer, Cham. https://doi.org/10.1007/978-3-031-42941-5_16
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