Abstract
Digitization of industrial processes requires an ever increasing amount of resources to store and process data. However, integration of the business process including expert knowledge and (real-time) process data remains a largely open challenge. Our study is a first step towards better integration of these aspects by means of knowledge graphs and machine learning. In particular we describe the framework that we use to operate with both: conceptual representation of the business process, and the sensor data measured in the process. Considering the existing limitations of graph data storage in processing large time-series data volumes, we suggest an approach that creates a bridge between a graph database, that models the processes as concepts, and a time-series database, that contains the sensor data. The main difficulty of this approach is the creation and maintenance of the vast number of links between these databases. We introduce the method of smart data segmentation that i) reduces the number of links between the databases, ii) minimizes data pre-processing overhead and iii) integrates graph and time-series databases efficiently.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cavoto, P., Santanchè, A.: ReGraph: bridging relational and graph databases. In: Proceedings of the 30th Brazilian Symposium on Databases (2015)
Fernandes, D., Bernardino, J.: Graph databases comparison: AllegroGraph, ArangoDB, InfiniteGraph, Neo4j, and OrientDB. In: Data, pp. 373–380 (2018)
Ferreira, L.P.Z.d.P.M.: Bridging the gap between SQL and NoSQL. Ph.D. thesis, Universidade do Minho (2012)
Hofer, D., Jäger, M., Mohamed, A., Küng, J.: On applying graph database time models for security log analysis. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds.) FDSE 2020. LNCS, vol. 12466, pp. 87–107. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63924-2_5
InfluxData: InfluxDB schema design (2022). https://docs.influxdata.com/influxdb/v2.1/write-data/best-practices/schema-design/. Accessed 25 Mar 2022
InfluxData: Neo4j graph database (2022). https://neo4j.com/product/. Accessed 25 Mar 2022
InfluxData: Optimize flux queries (2022). https://docs.influxdata.com/influxdb/v2.1/query-data/optimize-queries/. Accessed 25 Mar 2022
Solid IT gmbh: DB-engines ranking of graph DBMS (2022). https://db-engines.com/en/ranking/graph+dbms. Accessed 25 Mar 2022
Solid IT gmbh: DB-engines ranking of time series DBMS (2022). https://db-engines.com/en/ranking/time+series+dbms. Accessed 25 Mar 2022
Keogh, E., Chu, S., Hart, D., Pazzani, M.: Segmenting time series: a survey and novel approach. In: Data Mining in Time Series Databases, pp. 1–21. World Scientific (2004)
Naqvi, S.N.Z., Yfantidou, S., Zimányi, E.: Time series databases and InfluxDB. Studienarbeit, Université Libre de Bruxelles 12 (2017)
Perçuku, A., Minkovska, D., Stoyanova, L.: Modeling and processing big data of power transmission grid substation using neo4j. Procedia Comput. Sci. 113, 9–16 (2017). https://doi.org/10.1016/j.procs.2017.08.276. https://www.sciencedirect.com/science/article/pii/S187705091731685X. The 8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2017) / The 7th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2017) / Affiliated Workshops
Roijackers, J., Fletcher, G.: Bridging SQL and NoSQL. Eindhoven University of Technology Department of Mathematics and Computer Science Master’s thesis (2012)
Tan, R., Chirkova, R., Gadepally, V., Mattson, T.G.: Enabling query processing across heterogeneous data models: a survey. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 3211–3220. IEEE (2017)
Villalobos, K., Ramírez-Durán, V.J., Diez, B., Blanco, J.M., Goñi, A., Illarramendi, A.: A three level hierarchical architecture for an efficient storage of industry 4.0 data. Comput. Ind. 121, 103257 (2020)
Villalobos, K., Ramírez, V.J., Diez, B., Blanco, J.M., Goñi, A., Illarramendi, A.: A hierarchical storage system for industrial time-series data. In: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), vol. 1, pp. 699–705 (2019). https://doi.org/10.1109/INDIN41052.2019.8972120
Acknowledgements
The research reported in this paper has been funded by BMK, BMDW, and the State of Upper Austria in the frame of the COMET Programme managed by FFG.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Karetnikov, A. et al. (2022). Using Property Graphs to Segment Time-Series Data. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2022 Workshops. DEXA 2022. Communications in Computer and Information Science, vol 1633. Springer, Cham. https://doi.org/10.1007/978-3-031-14343-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-031-14343-4_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-14342-7
Online ISBN: 978-3-031-14343-4
eBook Packages: Computer ScienceComputer Science (R0)