Abstract
The Big Data era requires new processing architectures, among which streaming systems which have become very popular. Those systems are able to summarize infinite data streams with aggregates on the most recent data. However, up to now, only point events have been considered and spanning events, which come with a duration, have been let aside, restricted to the persistent databases world only. In this paper, we propose a unified framework to deal with such stream mechanisms on spanning events. To this end, we formally define a spanning event stream with new stream semantics and events properties, particularly considering how the event is received. We then review and extend usual stream windows to meet the new spanning event requirements. Eventually, we validate the soundness of our new framework with a set of experiments, based on a straightforward implementation, showing that aggregation of spanning event stream is providing as much new insight on the data as effectiveness in several use cases.
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
Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)
Arasu, A., Babu, S., Widom, J.: The CQL continuous query language: semantic foundations and query execution. VLDB J. 15(2), 121–142 (2006)
Böhlen, M.H., Dignös, A., Gamper, J., Jensen, C.S.: Temporal data management – an overview. In: Zimányi, E. (ed.) eBISS 2017. LNBIP, vol. 324, pp. 51–83. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96655-7_3
Böhlen, M.H., Dignös, A., Gamper, J., Jensen, C.S.: Database technology for processing temporal data (invited paper). In: 25th International Symposium on Temporal Representation and Reasoning, TIME 2018 (2018)
Carbone, P., Traub, J., Katsifodimos, A., Haridi, S., Markl, V.: Cutty: aggregate sharing for user-defined windows. In: CIKM 2016, pp. 1201–1210. Association for Computing Machinery, New York (2016)
Dignös, A., Böhlen, M.H., Gamper, J.: Temporal alignment. In: SIGMOD 2012, pp. 433–444. Association for Computing Machinery, New York (2012)
Dignos, A., Glavic, B., Niu, X., Bohlen, M., Gamper, J.: Snapshot semantics for temporal multiset relations. Proc. VLDB Endow. 12(6), 639–652 (2019)
Gedik, B.: Generic windowing support for extensible stream processing systems. Softw. Pract. Exp. 44(9), 1105–1128 (2014)
Hammad, M.A., Aref, W., Franklin, M., Mokbel, M., Elmagarmid, A.K.: Efficient execution of sliding window queries over data streams. Purdue University Department of Computer Sciences Technical Report Number CSD TR (2003)
Hirzel, M., Schneider, S., Tangwongsan, K.: Tutorial: sliding-window aggregation algorithms. In: DEBS 2017, pp. 11–14. Association for Computing Machinery, New York (2017)
Kaufmann, M., Fischer, P.M., May, N., Ge, C., Goel, A.K., Kossmann, D.: Bi-temporal timeline index: a data structure for processing queries on bi-temporal data. In: ICDE 2015, pp. 471–482. IEEE, New York (2015)
Kim, H.G., Kim, M.H.: A review of window query processing for data streams. J. Comput. Sci. Eng. 7(4), 220–230 (2013)
Krämer, J., Seeger, B.: Semantics and implementation of continuous sliding window queries over data streams. ACM Trans. Database Syst. 34(1) (2009)
Krishnamurthy, S., et al.: Continuous analytics over discontinuous streams. In: SIGMOD 2010, pp. 1081–1092. Association for Computing Machinery, New York (2010)
Li, J., Tufte, K., Shkapenyuk, V., Papadimos, V., Johnson, T., Maier, D.: Out-of-order processing: a new architecture for high-performance stream systems. Proc. VLDB Endow. 1(1), 274–288 (2008)
Moon, B., Lopez, I.F.V., Immanuel, V.: Efficient algorithms for large-scale temporal aggregation. IEEE Trans. Knowl. Data Eng. 15(3), 744–759 (2003)
Piatov, D., Helmer, S.: Sweeping-based temporal aggregation. In: Gertz, M., et al. (eds.) SSTD 2017. LNCS, vol. 10411, pp. 125–144. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64367-0_7
Shein, A.U., Chrysanthis, P.K., Labrinidis, A.: SlickDeque: high throughput and low latency incremental sliding-window aggregation. In: EDBT 2018, pp. 397–408. OpenProceedings.org, Kostanz, Germany (2018)
Snodgrass, R.T.: A Case Study of Temporal Data, pp. 1–21. Teradata Corporation (2010)
Srivastava, U., Widom, J.: Flexible time management in data stream systems. In: PODS 2004, pp. 263–274. Association for Computing Machinery, New York (2004)
Tangwongsan, K., Hirzel, M., Schneider, S.: Sliding-Window Aggregation Algorithms, pp. 1–6. Springer, Cham (2018)
Tangwongsan, K., Hirzel, M., Schneider, S., Wu, K.L.: General incremental sliding-window aggregation. Proc. VLDB Endow. 8(7), 702–713 (2015)
Traub, J., et al.: Efficient window aggregation with general stream slicing. In: EDBT 2019, pp. 97–108. OpenProceedings, Kostanz, Germany (2019)
Yang, P., Thiagarajan, S., Lin, J.: Robust, scalable, real-time event time series aggregation at Twitter. In: SIGMOD 2018, pp. 595–599. Association for Computing Machinery, New York (2018)
Zhang, D., Gunopulos, D., Tsotras, V.J., Seeger, B.: Temporal aggregation over data streams using multiple granularities. In: Advances in Database Technology: EDBT 2002. vol. 2287, pp. 646–663. Springer, Berlin, Heidelberg (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer-Verlag GmbH Germany, part of Springer Nature
About this chapter
Cite this chapter
Suzanne, A., Raschia, G., Martinez, J., Jaouen, R., Hervé, F. (2021). Temporal Aggregation of Spanning Event Stream: An Extended Framework to Handle the Many Stream Models. In: Hameurlain, A., Tjoa, A.M., Amann, B., Goasdoué, F. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIX. Lecture Notes in Computer Science(), vol 12920. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-64148-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-662-64148-4_1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-64147-7
Online ISBN: 978-3-662-64148-4
eBook Packages: Computer ScienceComputer Science (R0)