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2018 | OriginalPaper | Buchkapitel

Datalyzer: Streaming Data Applications Made Easy

verfasst von : Mario González-Jiménez, Juan de Lara

Erschienen in: Web Engineering

Verlag: Springer International Publishing

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Abstract

Nowadays, streaming data are continuously generated from thousands of sources, including social networks, mobile apps, sensors, e-commerce transactions, and many more. Hence, it becomes very useful to build applications able to process these data, with the purpose of filtering interesting parts, monitor their run-time evolution, persist valuable chunks, trigger events upon certain conditions are met and provide analytics. While several frameworks and systems have emerged to create this kind of applications, these systems tend to be low-level, based on complicated APIs, challenging to install and configure for end-users, and requiring from high performant hardware for their execution. Our goal is to lower the entry level to develop, deploy and run streaming applications.
To accomplish this goal, we propose Datalyzer, an approach to create streaming data applications on the cloud based on a visual language. This way, Datalyzer provides a facility to describe streaming data sources in an open way, and a visual language to describe the execution flow of the streaming application. Datalyzer is based on model-based development principles, where code is generated automatically, and then compiled, deployed and executed on the cloud. As a proof of concept, we describe a case study in enterprise systems, and how it can be built using our prototype tool.

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Literatur
1.
Zurück zum Zitat Brambilla, M., Cabot, J., Wimmer, M.: Model-Driven Software Engineering in Practice. Synthesis Lectures on Software Engineering, 2nd edn. Morgan & Claypool Publishers, San Rafael (2017) Brambilla, M., Cabot, J., Wimmer, M.: Model-Driven Software Engineering in Practice. Synthesis Lectures on Software Engineering, 2nd edn. Morgan & Claypool Publishers, San Rafael (2017)
2.
Zurück zum Zitat de Assunção, M.D., Veith, A.D.S., Buyya, R.: Distributed data stream processing and edge computing: a survey on resource elasticity and future directions. J. Netw. Comput. Appl. 103, 1–17 (2018)CrossRef de Assunção, M.D., Veith, A.D.S., Buyya, R.: Distributed data stream processing and edge computing: a survey on resource elasticity and future directions. J. Netw. Comput. Appl. 103, 1–17 (2018)CrossRef
3.
Zurück zum Zitat Dindar, N., Tatbul, N., Miller, R.J., Haas, L.M., Botan, I.: Modeling the execution semantics of stream processing engines with SECRET. VLDB J. 22(4), 421–446 (2013)CrossRef Dindar, N., Tatbul, N., Miller, R.J., Haas, L.M., Botan, I.: Modeling the execution semantics of stream processing engines with SECRET. VLDB J. 22(4), 421–446 (2013)CrossRef
4.
Zurück zum Zitat Harth, A., Knoblock, C.A., Stadtmüller, S., Studer, R., Szekely, P.A.: On-the-fly integration of static and dynamic sources. In: COLD. CEUR Workshop Proceedings, vol. 1034 (2013) Harth, A., Knoblock, C.A., Stadtmüller, S., Studer, R., Szekely, P.A.: On-the-fly integration of static and dynamic sources. In: COLD. CEUR Workshop Proceedings, vol. 1034 (2013)
5.
Zurück zum Zitat Hirzel, M., et al.: IBM streams processing language: analyzing big data in motion. IBM J. Res. Dev. 57(3/4), 7 (2013)CrossRef Hirzel, M., et al.: IBM streams processing language: analyzing big data in motion. IBM J. Res. Dev. 57(3/4), 7 (2013)CrossRef
6.
Zurück zum Zitat Luckham, D.C.: The power of events - an introduction to complex event processing in distributed enterprise systems. ACM (2005) Luckham, D.C.: The power of events - an introduction to complex event processing in distributed enterprise systems. ACM (2005)
7.
Zurück zum Zitat Rettig, L., Khayati, M., Cudré-Mauroux, P., Piórkowski, M.: Online anomaly detection over big data streams. In: 2015 IEEE International Conference on Big Data, pp. 1113–1122. IEEE (2015) Rettig, L., Khayati, M., Cudré-Mauroux, P., Piórkowski, M.: Online anomaly detection over big data streams. In: 2015 IEEE International Conference on Big Data, pp. 1113–1122. IEEE (2015)
9.
Zurück zum Zitat Tatbul, N.: Streaming data integration: challenges and opportunities. In: IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010), pp. 155–158 (2010) Tatbul, N.: Streaming data integration: challenges and opportunities. In: IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010), pp. 155–158 (2010)
12.
Zurück zum Zitat Zhuang, Z., Feng, T., Pan, Y., Ramachandra, H., Sridharan, B.: Effective multi-stream joining in Apache Samza framework. In: 2016 IEEE International Conference on Big Data, pp. 267–274. IEEE Computer Society (2016). See also https://samza.apache.org/ Zhuang, Z., Feng, T., Pan, Y., Ramachandra, H., Sridharan, B.: Effective multi-stream joining in Apache Samza framework. In: 2016 IEEE International Conference on Big Data, pp. 267–274. IEEE Computer Society (2016). See also https://​samza.​apache.​org/​
Metadaten
Titel
Datalyzer: Streaming Data Applications Made Easy
verfasst von
Mario González-Jiménez
Juan de Lara
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-91662-0_34