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DAGGTAX: A taxonomy of data aggregation processes
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0002-6952-1053
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0003-2898-9570
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0003-2870-2680
2017 (engelsk)Inngår i: Lecture Notes in Computer Science, vol. 10563, Springer Verlag , 2017, s. 324-339Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Data aggregation processes are essential constituents for data management in modern computer systems, such as decision support systems and Internet of Things (IoT) systems. Due to the heterogeneity and real-time constraints in such systems, designing appropriate data aggregation processes often demands considerable effort. A study on the characteristics of data aggregation processes is then desirable, as it provides a comprehensive view of such processes, potentially facilitating their design, as well as the development of tool support to aid designers. In this paper, we propose a taxonomy called DAGGTAX, which is a feature diagram that models the common and variable characteristics of data aggregation processes, with a special focus on the real-time aspect. The taxonomy can serve as the foundation of a design tool, which we also introduce, enabling designers to build an aggregation process by selecting and composing desired features, and to reason about the feasibility of the design. We apply DAGGTAX on industrial case studies, showing that DAGGTAX not only strengthens the understanding, but also facilitates the model-driven design of data aggregation processes. © 2017, Springer International Publishing AG.

sted, utgiver, år, opplag, sider
Springer Verlag , 2017. s. 324-339
Serie
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10563 LNCS
Emneord [en]
Data aggregation taxonomy, Feature model, Real-time data management, Artificial intelligence, Decision support systems, Internet of things, Real time systems, Taxonomies, Aggregation process, Data aggregation, Feature modeling, Industrial case study, Internet of Things (IOT), Modern computer systems, Real time constraints, Real time data management, Information management
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Identifikatorer
URN: urn:nbn:se:mdh:diva-38313DOI: 10.1007/978-3-319-66854-3_25ISI: 000439935200025Scopus ID: 2-s2.0-85030711559ISBN: 9783319668536 (tryckt)OAI: oai:DiVA.org:mdh-38313DiVA, id: diva2:1182216
Konferanse
7th International Conference on Model and Data Engineering (MEDI), Barcelona, SPAIN, OCT 04-06, 2017
Tilgjengelig fra: 2018-02-12 Laget: 2018-02-12 Sist oppdatert: 2018-08-17bibliografisk kontrollert

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Cai, SiminGallina, BarbaraNyström, DagSeceleanu, Cristina

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