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DAGGTAX: A Taxonomy of Data Aggregation Processes

Fulltext:


Publication Type:

Conference/Workshop Paper

Venue:

7th International Conference on Model and Data Engineering


Abstract

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.

Bibtex

@inproceedings{Cai4818,
author = {Simin Cai and Barbara Gallina and Dag Nystr{\"o}m and Cristina Seceleanu},
title = {DAGGTAX: A Taxonomy of Data Aggregation Processes},
month = {October},
year = {2017},
booktitle = {7th International Conference on Model and Data Engineering},
url = {http://www.es.mdh.se/publications/4818-}
}