Abstract
Data communication is at the heart of any distributed system. The adoption of generic data formats such as XML or JSON eases the exchange of information and interoperability among heterogeneous systems. However, the verbosity of those generic data formats usually requires system resources that might not be available in resource-constrained systems, e.g., embedded systems and those devices which are being integrated into the so-called IoT. In this work we present a method to reduce the cost of managing data models like XML or JSON by using templates and context based compression. We also provide a brief evaluation and comparison as a benchmark with current implementations of W3C’s Efficient XML Interchange (EXI) processor. Although the method described in this paper is still at its initial stage, it outperforms the EXI implementations in terms of memory usage and speed, while keeping similar compression rates. As a consequence, we believe that our approach fits better for constrained systems.
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- 1.
Context table before collapsing is available at [3].
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This is very relevant for resource-constrained devices as sorter processing cycles imply lower energy consumption.
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Acknowledgements
Research partially supported by the European Union Horizon 2020 Programme under grant 680708/HIT2GAP, by the Spanish Research Council, grant TIN2013-41123-P, and the University of the Basque Country UPV/EHU, grant UFI11/45.
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Berzosa, J., Gardeazabal, L., Cortiñas, R. (2016). Efficient Management of Data Models in Constrained Systems by Using Templates and Context Based Compression. In: García, C., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds) Ubiquitous Computing and Ambient Intelligence. IWAAL AmIHEALTH UCAmI 2016 2016 2016. Lecture Notes in Computer Science(), vol 10070. Springer, Cham. https://doi.org/10.1007/978-3-319-48799-1_38
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DOI: https://doi.org/10.1007/978-3-319-48799-1_38
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