TY - GEN
T1 - Classification and annotation of open internet of things datastreams
AU - Montori, Federico
AU - Liao, Kewen
AU - Jayaraman, Prem Prakash
AU - Bononi, Luciano
AU - Sellis, Timos
AU - Georgakopoulos, Dimitrios
PY - 2018
Y1 - 2018
N2 - The Internet of Things (IoT) is springboarding novel applications and has led to the generation of massive amounts of data that can offer valuable insights across multiple domains: Smart Cities, environmental monitoring, healthcare etc. In particular, the availability of open IoT data streaming from heterogeneous sources constitute a novel powerful knowledge base. However, due to the inherent distributed, heterogeneous and open nature of such data, metadata that describe the data is generally lacking. This happens especially in contexts where IoT data is contributed by users via cloud-based open data platforms, in which even the information about the type of data measured is often missing. Since metadata is of paramount importance for data reuse, there is a need to develop intelligent techniques that can perform automatic annotation of heterogeneous IoT datastreams. In this paper, we propose two novel IoT datastream classification algorithms: CBOS and TKSE for the task of metadata annotation. We validate our proposed techniques through extensive experiments using public IoT datasets and comparing the outcomes with state-of-the-art classification methods. Results show that our techniques bring significant improvements to classification accuracy.
AB - The Internet of Things (IoT) is springboarding novel applications and has led to the generation of massive amounts of data that can offer valuable insights across multiple domains: Smart Cities, environmental monitoring, healthcare etc. In particular, the availability of open IoT data streaming from heterogeneous sources constitute a novel powerful knowledge base. However, due to the inherent distributed, heterogeneous and open nature of such data, metadata that describe the data is generally lacking. This happens especially in contexts where IoT data is contributed by users via cloud-based open data platforms, in which even the information about the type of data measured is often missing. Since metadata is of paramount importance for data reuse, there is a need to develop intelligent techniques that can perform automatic annotation of heterogeneous IoT datastreams. In this paper, we propose two novel IoT datastream classification algorithms: CBOS and TKSE for the task of metadata annotation. We validate our proposed techniques through extensive experiments using public IoT datasets and comparing the outcomes with state-of-the-art classification methods. Results show that our techniques bring significant improvements to classification accuracy.
KW - Classification
KW - Internet of Things
KW - Metadata
KW - Open data
KW - Sensors
UR - http://www.scopus.com/inward/record.url?scp=85055939067&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-02925-8_15
DO - 10.1007/978-3-030-02925-8_15
M3 - Conference Paper published in Proceedings
AN - SCOPUS:85055939067
SN - 9783030029241
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 209
EP - 224
BT - Web Information Systems Engineering – WISE 2018 - 19th International Conference, 2018, Proceedings
A2 - Wang, Hua
A2 - Zhou, Rui
A2 - Paik, Hye-Young
A2 - Hacid, Hakim
A2 - Cellary, Wojciech
PB - Springer-Verlag London Ltd.
T2 - 19th International Conference on Web Information Systems Engineering, WISE 2018
Y2 - 12 November 2018 through 15 November 2018
ER -