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A Trie-based Indexing Scheme for Efficient Retrieval of Massive Spatio-Temporal IoT Sensor Data
Hawon Chu, Young-Kyoon Suh, Ryong Lee, Minwoo Park, Rae-Young Jang, Sang-Hwan Lee, Sa-Kwang Song
http://doi.org/10.5626/JOK.2020.47.12.1199
As the Internet-of-Things (IoT) sensors with enhanced communication technology and computing power have been widely utilized in many areas, a great deal of spatio-temporal data has been continuously generated. Thanks to the remarkable advances in storage technology, it is possible to collect such massive data into storage systems for further high-dimensional analysis. That said, it has been very challenging to speedily locate stored IoT data in a reasonable amount of time due to the heavy volume and complex spatial and temporal attributes. To address this concern, we propose a novel scalable indexing scheme, termed ST-Trie, to support the efficient querying of massive spatial-temporal data collected from IoT sensors. The key idea of our scheme is to encode three-dimensional spatiotemporal information into one-dimensional keys in consideration of time and space locality and then organize the keys into a logical trie structure. In our experiments with real datasets, the proposed scheme outperformed composite indexes by an average of up to 92 times in terms of query response time. In particular, we confirmed that ST-Trie scaled much better than the compared indexes with increasing time ranges.
An Efficient Method of Processing Spatio-Temporal Joins in IoT (Internet of Things) Environments
Ki Yong Lee, Minji Seo, Ryong Lee, Minwoo Park, Sang-Hwan Lee
http://doi.org/10.5626/JOK.2019.46.1.86
A spatio-temporal join is an operation that connects multiple sets of data with the same spatial and temporal values. Especially with the increasing spread of IoT (Internet of Things), the need for spatio-temporal joins is also increasing in order to retrieve data generated at the same time and location among the data generated by different things in the past. In this study, we propose an efficient method for processing spatio-temporal joins on IoT data. The proposed method divides the 3D spatio-temporal space into small subspaces and maintains the identifiers of things whose data are present in each subspace. When a spatio-temporal join between things is requested, the proposed method identifies the spaces in which the things’ data are close to each other. Then, it retrieves data contained in the identified spaces and performs the join only between them. Therefore, because only that data with the possibility of being joined are accessed, the execution cost is greatly reduced. The experimental results on a real IoT dataset show that the proposed method significantly reduces the execution time compared to the existing spatio-temporal methods.
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