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Geolocated time series, i.e., time series associated with certain locations, abound in many modern applications. In this paper, we consider hybrid queries for retrieving geolocated time series based on filters that combine spatial distance and time series similarity. For the latter, unlike existing work, we allow filtering based on local similarity, which is computed based on subsequences rather than the entire length of each series, thus allowing the discovery of more fine-grained trends and patterns. To efficiently support such queries, we first leverage the state-of-the-art BTSR-tree index, which utilizes bounds over both the locations and the shapes of time series to prune the search space. Moreover, we propose optimizations that check at specific timestamps to identify candidate time series that may exceed the required local similarity threshold. To further increase pruning power, we introduce the SBTSR-tree index, an extension to BTSR-tree, which additionally segments the time series temporally, allowing the construction of tighter bounds. Our experimental results on several real-world datasets demonstrate that SBTSR-tree can provide answers much faster for all examined query types. This paper has been published in the 27th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2019).

We present spaTScope, a web application for visual exploration of geolocated time series. Analyzing such data is becoming increasingly important in many domains, such as energy demand management, geomarketing and geosocial networks. spaTScope allows users to visually explore large collections of geolocated time series and obtain insights about trends and patterns in their area of interest. The provided functionalities leverage a hybrid index that allows to navigate and group the available time series based not only on their similarity but also on spatial proximity. The results are visualized using linked plots combining maps and timelines.

Water management field has concentrated great interest, with the potential to affect the long term well-being, the societal economy and security. In parallel, it imposes specific research challenges which have not been already met, due to the lack of fine-grained data. Knowledge extraction and decision making for efficient management in the energy field has attracted a lot of interest in Big Data research. However, the water domain is strikingly absent, with minimal focused work on data exploitation and useful information extraction. The goal of this work is to discover persistent and meaningful knowledge from water consumption data and provide efficient and scalable big data management and analysis services. We propose a novel methodology which exploits machine learning techniques and introduces a robust probabilistic classifier which is able to operate on data of arbitrary dimensionality and of huge volume. It also provides added value services and new operation models for the water management domain, inducing sustainable behavioural changes for consumers, which can further raise social awareness. It does so through a new k-Nearest Neighbour based algorithm, developed in a parallel and distributed environment, which operates over Big Data and discovers useful knowledge about consumption classes and other water related attitudinal properties. A detailed experimental evaluation assesses the effectiveness and efficiency of the algorithm on prediction precision along with the provision of analytics. The results show that this method is prosperous and provides accurate and interesting results that allow us to identify useful characteristics, not only for the households, but also for the water utilities.