Showing papers by "Yannis Theodoridis published in 2021"
31 Aug 2021
TL;DR: In this article, a survey of big data processing frameworks for mobility analytics is presented, focusing on the underlying techniques; indexing, partitioning, query processing are essential for enabling efficient and scalable data management.
Abstract: In the current era of big spatial data, the vast amount of produced mobility data (by sensors, GPS-equipped devices, surveillance networks, radars, etc.) poses new challenges related to mobility analytics. A cornerstone facilitator for performing mobility analytics at scale is the availability of big data processing frameworks and techniques tailored for spatial and spatio-temporal data. Motivated by this pressing need, in this paper, we provide a survey of big data processing frameworks for mobility analytics. Particular focus is put on the underlying techniques; indexing, partitioning, query processing are essential for enabling efficient and scalable data management. In this way, this report serves as a useful guide of state-of-the-art methods and modern techniques for scalable mobility data management and analytics.
8 citations
TL;DR: The i4sea research project as discussed by the authors provides effective and efficient big data integration, processing, and analysis technologies to deliver both real-time and historical operational snapshots of fishing ves...
Abstract: The i4sea research project provides effective and efficient big data integration, processing, and analysis technologies to deliver both real-time and historical operational snapshots of fishing ves...
6 citations
15 Jun 2021
TL;DR: ST_VISIONS as discussed by the authors is an easy-to-use Python library for interactive visualizations of spatial and spatio-temporal datasets by automating the low-level details of the underlying visualization library (Bokeh).
Abstract: In this demo paper we present ST_VISIONS, an easy-to-use Python library for interactive visualizations of spatial and spatio-temporal datasets. By automating the low-level details of the underlying visualization library (Bokeh), ST_VISIONS allows data scientists to create interactive, map-based visualizations, by writing Python code at a higher level of abstraction. Consequently, we accelerate the task of visualization from different sources, while we support interactive filtering, colorization, as well as multiple graphs, for various types of spatial and spatio-temporal data.
6 citations
TL;DR: This paper proposes a novel graph-based online co-movement pattern mining algorithm, called EvolvingClusters, which can be used to discover different collective movement behaviours in a unified way based on the activity of multiple concurrent objects through time and space.
Abstract: The advent of GPS technologies generates location data-streams and accentuates the importance of developing practical tools that can process and analyze the vast amounts of location data at a given...
5 citations
01 Jan 2021
TL;DR: Visual analytics science develops principles and methods for efficient human-computer collaboration in solving complex problems through visual and interactive techniques.
Abstract: Visual analytics science develops principles and methods for efficient human–computer collaboration in solving complex problems. Visual and interactive techniques are used to create conditions in which human analysts can effectively utilize their unique capabilities: the power of seeing, interpreting, linking, and reasoning. Visual analytics research deals with various types of data and analysis tasks from numerous application domains. A prominent research topic is analysis of spatiotemporal data, which may describe events occurring at different spatial locations, changes of attribute values associated with places or spatial objects, or movements of people, vehicles, or other objects. Such kinds of data are abundant in urban applications. Movement data are a quintessential type of spatiotemporal data because they can be considered from multiple perspectives as trajectories, as spatial events, and as changes of space-related attribute values. By example of movement data, we demonstrate the utilization of visual analytics techniques and approaches in data exploration and analysis.
1 citations
23 Aug 2021
TL;DR: MaSEC (Moving and Stationary Evolving Clusters) as discussed by the authors provides a unified solution that discovers both moving and stationary evolving clusters on streaming vessel position data in an online mode, which is evaluated over two real-world datasets from the maritime domain.
Abstract: The massive-scale data generation of positioning (tracking) messages, collected by various surveillance means, has posed new challenges in the field of mobility data analytics in terms of extracting valuable knowledge out of this data. One of these challenges is online cluster analysis, where the goal is to unveil hidden patterns of collective behaviour from streaming trajectories, such as co-movement and co-stationary (aka anchorage) patterns. Towards this direction, in this paper, we demonstrate MaSEC (Moving and Stationary Evolving Clusters), a system that discovers valuable behavioural patterns as above. In particular, our system provides a unified solution that discovers both moving and stationary evolving clusters on streaming vessel position data in an online mode. The functionality of our system is evaluated over two real-world datasets from the maritime domain.
01 Jan 2021
TL;DR: In this article, the authors focus on the exploration, preparation of data and application of several offline maritime data analytics techniques, such as trajectory clustering, group behaviour identification, hot-spot analysis, frequent route or network discovery and data-driven predictive analytics methods.
Abstract: The goal of mobility data analytics is to extract valuable knowledge out of a plethora of data sources that produce immense volumes of data. Focusing on the maritime domain, this relates to several challenging use-case scenarios, such as discovering valuable behavioural patterns of moving objects, identifying different types of activities in a region of interest, estimating fishing pressure or environmental fingerprint, etc. In this chapter, we focus on the exploration, preparation of data and application of several offline maritime data analytics techniques. Initially, we present several methods that assist an analyst to explore and gain insight of the data under analysis. Subsequently, we study several preprocessing techniques that aim to clean, transform, compress and partition long GPS traces into meaningful portions of movement. Finally, we overview some representative maritime knowledge discovery techniques, such as trajectory clustering, group behaviour identification, hot-spot analysis, frequent route or network discovery and data-driven predictive analytics methods.
Posted Content•
TL;DR: In the post-pandemic era, the intersection of sustainable urban mobility and movement data science fields has been discussed in this paper, where the authors briefly discuss some lessons learned out of this process based on recorded mobility figures, and raise challenges that are emerging in the post pandemic era.
Abstract: COVID-19 is the first pandemic of the modern world causing significant changes to the everyday life of billions of people in all continents. To reduce its expansion, most governments decided to mitigate a great percentage of daily movements of their citizens. For instance, they enforced strict controls (in space, time, etc.) on urban movement whereas they selectively prohibited international air and ground connections. In this short study, we briefly discuss some lessons learned out of this process based on recorded mobility figures, and we raise challenges that are emerging in the post-pandemic era, in the intersection of the sustainable urban mobility and movement data science fields.