Scalable enrichment of mobility data with weather information
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Scalable enrichment of mobility data with weather information Nikolaos Koutroumanis1 · Georgios M. Santipantakis1 · Apostolos Glenis1 · Christos Doulkeridis1 · George A. Vouros1 Received: 21 August 2019 / Revised: 11 July 2020 / Accepted: 4 September 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract More and more real-life applications for mobility analytics require the joint exploitation of positional information of moving objects together with weather data that correspond to the movement. In particular, this is evident in fleet management applications for improved routing and reduced fuel consumption, in the maritime domain for more accurate trajectory prediction, as well as in air-traffic management for predicting regulations and reducing delays. Motivated by such applications, in this paper, we present a system for the enrichment of mobility data with weather information. Our main application scenario concerns streaming positional information (such as GPS traces of vehicles) that is collected and is enriched in an online fashion with stored weather data. We present the system architecture of a centralized version that runs on a single machine and exploits caching to improve its efficiency. Also, we extend our approach to a parallel implementation on top of Apache Kafka, which can scale to hundreds of thousands of processed records when provided with more computing nodes. Furthermore, we present extensions of our system for: (a) enrichment of more complex geometries than point data, and (b) providing linked RDF data as output. Our experimental evaluation on a medium-sized cluster shows the scalability of our approach in terms of number of processed records per second. Keywords Mobility data · Trajectories · Weather integration Nikolaos Koutroumanis
[email protected] Georgios M. Santipantakis [email protected] Apostolos Glenis [email protected] Christos Doulkeridis [email protected] George A. Vouros [email protected] 1
Department of Digital Systems, School of Information and Communication Technologies, University of Piraeus, Pireas, Greece
Geoinformatica
1 Introduction Data integration is a challenging task aiming at combining data from heterogeneous sources. This is further intensified in the Big Data era [4], due to the variety dimension that essentially is used to refer to data with different representations, different formats, types and modalities. In the domain of mobile data management [10], several data integration tasks are of interest due to the existence of multiple data sources that provide raw data: surveillance systems, radars and GPS-enabled devices provide timestamped positions of moving objects, meteorological services provide weather forecasts, domain-specific databases provide miscellaneous types of static data (e.g., type and features of moving object), geographical databases provide information on points and areas of interest, etc. Integrating data from such disparate data sources raises various challenges regarding efficiency and effectiveness [2].
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