## 1 Introduction  
The Internet of things (IoT) is a consortium of active physical objects having attached sensors that regularly capture data and exchange it with other objects over communication networks including the Internet. With IoT devices being ubiquitous and utilized in every aspect of our life, the amount of georeferenced data accumulated are now inevitably and unprecedently on the scale of several zettabytes [1; 2]. Being it through social media such as Twitter, Instagram and Facebook, or GPS locations of trajectories of vehicles in urban areas, or even photos enriched with locational data where they have been captured. Mobility data per se is voluminous and offers promising opportunities for analysis to guide urban planning and knowledge discovery. Mobility data can be defined as any information tagged with locational reference on the form of longitude/latitude pairs of coordinates, which is generated by using digitally-enabled mobility devices (e.g., smart phones, on-board vehicle location readers, or GPS-enabled navigation systems such as Bing and google maps). However, analysts typically seek to analyze the correlation between mobility patterns and other contextual information surrounding those patterns. For example, we may seek answers to sophisticated questions related to the relationship between the density of vehicles in city areas and the levels of pollution substances such as Particulate Matters (PM\({}_{10}\) and PM\({}_{2.5}\)), to understand the contribution of vehicle movements in the elevated numbers of pollutants in those areas. Context constitutes any enriching information that is necessary in characterizing the environment surrounding objects, such as city dwellers, and cyclers [3]. Pollution, metrological and climatological information compose part of very important contextual enrichments that surround our daily activities. Various are the application scenarios that require joining georeferenced data from different domains. For example, understanding relationships between animals emigration/mobility patterns and the co-located weather conditions [4], or the effect of weather on human mobility [5]. Smart cities are technologically-enabled urban spaces that utilize sensors and other electronic methods to capture data that are relevant to the efficient management of city resources and services, thus enhancing intracity operations. In this sense, numerous smart city scenarios can emerge from unified views generated for mobility and environment contextual data combined [6]. A canonical example is that of city decision makers desiring to uncover the correlations between PM concentrations and the densities of city dwellers and cyclers daily sport activities, aiming basically to enforce future policies to protect the health of the community at large [7]. For example, by restricting the access of vehicles to specific zones of the city with elevated concentrations of pollutants where there is a high concentration of city dwellers, in an aim to reduce the adverse effect of such poisonous substances on the community's health [8; 9; 10; 11]. For such kinds of sophisticated analytics to be applicable they require unified views that integrate heterogeneous pollution, climatology, meteorological, human and vehicle mobility data in single database backdrops. However, the fact that such data typically is collected independently enlarges the challenge of achieving such combined analytics. This is so because normally mobility and meteorological data are heterogeneous and have different data structures, e.g., mobility data on the form of relational data structure such as tables, whereas meteorological data coming on the form of sophisticated structures such as NetCDF of GRIB files. Having said that, bringing those data sources into a unified format is challenging. Those data are georeferenced and joining them requires applying computationally expensive geometric operations such as spatial joins [12].