###### Abstract
Collecting such information is nowadays easy and convenient, thanks to the widespread use of mobile devices [13]. This led, over the years, to a very large amount of spatio-temporal data being collected via crowdsourcing and then stored in a variety of databases, some of which are open source. In this regard, the largest collaborative community project is OpenCellID (OpenCellID website: [[https://opencellid.org/](https://opencellid.org/)]([https://opencellid.org/](https://opencellid.org/)), (accessed on 13 April 2022)), which, on average, collects more than 1 million new measurements per day. Unfortunately, data are not properly arranged: the repository contains lists of different values stored in tabular formats, e.g., csv, without any specific data structure reflecting the organization of the cellular network [14]. In this paper, we propose a general and flexible yet complete database schema for cellular networks that is modeled after the information available in signal fingerprints and capable of fostering the crowdsourced collection of data. As we will see, the system supports several operations, ranging from outdoor positioning to advanced spatio-temporal analyses and validation tasks pertaining to the state of the cellular network. Even though each generation of cellular networks is based on standard specifications, a major challenge is the fact that each operator adopts its own organization and makes some changes that are usually not known by the external people. As a result, the only way to get complete and accurate knowledge of the cellular network is to systematically collect and analyze available data. The task is exacerbated due to several characteristics of such a network being time-dependent, i.e., they undergo continuous changes. For instance, a cell may be created o removed, merged with another, or even spatially relocated [15]. Additionally, this continuous network evolution is taken into account and effectively managed by our proposed system, which overall demonstrates how a deep knowledge of the cellular network arrangement can be achieved and maintained based only on crowdsourced information. To the best of our knowledge, no other comprehensive attempt to model and analyze the considered data has been reported in the literature. Previous works concentrated on limited network analyses, performed over specific technologies and with the purpose of solving precise problems, often relying on artificial intelligence techniques [16]. For instance, this has been the case with network optimization and planning tasks, such as the one in [17],where the authors propose an approach to determine groups of similarly behaving 3G cells, to support human experts in determining the state of the network; and the one in [18], where the collection of a large-scale dataset to foster mobile network planning is presented. In addition, a broad set of the literature focuses on anomaly detection and troubleshooting [19; 20; 21; 22; 23; 24; 25; 26]. The only work close in spirit to ours lies in the indoor positioning domain [27]. The authors developed a framework based on a relational database that pairs heterogeneous, sparsely collected fingerprints with building topology information. The aim, besides that of supporting fingerprint-based positioning tasks, was to allow for the detection of spatio-temporal changes in the radio map and to provide a basis for advanced analyses. This paper is organized as follows. Section 2 provides an account of the cellular network from the point of view of data modeling. It also presents the main issues related to the management of temporal aspects pertaining to the evolution of the network. Next, Section 3 presents the conceptual design of our original spatio-temporal database for the cellular network. The schema suitably models various generations of the network, independently of the data sources, and copes effectively with the previously described issues. The conceptual schema was translated into a logical relational schema and then physically implemented.