# Big Data in Smart City: Management Challenges
###### Abstract
They defined that main problems of big data in the context of smart cities can be shown through the five V definitions: value, velocity, variety, variability, and volume:  
* data are generated in real time, which leads to the tremendous amount of data to be dealt with--volume (identified by [13,14]);
* data are collected from different sources (smart phones, computers, GNSS, environmental sensors, cameras)--variety [6];
* big data are catalogued and stored in different platforms that often cause a problem in that some data are unused--velocity [15];
* smart city demands change in the approach of performing the complex analytics operations over such huge amount of data--velocity [16];
* big data systems must provide the data that are included in analytical processes with a focus on enhancing smart city applications--variability [14];
* a need to transform a huge amount of data into business based on big data collection, management, and analysis--value [12,17,18]. In the context of defined problems, limitations of the smart city based on the relational database systems are:* volume--related to the storage performances and the performances of the platform where the system is installed,
* variety--the conceptual model of the smart city solution does not have a possibility to add different sources to the platform because they work on different standard protocols,
* velocity--most of sources have their own storage,
* variability--a problem to perform complex analysis with data used from different sources,
* value--same as volume, limited with the storage and the platform performances. Geospatial data are analyzed in the context of big data in terms of collecting, storing, and processing large amounts of geospatial data. The management context of decision-making processes places big data as an additional building block in the system of exchange and processing of large amounts of geospatial data more efficiently than traditional solutions [19]. The amount of collected data largely exceeds the possibility of their storage on individual computers and requires storage on a cluster of computers [20]. Traditional relational database management systems have problems to handle all these data together, especially if big data concepts are considered. The 3D city models are an integral part of the smart city [21]. In this paper, GAMINESS is described as a new management system based on big data concepts, which provide management over different structured, semistructured, and non-structured smart city data. The system consists of two components: a geospatial component and a geosensor component. GAMINESS provides connectivity of different data sources regardless of the standardization rules and the data formats. The main advantage of such a system is that it represents a solution for the variety problems in big data. GAMINESS also speeds up performances vertically, allowing fast responses for the data requested by clients. The goals of this paper were:  
* migrating/transferring the standard smart city concept defined as the relational DB system to the big data framework;
* creating the GAMINESS conceptual model based on the CityGML 3.1.1 standard extended with the IndoorGML standard, the IoT standard, and the DigtialTwins smart city recommendations and extended with the connection protocols for different data sources and complex data types based on data sources;
* developing the model which integrates all data in one structure based on a map-reducing paradigm of big data. The paper is structured as follows: after the introduction in Section 1 and the related work review in Section 2, Section 3 presents a methodology in the form of a roadmap that was used to develop the model of the GAMINESS management system. Section 4 presents the implementation of GAMINESS into the big data framework. In Section 5, the case study for the city of Novi Sad is presented. Conclusions and future work are discussed afterward.