# Big Data in Smart City: Management Challenges
## 3 Materials and Methods  
Big data technologies are not a unique technology, but a combination of new and old technologies that help enable data management of large amounts of different data in structured, semi-structured, and unstructured forms. Figure 1 shows possibilities of the proposed GAMINESS management system. It integrates loading and storing data from different sources regardless of their structure according to the five V rules. Sources are connected to the management system as separate storages. This provides the velocity objective. Traditional systems only receive data which are structured according to a predefined concept such as CityGML. GAMINESS can recive data from sources without variablity problems. It uses algorithms to read such structures and map data by using user defined types. In traditional systems, the speed of transactions directly affects the performance of the system. The GAMINESS management system is characterized by big data processing using a cluster network, which speeds up performance vertically and improves overall system performance. Figure 1: Migration from standard city model to the GAMINESS big data model. The paper presents a proposal for a new solution that provides a different approach of support for the mentioned five characteristics compared to the most commonly used management systems such as PostgreSQL and Oracle. The research methodology in the form of a roadmap is shown in Figure 2. The proposed model, based on the concepts of the big data paradigm and using process parallelization algorithms for distributed sources, gives significant efficiency over large amounts of data stored (petabytes and larger). The advantage of the big data system is primarily reflected in the processing of large amounts of data, while, with smaller amounts of data, this advantage is not noticeable, and thus there is no particular need to use it. By sharing data through network system clusters, these agglomer algorithms provide higher processing speeds, which is explained in more detail in the discussion section, where a comparative analysis of the same amounts of data in the GAMINESS environment and the 3 DcityDB Postgres/Oracle is made. In smart city-related data processing systems, according to the DigitalTwins model, data appear multiple times with time validity details, meaning that the same data are repeated, leading to more complex data processing. In the GAMINESS management  
Figure 2: A roadmap to adopt the GAMINESS management system. system, this problem is overcome by using lifespan rules, and only currently valid data are inputs for processing. The proposed solution provides the possibility of transformation from heterogeneous data sources into the proposed structure, taking into account the relationship between data and data hierarchy in order to control the management of such large amounts of data. This stratification is defined by a data model in which there is a connection between data that are geospatially determined (point cloud combined with sensor observations integrated into a city model). This forms two separate units in the loading model, realized by user type definitions for the classes of point and sensor clouds, which provide loading of complex data types. In this way, the complexity problem is solved. The GAMINESS management system provides better functionality of the system based on the Apache Spark programming language in the MapReduce distributed data processing approach, shown through the five V concept. In the big data environment, the basic concept of MapReduce involves splitting the process into two subprocesses: the reduction phase and the data mapping phase. There are appropriate algorithms that perform adequate data merging without any loss. One of the most well-known data storage systems in this context is Hadoop, which has no developed geospatial component and is therefore not adequate for use in this research.