Technologies to manager big data of urban building projects

The transformation of the construction industry according to the Construction 4.0 concept is possible with the availability of technology for managing big data of construction projects, where the task of managing big data includes tasks: collection; processing; renewal; backup and save data. Today, the information technologies of urban construction projects are a complex of integrated software complexes, and the data of construction projects remain stored in various data repositories, which makes it difficult, and sometimes impossible, to use them for project implementation. The choice of big data management technologies, including, depends on the types of big data that are characteristic of the project. The purpose of this work is to define a list of technologies for managing various types of data of urban construction projects to enable their use for the automation of urban construction projects. To achieve the goal, the work analyzed the types and formats of data information systems of urban construction projects, namely: business process management systems; systems of interaction with interested parties; labor protection and risk management systems in construction; operation management systems; systems for designing and creating models of spatial objects; systems of augmented reality (VR/AR); systems for engineering analysis. Based on the analysis of data types and formats, it is determined that the data belongs to the following categories: structured, semi-structured and unstructured data types. Studying the developments of scientists regarding big data management technologies of certain types made it possible to compile a list of technologies for data management of urban construction projects, namely: for data collection – Apache Kafka, Apache Hbase, Apache Spark, Apache Hadoop, Stream Analytics, Scrapy, Twitter API, Facebook Graph API; for data processing – technologies "Intelligent analysis of texts", "Computer vision", machine and deep learning; 3) for data storage – AWS S3, AWS RDS SQL, Azure Data Lake, HDFS, Redi, CosmosDB, MongoDB, Azure Blob Storage; 4) for backup – AWS Backup, Google Cloud Storage, Microsoft Azure Backup, MongoDB Backup, Cassandra Backup; to update – Apache Kafka/Flink/Spark Streaming, SQL. Further research will consist in conducting an analysis of the effectiveness of the methods of the specified technologies for solving the tasks of data management of construction projects, depending on the nature of the data input.