Introduction. Systems and programming description management. Lambda and Kappa architectures for big data. Basic principles and features of big spatial and spatio-temporal data. Modelling of spatial and spatio-temporal data. Specification of relevant operations on spatial and spatio-temporal data. Indexing. Global and local indexes. Static and dynamic indexes. Geohashes. Spatio-temporal data streams. SQL-based analysis of spatio-temporal data streams within integrated big data platforms. Implementation of data types and operations in object-functional programming language and distributed dataflow platforms. Implementation based on API of integrated platform for distributed batch and data stream processing. Development of user-defined functions. Specification of spatial and spatio-temporal queries in SQL-like query languages. Data mining of big spatio-temporal data.
Learning Outcomes

•     Identify fundamental features of spatial and spatio-temporal big data
•     Identify fundamental features of spatioto-temporal data streams
•     Design and implement spatial and spatio-temporal data types in object-functional programming language and distributed data flow platforms
•     Develop simple algorithms for big spatio-temporal data management
•     Develop simple algorithms for spatio-temporal data streams management
•     Develop spatial and spatio-temporal queries using SQL-like expressions
•     Develop simple algorithms for spatio-temporal data mining and knowledge discovery.
•     Choose big data management technologies in spatio-temporal application domain

Type of methodology: Combination of lecture and hands-on

Paid training activity for participants: Yes, for some only

Participants prerequisite knowledge: No prerequisite knowledge

Language: English and Croatian