Data warehouses. Data storage architecture. Multidimensional data. The concept of data warehouses (HD). OLAP as a key component of the data warehouse. Relational data stores. Modeling of temporal (temporal) data in data warehouses. Metadata in data warehouses.
Semantic data modeling. Organization of access to
data warehouses. Virtual data stores. Data storage software market. SQL in data warehouses: analytical data processing. Data Mining Methods. Cloud technologies and data storage.
Formation in PhD doctoral students of ideas about how to process and store large amounts of information and data analysis, as well as acquiring skills in creating data warehouses and semantic data models.
LO 1.Know the concept of data, the concept of data storage, the main types and architectures of data storage, the concept of data storage (DW).
LO 2.Be able to apply various methods of semantic data analysis to solve various applied problems in professional activities;
LO 3.Have the skills to use semantic models in professional and scientific research.