Business Intelligence (BI) tools support making better business decisions by analyzing available organizational data. Data Warehouses (DWs), typically structured with the Multidimensional (MD) model, are used to store data from different internal and external sources processed using Extract-Transformation-Load (ETL) processes. On-Line Analytical Processing (OLAP) queries are applied on DWs to derive important business-critical knowledge. DW and OLAP technologies perform efficiently when they are applied on data that are static in nature and well organized in structure. Nowadays, Semantic Web (SW) technologies and the Linked Data (LD) principles inspire organizations to publish their semantic data, which allow machines to understand the meaning of data, using the Resource Description Framework (RDF) model. One of the reasons why semantic data has become so successful is that managing and making the data available is low effort and does not rely on a sophisticated schema.

In addition to the traditional (non-semantic) data, the incorporation of those (external) semantic data into a DW raises additional challenges of schema derivation, semantic annotation, semantic heterogeneity, as well as schema and data management model over traditional DW technologies and ETL tools. The main drawback of a state-of-the-art Relational Database Management System (RDBMS)-based DW is that it is strictly schema dependent and less flexible to evolving business requirements. To cover new business requirements, every step of the development cycle needs to be updated to cope with the new requirements. This update process is time-consuming and costly and is sometimes not adjustable with the current setup of the DW; hence, it introduces the need for a novel approach.

In summary, the limitations of traditional ETL tools to process semantic data sources are: (1) they do not fully support semantic-aware data, (2) they are entirely schema dependent (i.e., cannot handle data expressed without any schema), (3) they do not focus on meaningful semantic relationships to integrate data from disparate sources, and (4) they neither support to capture the semantics of data nor support to derive new information by active inference and reasoning on the data. Thus, a DW with semantic data sources in addition to traditional data sources requires more powerful techniques to define, integrate, transform, update, and load data semantically, which are the research challenges of group.

See also SETL

Once the data from different sources are semantic integrated into a Knowledge Graph (semantic data warehouse), the next task is how to apply business analytics or explore it efficiently. Here we develop deploy different methods, tools and techniques to explore a knowledge graph. We examine how skyline queries, OLAP queries, Federated and deep learning, and hidden pattern can be applied/derived in a knowledge graph.   

We also focus on efficient link discovery, resource linking, and data matching. 

Creating a multidimentional  semantic datawarehouse over covid-19 data and enable OLAP-style analysis over it. 

We also develop data intensive systems based on our clients' requirements