Data preparation is an iterative-agile process for exploring, combining, cleaning and transforming raw data into curated datasets self-service integration, science, discovery, BI/analytics. To perform preparation, tools are used by analysts, citizen scientists self- service. The also integrators engineers enablement to reduce the time complexity of interactively accessing, cataloging, harmonizi...

Business Intelligence (BI) plays important roles in executive decision making in organizations. Up-to-date information from good data sources always gives advantages to the organization. Nowadays, information in Semantic Webs is considered important source of BI data. It provides machine readable information using the Resource Description Framework (RDF). To perform BI activities on RDF documen...

Database mining can be defined as the process of mining for implicit, formerly unidentified, and potentially essential information from awfully huge databases by efficient knowledge discovery techniques. The privacy and security of user information have become significant public policy anxieties and these anxieties are receiving increased interest by the both public and government lawmaker and ...

The constant increase in use of geographic data in different application domains has resulted in large amounts of data stored in spatial databases and in the desire of data mining. Many solutions for spatial data mining have been proposed. Most create data mining languages or extend existing query languages to support data mining operations. This paper presents an interoperable framework for sp...

Filename D2.2 Data optimisation, enrichment, and preparation for analysis.docx Abstract: The intermediate data produced using various ETL pipelines during their development may not be consistent with the RDF Data Cube Vocabulary and the OpenBudgets.eu data model. This is why we developed several techniques for data optimisation, detecting such inconsistencies and reporting them to the pipeline ...

The application of logic-based learning algorithms in real-world domains, such as robotics, requires extensive data engineering, including the transformation of numerical tabular representations of real-world data to logic-based representations, feature and concept selection, the generation of the respective descriptions, and the composition of training and test sets, which meet the requirement...