Efficiently managing and merging heterogeneous, changing data sources has become a critical success factor for enterprises. AMPL (Automatic Meta Data Profiling and Lineage for Integrating Heterogeneous Data Sources) aims to develop a new tool for structuring, analyzing and exploring multiple heterogeneous, dynamic data sources. For this purpose, extensive data profiles are computed, consisting of statistics, correlations and complex provenance information (lineage). Machine learning assisted methods help in schema mapping between data sources as well as new methods for scalable and incremental computation of the data profiles.
As part of the BMBF funding initiative „KMU-innovativ“, ScaDS.AI Dresden/Leipzig started AMPL to develop a new tool for structuring, analyzing and exploring large volumes of heterogeneous, dynamic data sources. Partners from research and industry are working together in this project.
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