Glavic, BorisDittrich, KlausKemper, AlfonsSchöning, HaraldRose, ThomasJarke, MatthiasSeidl, ThomasQuix, ChristophBrochhaus, Christoph2020-02-112020-02-112007978-3-88579-197-3https://dl.gi.de/handle/20.500.12116/31801In many application areas like e-science and data-warehousing detailed information about the origin of data is required. This kind of information is often re- ferred to as data provenance or data lineage. The provenance of a data item includes information about the processes and source data items that lead to its creation and current representation. The diversity of data representation models and application domains has lead to a number of more or less formal definitions of provenance. Most of them are limited to a special application domain, data representation model or data processing facility. Not surprisingly, the associated implementations are also restricted to some application domain and depend on a special data model. In this paper we give a survey of data provenance models and prototypes, present a general categorization scheme for provenance models and use this categorization scheme to study the properties of the existing approaches. This categorization enables us to distinguish between different kinds of provenance information and could lead to a better understanding of provenance in general. Besides the categorization of provenance types, it is important to include the storage, transformation and query requirements for the different kinds of provenance information and application domains in our considerations. The analysis of existing approaches will assist us in revealing open research problems in the area of data provenance.enText/Conference Paper1617-5468