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Issues in Automatic Provenance Collection



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Issues in Automatic Provenance Collection Uri Braun Simson Garfinkel David A Holland Kiran Kumar Muniswamy Reddy Margo I Seltzer Harvard University Cambridge Massachusetts pass eecs harvard edu Abstract Automatic provenance collection describes systems that observe processes and data transformations inferring collecting and maintaining provenance about them Automatic collection is a powerful tool for analysis of objects and processes providing a level of transparency and pervasiveness not found in more conventional provenance systems Unfortunately automatic collection is also difficult We discuss the challenges we encountered and the issues we exposed as we developed an automatic provenance collector that runs at the operating system level 1 Introduction Today s provenance management systems usually take one of two approaches to provenance collection Either users enter it manually or applications explicitly collect provenance and enter it into a database There is however a third model automatic provenance collection In automatic collection the system observes the actions of users and programs and derives provenance storing it without user or application involvement Automatic collection is a powerful approach because it eliminates user error consistently collects provenance across all applications and captures more complete provenance than systems relying on a user s or application developer s assumptions about provenance For example automatic provenance collection in the operating system allows us to identify system configuration changes e g new tools or libraries identify environment variable modifications that alter program behavior debug faulty builds that are missing dependencies identify the source and creation of unusual files and create scripts that produce objects In earlier work we described a prototype Provenance Aware Storage System PASS built on Linux that automatically collects provenance at the operating system level 18 PASS is similar to systems such



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