Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Lineage tracing for general data warehouse transformations
The VLDB Journal — The International Journal on Very Large Data Bases
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
A survey of data provenance in e-science
ACM SIGMOD Record
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Provenance in Sensornet Republishing
Provenance and Annotation of Data and Processes
Facilitating fine grained data provenance using temporal data model
Proceedings of the Seventh International Workshop on Data Management for Sensor Networks
LIVE: a lineage-supported versioned DBMS
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part II
ProvenanceCurious: a tool to infer data provenance from scripts
Proceedings of the 16th International Conference on Extending Database Technology
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Fine-grained data provenance ensures reproducibility of results in decision making, process control and e-science applications. However, maintaining this provenance is challenging in stream data processing because of its massive storage consumption, especially with large overlapping sliding windows. In this paper, we propose an approach to infer fine-grained data provenance by using a temporal data model and coarse-grained data provenance of the processing. The approach has been evaluated on a real dataset and the result shows that our proposed inferring method provides provenance information as accurate as explicit fine-grained provenance at reduced storage consumption.