## IIT Database Group

### Abstract

The current state of the art for provenance in data stream management systems (DSMS) is to provide provenance at a high level of abstraction (such as, from which sensors in a sensor network an aggregated value is derived from). This limitation was imposed by high-throughput requirements and an anticipated lack of application demand for more detailed provenance information. In this work, we first demonstrate by means of well-chosen use cases that this is a misconception, i.e., coarse-grained provenance is in fact insufficient for many application domains. We then analyze the requirements and challenges involved in integrating support for fine-grained provenance into a streaming system and outline a scalable solution for supporting tuple-level provenance in DSMS.

### bibtex

@inproceedings{GE11,
author = {Glavic, Boris and Esmaili, Kyumars Sheykh and Fischer, Peter M. and Tatbul, Nesime},
bibsource = {DBLP, http://dblp.uni-trier.de},
booktitle = {Proceedings of the 1st Workshop on Data Streams and Event Processing collocated with BTW},
crossref = {DBLP:conf/btw/2011w},
date-modified = {2012-12-18 17:16:17 +0000},
isworkshop = {true},