Provenance in Sensornet Republishing. Park, U. & Heidemann, J. In Proceedings of the 2ndInternational Provenance and Annotation Workshop , pages 208–292, Salt Lake City, Utah, USA, June, 2008. Springer-Verlag.
Provenance in Sensornet Republishing [link]Paper  abstract   bibtex   
Sensornets are being deployed and increasingly brought on-line to share data as it is collected. Sensornet \emphrepublishing is the process of transforming on-line sensor data and sharing the filtered, aggregated, or improved data with others. We explore the need for data provenance in this system to allow users to understand how processed results are derived and detect and correct anomalies. We describe our sensornet provenance system, exploring design alternatives and quantifying storage trade-offs in the context of a city-sized temperature monitoring application. In that application, our link approach outperforms other alternatives on saving storage requirement and our \emphincremental compression scheme save the storage further up to 83%.
@InProceedings{Park08b,
	author = 	"Unkyu Park and John Heidemann",
	title = 	"Provenance in Sensornet Republishing",
	booktitle = 	"Proceedings of the " # "2nd" # " International Provenance and Annotation Workshop ",
	year = 		2008,
	sortdate = "2008-06-01",
	project = "ilense, siss",
	jsubject = "sensornet_sharing",
	publisher =	"Springer-Verlag",
	address =	"Salt Lake City, Utah, USA",
	month =		jun,
	pages =		"208--292",
	xnote =		"(also released as tech report ISI-TR-2008-650",
	location =	"johnh: pafile",
	keywords =	"sensornet, data provenance",
	url =		"http://www.isi.edu/%7ejohnh/PAPERS/Park08a.html",
	pdfurl =	"http://www.isi.edu/%7ejohnh/PAPERS/Park08a.pdf",
	abstract = "
Sensornets are being deployed and increasingly brought on-line to
share data as it is collected.  Sensornet \emph{republishing} is the
process of transforming on-line sensor data and sharing the filtered,
aggregated, or improved data with others.  We explore the need for
data provenance in this system to allow users to understand how
processed results are derived and detect and correct anomalies.  We
describe our sensornet provenance system, exploring design
alternatives and quantifying storage trade-offs in the context of a
city-sized temperature monitoring application.  In that application,
our link approach outperforms other alternatives on saving storage
requirement and our \emph{incremental compression} scheme save the
storage further up to 83\%.
",
}

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