File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Managing uncertainty in sensor databases

TitleManaging uncertainty in sensor databases
Authors
Issue Date2003
Citation
Sigmod Record, 2003, v. 32 n. 4, p. 41-46 How to Cite?
AbstractSensors are often employed to monitor continuously changing entities like locations of moving objects and temperature. The sensor readings are reported to a centralized database system, and are subsequently used to answer queries. Due to continuous changes in these values and limited resources (e.g., network bandwidth and battery power), the database may not be able to keep track of the actual values of the entities, and use the old values instead. Queries that use these old values may produce incorrect answers. However, if the degree of uncertainty between the actual data value and the database value is limited, one can place more confidence in the answers to the queries. In this paper, we present a framework that represents uncertainty of sensor data. Depending on the amount of uncertainty information given to the application, different levels of imprecision are presented in a query answer. We examine the situations when answer imprecision can be represented qualitatively and quantitatively. We propose a new kind of probabilistic queries called Probabilistic Threshold Query, which requires answers to have probabilities larger than a certain threshold value. We also study techniques for evaluating queries under different details of uncertainty, and investigate the tradeoff between data uncertainty, answer accuracy and computation costs.
Persistent Identifierhttp://hdl.handle.net/10722/152316
ISSN
2015 Impact Factor: 0.698
2015 SCImago Journal Rankings: 0.543
References

 

DC FieldValueLanguage
dc.contributor.authorCheng, Ren_US
dc.contributor.authorPrabhakar, Sen_US
dc.date.accessioned2012-06-26T06:37:08Z-
dc.date.available2012-06-26T06:37:08Z-
dc.date.issued2003en_US
dc.identifier.citationSigmod Record, 2003, v. 32 n. 4, p. 41-46en_US
dc.identifier.issn0163-5808en_US
dc.identifier.urihttp://hdl.handle.net/10722/152316-
dc.description.abstractSensors are often employed to monitor continuously changing entities like locations of moving objects and temperature. The sensor readings are reported to a centralized database system, and are subsequently used to answer queries. Due to continuous changes in these values and limited resources (e.g., network bandwidth and battery power), the database may not be able to keep track of the actual values of the entities, and use the old values instead. Queries that use these old values may produce incorrect answers. However, if the degree of uncertainty between the actual data value and the database value is limited, one can place more confidence in the answers to the queries. In this paper, we present a framework that represents uncertainty of sensor data. Depending on the amount of uncertainty information given to the application, different levels of imprecision are presented in a query answer. We examine the situations when answer imprecision can be represented qualitatively and quantitatively. We propose a new kind of probabilistic queries called Probabilistic Threshold Query, which requires answers to have probabilities larger than a certain threshold value. We also study techniques for evaluating queries under different details of uncertainty, and investigate the tradeoff between data uncertainty, answer accuracy and computation costs.en_US
dc.languageengen_US
dc.relation.ispartofSIGMOD Recorden_US
dc.titleManaging uncertainty in sensor databasesen_US
dc.typeArticleen_US
dc.identifier.emailCheng, R:ckcheng@cs.hku.hken_US
dc.identifier.authorityCheng, R=rp00074en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1145/959060.959068en_US
dc.identifier.scopuseid_2-s2.0-14344250095en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-14344250095&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume32en_US
dc.identifier.issue4en_US
dc.identifier.spage41en_US
dc.identifier.epage46en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridCheng, R=7201955416en_US
dc.identifier.scopusauthoridPrabhakar, S=7101672592en_US
dc.identifier.citeulike1582702-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats