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dc.contributor.authorCheng, Yongqiang
dc.contributor.authorJiang, Ping
dc.contributor.authorPeng, Yonghong
dc.date.accessioned2016-09-21T15:37:34Z
dc.date.available2016-09-21T15:37:34Z
dc.date.issued2015
dc.identifier.citationCheng Y, Jiang P and Peng Y (2015) Increasing big data front end processing efficiency via locally sensitive Bloom filter for elderly healthcare. In: IEEE Symposium Series on Computational Intelligence in Big Data 2014, CIBD 2014. 9-12 Dec 2014, Orlando, FL, USA: 7011524.
dc.identifier.urihttp://hdl.handle.net/10454/9133
dc.descriptionNo
dc.description.abstractIn support of the increasing number of elderly population, wearable sensors and portable mobile devices capable of monitoring, recording, reporting and alerting are envisaged to enable them an independent lifestyle without relying on intrusive care programmes. However, the big data readings generated from the sensors are characterized as multidimensional, dynamic and non-linear with weak correlation with observable human behaviors and health conditions which challenges the information transmission, storing and processing. This paper proposes to use Locality Sensitive Bloom Filter to increase the Instance Based Learning efficiency for the front end sensor data pre-processing so that only relevant and meaningful information will be sent out for further processing aiming to relieve the burden of the above big data challenges. The approach is proven to optimize and enhance a popular instance-based learning method benefits from its faster speed, less space requirements and is adequate for the application.
dc.relation.isreferencedbyhttp://dx.doi.org/10.1109/CIBD.2014.7011524
dc.subjectData classification; Locality sensitive bloom filter; Big data; Instance-based learning
dc.titleIncreasing big data front end processing efficiency via locally sensitive Bloom filter for elderly healthcare
dc.status.refereedYes
dc.typeConference Paper
dc.type.versionNo full-text available in the repository


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