KDE-Track: An Efficient Dynamic Density Estimator for Data Streams

Handle URI:
http://hdl.handle.net/10754/621861
Title:
KDE-Track: An Efficient Dynamic Density Estimator for Data Streams
Authors:
Qahtan, Abdulhakim Ali Ali ( 0000-0001-8254-1764 ) ; Wang, Suojin; Zhang, Xiangliang ( 0000-0002-3574-5665 )
Abstract:
Recent developments in sensors, global positioning system devices and smart phones have increased the availability of spatiotemporal data streams. Developing models for mining such streams is challenged by the huge amount of data that cannot be stored in the memory, the high arrival speed and the dynamic changes in the data distribution. Density estimation is an important technique in stream mining for a wide variety of applications. The construction of kernel density estimators is well studied and documented. However, existing techniques are either expensive or inaccurate and unable to capture the changes in the data distribution. In this paper, we present a method called KDE-Track to estimate the density of spatiotemporal data streams. KDE-Track can efficiently estimate the density function with linear time complexity using interpolation on a kernel model, which is incrementally updated upon the arrival of new samples from the stream. We also propose an accurate and efficient method for selecting the bandwidth value for the kernel density estimator, which increases its accuracy significantly. Both theoretical analysis and experimental validation show that KDE-Track outperforms a set of baseline methods on the estimation accuracy and computing time of complex density structures in data streams.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Qahtan A, Wang S, Zhang X (2016) KDE-Track: An Efficient Dynamic Density Estimator for Data Streams. IEEE Transactions on Knowledge and Data Engineering: 1–1. Available: http://dx.doi.org/10.1109/TKDE.2016.2626441.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Knowledge and Data Engineering
Issue Date:
8-Nov-2016
DOI:
10.1109/TKDE.2016.2626441
Type:
Article
ISSN:
1041-4347
Additional Links:
http://ieeexplore.ieee.org/document/7738463/
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorQahtan, Abdulhakim Ali Alien
dc.contributor.authorWang, Suojinen
dc.contributor.authorZhang, Xiangliangen
dc.date.accessioned2016-11-22T13:40:08Z-
dc.date.available2016-11-22T13:40:08Z-
dc.date.issued2016-11-08en
dc.identifier.citationQahtan A, Wang S, Zhang X (2016) KDE-Track: An Efficient Dynamic Density Estimator for Data Streams. IEEE Transactions on Knowledge and Data Engineering: 1–1. Available: http://dx.doi.org/10.1109/TKDE.2016.2626441.en
dc.identifier.issn1041-4347en
dc.identifier.doi10.1109/TKDE.2016.2626441en
dc.identifier.urihttp://hdl.handle.net/10754/621861-
dc.description.abstractRecent developments in sensors, global positioning system devices and smart phones have increased the availability of spatiotemporal data streams. Developing models for mining such streams is challenged by the huge amount of data that cannot be stored in the memory, the high arrival speed and the dynamic changes in the data distribution. Density estimation is an important technique in stream mining for a wide variety of applications. The construction of kernel density estimators is well studied and documented. However, existing techniques are either expensive or inaccurate and unable to capture the changes in the data distribution. In this paper, we present a method called KDE-Track to estimate the density of spatiotemporal data streams. KDE-Track can efficiently estimate the density function with linear time complexity using interpolation on a kernel model, which is incrementally updated upon the arrival of new samples from the stream. We also propose an accurate and efficient method for selecting the bandwidth value for the kernel density estimator, which increases its accuracy significantly. Both theoretical analysis and experimental validation show that KDE-Track outperforms a set of baseline methods on the estimation accuracy and computing time of complex density structures in data streams.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/7738463/en
dc.rights(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.subjectInterpolationen
dc.subjectAdaptive Resamplingen
dc.subjectBandwidth Selectionen
dc.subjectData Streamsen
dc.subjectDynamic Density Estimationen
dc.titleKDE-Track: An Efficient Dynamic Density Estimator for Data Streamsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalIEEE Transactions on Knowledge and Data Engineeringen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Statistics, Texas A&M University (TAMU), College Station, TX 77843, USAen
kaust.authorQahtan, Abdulhakim Ali Alien
kaust.authorZhang, Xiangliangen
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