Applying learned indexing on embedded devices for time series data
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Ding, Yiming
issued: 2023-07-31T21:36:15Z
issued: 2023
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: To meet the demand for increasingly accurate sensor monitoring and forecasting, time series datasets have grown to take larger, more detailed samples with more frequent sampling rates. As a result, the size of time series datasets has grown larger and now require more efficient indexing methods to manage properly. Time series databases have unique traits that allow for efficient indexing structures to be used. The timestamps are always increasing in an append-only fashion, a characteristic which can be exploited to create more efficient indexing structures. In this research, two different indexing algorithms for time series databases are evaluated. The spline index model uses existing points of the time series data to form a series of linear approximations. The other index model examined is the piece-wise geometric model (PGM), which forms fully independent lines that approximate the underlying time series data. Experimental results show both the Spline and PGM learned indexes outperform conventional indexes for time series data. Performance metrics for binary search and simpler single line approximations are also included for comparison.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/85355
note: Applying Learned Indexing onEmbedded Devices for Time Series DatabyYiming DingB.Sc., The University of British Columbia, 2020A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe College of Graduate Studies(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)July, 2023© Yiming Ding, 2023The following individuals certify that they have read, and recommend tothe College of Graduate Studies for acceptance, a thesis/dissertation entitled:Applying Learned Indexing on Embedded Devices for Time Series Datasubmitted by Yiming Ding in partial fulfilment of the requirements of thedegree of Master of Science.Dr. Ramon Lawrence, Irving K. Barber Faculty of ScienceSupervisorDr. Khalad Hasan, Irving K. Barber Faculty of ScienceSupervisory Committee MemberDr. Yong Gao, Irving K. Barber Faculty of ScienceSupervisory Committee MemberDr. Barb Marcolin, Faculty of ManagementUniversity ExamineriiAbstractTo meet the demand for increasingly accurate sensor monitoring andforecasting, time series datasets have grown to take larger, more detailedsamples with more frequent sampling rates. As a result, the size of timeseries datasets has grown larger and now require more efficient indexingmethods to manage properly. Time series databases have unique traits thatallow for efficient indexing structures to be used. The timestamps are alwaysincreasing in an append-only fashion, a characteristic which can be exploitedto create more efficient indexing structures. In this research, two differentindexing algorithms for time series databases are evaluated. The spline indexmodel uses existing points of the time series data to form a series of linearapproximations. The other index model examined is the piece-wise geometricmodel (PGM), which forms fully independent lines that approximate theunderlying time series data. Experimental results show both the Spline andPGM learned indexes outperform conventional indexes for time series data.Performance met…
has type: Thesis/Dissertation
date issued: 2023-09
is shown at: 10.14288/1.0434631
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Lawrence, Ramon
type: Text
identifier URI: http://hdl.handle.net/2429/85355