Time Lattice: A Data Structure for the Interactive Visual Analysis of Large Time Series

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dc.contributor.authorMiranda, Fabioen_US
dc.contributor.authorLage, Marcosen_US
dc.contributor.authorDoraiswamy, Harishen_US
dc.contributor.authorMydlarz, Charlieen_US
dc.contributor.authorSalamon, Justinen_US
dc.contributor.authorLockerman, Yitzchaken_US
dc.contributor.authorFreire, Julianaen_US
dc.contributor.authorSilva, Claudio T.en_US
dc.contributor.editorJeffrey Heer and Heike Leitte and Timo Ropinskien_US
dc.date.accessioned2018-06-02T18:07:01Z
dc.date.available2018-06-02T18:07:01Z
dc.date.issued2018
dc.description.abstractAdvances in technology coupled with the availability of low-cost sensors have resulted in the continuous generation of large time series from several sources. In order to visually explore and compare these time series at different scales, analysts need to execute online analytical processing (OLAP) queries that include constraints and group-by's at multiple temporal hierarchies. Effective visual analysis requires these queries to be interactive. However, while existing OLAP cube-based structures can support interactive query rates, the exponential memory requirement to materialize the data cube is often unsuitable for large data sets. Moreover, none of the recent space-efficient cube data structures allow for updates. Thus, the cube must be re-computed whenever there is new data, making them impractical in a streaming scenario. We propose Time Lattice, a memory-efficient data structure that makes use of the implicit temporal hierarchy to enable interactive OLAP queries over large time series. Time Lattice is a subset of a fully materialized cube and is designed to handle fast updates and streaming data. We perform an experimental evaluation which shows that the space efficiency of the data structure does not hamper its performance when compared to the state of the art. In collaboration with signal processing and acoustics research scientists, we use the Time Lattice data structure to design the Noise Profiler, a web-based visualization framework that supports the analysis of noise from cities. We demonstrate the utility of Noise Profiler through a set of case studies.en_US
dc.description.number3
dc.description.sectionheadersMultiple Fields and Time
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume37
dc.identifier.doi10.1111/cgf.13398
dc.identifier.issn1467-8659
dc.identifier.pages23-35
dc.identifier.urihttps://doi.org/10.1111/cgf.13398
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13398
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleTime Lattice: A Data Structure for the Interactive Visual Analysis of Large Time Seriesen_US
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