Using signature files for querying time-series data

André-Jönsson, Henrik; Badal, Dushan Z.

doi:10.1007/3-540-63223-9_120

Cited by 57 publications

(36 citation statements)

References 3 publications

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“…Some of the representations that have been proposed include the Fourier transform [10,25], many different wavelets [23,7], piecewise polynomials [28,6], singular value decomposition [6] and symbolic approximations [2]. Many of the above approximation techniques have been adapted to work in an online fashion.…”

Section: Related Workmentioning

confidence: 99%

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Online amnesic approximation of streaming time series

Palpanas

Vlachos

Keogh

et al.

Proceedings. 20th International Conference on Data Engineering

103

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Section: Related Workmentioning

confidence: 99%

“…In this situation, we cannot apply approximation techniques that require knowledge of the entire series, such as singular value decomposition [6] and most symbolic approaches [2]. Furthermore, all current time series representations treat every point of the time series equally.…”

Section: Introductionmentioning

confidence: 99%

Online amnesic approximation of streaming time series

Palpanas

Vlachos

Keogh

et al.

Proceedings. 20th International Conference on Data Engineering

103

View full text Add to dashboard Cite

show abstract

“…Even though it seems a straightforward solution, it comes with substantial advantages over existing algorithms and data structures that enable the efficient manipulations of symbolic representations in addition to allowing the framing of time. However, general symbolic representation methods are not capable of calculating distance in symbolic space and supporting lower bounding at the same time [42], [43]. Symbolic Aggregate Approximation (SAX) is a symbolic representation technique on time series analysis [3] that is not only capable of providing significant data reduction but also a support for lower bounding principle.…”

Section: Time-series Analysismentioning

confidence: 99%

On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data

Kolozali

Puschmann

Bermúdez-Edo

et al. 2016

IEEE Internet Things J.

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Abstract-With the growing popularity of Information and Communications Technologies (ICT) and information sharing and integration, cities are evolving into large interconnected ecosystems by using smart objects and sensors that enable interaction with the physical world. However, it is often difficult to perform real-time analysis of large amount on heterogeneous data and sensory information that are provided by various resources. This paper describes a framework for real-time semantic annotation and aggregation of data streams to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMQP). We provide a comprehensive analysis on the effect of adaptive and non-adaptive window size in segmentation of time series using SensorSAX and SAX approaches for data streams with different variation and sampling rate in real-time processing. The framework is evaluated with 3 parameters, namely window size parameter of the SAX algorithm, sensitivity level and minimum window size parameters of the SensorSAX algorithm based on the average data aggregation and annotation time, CPU consumption, data size, and data reconstruction rate. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost of the stream-processing framework. Our results suggests that regardless of utilised segmentation approach, due to the fact that each geographically different sensory environment has got different dynamicity level, it is desirable to find the optimal data aggregation parameters in order to reduce the energy consumption and improve the data aggregation quality.

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“…There have been several dozen research efforts that propose to facilitate time series search by first symbolizing the raw data (André-Jönsson and Badal 1997;Huang and Yu 1999;Megalooikonomou et al 2005). However, in every case, the authors introduced a distance measure defined on the newly derived symbols.…”

Section: Treesmentioning

confidence: 99%

iSAX: disk-aware mining and indexing of massive time series datasets

Shieh

Keogh

2009

Data Min Knowl Disc

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Current research in indexing and mining time series data has produced many interesting algorithms and representations. However, the algorithms and the size of data considered have generally not been representative of the increasingly massive datasets encountered in science, engineering, and business domains. In this work, we introduce a novel multi-resolution symbolic representation which can be used to index datasets which are several orders of magnitude larger than anything else considered in the literature. To demonstrate the utility of this representation, we constructed a simple tree-based index structure which facilitates fast exact search and orders of magnitude faster, approximate search. For example, with a database of one-hundred million time series, the approximate search can retrieve high quality nearest neighbors in slightly over a second, whereas a sequential scan would take tens of minutes. Our experimental evaluation demonstrates that our representation allows index performance to scale well with increasing dataset sizes. Additionally, we provide analysis concerning parameter sensitivity, approximate search effectiveness, and lower bound comparisons between time series representations in a bit constrained environment. We further show how to exploit the combination of both exact and approximate search as sub-routines in data mining algorithms, allowing for the exact mining of truly massive real world datasets, containing tens of millions of time series.Responsible editor: Bart Goethals.

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Using signature files for querying time-series data

Cited by 57 publications

References 3 publications

Online amnesic approximation of streaming time series

Online amnesic approximation of streaming time series

On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data

iSAX: disk-aware mining and indexing of massive time series datasets

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