An efficient external sorting with minimal space requirement

Motzkin, Dalia; Hansen, Christina L

doi:10.1007/bf00996816

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…We compute the distance of each data point from r and sort all such distances in ascending order. As the data is on the disk, any efficient external sorting algorithm can be used for this purpose (Motzkin and Hansen 1982;Nyberg et al 1995). A snap shot of the database after sorting is shown in Fig.…”

Section: A Detailed Intuition Of Our Algorithmmentioning

confidence: 99%

A disk-aware algorithm for time series motif discovery

Mueen

Keogh

Zhu

et al. 2010

Data Min Knowl Disc

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Time series motifs are sets of very similar subsequences of a long time series. They are of interest in their own right, and are also used as inputs in several higher-level data mining algorithms including classification, clustering, rule-discovery and summarization. In spite of extensive research in recent years, finding time series motifs exactly in massive databases is an open problem. Previous efforts either found approximate motifs or considered relatively small datasets residing in main memory. In this work, we leverage off previous work on pivot-based indexing to introduce a disk-aware algorithm to find time series motifs exactly in multi-gigabyte databases which contain on the order of tens of millions of time series. We have evaluated our algorithm on datasets from diverse areas including medicine, anthropology, computer networking and image processing and show that we can find interesting and meaningful motifs in datasets that are many orders of magnitude larger than anything considered before.

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Section: A Detailed Intuition Of Our Algorithmmentioning

confidence: 99%

A disk-aware algorithm for time series motif discovery

Mueen

Keogh

Zhu

et al. 2010

Data Min Knowl Disc

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show abstract

“…We compute the distance of each data point from r and sort all such distances in ascending order. As the data is on the disk, any efficient external sorting algorithm can be used for this purpose [18] [21]. A snap shot of the database after sorting is shown in Figure 1B.…”

Section: A a Detailed Intuition Of Our Algorithmmentioning

confidence: 99%

Finding Time Series Motifs in Disk-Resident Data

Mueen

Keogh

Bigdely-Shamlo

2009

2009 Ninth IEEE International Conference on Data Mining

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Abstract-Time series motifs are sets of very similar subsequences of a long time series. They are of interest in their own right, and are also used as inputs in several higher-level data mining algorithms including classification, clustering, rule-discovery and summarization. In spite of extensive research in recent years, finding exact time series motifs in massive databases is an open problem. Previous efforts either found approximate motifs or considered relatively small datasets residing in main memory. In this work, we describe for the first time a disk-aware algorithm to find exact time series motifs in multi-gigabyte databases which contain on the order of tens of millions of time series. We have evaluated our algorithm on datasets from diverse areas including medicine, anthropology, computer networking and image processing and show that we can find interesting and meaningful motifs in datasets that are many orders of magnitude larger than anything considered before.

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An Optimized External Sort Algorithm

Zhou

Lin

1994

Computer Science 2

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An efficient external sorting with minimal space requirement

Cited by 6 publications

References 10 publications

A disk-aware algorithm for time series motif discovery

A disk-aware algorithm for time series motif discovery

Finding Time Series Motifs in Disk-Resident Data

An Optimized External Sort Algorithm

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