T3

On mapping Text To Time series

Tao Yang, Dongwon Lee

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

We investigate if the mapping between text and time series data is feasible such that relevant data mining problems in text can find their counterparts in time series (and vice versa). As a preliminary work, we present the T 3 (Text To Time series) framework that utilizes different combinations of granularity (e.g., character or word level) and n-grams (e.g., unigram or bigram). To assign appropriate numeric values to each character, T3 adopts different space-filling curves (e.g., linear, Hilbert, Z orders) based on the keyboard layout. When we applied T3 approach to the "record linkage" problem, despite the lossy transformation, T 3 achieved comparable accuracy with considerable speed-up.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
Volume450
StatePublished - Dec 1 2009

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Time series
Data mining

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

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title = "T3: On mapping Text To Time series",
abstract = "We investigate if the mapping between text and time series data is feasible such that relevant data mining problems in text can find their counterparts in time series (and vice versa). As a preliminary work, we present the T 3 (Text To Time series) framework that utilizes different combinations of granularity (e.g., character or word level) and n-grams (e.g., unigram or bigram). To assign appropriate numeric values to each character, T3 adopts different space-filling curves (e.g., linear, Hilbert, Z orders) based on the keyboard layout. When we applied T3 approach to the {"}record linkage{"} problem, despite the lossy transformation, T 3 achieved comparable accuracy with considerable speed-up.",
author = "Tao Yang and Dongwon Lee",
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journal = "CEUR Workshop Proceedings",
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T3 : On mapping Text To Time series. / Yang, Tao; Lee, Dongwon.

In: CEUR Workshop Proceedings, Vol. 450, 01.12.2009.

Research output: Contribution to journalConference article

TY - JOUR

T1 - T3

T2 - On mapping Text To Time series

AU - Yang, Tao

AU - Lee, Dongwon

PY - 2009/12/1

Y1 - 2009/12/1

N2 - We investigate if the mapping between text and time series data is feasible such that relevant data mining problems in text can find their counterparts in time series (and vice versa). As a preliminary work, we present the T 3 (Text To Time series) framework that utilizes different combinations of granularity (e.g., character or word level) and n-grams (e.g., unigram or bigram). To assign appropriate numeric values to each character, T3 adopts different space-filling curves (e.g., linear, Hilbert, Z orders) based on the keyboard layout. When we applied T3 approach to the "record linkage" problem, despite the lossy transformation, T 3 achieved comparable accuracy with considerable speed-up.

AB - We investigate if the mapping between text and time series data is feasible such that relevant data mining problems in text can find their counterparts in time series (and vice versa). As a preliminary work, we present the T 3 (Text To Time series) framework that utilizes different combinations of granularity (e.g., character or word level) and n-grams (e.g., unigram or bigram). To assign appropriate numeric values to each character, T3 adopts different space-filling curves (e.g., linear, Hilbert, Z orders) based on the keyboard layout. When we applied T3 approach to the "record linkage" problem, despite the lossy transformation, T 3 achieved comparable accuracy with considerable speed-up.

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M3 - Conference article

VL - 450

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

ER -