T3: On Mapping Text To Time Series

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 T3 (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, T3 achieved comparable accuracy with considerable speed-up.

• Type: Conference
• Year: 2009
• Where: AMW
• Authors: Tao Yang, Dongwon Lee
• Keywords: AMW 2009, AMW 2010, Text To Time, Time Series, Time Series Data