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 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.