title: JTSA: an open source framework for time series abstractions.
abstract: OBJECTIVE
  The evaluation of the clinical status of a patient is frequently based on the temporal evolution of some parameters, making the detection of temporal patterns a priority in data analysis. Temporal abstraction (TA) is a methodology widely used in medical reasoning for summarizing and abstracting longitudinal data.
  
  
  METHODS
  This paper describes JTSA (Java Time Series Abstractor), a framework including a library of algorithms for time series preprocessing and abstraction and an engine to execute a workflow for temporal data processing. The JTSA framework is grounded on a comprehensive ontology that models temporal data processing both from the data storage and the abstraction computation perspective. The JTSA framework is designed to allow users to build their own analysis workflows by combining different algorithms. Thanks to the modular structure of a workflow, simple to highly complex patterns can be detected. The JTSA framework has been developed in Java 1.7 and is distributed under GPL as a jar file.
  
  
  RESULTS
  JTSA provides: a collection of algorithms to perform temporal abstraction and preprocessing of time series, a framework for defining and executing data analysis workflows based on these algorithms, and a GUI for workflow prototyping and testing. The whole JTSA project relies on a formal model of the data types and of the algorithms included in the library. This model is the basis for the design and implementation of the software application. Taking into account this formalized structure, the user can easily extend the JTSA framework by adding new algorithms. Results are shown in the context of the EU project MOSAIC to extract relevant patterns from data coming related to the long term monitoring of diabetic patients.
  
  
  CONCLUSIONS
  The proof that JTSA is a versatile tool to be adapted to different needs is given by its possible uses, both as a standalone tool for data summarization and as a module to be embedded into other architectures to select specific phenotypes based on TAs in a large dataset.
authors.affiliation: Department of Electrical Computer and Biomedical Engineering of the University of Pavia, Via Ferrata 5, 27100 Pavia, Italy. Electronic address: lucia.sacchi@unipv.it.
authors.forename: Lucia
authors.initials: L
authors.lastname: Sacchi
authors.affiliation: BIOMERIS srl, Via Ferrata 5, 27100 Pavia, Italy. Electronic address: davide.capozzi@biomeris.com.
authors.forename: Davide
authors.initials: D
authors.lastname: Capozzi
authors.affiliation: Department of Electrical Computer and Biomedical Engineering of the University of Pavia, Via Ferrata 5, 27100 Pavia, Italy. Electronic address: riccardo.bellazzi@unipv.it.
authors.forename: Riccardo
authors.initials: R
authors.lastname: Bellazzi
authors.affiliation: Department of Electrical Computer and Biomedical Engineering of the University of Pavia, Via Ferrata 5, 27100 Pavia, Italy. Electronic address: cristiana.larizza@unipv.it.
authors.forename: Cristiana
authors.initials: C
authors.lastname: Larizza
mesh_terms: D000465:Algorithms; D057225:Data Mining; D006801:Humans; D012984:Software
keywords: Biomedical data mining software tool; Data analysis workflow; Temporal abstractions; Temporal pattern discovery; Time series analysis