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A Dockerized String Analysis Workflow for Big Data

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1064)


Nowadays, a wide range of sciences are moving towards the Big Data era, producing large volumes of data that require processing for new knowledge extraction. Scientific workflows are often the key tools for solving problems characterized by computational complexity and data diversity, whereas cloud computing can effectively facilitate their efficient execution. In this paper, we present a generative big data analysis workflow that can provide analytics, clustering, prediction and visualization services to datasets coming from various scientific fields, by transforming input data into strings. The workflow consists of novel algorithms for data processing and relationship discovery, that are scalable and suitable for cloud infrastructures. Domain experts can interact with the workflow components, set their parameters, run personalized pipelines and have support for decision-making processes. As case studies in this paper, two datasets consisting of (i) Documents and (ii) Gene sequence data are used, showing promising results in terms of efficiency and performance.


  • Workflow
  • Docker
  • Big data analytics
  • String analysis

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  • DOI: 10.1007/978-3-030-30278-8_55
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Correspondence to Maria Th. Kotouza .

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Kotouza, M.T., Psomopoulos, F.E., Mitkas, P.A. (2019). A Dockerized String Analysis Workflow for Big Data. In: , et al. New Trends in Databases and Information Systems. ADBIS 2019. Communications in Computer and Information Science, vol 1064. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30277-1

  • Online ISBN: 978-3-030-30278-8

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