RawVis: Visual Exploration over Raw Data

  • Nikos BikakisEmail author
  • Stavros Maroulis
  • George Papastefanatos
  • Panos Vassiliadis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11019)


Data exploration and visual analytics systems are of great importance in Open Science scenarios, where less tech-savvy researchers wish to access and visually explore big raw data files (e.g., json, csv) generated by scientific experiments using commodity hardware and without being overwhelmed in the tedious processes of data loading, indexing and query optimization. In this work, we present our work for enabling efficient query processing on raw data files for interactive visual exploration scenarios. We introduce a framework, named RawVis, built on top of a lightweight in-memory tile-based index, VALINOR, that is constructed on-the-fly given the first user query over a raw file and adapted based on the user interaction. We evaluate the performance of prototype implementation compared to three other alternatives and show that our method outperforms in terms of response time, disk accesses and memory consumption.


In situ query Big raw data Adaptive processing Visual analytics Visualization Indexing User interaction Exploratory data analysis 



This research is implemented through the Operational Program “Human Resources Development, Education and Lifelong Learning” and is co-financed by the European Union (European Social Fund) and Greek national funds.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of IoanninaIoanninaGreece

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