Digital Visual Exploration Library Nicholas Tan Jerome and Andreas Kopmann Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany Keywords: Digital Library, Visual Exploration, Scientific Visualisation, Information Visualisation. Abstract: With the advancement of instrument precision, research facilities are generating data at an unprecedented rate. These experimental results are stored in a digital library platform which the contents are later acces- sible from within the facility or the public. However, the sheer volume of collected data is overwhelming the capacity of researchers and impedes the process of browsing for the desired data. In this paper, we pre- sent a concept of Digital Visual Exploration Library (DVEL) based on the confluence of two major research domains—digital library and visualisation—that enables efficient browsing of the growing data within a di- gital library. We complement the current state-of-the-art textual metadata description by integrating visual exploration to address big complex data, i.e., data of large size, multimodal data and multivariate data. We describe our concept based on use cases from three unique domains: climate research with Doppler wind lidar, X-ray-imaging for entomology research, and medical imaging with ultrasound computer tomography. 1 INTRODUCTION We are entering an era of data-intensive science where significant data are produced by scientific experi- ments worldwide. These data hold the key to verify scientific hypothesis and having more data can help drawing more reliable research conclusions. Hence, numerous works focus on advancing the instrument precision which allows researchers to produce data at an unprecedented rate. For instance, the Large Ha- dron Collider (LHC) particle accelerator can generate 60 terabytes of data per day (Brumfiel, 2011). With LHC serving as an extreme case in the data-intensive science, such large data are no stranger in most expe- riments. Often, the data are stored in a digital library which the contents are accessible from within the re- search facility or from the public. By storing all the information into the digital library, the sheer volume of data complicates the data exploration process. The main problem lies with users having little knowledge of the large amounts of data. To date, many scientific experiments are starting to provide open data access to the public (Molloy, 2011) which at the same time enables citizen science movement—citizens (non-scientists) taking part in real-world experiments. There is a growing demand to access scientific data from the public and impro- ving the digital library framework is essential. To fa- cilitate the exploration and analysis of a digital library collection (Mathew et al., 2017), researchers emp- hasise metadata analysis, i.e., word-frequency analy- sis (Shubankar et al., 2011), co-occurrence word ana- lysis (Isenberg et al., 2017), and probabilistic methods like Latent Dirichlet Allocation (Griffiths and Stey- vers, 2004). While these methods offer a generic so- lution, but they fail to provide information related to a particular domain (Mesbah et al., 2017). Moreover, if the metadata is not complete or not informative, users have no other options but to download a copy for off- line verification which is cumbersome. We propose instead to integrate visualisation techniques into the framework of a digital library— Digitial Visual Exploration Library (DVEL). In parti- cular, we want to utilise visualisation to improve the data browsing experience. With little knowledge of the data, the visual presentation in the DVEL enables users to explore the data faster and with higher con- fidence. Since the user is the main actor throughout the exploration process, the interactive interface of the DVEL allows users to formulate hypothesis while ad- justing their exploration goals (Keim, 2001). Keim outlined the benefits of visualisation techniques for browsing large data sets but has hitherto received little attention in the digital library domain. In this paper, we discuss design considerations to provide appropriate visualisation interface as part of the DVEL framework. We present three use cases that illustrate the large data size, multimodal data and mul- tivariate data. As digital library often provide its data through a web portal, we are inherently limited by the Jerome, N. and Kopmann, A. Digital Visual Exploration Library. DOI: 10.5220/0006650003410348 In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 3: IVAPP, pages 341-348 ISBN: 978-989-758-289-9 Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved 341