Abstract
Nowadays, an exponential growth in biological data has been recorded, including both structured and unstructured data. One of the main computational and scientific challenges in the modern age is to extract useful information from unstructured textual corpora to effectively support the decision making process. Since the emergence of topic modelling, new and interesting approaches to compactly represent the content of a document collection have been proposed. However, the effective exploitation of the proposed strategies requires a lot of expertise.
This paper presents a new scalable and exploratory data visualisation engine, named ACE-HEALTH (AutomatiC Exploration of textual collections for HEALTH-care), whose target is to easily analyse medical document collections through the Latent Dirichlet Allocation. To streamline the analytics process and enhance the effectiveness of data and knowledge exploration, a variety of data visualisation techniques have been integrated in the engine to provide navigable informative dashboards without requiring any a-priori knowledge on the analytics techniques.
Preliminary results obtained on a real PubMed collection show the effectiveness of ACE-HEALTH in correctly capturing the high-level overview of textual medical collections through innovative visualisation techniques.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Cerquitelli, T., Baralis, E., Morra, L., Chiusano, S.: Data mining for better healthcare: a path towards automated data analysis? In: 2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW), pp. 60–63. IEEE (2016)
Cerquitelli, T., Di Corso, E., Ventura, F., Chiusano, S.: Data miners’ little helper: data transformation activity cues for cluster analysis on document collections. In: Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, p. 27. ACM (2017)
Di Corso, E., Cerquitelli, T., Ventura, F.: Self-tuning techniques for large scale cluster analysis on textual data collections. In: Proceedings of the Symposium on Applied Computing, pp. 771–776. ACM (2017)
Heimerl, F., Lohmann, S., Lange, S., Ertl, T.: Word cloud explorer: text analytics based on word clouds. In: 2014 47th Hawaii International Conference on System Sciences (HICSS), pp. 1833–1842. IEEE (2014)
Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Nakov, P., Popova, A., Mateev, P.: Weight functions impact on LSA performance. In: EuroConference RANLP 2001 Recent Advances in NLP, pp. 187–193 (2001)
Proto, S., Di Corso, E., Ventura, F., Cerquitelli, T.: Useful ToPIC: self-tuning strategies to enhance latent dirichlet allocation. In: 2018 IEEE International Congress on Big Data (BigData Congress), pp. 33–40. IEEE (2018)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Acknowledgements
The research leading to these results was partially funded by Project CANP, POR-FESR 2014-2020 - Technology Platform “Health and Wellness” - Piedmont Region, Italy.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Di Corso, E., Proto, S., Cerquitelli, T., Chiusano, S. (2019). Towards Automated Visualisation of Scientific Literature. In: Welzer, T., et al. New Trends in Databases and Information Systems. ADBIS 2019. Communications in Computer and Information Science, vol 1064. Springer, Cham. https://doi.org/10.1007/978-3-030-30278-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-30278-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30277-1
Online ISBN: 978-3-030-30278-8
eBook Packages: Computer ScienceComputer Science (R0)