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Archivio Istituzionale della Ricerca
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishNowadays, 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.
| Titolo: | Towards automated visualisation of scientic literature |
| Autori: | |
| Data di pubblicazione: | 2019 |
| Abstract: | Nowadays, an exponential growth in biological data has been recorded, including both structured a...nd 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. |
| ISBN: | 978-3-030-30277-1 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| ADBIS2019_cameraReady.pdf | Articolo principale | 2. Post-print | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
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