2 Search Results for "Migliorini, Sara"

Document
DOI: 10.4230/LIPIcs.TIME.2025.13
Temporal Ensemble Logic for Integrative Representation of the Entirety of Clinical Trials

Authors: Xiaojin Li, Yan Huang, Rashmie Abeysinghe, Zenan Sun, Hongyu Chen, Pengze Li, Xing He, Shiqiang Tao, Cui Tao, Jiang Bian, Licong Cui, and Guo-Qiang Zhang

Published in: LIPIcs, Volume 355, 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)

Abstract
Clinical trials are typically specified with protocols that define eligibility criteria, treatment regimens, follow-up schedules, and outcome assessments. Temporality is a hallmark of all clinical trials, reflected within and across trial components, with complex dependencies unfolding across multiple time points. Despite their importance, clinical trial protocols are described in free-text format, limiting their semantic precision and the ability to support automated reasoning, leverage data across studies and sites, or simulate trial execution under varying assumptions using Real-World Data. This paper introduces a formalized representation of clinical trials using Temporal Ensemble Logic (TEL). TEL incorporates metricized modal operators, such as "always until t" (□_t) and "possibly until t" (◇_t), where t is a time-length parameter, to offer a logical framework for capturing phenotypes in biomedicine. TEL is more expressive in syntax than classical linear temporal logic (LTL) while maintaining the simplicity of semantic structures. The attributes of TEL are exploited in this paper to formally represent not only individual clinical trial components, but also the timing and sequential dependencies of these components as a whole. Modeling strategies and demonstration case studies are provided to show that TEL can represent the entirety of clinical trials, whereby providing a formal logical framework that can be used to represent the intricate temporal dependencies in trial structure specification. Since clinical trials are a cornerstone of evidence-based medicine, serving as the scientific basis for evaluating the safety, efficacy, and comparative effectiveness of therapeutic interventions, results reported here can serve as a stepping stone that leads to scalable, consistent, and reproducible representation and simulation of clinical trials across all disease domains.
Cite as

Xiaojin Li, Yan Huang, Rashmie Abeysinghe, Zenan Sun, Hongyu Chen, Pengze Li, Xing He, Shiqiang Tao, Cui Tao, Jiang Bian, Licong Cui, and Guo-Qiang Zhang. Temporal Ensemble Logic for Integrative Representation of the Entirety of Clinical Trials. In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 13:1-13:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{li_et_al:LIPIcs.TIME.2025.13,
  author =	{Li, Xiaojin and Huang, Yan and Abeysinghe, Rashmie and Sun, Zenan and Chen, Hongyu and Li, Pengze and He, Xing and Tao, Shiqiang and Tao, Cui and Bian, Jiang and Cui, Licong and Zhang, Guo-Qiang},
  title =	{{Temporal Ensemble Logic for Integrative Representation of the Entirety of Clinical Trials}},
  booktitle =	{32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)},
  pages =	{13:1--13:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-401-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{355},
  editor =	{Vidal, Thierry and Wa{\l}\k{e}ga, Przemys{\l}aw Andrzej},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2025.13},
  URN =		{urn:nbn:de:0030-drops-244595},
  doi =		{10.4230/LIPIcs.TIME.2025.13},
  annote =	{Keywords: Temporal ensemble logic, Clinical trials, Logic-based modeling}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.2
What Makes Spatial Data Big? A Discussion on How to Partition Spatial Data

Authors: Alberto Belussi, Damiano Carra, Sara Migliorini, Mauro Negri, and Giuseppe Pelagatti

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
The amount of available spatial data has significantly increased in the last years so that traditional analysis tools have become inappropriate to effectively manage them. Therefore, many attempts have been made in order to define extensions of existing MapReduce tools, such as Hadoop or Spark, with spatial capabilities in terms of data types and algorithms. Such extensions are mainly based on the partitioning techniques implemented for textual data where the dimension is given in terms of the number of occupied bytes. However, spatial data are characterized by other features which describe their dimension, such as the number of vertices or the MBR size of geometries, which greatly affect the performance of operations, like the spatial join, during data analysis. The result is that the use of traditional partitioning techniques prevents to completely exploit the benefit of the parallel execution provided by a MapReduce environment. This paper extensively analyses the problem considering the spatial join operation as use case, performing both a theoretical and an experimental analysis for it. Moreover, it provides a solution based on a different partitioning technique, which splits complex or extensive geometries. Finally, we validate the proposed solution by means of some experiments on synthetic and real datasets.
Cite as

Alberto Belussi, Damiano Carra, Sara Migliorini, Mauro Negri, and Giuseppe Pelagatti. What Makes Spatial Data Big? A Discussion on How to Partition Spatial Data. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 2:1-2:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{belussi_et_al:LIPIcs.GISCIENCE.2018.2,
  author =	{Belussi, Alberto and Carra, Damiano and Migliorini, Sara and Negri, Mauro and Pelagatti, Giuseppe},
  title =	{{What Makes Spatial Data Big? A Discussion on How to Partition Spatial Data}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{2:1--2:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.2},
  URN =		{urn:nbn:de:0030-drops-93306},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.2},
  annote =	{Keywords: Spatial join, SpatialHadoop, MapReduce, partitioning, big data}
}
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