29 Search Results for "Combi, Carlo"

Volume

LIPIcs, Volume 206

28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

TIME 2021, September 27-29, 2021, Klagenfurt, Austria

Editors: Carlo Combi, Johann Eder, and Mark Reynolds

Document
DOI: 10.4230/LIPIcs.TIME.2025.9
Temporal GraphQL: A Tree Grammar Approach

Authors: Curtis E. Dyreson and Bishal Sarkar

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

Abstract
This paper presents a novel system, called Temporal GraphQL, for supporting temporal data in web services. A temporal web service is a service that provides a temporal view of data, that is, a view of the current data as well as past or future states of the data. Capturing the history of the data is important in data forensics, data auditing, and subscriptions, where an application continuously reads data. GraphQL is a technology for improving the development and management of web services. Originally developed by Facebook and widely used in industry, GraphQL is a query language for web services. This paper introduces Temporal GraphQL. We show how to use tree grammars to model GraphQL schemas, data, and queries, and propose temporal tree grammars to model Temporal GraphQL. We extend GraphQL with temporal snapshot, slice, and delta operators. To the best of our knowledge, this is the first work on Temporal GraphQL and temporal tree grammars.
Cite as

Curtis E. Dyreson and Bishal Sarkar. Temporal GraphQL: A Tree Grammar Approach. In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 9:1-9:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{dyreson_et_al:LIPIcs.TIME.2025.9,
  author =	{Dyreson, Curtis E. and Sarkar, Bishal},
  title =	{{Temporal GraphQL: A Tree Grammar Approach}},
  booktitle =	{32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)},
  pages =	{9:1--9:14},
  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.2025.9},
  URN =		{urn:nbn:de:0030-drops-244556},
  doi =		{10.4230/LIPIcs.TIME.2025.9},
  annote =	{Keywords: Temporal databases, temporal queries, GraphQL, web services}
}
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
Short Paper
DOI: 10.4230/LIPIcs.TIME.2025.19
Temporal Association Rules from Motifs (Short Paper)

Authors: Mauro Milella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan

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

Abstract
A motif is defined as a frequently occurring pattern within a (multivariate) time series. In recent years, various techniques have been developed to mine time series data. However, only a few studies have explored the idea of using motif discovery in temporal association rule mining. Interval-based temporal association rules have been recently defined and studied, along with the temporal version of the known frequent patterns, and therefore, association rule extraction algorithms (such as APRIORI and FP-Growth). In this work, we define a vocabulary of propositional letters wrapping motifs, and show how to extract temporal association rules starting from such a vocabulary. We apply our methodology to time series datasets in the fields of hand signs execution and gait recognition, and we discuss how they capture curious insights within data, keeping a high level of interpretability.
Cite as

Mauro Milella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan. Temporal Association Rules from Motifs (Short Paper). In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 19:1-19:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{milella_et_al:LIPIcs.TIME.2025.19,
  author =	{Milella, Mauro and Pagliarini, Giovanni and Sciavicco, Guido and Stan, Ionel Eduard},
  title =	{{Temporal Association Rules from Motifs}},
  booktitle =	{32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)},
  pages =	{19:1--19:7},
  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.2025.19},
  URN =		{urn:nbn:de:0030-drops-244653},
  doi =		{10.4230/LIPIcs.TIME.2025.19},
  annote =	{Keywords: Motifs, Interval Temporal Logic, Association Rules}
}
Document
DOI: 10.4230/LIPIcs.TIME.2024.4
Agile Controllability of Simple Temporal Networks with Uncertainty and Oracles

Authors: Johann Eder, Roberto Posenato, Carlo Combi, Marco Franceschetti, and Franziska S. Hollauf

Published in: LIPIcs, Volume 318, 31st International Symposium on Temporal Representation and Reasoning (TIME 2024)

Abstract
Simple temporal networks with uncertainty (STNUs) have achieved wide attention and are the basis of many applications requiring the representation of temporal constraints and checking whether they are conflicting. Dynamic controllability is currently the most relaxed notion to check whether a system can be controlled without violating temporal constraints despite uncertainties. However, dynamic controllability assumes that the actual duration of a contingent activity is only known when the end event of this activity takes place. The recently introduced notion of agile controllability considers when this duration is known earlier, leading to a more relaxed notion of temporal feasibility. We extend the definition of STNUs to STNUOs (Simple Temporal Networks with Uncertainty and Oracles) to represent the point in time at which information about a contingent duration is available. We formally define agile controllability as a generalization of dynamic controllability considering the timepoints of information availability. We propose a set of constraint propagation rules for STNUOs leading to an algorithm for checking agile controllability.
Cite as

Johann Eder, Roberto Posenato, Carlo Combi, Marco Franceschetti, and Franziska S. Hollauf. Agile Controllability of Simple Temporal Networks with Uncertainty and Oracles. In 31st International Symposium on Temporal Representation and Reasoning (TIME 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 318, pp. 4:1-4:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{eder_et_al:LIPIcs.TIME.2024.4,
  author =	{Eder, Johann and Posenato, Roberto and Combi, Carlo and Franceschetti, Marco and Hollauf, Franziska S.},
  title =	{{Agile Controllability of Simple Temporal Networks with Uncertainty and Oracles}},
  booktitle =	{31st International Symposium on Temporal Representation and Reasoning (TIME 2024)},
  pages =	{4:1--4:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-349-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{318},
  editor =	{Sala, Pietro and Sioutis, Michael and Wang, Fusheng},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2024.4},
  URN =		{urn:nbn:de:0030-drops-212115},
  doi =		{10.4230/LIPIcs.TIME.2024.4},
  annote =	{Keywords: Temporal constraint networks, contingent durations, agile controllability}
}
Document
DOI: 10.4230/LIPIcs.TIME.2023.4
Discovering Predictive Dependencies on Multi-Temporal Relations

Authors: Beatrice Amico, Carlo Combi, Romeo Rizzi, and Pietro Sala

Published in: LIPIcs, Volume 278, 30th International Symposium on Temporal Representation and Reasoning (TIME 2023)

Abstract
In this paper, we propose a methodology for deriving a new kind of approximate temporal functional dependencies, called Approximate Predictive Functional Dependencies (APFDs), based on a three-window framework and on a multi-temporal relational model. Different features are proposed for the Observation Window (OW), where we observe predictive data, for the Waiting Window (WW), and for the Prediction Window (PW), where the predicted event occurs. We then discuss the concept of approximation for such APFDs, introduce two new error measures. We prove that the problem of deriving APFDs is intractable. Moreover, we discuss some preliminary results in deriving APFDs from real clinical data using MIMIC III dataset, related to patients from Intensive Care Units.
Cite as

Beatrice Amico, Carlo Combi, Romeo Rizzi, and Pietro Sala. Discovering Predictive Dependencies on Multi-Temporal Relations. In 30th International Symposium on Temporal Representation and Reasoning (TIME 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 278, pp. 4:1-4:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{amico_et_al:LIPIcs.TIME.2023.4,
  author =	{Amico, Beatrice and Combi, Carlo and Rizzi, Romeo and Sala, Pietro},
  title =	{{Discovering Predictive Dependencies on Multi-Temporal Relations}},
  booktitle =	{30th International Symposium on Temporal Representation and Reasoning (TIME 2023)},
  pages =	{4:1--4:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-298-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{278},
  editor =	{Artikis, Alexander and Bruse, Florian and Hunsberger, Luke},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2023.4},
  URN =		{urn:nbn:de:0030-drops-190945},
  doi =		{10.4230/LIPIcs.TIME.2023.4},
  annote =	{Keywords: temporal databases, temporal data mining, functional dependencies}
}
Document
Complete Volume
DOI: 10.4230/LIPIcs.TIME.2021
LIPIcs, Volume 206, TIME 2021, Complete Volume

Authors: Carlo Combi, Johann Eder, and Mark Reynolds

Published in: LIPIcs, Volume 206, 28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

Abstract
LIPIcs, Volume 206, TIME 2021, Complete Volume
Cite as

28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, pp. 1-244, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@Proceedings{combi_et_al:LIPIcs.TIME.2021,
  title =	{{LIPIcs, Volume 206, TIME 2021, Complete Volume}},
  booktitle =	{28th International Symposium on Temporal Representation and Reasoning (TIME 2021)},
  pages =	{1--244},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-206-8},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{206},
  editor =	{Combi, Carlo and Eder, Johann and Reynolds, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2021},
  URN =		{urn:nbn:de:0030-drops-147755},
  doi =		{10.4230/LIPIcs.TIME.2021},
  annote =	{Keywords: LIPIcs, Volume 206, TIME 2021, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/LIPIcs.TIME.2021.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Carlo Combi, Johann Eder, and Mark Reynolds

Published in: LIPIcs, Volume 206, 28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, pp. 0:i-0:xiv, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{combi_et_al:LIPIcs.TIME.2021.0,
  author =	{Combi, Carlo and Eder, Johann and Reynolds, Mark},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{28th International Symposium on Temporal Representation and Reasoning (TIME 2021)},
  pages =	{0:i--0:xiv},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-206-8},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{206},
  editor =	{Combi, Carlo and Eder, Johann and Reynolds, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2021.0},
  URN =		{urn:nbn:de:0030-drops-147761},
  doi =		{10.4230/LIPIcs.TIME.2021.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Invited Talk
DOI: 10.4230/LIPIcs.TIME.2021.1
Simple Temporal Networks: A Practical Foundation for Temporal Representation and Reasoning (Invited Talk)

Authors: Luke Hunsberger and Roberto Posenato

Published in: LIPIcs, Volume 206, 28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

Abstract
Since Simple Temporal Networks (STNs) were first introduced in 1991, there have been numerous theoretic and algorithmic advances that have made them practical for a wide variety of applications. However, the presentation of most of the important advances have been scattered across numerous conference papers and journal articles. As a result, it is too easy for even experienced researchers to be unaware of results that could positively impact their work. In this talk we review the most important results about STNs for researchers in Artificial Intelligence who are interested in incorporating the management of time and temporal constraints into their projects.
Cite as

Luke Hunsberger and Roberto Posenato. Simple Temporal Networks: A Practical Foundation for Temporal Representation and Reasoning (Invited Talk). In 28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, pp. 1:1-1:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{hunsberger_et_al:LIPIcs.TIME.2021.1,
  author =	{Hunsberger, Luke and Posenato, Roberto},
  title =	{{Simple Temporal Networks: A Practical Foundation for Temporal Representation and Reasoning}},
  booktitle =	{28th International Symposium on Temporal Representation and Reasoning (TIME 2021)},
  pages =	{1:1--1:5},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-206-8},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{206},
  editor =	{Combi, Carlo and Eder, Johann and Reynolds, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2021.1},
  URN =		{urn:nbn:de:0030-drops-147770},
  doi =		{10.4230/LIPIcs.TIME.2021.1},
  annote =	{Keywords: Simple Temporal Networks, Consistency Checking, Restoring Consistency, Dispatchability, Temporal Decoupling Problem}
}
Document
Invited Talk
DOI: 10.4230/LIPIcs.TIME.2021.2
Extreme-Scale Model-Based Time Series Management with ModelarDB (Invited Talk)

Authors: Torben Bach Pedersen

Published in: LIPIcs, Volume 206, 28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

Abstract
To monitor critical industrial devices such as wind turbines, high quality sensors sampled at a high frequency are increasingly used. Current technology does not handle these extreme-scale time series well [Søren Kejser Jensen et al., 2017], so only simple aggregates are traditionally stored, removing outliers and fluctuations that could indicate problems. As a remedy, we present a model-based approach for managing extreme-scale time series that approximates the time series values using mathematical functions (models) and stores only model coefficients rather than data values. Compression is done both for individual time series and for correlated groups of time series. The keynote will present concepts, techniques, and algorithms from model-based time series management and our implementation of these in the open source Time Series Management System (TSMS) ModelarDB[Søren Kejser Jensen et al., 2018; Søren Kejser Jensen et al., 2019; Søren Kejser Jensen et al., 2021] . Furthermore, it will present our experimental evaluation of ModelarDB on extreme-scale real-world time series, which shows that that compared to widely used Big Data formats, ModelarDB provides up to 14× faster ingestion due to high compression, 113× better compression due to its adaptability, 573× faster aggregatation by using models, and close to linear scale-out scalability. ModelarDB is being commercialized by the spin-out company ModelarData.
Cite as

Torben Bach Pedersen. Extreme-Scale Model-Based Time Series Management with ModelarDB (Invited Talk). In 28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, pp. 2:1-2:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{pedersen:LIPIcs.TIME.2021.2,
  author =	{Pedersen, Torben Bach},
  title =	{{Extreme-Scale Model-Based Time Series Management with ModelarDB}},
  booktitle =	{28th International Symposium on Temporal Representation and Reasoning (TIME 2021)},
  pages =	{2:1--2:2},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-206-8},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{206},
  editor =	{Combi, Carlo and Eder, Johann and Reynolds, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2021.2},
  URN =		{urn:nbn:de:0030-drops-147785},
  doi =		{10.4230/LIPIcs.TIME.2021.2},
  annote =	{Keywords: Model-based storage, approximate query processing, time series management, extreme-scale data}
}
Document
Invited Talk
DOI: 10.4230/LIPIcs.TIME.2021.3
Kernel Machines in Time (Invited Talk)

Authors: Johan Suykens

Published in: LIPIcs, Volume 206, 28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

Abstract
Kernel machines is a powerful class of models in machine learning with solid foundations and many existing application fields. The scope of this talk is kernel machines in time with a main focus on least squares support vector machines, and other related methods such as kernel principal component analysis and kernel spectral clustering. For dynamical systems modelling different possible input-output and state space model structures will be discussed. Applications will be shown on electricity load forecasting and temperature prediction in weather forecasting. Approximate closed-form solutions can be given to ordinary and partial differential equations. Kernel spectral clustering applications to identifying customer profiles, pollution modelling and detecting topological changes in time-series of bridges will be shown. Finally, new synergies between kernel machines and deep learning will be presented, leading for example to generative kernel machines, with new insights on disentangled representations, explainability and latent space exploration. Application of these models will be illustrated on out-of-distribution detection of time-series data.
Cite as

Johan Suykens. Kernel Machines in Time (Invited Talk). In 28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, p. 3:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{suykens:LIPIcs.TIME.2021.3,
  author =	{Suykens, Johan},
  title =	{{Kernel Machines in Time}},
  booktitle =	{28th International Symposium on Temporal Representation and Reasoning (TIME 2021)},
  pages =	{3:1--3:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-206-8},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{206},
  editor =	{Combi, Carlo and Eder, Johann and Reynolds, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2021.3},
  URN =		{urn:nbn:de:0030-drops-147791},
  doi =		{10.4230/LIPIcs.TIME.2021.3},
  annote =	{Keywords: SVM, Time series analysis}
}
Document
Panel Description
DOI: 10.4230/LIPIcs.TIME.2021.4
Temporal Big Data Analytics: New Frontiers for Big Data Analytics Research (Panel Description)

Authors: Alfredo Cuzzocrea

Published in: LIPIcs, Volume 206, 28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

Abstract
Big data analytics is an emerging research area with many sophisticated contributions in the actual literature. Big data analytics aims at discovering actionable knowledge from large amounts of big data repositories, based on several approaches that integrate foundations of a wide spectrum of disciplines, ranging from data mining to machine learning and artificial intelligence. Among the concrete innovative topics of big data analytics, temporal big data analytics covers a first-class role and it is attracting the attention of larger and larger communities of academic and industrial researchers. Basically, temporal big data analytics aims at modeling, capturing and analyzing temporal aspects of big data during analytics phase, including specialized tasks such as big data versioning over time, building temporal relations among ad-hoc big data structures (such as nodes of big graphs) and temporal queries over big data. It is worth to notice that temporal big data analytics research is characterized by several open challenges, which range from foundations, including temporal big data representation and processing, to applications, including smart cities and bio-informatics tools. Inspired by these considerations, this paper focuses on models, paradigms, techniques and future challenges of temporal big data analytics, by reporting on state-of-the-art results as well as emerging trends, with also criticisms on future work that we should expect from the community.
Cite as

Alfredo Cuzzocrea. Temporal Big Data Analytics: New Frontiers for Big Data Analytics Research (Panel Description). In 28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, pp. 4:1-4:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{cuzzocrea:LIPIcs.TIME.2021.4,
  author =	{Cuzzocrea, Alfredo},
  title =	{{Temporal Big Data Analytics: New Frontiers for Big Data Analytics Research}},
  booktitle =	{28th International Symposium on Temporal Representation and Reasoning (TIME 2021)},
  pages =	{4:1--4:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-206-8},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{206},
  editor =	{Combi, Carlo and Eder, Johann and Reynolds, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2021.4},
  URN =		{urn:nbn:de:0030-drops-147804},
  doi =		{10.4230/LIPIcs.TIME.2021.4},
  annote =	{Keywords: Big Data Analytics, Big Data Management, Temporal Big Data Analytics, Temporal Big Data Management}
}
Document
DOI: 10.4230/LIPIcs.TIME.2021.5
Model Checking of Stream Processing Pipelines

Authors: Alexis Bédard and Sylvain Hallé

Published in: LIPIcs, Volume 206, 28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

Abstract
Event stream processing (ESP) is the application of a computation to a set of input sequences of arbitrary data objects, called "events", in order to produce other sequences of data objects. In recent years, a large number of ESP systems have been developed; however, none of them is easily amenable to a formal verification of properties on their execution. In this paper, we show how stream processing pipelines built with an existing ESP library called BeepBeep 3 can be exported as a Kripke structure for the NuXmv model checker. This makes it possible to formally verify properties on these pipelines, and opens the way to the use of such pipelines directly within a model checker as an extension of its specification language.
Cite as

Alexis Bédard and Sylvain Hallé. Model Checking of Stream Processing Pipelines. In 28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, pp. 5:1-5:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

Copy BibTex To Clipboard

@InProceedings{bedard_et_al:LIPIcs.TIME.2021.5,
  author =	{B\'{e}dard, Alexis and Hall\'{e}, Sylvain},
  title =	{{Model Checking of Stream Processing Pipelines}},
  booktitle =	{28th International Symposium on Temporal Representation and Reasoning (TIME 2021)},
  pages =	{5:1--5:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-206-8},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{206},
  editor =	{Combi, Carlo and Eder, Johann and Reynolds, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2021.5},
  URN =		{urn:nbn:de:0030-drops-147819},
  doi =		{10.4230/LIPIcs.TIME.2021.5},
  annote =	{Keywords: stream processing, model checking}
}
Document
DOI: 10.4230/LIPIcs.TIME.2021.6
Investigation of Database Models for Evolving Graphs

Authors: Alexandros Spitalas, Anastasios Gounaris, Kostas Tsichlas, and Andreas Kosmatopoulos

Published in: LIPIcs, Volume 206, 28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

Abstract
We deal with the efficient implementation of storage models for time-varying graphs. To this end, we present an improved approach for the HiNode vertex-centric model based on MongoDB. This approach, apart from its inherent space optimality, exhibits significant improvements in global query execution times, which is the most challenging query type for entity-centric approaches. Not only significant speedups are achieved but more expensive queries can be executed as well, when compared to an implementation based on Cassandra due to the capability to exploit indices to a larger extent and benefit from in-database query processing.
Cite as

Alexandros Spitalas, Anastasios Gounaris, Kostas Tsichlas, and Andreas Kosmatopoulos. Investigation of Database Models for Evolving Graphs. In 28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, pp. 6:1-6:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{spitalas_et_al:LIPIcs.TIME.2021.6,
  author =	{Spitalas, Alexandros and Gounaris, Anastasios and Tsichlas, Kostas and Kosmatopoulos, Andreas},
  title =	{{Investigation of Database Models for Evolving Graphs}},
  booktitle =	{28th International Symposium on Temporal Representation and Reasoning (TIME 2021)},
  pages =	{6:1--6:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-206-8},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{206},
  editor =	{Combi, Carlo and Eder, Johann and Reynolds, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2021.6},
  URN =		{urn:nbn:de:0030-drops-147821},
  doi =		{10.4230/LIPIcs.TIME.2021.6},
  annote =	{Keywords: Temporal Graphs, Indexing}
}
Document
DOI: 10.4230/LIPIcs.TIME.2021.7
Interval Temporal Random Forests with an Application to COVID-19 Diagnosis

Authors: Federico Manzella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan

Published in: LIPIcs, Volume 206, 28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

Abstract
Symbolic learning is the logic-based approach to machine learning. The mission of symbolic learning is to provide algorithms and methodologies to extract logical information from data and express it in an interpretable way. In the context of temporal data, interval temporal logic has been recently proposed as a suitable tool for symbolic learning, specifically via the design of an interval temporal logic decision tree extraction algorithm. Building on it, we study here its natural generalization to interval temporal random forests, mimicking the corresponding schema at the propositional level. Interval temporal random forests turn out to be a very performing multivariate time series classification method, which, despite the introduction of a functional component, are still logically interpretable to some extent. We apply this method to the problem of diagnosing COVID-19 based on the time series that emerge from cough and breath recording of positive versus negative subjects. Our experiment show that our models achieve very high accuracies and sensitivities, often superior to those achieved by classical methods on the same data. Although other recent approaches to the same problem (based on different and more numerous data) show even better statistical results, our solution is the first logic-based, interpretable, and explainable one.
Cite as

Federico Manzella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan. Interval Temporal Random Forests with an Application to COVID-19 Diagnosis. In 28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, pp. 7:1-7:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{manzella_et_al:LIPIcs.TIME.2021.7,
  author =	{Manzella, Federico and Pagliarini, Giovanni and Sciavicco, Guido and Stan, Ionel Eduard},
  title =	{{Interval Temporal Random Forests with an Application to COVID-19 Diagnosis}},
  booktitle =	{28th International Symposium on Temporal Representation and Reasoning (TIME 2021)},
  pages =	{7:1--7:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-206-8},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{206},
  editor =	{Combi, Carlo and Eder, Johann and Reynolds, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2021.7},
  URN =		{urn:nbn:de:0030-drops-147837},
  doi =		{10.4230/LIPIcs.TIME.2021.7},
  annote =	{Keywords: Interval temporal logic, decision trees, random forests, sound-based diagnosis}
}
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