DROPS

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.ISAAC.2025.47

Hardness and Fixed Parameter Tractability for Pinwheel Scheduling Problems

Authors: Yusuke Kobayashi and Bingkai Lin

Published in: LIPIcs, Volume 359, 36th International Symposium on Algorithms and Computation (ISAAC 2025)

Abstract

In the Pinwheel Packing problem, we are given a set of recurring tasks, each associated with a positive integer a_i for task i. The objective is to select one task to perform each day such that every task i is performed at least once within every a_i consecutive days. The exact computational complexity of this problem, where ∑ 1/a_i = 1, has remained an open question for more than 30 years; in particular, it is still unknown whether the problem is NP-hard. The first contribution of this paper is to show that Pinwheel Packing cannot be solved in polynomial time under a standard complexity assumption, improving upon the hardness result shown by Jacobs and Longo. Additionally, we present fixed-parameter algorithms for variants of Pinwheel Packing, parameterized by the number of tasks.

Cite as

Yusuke Kobayashi and Bingkai Lin. Hardness and Fixed Parameter Tractability for Pinwheel Scheduling Problems. In 36th International Symposium on Algorithms and Computation (ISAAC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 359, pp. 47:1-47:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{kobayashi_et_al:LIPIcs.ISAAC.2025.47,
  author =	{Kobayashi, Yusuke and Lin, Bingkai},
  title =	{{Hardness and Fixed Parameter Tractability for Pinwheel Scheduling Problems}},
  booktitle =	{36th International Symposium on Algorithms and Computation (ISAAC 2025)},
  pages =	{47:1--47:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-408-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{359},
  editor =	{Chen, Ho-Lin and Hon, Wing-Kai and Tsai, Meng-Tsung},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2025.47},
  URN =		{urn:nbn:de:0030-drops-249558},
  doi =		{10.4230/LIPIcs.ISAAC.2025.47},
  annote =	{Keywords: Pinwheel Scheduling, Polynomial-time Solvability, Packing and Covering, Fixed Parameter Algorithms}
}

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

Survey

DOI: 10.4230/TGDK.1.1.11

How Does Knowledge Evolve in Open Knowledge Graphs?

Authors: Axel Polleres, Romana Pernisch, Angela Bonifati, Daniele Dell'Aglio, Daniil Dobriy, Stefania Dumbrava, Lorena Etcheverry, Nicolas Ferranti, Katja Hose, Ernesto Jiménez-Ruiz, Matteo Lissandrini, Ansgar Scherp, Riccardo Tommasini, and Johannes Wachs

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract

Openly available, collaboratively edited Knowledge Graphs (KGs) are key platforms for the collective management of evolving knowledge. The present work aims t o provide an analysis of the obstacles related to investigating and processing specifically this central aspect of evolution in KGs. To this end, we discuss (i) the dimensions of evolution in KGs, (ii) the observability of evolution in existing, open, collaboratively constructed Knowledge Graphs over time, and (iii) possible metrics to analyse this evolution. We provide an overview of relevant state-of-the-art research, ranging from metrics developed for Knowledge Graphs specifically to potential methods from related fields such as network science. Additionally, we discuss technical approaches - and their current limitations - related to storing, analysing and processing large and evolving KGs in terms of handling typical KG downstream tasks.

Cite as

Axel Polleres, Romana Pernisch, Angela Bonifati, Daniele Dell'Aglio, Daniil Dobriy, Stefania Dumbrava, Lorena Etcheverry, Nicolas Ferranti, Katja Hose, Ernesto Jiménez-Ruiz, Matteo Lissandrini, Ansgar Scherp, Riccardo Tommasini, and Johannes Wachs. How Does Knowledge Evolve in Open Knowledge Graphs?. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 11:1-11:59, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{polleres_et_al:TGDK.1.1.11,
  author =	{Polleres, Axel and Pernisch, Romana and Bonifati, Angela and Dell'Aglio, Daniele and Dobriy, Daniil and Dumbrava, Stefania and Etcheverry, Lorena and Ferranti, Nicolas and Hose, Katja and Jim\'{e}nez-Ruiz, Ernesto and Lissandrini, Matteo and Scherp, Ansgar and Tommasini, Riccardo and Wachs, Johannes},
  title =	{{How Does Knowledge Evolve in Open Knowledge Graphs?}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{11:1--11:59},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.11},
  URN =		{urn:nbn:de:0030-drops-194855},
  doi =		{10.4230/TGDK.1.1.11},
  annote =	{Keywords: KG evolution, temporal KG, versioned KG, dynamic KG}
}

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)

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@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}
}

Document

DOI: 10.4230/LIPIcs.TIME.2021.8

Past Matters: Supporting LTL+Past in the BLACK Satisfiability Checker

Authors: Luca Geatti, Nicola Gigante, Angelo Montanari, and Gabriele Venturato

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

Abstract

LTL+Past is the extension of Linear Temporal Logic (LTL) supporting past temporal operators. The addition of the past does not add expressive power, but does increase the usability of the language both in formal verification and in artificial intelligence, e.g., in the context of multi-agent systems. In this paper, we add the support of past operators to BLACK, a satisfiability checker for LTL based on a SAT encoding of a tree-shaped tableau system. We implement two ways of supporting the past in the tool. The first one is an equisatisfiable translation that removes the past operators, obtaining a future-only formula that can be solved with the original LTL engine. The second one extends the SAT encoding of the underlying tableau to directly support the tableau rules that deal with past operators. We describe both approaches and experimentally compare the two between themselves and with the νXmv model checker, obtaining promising results.

Cite as

Luca Geatti, Nicola Gigante, Angelo Montanari, and Gabriele Venturato. Past Matters: Supporting LTL+Past in the BLACK Satisfiability Checker. In 28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, pp. 8:1-8:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{geatti_et_al:LIPIcs.TIME.2021.8,
  author =	{Geatti, Luca and Gigante, Nicola and Montanari, Angelo and Venturato, Gabriele},
  title =	{{Past Matters: Supporting LTL+Past in the BLACK Satisfiability Checker}},
  booktitle =	{28th International Symposium on Temporal Representation and Reasoning (TIME 2021)},
  pages =	{8:1--8:17},
  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.2021.8},
  URN =		{urn:nbn:de:0030-drops-147846},
  doi =		{10.4230/LIPIcs.TIME.2021.8},
  annote =	{Keywords: SAT, LTL, LTL+Past, Tableaux}
}

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