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Large-Scale
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Abdelkader Hameurlain • A Min Tjoa
Editors-in-Chief
Lecture Notes in Computer Science 13840
FoundingEditors
GerhardGoos
JurisHartmanis
EditorialBoardMembers
ElisaBertino,PurdueUniversity,WestLafayette,IN,USA
WenGao,PekingUniversity,Beijing,China
BernhardSteffen ,TUDortmundUniversity,Dortmund,Germany
MotiYung ,ColumbiaUniversity,NewYork,NY,USA
TheseriesLectureNotesinComputerScience(LNCS),includingitssubseriesLecture
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Abdelkader Hameurlain · A Min Tjoa
Editors
Transactions on
Large-Scale
Data- and Knowledge-
Centered Systems LIII
Editors-in-Chief
AbdelkaderHameurlain AMinTjoa
IRIT,PaulSabatierUniversity IFS,TechnicalUniversityofVienna
Toulouse,France Vienna,Austria
ISSN 0302-9743 ISSN 1611-3349 (electronic)
LectureNotesinComputerScience
ISSN 1869-1994 ISSN 2510-4942 (electronic)
TransactionsonLarge-ScaleData-andKnowledge-CenteredSystems
ISBN 978-3-662-66862-7 ISBN 978-3-662-66863-4 (eBook)
https://doi.org/10.1007/978-3-662-66863-4
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Preface
This volume contains six fully revised regular papers, covering a wide range of very
hot topics in the fields of time series management from edge to cloud, segmentation
for time series representation, similarity research, semantic similarity in a taxonomy,
linkeddatasemanticdistance,linguistics-informednaturallanguageprocessing,graph
neuralnetworks,protectedfeatures,imbalanceddata,causalconsistencyindistributed
databases,actormodels,andelastichorizontalscalability.
We would like to sincerely thank the editorial board for thoroughly refereeing the
submittedpapersandensuringthehighqualityofthisvolume.Furthermore,wewould
like to wholeheartedly thank the Springer team for their ready availability, manage-
mentefficiency,andverypleasantcollaborationintherealizationoftheTLDKSjournal
volumes.
December2022 AbdelkaderHameurlain
AMinTjoa
Organization
Editors-in-Chief
AbdelkaderHameurlain PaulSabatierUniversity,IRIT,France
AMinTjoa TechnicalUniversityofVienna,IFS,Austria
EditorialBoard
RezaAkbarinia Inria,France
DagmarAuer JohannesKeplerUniversityLinz,Austria
DjamalBenslimane UniversityLyon1,France
StéphaneBressan NationalUniversityofSingapore,Singapore
MirelCosulschi UniversityofCraiova,Romania
JohannEder AlpenAdriaUniversityofKlagenfurt,Austria
AnnaFormica NationalResearchCouncilinRome,Italy
ShahramGhandeharizadeh UniversityofSouthernCalifornia,USA
AnastasiosGounaris AristotleUniversityofThessaloniki,Greece
SergioIlarri UniversityofZaragoza,Spain
PetarJovanovic UniversitatPolitècnicadeCatalunyaand
BarcelonaTech,Spain
AidaKamišalic´ Latific´ UniversityofMaribor,Slovenia
DieterKranzlmüller Ludwig-Maximilians-UniversitätMünchen,
Germany
PhilippeLamarre INSALyon,France
LenkaLhotská TechnicalUniversityofPrague,CzechRepublic
VladimirMarik TechnicalUniversityofPrague,CzechRepublic
JorgeMartinezGil SoftwareCompetenceCenterHagenberg,Austria
FranckMorvan PaulSabatierUniversity,IRIT,France
TorbenBachPedersen AalborgUniversity,Denmark
GüntherPernul UniversityofRegensburg,Germany
VieraRozinajova KempelenInstituteofIntelligentTechnologies,
Slovakia
SororSahri LIPADE,UniversitéParisCité,France
JosephVella UniversityofMalta,Malta
ShaoyiYin PaulSabatierUniversity,IRIT,France
Feng“George”Yu YoungstownStateUniversity,USA
Contents
ModelarDB: Integrated Model-Based Management of Time Series
fromEdgetoCloud .................................................... 1
SørenKejserJensen,ChristianThomsen,andTorbenBachPedersen
Variable-SizeSegmentationforTimeSeriesRepresentation ................. 34
LamiaDjebour,RezaAkbarinia,andFlorentMasseglia
Semantic Similarity in a Taxonomy by Evaluating the Relatedness
ofConceptSenseswiththeLinkedDataSemanticDistance ................. 66
AnnaFormicaandFrancescoTaglino
Constituency-InformedandConstituency-ConstrainedExtractiveQuestion
AnsweringwithHeterogeneousGraphTransformer ........................ 90
MingzheDu,MouadHakam,See-KiongNg,andStéphaneBressan
FAPFID: A Fairness-Aware Approach for Protected Features
andImbalancedData ................................................... 107
GinelDorleon,ImenMegdiche,NathalieBricon-Souf,andOlivierTeste
D-Thespis:ADistributedActor-BasedCausallyConsistentDBMS ........... 126
CarlCamilleri,JosephG.Vella,andVitezslavNezval
AuthorIndex ......................................................... 167
ModelarDB: Integrated Model-Based
Management of Time Series from Edge
to Cloud
B
Søren Kejser Jensen( ) , Christian Thomsen , and Torben Bach Pedersen
Department of Computer Science, Aalborg University, Aalborg, Denmark
{skj,chr,tbp}@cs.aau.dk
Abstract. To ensure critical infrastructure is operating as expected,
high-qualitysensorsareincreasinglyinstalled.However,duetotheenor-
mousamountsofhigh-frequencytimeseriestheyproduce,itisimpossible
orinfeasibletotransferorevenstorethesetimeseriesinthecloudwhen
using state-of-the-practice compression methods. Thus, simple aggre-
gates, e.g., 1–10-minutes averages, are stored instead of the raw time
series.However,byonlystoringthesesimpleaggregates,informativeout-
liers and fluctuations are lost. Many Time Series Management System
(TSMS) have been proposed to efficiently manage time series, but they
are generally designed for either the edge or the cloud. In this paper,
wedescribeanewversionoftheopen-sourcemodel-basedTSMSMode-
larDB.Thesystemisdesignedtobemodularandthesamebinarycanbe
efficientlydeployedontheedgeandinthecloud.Italsosupportscontin-
uously transferring high-frequency time series compressed using models
fromtheedgetothecloud.WefirstprovideanoverviewofModelarDB,
analyzetherequirementsandlimitationsoftheedge,andevaluateexist-
ingqueryenginesanddatastoresforuseontheedge.Then,wedescribe
how ModelarDB has been extended to efficiently manage time series on
theedge,anovelfile-baseddatastore,howModelarDB’scompressionhas
been improved by not storing time series that can be derived from base
time series, and how ModelarDB transfers high-frequency time series
from the edge to the cloud. As the work that led to ModelarDB began
in 2015, we also reflect on the lessons learned while developing it.
1 Introduction
High-quality sensors are increasingly used for monitoring and to facilitate
automation and error detection, e.g., for Renewable Energy Sources (RES)
installations like wind turbines and solar panels. However, the limited amount
of bandwidth available for transferring data between the edge and the cloud
generally makes transferring high-frequency time series impossible. In addition,
itisofteneitherinfeasibleorprohibitivelyexpensivetostorethehigh-frequency
time series in the cloud. Thus, simple aggregates, e.g., 1–10-minutes averages,
are generally stored in the cloud instead of the high-frequency time series. How-
ever, this removes informative outliers and fluctuations. To remedy this prob-
lem, many Time Series Management System (TSMS) have been proposed to
(cid:2)c TheAuthor(s)2023
A.HameurlainandA.M.Tjoa(Eds.):TLDKSLIII,LNCS13840,pp.1–33,2023.
https://doi.org/10.1007/978-3-662-66863-4_1
2 S. K. Jensen et al.
efficiently ingest, query, and store time series [4,10,11]. However, most TSMSs
are designed to be deployed on either the edge (e.g., in a wind turbine) or the
cloud. Thus, these TSMSs cannot optimize ingestion, query processing, storage,
anddatatransferacrosstheedgeandcloud.Usersmustalsooftenaddadditional
systems, e.g., Apache Kafka, to continuously transfer data points from the edge
to the cloud.
ModelarDB is a state-of-the-art modular model-based TSMS that was origi-
nallydesignedspecificallyfordeploymentonaclusterofcloudnodes[12–14].In
the context of ModelarDB, a model is any representation that can reconstruct
the values of a group of time series within a user-defined error bound (possibly
0%).Forexample,alinearfunctionisamodelthatcanapproximatelyrepresent
increasing, decreasing, or constant values from a group of time series efficiently.
In general, model-based compression of time series provides much better com-
pression than general-purpose compression methods like DEFLATE, LZ4, and
Snappy, especially if a small amount of error is allowed [9,12–14,26]. This paper
provides an overview of the current version of ModelarDB and describes how it
has been extended so it can be deployed on individual edge nodes, be deployed
onaclusterofcloudnodes,andcontinuouslytransferdatapointsefficientlycom-
pressed using models from the edge nodes to the cloud nodes. ModelarDB is an
open-sourceprojectandthesourcecodefortheversionofModelarDBdescribed
in this paper is available at https://github.com/skejserjensen/ModelarDB. To
differentiate between the different versions of ModelarDB referenced in this
paper,weusethe previous version of ModelarDB torefertotheversionofMod-
elarDB documented in our previous papers [12–14], while we use the current
version of ModelarDB to refer to the version of ModelarDB with the extensions
described in this paper. In this paper we make the following contributions:
1. Provide an overview of the open-source model-based TSMS ModelarDB.
2. Analyze the requirements and limitations of the edge for RES installations.
3. Analyze existing query engines and data stores and evaluate which of them
are most suitable for use by ModelarDB when deployed on the edge.
4. Extend ModelarDB to efficiently ingest, query, and store groups of similar
time series using multiple different types of models on the edge.
5. ExtendModelarDBwithanovel file-baseddata storefor theedgeandcloud.
6. Extend ModelarDB to reduce the number of time series that are physically
stored by efficiently recomputing the time series that can be derived from a
base time series using user-defined functions and dynamic code generation.
7. Extend ModelarDB with a data transfer component that continuously trans-
fers data points compressed using models from the edge to the cloud.
8. Reflect on the lesson learned while designing and developing ModelarDB.
The paper is structured as follows. Definitions are provided in Sect.2.
Section3 describes the architecture of ModelarDB. The extensions added to
ModelarDBsoitcanbeefficientlydeployedontheedgearedescribedinSect.4.
Then each part of ModelarDB is described in detail with ingestion described
in Sect.5, query processing described in Sect.6, storage described in Sect.7,
