# More power to SPARQL: From paths to trees

Angelos Christos Anadiotis*

Oracle, Switzerland
angelos.anadiotis@oracle.com

Ioana Manolescu

Inria and IPP, France
ioana.manolescu@inria.fr

Madhulika Mohanty

Inria and IPP, France
madhulika.mohanty@inria.fr

## ABSTRACT

Exploring Knowledge Graphs (KGs, in short) to discover facts and links is tedious even for experts with knowledge of SPARQL due to their unfamiliarity with the structure and labels of entities, classes and relations. Some KG applications require finding the connections between groups of nodes, even if users ignore the shape of these connections. However, SPARQL only allows checking if paths exist, not returning them. A recent property graph query language, GPML, allows also returning connecting paths, but not connections between three or more nodes.

We propose to demonstrate RELSEARCH, a system supporting *extended SPARQL* queries, featuring standard Basic Graph Patterns (BGPs) as well as novel Connecting Tree Patterns (CTPs); each CTP requests *the connections* (paths, or trees) between nodes bound to variables. RELSEARCH evaluates such extended queries using novel

algorithms [1] which, unlike prior keyword search methods, return connections regardless of the edge directions and are independent of how we measure the quality (score) of each connection. We will demonstrate RELSEARCH's expressivity and efficiency using a variety of RDF graphs, user-selected score functions, and search exploration orders.

## REFERENCES

[1] Angelos-Christos Anadiotis, Ioana Manolescu, and Madhulika Mohanty. Integrating connection search in graph queries. In *ICDE*, 2023.

<footnote>*Work done while at Ecole Polytechnique.</footnote>

<footnote>© 2023, Copyright is with the authors. Published in the Proceedings of the BDA 2023 Conference (October 23-26, 2023, Montpellier, France). Distribution of this article is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0.</footnote>

<footnote>© 2023, Droits restant aux auteurs. Published dans les actes de la conférence BDA 2023 (23-26 octobre 2023, Montpellier, France). Redistribution de cet article autorisée selon les termes de la licence Creative Commons CC-by-nc-nd 4.0.</footnote>
# GSTSM Package: Finding Frequent Sequences in Constrained Space and Time

Antonio Castro
Heraldo Borges

antonio.castro@eic.cefet-rj.br
heraldo.borges@eic.cefet-rj.br
CEFET/RJ

Rio de Janeiro, RJ, Brazil

Cassio Souza
Jorge Rodrigues

cassio.souza@eic.cefet-rj.br
jorge.rodrigues@eic.cefet-rj.br
CEFET/RJ

Rio de Janeiro, RJ, Brazil

Fabio Porto

fporto@lncc.br
LNCC
Petrópolis, RJ, Brazil

Esther Pacitti

esther.pacitti@lirmm.fr
INRIA & University of Montpellier
Montpellier, France

Rafaelli Coutinho

rafaelli.coutinho@cefet-rj.br
CEFET/RJ

Rio de Janeiro, RJ, Brazil

Eduardo Ogasawara

eogasawara@ieee.org
CEFET/RJ
Rio de Janeiro, RJ, Brazil

## ABSTRACT

Spatial time-stamped sequences have information about time and space where events occur. Mining such sequences can bring important insights. However, not all sequences are frequent over an entire dataset. Some are only common in subsets of time and space. This article explains the first tool for mining these sequences in constrained space and time: the GSTSM R package. It allows users to search for spatio-temporal patterns that are not frequent in the entire database, but are dense in restricted time-space intervals. Thus, making it possible to find non-trivial patterns that would not be found using common data mining tools.

## CCS CONCEPTS

* Information systems → Data mining.

## KEYWORDS

Data Mining, Spatial-Temporal, Time Series, Sequential Mining

## 1 INTRODUCTION

Data mining tools have been used to find interesting patterns in different areas of knowledge in various problems [1]. The sequence mining knowledge area is a specialization of data mining, focused on finding sequences or series of events in datasets [12, 15]. Such sequences may form patterns, a set of frequent attributes that appear persistently among the dataset. It means that its frequency exceeds a user-defined minimum threshold [3].

Several types of events involve both temporal and spatial data, such as financial to understand sales patterns over time and space [1], and hydrological data for river water quality monitoring in different points over time [1, 2]. They correspond to Time-stamped Sequence (TS) events distributed in space [11]. Mining sequences related to space and time enables to find knowledge related to phenomena that involve both spatial and temporal components,

trying to find all sequences of significant, useful, interesting, and non-trivial events [1, 3, 13].

However, spatio-temporal sequential patterns may have low support if considered the entire dataset, but they can be frequent if considered only a period and region [8]. The Generalized Spatial-Time Sequence Miner (GSTSM) package can find these patterns, being able to efficiently discover the region and period where they occur. This way, GSTSM would be the right tool to find time-localized patterns.

This work describes the process, structure, and usage of the GSTSM package using a synthetic (but still complete) example. However, we also provide a glimpse of applicability in a real-world dataset. GSTSM was able to find sequential patterns in seismic data. They correspond to seismic horizons, which are important elements in the application domain.

## 2 RELATED WORK

There are different methods for spatio-temporal data mining. Some use only data mining, searching for frequent patterns, considering only time [10]. Others combine techniques by seeking in time and then grouping in space [7]. Furthermore, there is a diversity in how constraints are handled. Some use global support, a value that is valid for the entire dataset [2]. Others consider local support, using predefined windows of time and space [9].

This work differs by seeking frequent sequences in time that occur in spatial groups. Instead of using predefined constraints for time and space, three density parameters are established: a minimum frequency to be achieved within the period, a maximum distance that a position can be from any other in the group, and a lower limit of distinct positions in the group. Thus, the formalization presented in this work can find different sizes of sequences, time intervals, and spatial regions where a sequence is frequent, based on the concepts of RG, KRG, and SRG introduced in [5].

As far as the conducted research has reached, the only work with a similar approach found in the literature is proposed by [4], which considers one-dimensional space. The present work is a generalization that presents a formalization considering space in its three-dimensional form.

<footnote>© 2023, Copyright is with the authors. Published in the Proceedings of the BDA 2023 Conference (October 23-26, 2023, Montpellier, France). Distribution of this article is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0.</footnote>

<footnote>© 2023, Droits restant aux auteurs. Published dans les actes de la conférence BDA 2023 (23-26 octobre 2023, Montpellier, France). Redistribution de cet article autorisée selon les termes de la licence Creative Commons CC-by-nc-nd 4.0.</footnote>
## 3 DEMONSTRATION OVERVIEW

GSTSM is a package which provides polymorphic functions that let the user extends its functionalities, as it is based on R [14] language S3 classes. The source code can be found at GitHub [6]. The package has a main class named GSTSM, that needs the parameters D, P, γ, β, and σ to instantiate an object, explained as follows:

* D and P represents the TS dataset with their respective positions. Each TS must be associated with one position. It means that the number of timestamped sequences (columns) in D must be equal to the number of positions (rows) in P.

* for the user defined thresholds values in the range [0, 1] for γ, values starting from 2 for β, and integer values starting from 1 for σ.

A GSTSM object is an instance of S3 Class built as a list with all this information. Furthermore, it generates an adjacency matrix that informs each position which other positions are at a maximum distance of σ.

The GSTSM package has the *mine()* method that implements the entire process of finding frequent sequences. It receives as input a GSTSM object and provides as output a list of the SRGs of all sizes found. The user does not need to call any other method to get the results. This method calls and passes all the necessary parameters to make the entire process transparent to the user.

The other methods used in each process step are polymorphic and can be extended by the user. It gives the user the ability to try its implementation. These are described as follows:

* *find()* has two input parameters: a GSTSM object and a set of candidate sequences of size *k*. It provides as output the KRGs for each candidate.

* *merge()* has also two input parameters: a GSTSM object and a set of candidate sequences of size *k* containing information about the KRG of each one. The method returns the SRGs with the candidate sequences of size *k*.

* *generate_candidates()* has two input parameters: a GSTSM object and a set of SRGs of size *k*. There are no SRGs to pass to generate candidates of size one, a NULL value can be used. The method provides the candidate sequences of size *k* + 1.

An illustrative example shows the use of the GSTSM package functions. To start, the first action is installing and loading GSTSM package and then setting all the inputs for the package: *D*, *P*, *γ*, *β*, and *σ*. For *D*, we use a simple dataset. For *P*, positions in a row are used, with one unit distance. Each position is associated with a time series, such as *p*₁ to *t*₁ and *p*₂ to *t*₂. The values for the user-defined thresholds are: *γ* = 0.8, *β* = 2, and *σ* = 1. After setting the input parameters, we can instantiate the GSTSM object and execute the *mine()* method. Listing 1 shows the code using the R command line.

### Listing 1: R example of the use of GSTSM

```R
# loading the GSTSM package
> library("gstsm")
# loading Spatial Timestamped Sequence
> path <-
"https://eic.cefet-rj.br/~dal/wp-content/uploads/2023/05/*
> load(url(paste(path, "dataset.rdata"))) # dataset D
> load(url(paste(path, "positions.rdata"))) # positions P
# mining dataset
> gstsm_object <- gstsm(D, P, gamma=0.8, beta=2, sigma=1)
> result <- mine(gstsm_object)
```

## 4 CONCLUSION

GSTSM is the first tool for mining sequences in spatial time-stamped sequences datasets able to discover constrained patterns in time and space with all three dimensions. The package discovers patterns that may not be frequent over an entire dataset but are grouped in space and frequent in a time interval. It would not be easy to find these patterns without this tool. The results can differ from conventional data mining tools and give different insights about data behavior. The patterns are groups of positions and periods where the sequences are frequent according to the input parameters. The package is also extensible, enabling users to incorporate heuristics and optimizations to drive the discovery of patterns.

## ACKNOWLEDGMENTS

The authors thank CNPq, CAPES, and FAPERJ for partially supporting this research.

## REFERENCES

[1] H. Alatrista-Salas, J. Azé, S. Bringay, F. Cernesson, N. Selmaoui-Folcher, and M. Teisseire. 2015. A knowledge discovery process for spatiotemporal data: Application to river water quality monitoring. *Ecological Informatics* 26, P2 (2015), 127–139. https://doi.org/10.1016/j.ecoinf.2014.05.011

[2] H. Alatrista-Salas, S. Bringay, F. Flouvat, N. Selmaoui-Folcher, and M. Teisseire. 2016. Spatio-sequential patterns mining: Beyond the boundaries. *Intelligent Data Analysis* 20, 2 (2016), 293–316. https://doi.org/10.3233/IDA-160806

[3] B. Aydin and R.A. Angryk. 2016. Spatiotemporal event sequence mining from evolving regions. In *Proceedings - International Conference on Pattern Recognition*, Vol. 0. 4172–4177. https://doi.org/10.1109/ICPR.2016.7900288

[4] R. Campisano, H. Borges, F. Porto, F. Perosi, E. Pacitti, F. Masseglia, and E. Ogasawara. 2018. Discovering tight space-time sequences. In *Lecture Notes in Computer Science*, Vol. 11031. 247–257. https://doi.org/10.1007/978-3-319-98539-8_19

[5] A. Castro, H. Borges, R. Campisano, E. Pacitti, F.Porto, R. Coutinho, and E. Ogasawara. 2021. Generalização de Mineração de Sequências Restritas no Espaço e no Tempo. In *Anais do Simpósio Brasileiro de Banco de Dados (SBBD)*. SBC, 313–318. https://doi.org/10.5753/sbbd.2021.17891

[6] A. Castro, C. Souza, J. Rodrigues, E. Pacitti, F. Masseglia, R. Coutinho, and E. Ogasawara. 2022. GSTSM Package. Technical Report. https://cran.rstudio.com/web/packages/gstsm/index.html, CRAN.

[7] C. Flamand, M. Fabregue, S. Bringay, V. Ardillon, P. Quénel, J.C. Desenclos, and M. Teisseire. 2014. Mining local climate data to assess spatiotemporal dengue fever epidemic patterns in French Guiana. *Journal of the American Medical Informatics Association: JAMIA* 21, e2 (2014), e232–240. https://doi.org/10.1136/amiajnl-2013-002348

[8] Y. Huang, L. Zhang, and P. Zhang. 2008. A framework for mining sequential patterns from spatio-temporal event data sets. *IEEE Transactions on Knowledge and Data Engineering* 20, 4 (2008), 433–448. https://doi.org/10.1109/TKDE.2007.190712

[9] B. Koseoglu, E. Kaya, S. Balcisoy, and B. Bozkaya. 2020. ST Sequence Miner: visualization and mining of spatio-temporal event sequences. *Visual Computer* 36, 10–12 (2020), 2369–2381. https://doi.org/10.1007/s00371-020-01894-6

[10] K. Li and Y. Fu. 2014. Prediction of human activity by discovering temporal sequence patterns. *IEEE Transactions on Pattern Analysis and Machine Intelligence* 36, 8 (2014), 1644–1657. https://doi.org/10.1109/TPAMI.2013.2297321

[11] C.H. Mooney and J.F. Roddick. 2013. Sequential pattern mining - Approaches and algorithms. *Comput. Surveys* 45, 2 (2013). https://doi.org/10.1145/2431211.2431218

[12] S. Parthasarathy, M.J. Zaki, M. Ogihara, and S. Dwarkadas. 1999. Incremental and interactive sequence mining. In *International Conference on Information and Knowledge Management, Proceedings*, 251–258. https://doi.org/10.1145/319950.320010

[13] G. Sunitha and A. Rama Mohan Reddy. 2014. Mining frequent patterns from spatiotemporal data sets: A survey. *Journal of Theoretical and Applied Information Technology* 68, 2 (2014), 265–274.

[14] R. Core Team. 2020. *R: A Language and Environment for Statistical Computing*. Technical Report. https://www.R-project.org/, Vienna, Austria.

[15] Mohammed J. Zaki. 2000. Sequence mining in categorical domains: incorporating constraints. In *Proceedings of the ninth international conference on Information and knowledge management (CIKM '00)*. Association for Computing Machinery, New York, NY, USA, 422–429. https://doi.org/10.1145/354756.354849
# InteGraal: a Tool for Data-Integration and Reasoning on Heterogeneous and Federated Sources

Jean-François Baget, Pierre Bisquert, Michel Leclère, Marie-Laure Mugnier, Guillaume Pérution-Kihli, Florent Tornil, Federico Ulliana

first.last@inria.fr

LIRMM, Inria, Univ Montpellier, CNRS, INRAE

Montpellier, France

## ABSTRACT

We propose to demonstrate InteGraal, a tool for reasoning over heterogeneous and federated data sources. InteGraal is a highly modular tool constituted by two main components. The first is the data-integration layer which allows the users to build a federated factbase over a collection of sources. The second is the automated reasoning layer, which provides powerful means for the declarative exploitation of data through the expressive formalism of *existential rules*. This demonstration proposes to showcase the use of the tool in use-cases of data exploitation as well as to present its architecture and its dedicated query answering mechanisms.

## ACKNOWLEDGMENTS

We are grateful to Julien Cufi and Patrice Buche for their help setting up the demonstration scenario. This work was partially supported by the Inria-DFKI project R4Agri and the ANR project CQFD (ANR-18-CE23-0003).

© 2023, Copyright is with the authors. Published in the Proceedings of the BDA 2023 Conference (October 23-26, 2023, Montpellier, France). Distribution of this article is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0.

© 2023, Droits restant aux auteurs. Published dans les actes de la conférence BDA 2023 (23-26 octobre 2023, Montpellier, France). Redistribution de cet article autorisée selon les termes de la licence Creative Commons CC-by-nc-nd 4.0.
# PathWays: entity-focused exploration of heterogeneous data graphs

Nelly Barret

nelly.barret@inria.fr

Inria and Institut Polytechnique de Paris, France

Jia Jean Law

jia-jean.law@polytechnique.edu

Ecole Polytechnique, France

Antoine Gauquier

antoine.gauquier@etu.imt-nord-europe.fr

Institut Mines Télécom, France

Ioana Manolescu

ioana.manolescu@inria.fr

Inria and Institut Polytechnique de Paris, France

## ABSTRACT

Graphs, and notably RDF graphs, are a prominent way of sharing data. As data usage democratizes, users need help figuring out the useful content of a graph dataset. In particular, journalists with whom we collaborate [1] are interested in identifying, in a graph, the *connections between entities*, e.g., people, organizations, emails, etc.

We propose PathWays, an interactive tool for exploring data graphs through their *data paths connecting Named Entities (NEs, in short)*; each data path leads to a tabular-looking set of results. NEs are extracted from the data through dedicated Information Extraction modules. PathWays leverages the ConnectionLens platform [2, 4] and follow-up work on dataset abstraction [3]. Its novelty lies in its interactive and efficient approach to enumerate, compute, and analyze NE paths.

## REFERENCES

[1] ANADIOTIS, A., BALALAU, O., BOUGANIM, T., ET AL. Empowering investigative journalism with graph-based heterogeneous data management. *IEEE DEBull*. (2021).

[2] ANADIOTIS, A., BALALAU, O., CONCEICAO, C., ET AL. Graph integration of structured, semistructured and unstructured data for data journalism. *Inf. Systems* 104 (2022).

[3] BARRET, N., MANOLESCU, I., AND UPADHYAY, P. Abstra: toward generic abstractions for data of any model (demonstration). In *CIKM* (2022).

[4] CHANIAL, C., DZIRI, R., GALHARDAS, H., ET AL. ConnectionLens: Finding connections across heterogeneous data sources (demonstration). *PVLDB* 11, 12 (2018).

© 2023, Copyright is with the authors. Published in the Proceedings of the BDA 2023 Conference (October 23-26, 2023, Montpellier, France). Distribution of this article is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0.

© 2023, Droits restant aux auteurs. Published in *Les actes de la conférence BDA 2023* (23-26 octobre 2023, Montpellier, France). Redistribution de cet article autorisée selon les termes de la licence Creative Commons CC-by-nc-nd 4.0.
# Interpretable Clustering of Multivariate Time Series with Time2Feat

Angela Bonifati

Lyon 1 University, Liris CNRS
Lyon, France
angela.bonifati@univ-lyon1.fr

Francesco Del Buono

University of Modena and Reggio
Emilia
Modena, Italy
francesco.delbuono@unimore.it

Francesco Guerra

University of Modena and Reggio
Emilia
Modena, Italy
francesco.guerra@unimore.it

Miki Lombardi

Adobe
Paris, France
mlombardi@adobe.com

Donato Tiano

University of Modena and Reggio
Emilia
Modena, Italy
donato.tiano@unimore.it

**CCS Concepts:** • Computing methodologies → Cluster analysis; Semi-supervised learning settings.

**Keywords:** Clustering, Features Selection, Semi Supervised Clustering, Interpretability

## 1 Introduction

A Multivariate Time Series (MTS) is a collection of multiple univariate time series (signals) that are observed simultaneously over time and provide insight into time-dependent phenomena. MTS analysis enables the examination of variable relationships over time, offering important insights into underlying phenomena. This can be useful for modeling complex systems, making data-driven decisions, and improving efficiency and productivity across various domains, such as finance and economics, environmental science, healthcare, and social science. MTS analytics involves both supervised and unsupervised techniques, which cover a range of tasks, including classification, clustering, pattern discovery, and forecasting. Among these, clustering analysis has become popular in applications where sensors generate large amounts of data.

While there has been a lot of research on clustering techniques for univariate time series (UTS), the field of clustering multivariate time series is still in its early stages. Proposals adapt clustering approaches designed for UTS to MTS after applying dimensionality reduction techniques. Examples of such techniques are based on the Principal Component Analysis (PCA), which enables the conversion of a set of correlated features in the high dimensional space into a set

<figure><img src="image_28.png" /><figcaption>Figure 1. The Time2Feat pipeline.</figcaption></figure>

of uncorrelated features in the low dimensional space. Nevertheless, the resulting clusters suffer from poor explainability as the original dimensions are lost. More recently, approaches based on Deep Neural Networks (DNNs), and in particular Variational Autoencoders (VAEs) have been used to generate MTS encodings before applying clustering methods. Although these solutions might exhibit high performance, the resulting clusters are based on latent dimensions that remain unexplainable to the end-users.

In this paper, we demonstrate Time2Feat[1, 2], the first system that deals with explainable results of Multivariate Time Series clustering using an end-to-end feature-based pipeline. The pipeline applies clustering techniques to interpretable features automatically extracted from the signals composing the MTS. While the pipeline can be executed without any user interaction (i.e., running the *unsupervised mode*), Time2Feat can also incorporate user annotations on small dataset samples to select generated features. This kind of user's *semi-supervision* can improve the accuracy of results and the quality of explanations.

Through the demonstration, users can experiment with the end-to-end process implemented in the pipeline to generate clusters and explanations from multiple MTS datasets using both unsupervised and semi-supervised modes.

## 2 The Time2Feat system

The Time2Feat data analysis pipeline is comprised of three distinct components: feature extraction, feature selection,

<footnote>© 2023, Copyright is with the authors. Published in the Proceedings of the BDA 2023 Conference (October 23-26, 2023, Montpellier, France). Distribution of this article is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0.</footnote>

<footnote>© 2023, Droits restant aux auteurs. Publié dans les actes de la conférence BDA 2023 (23-26 octobre 2023, Montpellier, France). Redistribution de cet article autorisée selon les termes de la licence Creative Commons CC-by-nc-nd 4.0.</footnote>
and clustering, as depicted in Figure 1. The input to the pipeline is a Multivariate Time Series, along with the number of desired clusters. If the number of clusters is not explicitly specified, the pipeline can determine it using a heuristic approach.

There are two operational modes for Time2Feat, unsupervised mode and semi-supervised mode. In unsupervised mode, the pipeline requires no additional input beyond the Multivariate Time Series and the number of desired clusters. The pipeline automatically extracts relevant features, selects the most important ones, and performs clustering to identify the number of specified clusters.

In semi-supervised mode, the user has the option of providing a subset of labeled samples to improve the accuracy of the clustering process. This information is utilized to fine-tune the feature selection and clustering steps, ultimately resulting in improved clustering accuracy.

**Feature Extraction.** The goal of this component is to generate a comprehensive and detailed representation of the MTS in the dataset via the extraction of a large spectrum of features, each describing the signals composing the MTS in isolation or pairs. *Intra-signal Features* are extracted by applying the library `tsfresh`, which can extract 700+ features encoding the signal description from the perspective offered by a specific analysis method, such as Distribution Analysis, Statistical Analysis, etc. `tsfresh` extracts features that are interpretable for users who know how the statistical measure summarizes the time series values. Moreover, Time2Feat captures *inter-signal relationships* by quantifying the relatedness between pairs of signals using eight metrics, such as correlation and Euclidean distance. All of the features extracted via this process represent the primary characteristics of the time series. These features are entirely based on statistical calculations, which results in them being fully explainable and interpretable.

**Features Extraction at work.** The RacketSports dataset<fnref n="1" /> describes four kinds of shots performed by people playing badminton or squash. Two sensors gather data in a three-dimensional space, thus producing three signals per sensor. Suppose we are asked to analyze the dataset and no details on the activities that MTS describes are provided to us. Time2Feat through the Feature Extraction component, generates 4722 intra-signal and 120 inter-signal features to describe the dataset. While individually interpretable, the sheer number of features can produce noise in the generation of the clusters and cannot be managed by users for their interpretation.

**Feature Selection.** The feature extraction process generates a vast number of features, making it necessary to reduce

its dimensionality to improve interpretability and clustering performance. If Time2Feat is running with the *semi-supervised mode*, the available labels are used to rank the relevance of the features for identifying a subset capable of generating clusters via an ANOVA based analysis. Irrespective of the availability of labels, the Principal Feature Analysis (PFA) technique is applied to select the most meaningful features. The PFA technique is chosen because it not only ensures conciseness but also promotes diversity by selecting the principal features that retain the maximum variability of the features in the lower-dimensional space.

**Features Selection at work.** The Feature Selection component reduces the number of features to 142 intra-signal and 11 inter-signal features when running in the semi-supervised mode. If the user can provide labels for at least 20% of the MTS, the number of features is reduced to 12 intra-signal and 3 inter-signal features.

**Clustering.** Time2Feat includes three techniques (Hierarchical, KMeans, Spectral) for generating the clusters. The hierarchical clustering technique is selected as default, since it achieved the best accuracy in our experiments. The Time2Feat system leverages state-of-the-art heuristics (e.g., applying the well-known Elbow method) or user preferences to select the number of clusters to generate. The clusters are wholly derived from the previously selected features. Given the interpretability of these features, it is feasible to analyze the resulting clusters and gain an understanding of the factors that influenced their creation.

**Clustering Component at work.** If the user selects to generate 4 clusters and the system runs in the unsupervised mode, the quality of the result measured with the Adjusted Mutual Information (AMI) is around 0.35. The reason for the low quality result (that in any case overcomes the competing approaches) is mainly due to the complexity of the problem. By running the semi-supervised mode and providing 20% of labels, the quality of the clusters improves to 0.56. If the user selects to generate 2 clusters, Time2Feat is able to better discriminate between the activities. The AMI obtained with the unsupervised approach is 0.77 and the one with the semi-supervised approach is 0.86.

## References

[1] Angela Bonifati, Francesco Del Buono, Francesco Guerra, Miki Lombardi, and Donato Tiano. 2023. Interpretable Clustering of Multivariate Time Series with Time2Feat. *Proceedings of the VLDB Endowment* 16, 12 (2023), 3994–3997.

[2] Angela Bonifati, Francesco Del Buono, Francesco Guerra, and Donato Tiano. 2022. Time2Feat: learning interpretable representations for multivariate time series clustering. *Proceedings of the VLDB Endowment* 16, 2 (2022), 193–201.

<footnote n="1">¹http://www.timeseriesclassification.com/description.php?Dataset=RacketSports</footnote>
# HEADWORK: Powering the Crowd with Tuple Artifacts

David Gross-Amblard
first.last@irisa.fr
Univ Rennes / Irisa Lab
France

Constance Thierry
first.last@irisa.fr
Univ Rennes / Irisa Lab
France

Marion Tommasi
Inria Lille-Nord Europe
Université de Lille
France

Rituraj Singh
Univ Rennes / Irisa Lab
France

Iandry Rakotoniaina
UMR 7204, MNHN-CNRS-UPMC,
CESCO, Paris
France

Leo Jacoboni
Wirk
France

## ABSTRACT

In this demo we introduce HEADWORK, an open-source academic platform for the crowdsourcing of complex tasks. Besides classical crowdsourcing features, HEADWORK eases the development of crowdsourcing campaigns through a full relational abstraction of relevant concepts (participants, skills, tasks, current answers, decision procedures, GUI, etc.). It allows the orchestration of complex dynamic tasks using so-called tuple artifacts (i.e. finite-state automata which transition guards and actions are SQL-defined, on an evolving database). The demo will illustrate these key features, both from the participant and developer point of view.

## 1 INTRODUCTION

Crowdsourcing is a technique to solve tasks by automatically asking questions to humans. Successful examples are Zooniverse [8], Foldit[3] for participative science, and Amazon Mechanical Turk for rewarded tasks. At the core of crowdsourcing platforms are micro-tasks: simple questions awaiting for a simple answer. But while the crowdsourcing of micro-tasks is well studied, recent works turn their attention to macro-tasks [5], that require a chain of interactions with humans, using various steps and intermediate decisions. Several systems has been considered to handle this kind of tasks [1, 6], but they rely on a low-level, procedural description of interactions.

In this demo, we present HEADWORK, a ready-to-use, academic crowdsourcing platform for the deployment of complex tasks. In order to limit the task designer's efforts, the HEADWORK platform proposes a full relational abstraction of relevant crowd concepts and algorithms. The orchestration of macro-tasks is realized through tuple artifacts [4], that are finite state automata operating on a database, which transitions are guarded by SQL conditions and which trigger SQL actions.

To promote the adoption of HEADWORK, the platform is fully open-source (AGPL), and a demo server is available<fnref n="1" />. The platform has already been used for participative science campaigns, and is compatible with rewarded crowdsourcing.

## 2 MODEL

The HEADWORK platform is data-oriented. Our goal is to focus on transforming data from the crowd rather than dealing with low-level programming issues. We illustrate below our relational abstraction, the template mechanism, and explain the deployment of micro and macro-tasks.

### 2.1 Relational Abstraction

Several built-in tables are available. Basically:

* user : gathers information about crowd participants;

* skill : contains skill definitions (as keywords and levels of expertise), used for tasks and user profiles;

* template : provides classical user interactions (expressed in HTML and Javascript);

* task : contains the questions for the crowd;

* profile : allows to specify which skill is relevant for a task;

* answer : saves participant contributions and intermediate computations.

Micro-tasks are then built on these notions.

### 2.2 Micro-Tasks

HEADWORK comes with different language flavour. A domain-specific language that we call *Crowdy* is available, allowing to express simply a wide variety of micro-tasks. If needed, task designers have full control of the SQL counterpart. SQL expressions can also be used in specific Crowdy statements. It is noteworthy that letting the task designer access to a full SQL engine is a potential security threat. We will come back to this question in the next section.

### 2.3 Template Mechanism

HEADWORK features an extensible template mechanism, that allows the task designer to re-use typical crowd interactions, but also to propose new ones to the community. Basic templates are classical HTML form-like inputs such as text, text area, lists and radio buttons. More sophisticated templates such as selectors for geographical maps, image selectors, audio/video playing and audio recording are also available.

The general architecture of a template is an HTML snippet whose interaction is driven by a Javascript code. The code can contain text tags that are populated by a Crowdy statement. The only constraint is to provide the output as a specific field in JSON format, so that HEADWORK is able to process it into the answer table.

<footnote n="1">¹https://headwork.irisa.fr</footnote>

<footnote>©2023, Copyright is with the authors. Published in the Proceedings of the BDA 2023 Conference (October 23-26, 2023, Montpellier, France). Distribution of this article is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0.</footnote>

<footnote>©2023, Droits restant aux auteurs. Published dans les actes de la conférence BDA 2023 (23-26 octobre 2023, Montpellier, France). Redistribution de cet article autorisée selon les termes de la licence Creative Commons CC-by-nc-nd 4.0.</footnote>
BDA'23, 23-26 oct. 2023, Montpellier (France),

Gross Amblard et al.

<figure><img src="image_29.png" /><figcaption>Figure 1: The HEADWORK architecture</figcaption></figure>

## 2.4 Macro-Tasks

In short, macro-tasks are workflows of simple tasks, which order and content can evolve according to participant answers and crowd decisions. In HEADWORK, a macro-task is driven by a tuple artifact : a finite state automaton which transition conditions (guards) and actions are expressed in Crowdy (hence SQL at a low level). Generally speaking, a transition in a tuple artifact has the following structure:

$$ \text{state s} \xrightarrow{\text{actions: } \alpha} \text{state s'} $$

meaning that, if we are in state s with database DB, and the guard query $\gamma(DB)$ is true, then we go to state $s'$, with the new database $\alpha(DB)$.

Since guards and actions can be defined completely with queries on the HEADWORK relational schema, and since any number of states can be envisioned, a wide set of task compositions can be expressed: sequences of questions, conditional branching, loops. Computations and aggregations benefit from the full power of SQL, extended with crowd-style operators such as majority voting. Specific cohort of participants can be defined thanks to queries on the skill and profile tables.

## 3 THE HEADWORK PLATFORM

The platform is organized as follows (Figure 1). Task providers submit a job as a JSON file encoding the crowd data oriented workflow, in the SQL or Crowdy language, based on the various available templates. The workflow engine (written in PHP hosted by Apache) then processes the automaton and render tasks to participants through the Web interface (Javascript, Bootstrap, Figure 2). Participants can create an account, give their profile (skills), see the list of available tasks ranked according to their skills, and start contributing. All information are available in a Mysql database. If required, HEADWORK is compatible with a rewarded pool of participants through the Wirk service, to speed-up macro-tasks that could not wait for benevolent participants.

<figure><img src="image_30.png" /><figcaption>Figure 2: HEADWORK main interface, with projects, activities and tasks, ordered by relevance wrt. participant's skills</figcaption></figure>

## 4 FUTUR WORK

Our hope is to make HEADWORK an academic laboratory for studies in macro-task crowdsourcing, while hosting real participative and citizen science projects. In the short future we plan to implement richer, hierarchical skill models [7] and to allow for automatic workflow verification [2].

## ACKNOWLEDGEMENTS

We would like to thank the numerous interns from Rennes University that contributed to pieces of this project. This work was also partially funded by the French National Research Agency (ANR) grant HEADWORK<fnref n="2" /> (ANR-16-CE23-0015).

## REFERENCES

[1] Salman Ahmad, Alexis Battle, Zahan Malkani, and Sepander Kamvar. The jabberwocky programming environment for structured social computing. In *Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology*, UIST '11, pages 53–64, New York, NY, USA, 2011. ACM.

[2] Pierre Bourhis, Loïc Hélouët, Zoltán Miklós, and Ritraj Singh. Data centric workflows for crowdsourcing. In Ryszard Janicki, Natalia Sidorova, and Thomas Chatain, editors, *Application and Theory of Petri Nets and Concurrency - 41st International Conference, PETRI NETS 2020, Paris, France, June 24-25, 2020, Proceedings*, volume 12152 of *Lecture Notes in Computer Science*, pages 24–45. Springer, 2020.

[3] Seth Cooper, Firas Khatib, Adrien Treuille, Janos Barbero, Jeehyung Lee, Michael Beenen, Andrew Leaver-Fay, David Baker, Zoran Popović, and Foldit players. Predicting protein structures with a multiplayer online game. *Nature*, 466(7307):756–760, 2010.

[4] Alin Deutsch, Richard Hull, Fabio Patrizi, and Victor Vianu. Automatic verification of data-centric business processes. In *Proceedings of the 12th International Conference on Database Theory*, ICDT '09, page 252–267, New York, NY, USA, 2009. Association for Computing Machinery.

[5] Vassillis-Javed Khan, Konstantinos Papangelis, Ioanna Lykourentzou, and Panos Markopoulos. Macrotask crowdsourcing. Cham: Springer International Publishing, 2019.

[6] Greg Little, Lydia B. Chilton, Max Goldman, and Robert C. Miller. Turkit: Human computation algorithms on mechanical turk. In *Proceedings of the 23Nd Annual ACM Symposium on User Interface Software and Technology*, UIST '10, pages 57–66, New York, NY, USA, 2010. ACM.

[7] Panagiotis Mavridis, David Gross-Amblard, and Zoltán Miklós. Using hierarchical skills for optimized task assignment in knowledge-intensive crowdsourcing. In *Proceedings of the 25th International Conference on World Wide Web*, pages 843–853. International World Wide Web Conferences Steering Committee, 2016.

[8] Robert Simpson, Kevin R Page, and David De Roure. Zooniverse: observing the world's largest citizen science platform. In *Proceedings of the 23rd international conference on world wide web*, pages 1049–1054, 2014.

<footnote n="2">²https://headwork.irisa.fr/headwork-web/</footnote>
# qEndpoint: A Wikidata SPARQL endpoint on commodity hardware

Antoine Willerval

antoine.willerval@etu.univ-lyon1.fr
Lyon 1 University, CNRS Liris
Lyon, France
The QA Company SAS
Saint Etienne, France

Dennis Diefenbach

dennis.diefenbach@the-qa-company.com
The QA Company SAS
Saint Etienne, France

Angela Bonifati

angela.bonifati@univ-lyon1.fr
CNRS, LIRIS UMR 5205, Lyon 1
University
Lyon, France

## ABSTRACT

In this work, we demonstrate how to setup a Wikidata SPARQL endpoint on commodity hardware resources. We achieve this by using a novel triple store called qEndpoint, which uses a read-only partition based on HDT and a write partition based on RDF4J. We show that qEndpoint can index and query the entire Wikidata dump (currently 17 billion triples) on a machine with 600GB SSD, 10 cores and 10GB of RAM, while keeping the query performance comparable with other SPARQL endpoints indexing Wikidata.
In a nutshell, we present the first SPARQL endpoint over Wikidata that can run on commodity hardware while preserving the query run time of existing implementations. Our work goes in the direction of democratizing the access to Wikidata as well as to other large-scale Knowledge Graphs published on the Web. The source code of qEndpoint along with the query workloads are publicly available.

<table><caption>Table 1: Wikidata Index characteristics for different endpoints</caption><thead><tr><th>System</th><th>Loading Time</th><th>#Triples</th><th>RAM</th><th>Index size</th><th>Doc</th></tr></thead><tbody><tr><td>Apache Jena</td><td>9d 21h</td><td>13.8 B</td><td>64 GB</td><td>2TB</td><td>1</td></tr><tr><td>Virtuoso</td><td>several days¹ (preprocessing) + 10h</td><td>11.9 B</td><td>378 GB</td><td>NA</td><td>2</td></tr><tr><td>Blazegraph</td><td>~5.5d</td><td>11.9 B</td><td>128 GB</td><td>1.1 T</td><td>3</td></tr><tr><td>Stardog</td><td>9.5 h</td><td>16.7 B</td><td>256 GB</td><td>NA</td><td>4</td></tr><tr><td>QLever</td><td>14.3 h</td><td>17 B</td><td>128 GB</td><td>823 GB</td><td>5</td></tr><tr><td>qEndpoint</td><td>50 h</td><td>17.4 B</td><td>10 GB</td><td>294 GB</td><td>6</td></tr></tbody></table>

(1) https://wiki.bitplan.com/index.php/WikiData_Import_2020-08-15

(2) https://community.openlinksw.com/t/loading-wikidata-into-virtuoso-open-source-or-enterprise-edition/2717

(3) https://addshore.com/2019/10/your-own-wikidata-query-service-with-no-limits/

(4) https://www.stardog.com/labs/blog/wikidata-in-stardog/

(5) https://github.com/ad-freiburg/qlever/wiki/Using-QLever-for-Wikidata

(6) https://github.com/the-qa-company/qEndpoint/wiki/Use-qEndpoint-to-index-a-dataset

## CCS CONCEPTS

* Information systems → DBMS engine architectures.

## KEYWORDS

qEndpoint, HDT, Wikidata, SPARQL

<table><caption>Table 2: Time split during the loading of the Wikidata dataset.</caption><thead><tr><th>Task</th><th>Time</th><th>Description</th></tr></thead><tbody><tr><td>Dataset download</td><td>7 h</td><td>Download the dataset²</td></tr><tr><td>HDT compression</td><td>45 h</td><td>Creating HDT</td></tr><tr><td>HDT co-index gen</td><td>5 h</td><td>Creating OPS/PSO/POS indexes</td></tr><tr><td>Loading the index</td><td>2 min</td><td>Start the endpoint</td></tr></tbody></table>

<table><caption>Table 3: Sizes of each components of qEndpoint (total: 296GB)</caption><thead><tr><th>File name</th><th>File size</th><th>Usage</th></tr></thead><tbody><tr><td>index_dev.hdt</td><td>183GB</td><td>Dictionary + SPO index</td></tr><tr><td>index_dev.hdt.index.v1-1</td><td>113GB</td><td>OPS/PSO/POS indexes</td></tr><tr><td>native-store</td><td>16KB</td><td>RDF4J store</td></tr><tr><td>qendpoint.jar</td><td>82MB</td><td>qEndpoint</td></tr></tbody></table>

Received 3 February 2023; revised ??; accepted ??

<footnote>© 2023, Copyright is with the authors. Published in the Proceedings of the BDA 2023 Conference (October 23-26, 2023, Montpellier, France). Distribution of this article is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0.</footnote>

<footnote>© 2023, Droits restant aux auteurs. Published in the Proceedings of the BDA 2023 (23-26 octobre 2023, Montpellier, France). Redistribution de cet article autorisée selon les termes de la licence Creative Commons CC-by-nc-nd 4.0.</footnote>
# 7 Résumés des articles de doctorant
# Representation Learning for Relational Structures

Alan Gany*

alan.gany@universite-paris-saclay.fr

CNRS-LISN

Paris, France

## ABSTRACT

Over the last decades, important improvements in deep learning based methods have been made, allowing to learn rich and versatile representations for text, graphs, or images. In the data management area, an important question is to explore to what extent these methods may be useful to represent relational structures, which may be incomplete (e.g., with missing values) or uncertain. Indeed, many recent research works focus on reconstructing or extracting data from relational structures using learned embeddings, for either tuple classification, entity resolution, or other data cleaning tasks. In this work, we intend to pursue in this research direction, focusing on the important problem of *missing value imputation*.

Our aim is to extend the state of the art for this problem, which focused mainly on machine learning datasets consisting of a single relation, to general and practically relevant relational structure. Concretely, we will build a relational structure representation that can capture both syntactic and semantic similarities and correlations, capturing all data facets, in order to have the most effective embeddings.

Our proposal is to construct a tripartite graph representation of the relational data, including the attribute values, the attribute names, and the tuples of the relational structure. This graph captures constraints such primary / foreign key, as well as inclusion dependencies.

The subsequent objective is then to generate embeddings for the nodes of the graph and, based on them, to retrieve for each missing value the most likely candidate one from the same domain. For this embedding task, we have used either task agnostic models such as random walks or tasks-specific deep graph learning models. As we are mapping our relation problem to a graph one, we must ensure that the generated embeddings retain properties such as isomorphism between the different graphs that can be built from the same relational structure. Relations are order (permutation) invariant, and should also transfer to the corresponding graph representations thereof.

Finally, we propose two data imputation methods that can be applied to relation schemas consisting of several interconnected relations. The first method exploits the geometric properties of the generated embeddings to fill each missing value of the database. The second method exploits the task specific deep learning model in order to find candidates for the missing values. Both of these models have the advantage to work on tuples having more than one missing element, without being specific to a single attribute. Moreover, because the graph we build can capture foreign / primary

key constraints, the methods proposed allow to retrieve candidate values across different relations.

## KEYWORDS

Representation Learning, Graph Learning, Relational Structures

© 2023, Copyright is with the authors. Published in the Proceedings of the BDA 2023 Conference (October 23-26, 2023, Montpellier, France). Distribution of this article is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0.

© 2023, Droits restant aux auteurs. Published dans les actes de la conférence BDA 2023 (23-26 octobre 2023, Montpellier, France). Redistribution de cet article autorisée selon les termes de la licence Creative Commons CC-by-nc-nd 4.0.
# Graph versioning for evolving urban data
## Versionnement de graphe pour les données urbaines évolutives

Jey Puget Gil

jey.puget-gil@liris.cnrs.fr

Université de Lyon, Université Claude Bernard, LIRIS,
UMR-CNRS 5205
Villeurbanne, FRANCE

John Samuel

john.samuel@cpe.fr

Université de Lyon, CPE Lyon, LIRIS, UMR-CNRS 5205
Villeurbanne, FRANCE

Emmanuel Coquery

emmanuel.coquery@univ-lyon1.fr

Université Claude Bernard, LIRIS, UMR-CNRS 5205
Villeurbanne, FRANCE

Gilles Gesquière

gilles.gesquiere@univ-lyon2.fr

Université de Lyon, Université Lumière Lyon 2, LIRIS,
UMR-CNRS 5205
Villeurbanne, FRANCE

## ABSTRACT

The continuous evolution of cities poses significant challenges in terms of managing and understanding their complex dynamics. With the increasing demand for transparency and the growing availability of open urban data, it has become important to ensure the reproducibility of scientific research and computations in urban planning. To understand past decisions and other possible scenarios, we require solutions that go beyond the management of urban knowledge graphs. In this work, we explore existing solutions and their limits and explain the need and possible approaches for querying across multiple graph versions.

## RÉSUMÉ

L'évolution continue des villes pose des défis importants en termes de gestion et de compréhension de leurs dynamiques complexes. Avec la demande croissante de transparence et la disponibilité gran-dissante de données urbaines ouvertes, il est devenu important d'assurer la reproductibilité de la recherche scientifique et des calculs dans le domaine de l'urbanisme. Pour comprendre les décisions passées et d'autres scénarios possibles, nous avons besoin de solutions qui vont au-delà de la gestion des graphes de connaissances urbaines. Dans ce travail, nous explorons les solutions existantes et leurs limites, et expliquons le besoin et les approches possibles pour l'interrogation à travers de multiples versions de graphes.

## CCS CONCEPTS

* Information systems → Geographic information systems; Resource Description Framework (RDF); Web Ontology Language (OWL).

## KEYWORDS

RDF, versioning, graph, urban data, deduction

## MOTS CLÉS

RDF, versionnement, graphe, données urbaines, déduction

# 1 INTRODUCTION AND MOTIVATION

Urban planners, historians, archaeologists, and researchers are continuously analyzing the constant development of cities. They are interested in an understanding of the possible versions and scenarios of the city [2], both in the past and in the future, if certain decisions were to be made. The choices made and the lessons learned in urban planning in the past serve as a guide for future decisions.

As the availability of open data increases across all sectors, so too does the demand for transparency in decision-making. Urban data come in different forms, they can be structured (sensors, building data, ...), semi-structured (urban system logs, ...) or unstructured (images, text, ...). Decisions are made on the basis of the data available at a given point in time. In other words, both the most recent version of the city and the previous versions are taken into account. In certain cases, complex calculations on this existing data guide the decision-makers. Reproducibility of these calculations is therefore also a requirement for transparency.

We provide the following sample queries from urban planning project proposals to better illustrate our research work:

* Q1: Which city versions have a metro station accessible to people with disabilities?

* Q2: Across multiple concurrent city versions, what is the maximum known height of a particular building? (aggregation)

Our previous research work proposed the use of graph formats [4] for the transformation and management of heterogeneous and concurrent urban data. In this work, we want to go beyond this and explore and develop a system that can query multiple versions of the graph simultaneously to answer complex queries like the ones above. This requires not only versioning of code (complex calculations) and data. It also requires efficient querying techniques across versions.

This article briefly reviews different ways to address the need for effective tools and methodologies to analyze urban development, emphasizing the importance of versioned data management.

<footnote>© 2023, Copyright is with the authors. Published in the Proceedings of the BDA 2023 Conference (October 23-26, 2023, Montpellier, France). Distribution of this article is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0.</footnote>

<footnote>© 2023, Droits restant aux auteurs. Published dans les actes de la conférence BDA 2023 (23-26 octobre 2023, Montpellier, France). Redistribution de cet article autorisée selon les termes de la licence Creative Commons CC-by-nc-nd 4.0.</footnote>
BDA, 23-26 oct. 2023, Montpellier, FRANCE

Jey Puget Gil, Emmanuel Coquery, John Samuel, and Gilles Gesquière

## 2 STATE OF THE ART

Data and code evolution have been at the heart of many recent research and industrial advances. Taken together, they make up an important part of urban knowledge evolution. Given their growing use, our research is particularly focused on version control systems.

### 2.1 Code and Data versioning

Versioned repositories are systems that track and manage changes to data and code over time, allowing researchers to maintain a historical record of their work and facilitate collaboration. When these two concepts are integrated, they provide several benefits in scientific research, such as reproducibility, transparency, and collaboration. Version control allows different deductive paths to be explored and merged, facilitating collaboration among researchers with different expertise. Code versioning systems like GIT and SVN play a critical role in software development, enabling collaborative work, code reuse, and traceability. There also exist some dedicated solutions for versioning data such as DVC, DagsHub, Delta Lake, Dolt, Qri, Weights and Biases, Git LFS, Comet and LakeFS.

These different approaches are not suitable for our case study, since we want to work with concurrent versions and scenarios [2]. To answer even simple queries like **Q1**, current solutions require querying multiple database instances or checking out multiple version commits, which limits query response times.

### 2.2 Database versioning

A recent interesting solution called DoltHub, an online platform and hosting service provides version control for databases. This technology allows users to create, manage, and collaborate on databases using Git-style workflows and supports branching and merging, enabling teams to work on different features or versions of a database. However, cross-version querying of RDF data with such a solution is a challenge. Native SPARQL (SPARQL Protocol and RDF Query Language) queries are not inherently version-aware. Another solution called QuitStore [1] is an RDF data versioning system that addresses the need for efficient data retrieval across different versions. By implementing an RDF-based approach, QuitStore allows users to track changes and revisions to their semantic data over time. However, these versioning systems provide little support for cross-version queries. Indeed, they can either query metadata on multiple versions or query data on a single version.

Temporal databases, also known as historicized databases, are specialized databases that are designed to capture and store historical snapshots of data over a while. Some advanced temporal databases allow the analysis and querying of data at different points in time from two perspectives: how the data appeared in the real world and how it evolved within the database. However, by their nature, such databases are limited to a linear history and cannot be directly used to store a dataset with a branching history.

## 3 CONTRIBUTIONS PERSPECTIVES

Our motivation is to find a method for retrieving knowledge from a set of urban data versions stored in RDF format[4]. Resource Description Framework (RDF) offers a flexible and standardized format for representing the state of the city. RDF's graph-based structure allows the integration of diverse data sources, enabling a

comprehensive view of the city's attributes and relationships. By versioning the city dataset, we can systematically track and record changes, modifications, and additions over time. This comprehensive version of history provides a foundation for analyzing the city's evolution, identifying trends, and extracting valuable knowledge. For example, if we have an urban dataset, we can identify the following problem: *How to analyze a set of versions of a city to produce additional knowledge?*

From a semantics point of view, a versioned graph can be assimilated to a collection of graphs, that is one graph for each version. Together with the GRAPH statement in SPARQL, this provides a way to query multiple versions at the same time. For example, the accessibility status of a given metro (**Q1**) or the height of the building (**Q2**). However separately storing each version would cost too much space and would probably lead to inefficient query processing.

Borrowing from historicized databases, one can associate version metadata to RDF triples. However, while a tuple in historicized database can be associated with a validity time interval, the branching nature of versioning history requires a different representation. Using provenance techniques [3], this information could then be used at the query engine level to compute partial answers for several versions at once. We aim at implementing such a query engine and compare its efficiency with solutions using existing approaches with version checkout. We also aim at comparing the efficiency of different representations of version metadata associated to triples (for example representing the set of versions in extension or by a set of version intervals).

Note that this representation is independent from version metadata, we can thus reuse representation of version graph such as in [1] to trace the origin and lineage of data, for example to reference the code used to produce the data. It helps to understand its authenticity and assess its trustworthiness.

## ACKNOWLEDGEMENTS

This work *Knowledge Hub for Evolving Urban Cities* is supported and funded by the IADoc@UDL (Université de Lyon, Université de Lyon 1) and LIRIS UMR 5205. We also acknowledge the BD team and the Virtual City Project<fnref n="1" /> members for their invaluable advice and assistance.

## REFERENCES

[1] Natanael Arndt. 2020. *Distributed Collaboration on Versioned Decentralized RDF Knowledge Bases*. Ph. D. Dissertation. Universität Leipzig. https://doi.org/10.33968/9783966270205-00

[2] John Samuel, Sylvie Servigne, and Gilles Gesquière. 2020. Representation of concurrent points of view of urban changes for city models. *Journal of Geographical Systems* (Feb. 2020). https://doi.org/10.1007/s10109-020-00319-1

[3] Leslie F. Sikos and Dean Philp. 2020. Provenance-Aware Knowledge Representation: A Survey of Data Models and Contextualized Knowledge Graphs. *Data Science and Engineering* 5, 3 (Sept. 2020), 293–316.

[4] Diego Vinasco-Alvarez, John Samuel, Sylvie Servigne, and Gilles Gesquière. 2021. Towards a semantic web representation from a 3D geospatial urban data model. In *SAGEO 2021, 16ème Conférence Internationale de la Géomatique, de l'Analyse Spatiale et des Sciences de l'Information Géographique*. 227–238. https://hal.science/hal-03240567/file/SAGEO_2021.pdf

Received June 7, 2023; revised August 21, 2023

<footnote n="1">¹https://projet.liris.cnrs.fr/vcity/</footnote>
# Un graphe de données pour mesurer les Objectifs de Développement Durable et comprendre l'héritage des événements sportifs sur les villes

Wissal Benjira*
DVRC, Pôle Léonard de Vinci
LASTIG, Univ. Gustave Eiffel, IGN
Paris La Défense, France
wissal.benjira@devinci.fr,ign.fr}

Faten Atigui
CEDRIC, CNAM
Paris, France
faten.atigui@lecnam.net

Bénédicte Bucher
LASTIG, Univ. Gustave Eiffel, IGN
Saint Mandé, France
benedicte.bucher@ign.fr

Malika Grim-Yefsah
LASTIG, Univ. Gustave Eiffel, ENSG
Marne la Vallée, France
malika.grim-yefsah@ensg.eu

Nicolas Travers
DVRC, Pôle Léonard de Vinci
Paris La Défense, France
nicolas.travers@devinci.fr

## ABSTRACT

La disponibilité croissante de données joue un rôle majeur dans le suivi du développement durable des villes. Produire des indicateurs mesurant les Objectifs de Développement Durable (ODD) est un enjeu sociétal, nécessitant des sources hétérogènes, pour établir des comparaisons spatio-temporelles. Notre travail se focalise particulièrement sur l'étude de l'impact des événements sportifs sur les indicateurs de Développement Durable en mettant en évidence les enjeux managériaux liés à l'organisation de tels événements. Nous proposons une approche reposant sur un framework simplifié pour évaluer l'héritage laissé par ces événements, en utilisant un lac sémantique représenté par une base de données graphe stockée dans Neo4j. Cette approche est illustrée par une étude de cas basée sur des données variées, permettant de comparer l'état du territoire hôte avant, pendant et après la Coupe d'Europe de Football 2016 et d'évaluer l'évolution d'indicateurs dans différentes zones géographiques.

## ACM Reference Format:

Wissal Benjira, Faten Atigui, Bénédicte Bucher, Malika Grim-Yefsah, and Nicolas Travers. Un graphe de données pour mesurer les Objectifs de Développement Durable et comprendre l'héritage des événements sportifs sur les villes. In *Proceedings of 39ème Conférence sur la Gestion de Données – Principes, Technologies et Applications (BDA'23)*.

## 1 CONTEXTE GÉNÉRAL

La disponibilité croissante de données couvrant des aspects variés de la réalité est une opportunité pour mieux observer et comprendre cette réalité dans sa complexité. Dans le domaine du développement durable urbain, ces données jouent un rôle crucial pour évaluer et mesurer l'impact des actions entreprises dans les villes [1, 2, 6]. Les Objectifs de Développement Durable (ODD) définis et reconnus par les pays membres des Nations Unies, mettent à disposition des indicateurs destinés à servir de fondement principal pour suivre les progrès vers la réalisation de ces ODD [8].

L'émergence d'événements sportifs, tels que les Jeux Olympiques, suscite un intérêt particulier en raison de leur influence significative sur les territoires hôtes. Ces évènements sportifs sont des phénomènes spatio-temporels qui affectent structurellement, économiquement et socialement un territoire accueillant ces évènements, générant ainsi un « héritage » [4, 5, 9]. L'étude de cet héritage repose sur la mesure comparative des différents impacts (ex. sociétaux, structurels, environnementaux, pratiques sportives, etc.) de ces événements à la fois sur une zone géographique et sur leurs évolutions au cours du temps.

L'étude de cet héritage sur les territoires nécessite différentes sources de données. Toutefois, la collecte des données peut s'avérer particulièrement complexe en raison de la diversité des sources, de la variété des formats, et finalement du manque de relations explicites entre les différentes données. L'examen de la littérature démontre une absence de cadre unificateur permettant de mobiliser des données pourvues d'hétérogénéités sémantiques.

Le sujet de la thèse s'intéresse plus précisément à la structuration de données et de métadonnées afin d'effectuer des analyses critiques et des comparaisons relatives à l'impact d'évènements sur les espaces urbains. Ainsi, l'objectif principal est de fournir un framework de croisement de données pour élaborer et exploiter des indicateurs associés aux ODD. Ce sujet prend tout son intérêt pour les collectivités territoriales, pour des porteurs de projets numériques autour des pratiques sportives et pour les sponsors de grands évènements.

## 2 MODÉLISATION PROPOSÉE

Dans la littérature, différentes approches de modélisation de schémas sont adaptées aux bases orientées documents ou colonnes, mais peu sur des bases de données graphes géolocalisées [7]. Ainsi, nous proposons une méthode de conception de lacs de données basée sur des bases de données graphes liés au contexte géomatique, i.e. intégrant des données géographiques, favorisant l'extraction et l'exploitation d'indicateurs ODD.

L'approche proposée repose sur un framework qui simplifie et facilite les différentes étapes d'évaluation de l'héritage en matière de développement durable; à savoir l'identification, le traitement et l'interprétation des données croisées pour obtenir des résultats plus

<footnote>*Auteur correspondant</footnote>
BDA'23, Octobre 2023, Montpellier, France

Benjira W. et al.

<figure><img src="image_31.png" /><figcaption>Figure 1: Sous-graphe Neo4j autour de l'événement sportif de la Coupe d'Europe de Football 2016</figcaption></figure>

significatifs. Le lac sémantique obtenu est représenté et structuré sous forme de base de données graphe facilitant le rapprochement des données avec un schéma de graphe de données unifié, des règles d'enrichissement du graph et des opérations sur le graph pour produire des mesures et leur composition pour définir les indicateurs.

## 3 CAS D'ÉTUDE

Pour implémenter l'approche sémantique, nous développons une méthodologie de conception de base de données graphe guidée par les cas d'usages. Le prototype permet de valider et évaluer l'approche proposée. Nous avons choisi de considérer l'évaluation de l'impact de l'organisation d'événements sportifs sur l'indicateur du taux de chômage (commun à plusieurs ODD) en utilisant les différentes sources de données :

* Recensement de l'activité professionnelle par l'INSEE. La donnée est disponible à l'échelle des IRIS<fnref n="1" /> et renseigne sur les caractéristiques des actifs, ex. le sexe, l'âge ou la catégorie socio-professionnelle.

* Géolocalisations des gares et stations par SmartIDF. Il s'agit d'un recensement des stations de transports en communs.

* Sites sportifs et installations sportives par SmartIDF. Il s'agit d'un recensement des installations sportives, ex. stades de football.

Pour illustrer, prenons l'événement « Coupe d'Europe de Football 2016 ». Nous avons pu établir le sous-graphe de la figure 1 offrant une vue globale des interconnexions entre les différentes entités.

Ce modèle de graphe facilite l'établissement d'indicateurs comparables dans le temps et dans l'espace, permettant ainsi des comparaisons du taux de chômage avant, pendant et après l'événement: l'héritage. Afin de manipuler le graphe et les indicateurs, le graphe est stocké dans Neo4j. Des requêtes Cypher interrogent aussi bien l'évolution temporelle du graphe que la géolocalisation du lieu.

Cette géolocalisation est représentée à partir de sa description [3]. Par ailleurs, du fait que les indicateurs ODD reposent sur la composition de plusieurs mesures, notre Framework combine différentes requêtes pour calculer ces indicateurs de plus haut niveau.

Ainsi, l'évolution du graphe offre également la possibilité d'analyser ces indicateurs et d'observer les variations dans différentes zones géographiques. Les tendances démographiques, telles que les mouvements de population par sexe et par tranche d'âge, peuvent être analysées. De plus, l'analyse géopatiale permet d'identifier les zones où l'impact de l'événement est plus significatif. Les résultats obtenus peuvent être rapprochés à des cibles ODD et ainsi à des mesures d'héritages, permettant ainsi d'évaluer l'impact de l'événement par rapport à ces objectifs globaux de durabilité.

## REFERENCES

[1] K. Anderson, B. Ryan, W. Sonntag, A. Kavvada, and L. Friedl. 2017. Earth observation in service of the 2030 Agenda for Sustainable Development. *Geo-spatial Information Science* 20 (2017).

[2] L. Ballerini and S. Bergh. 2021. Using citizen science data to monitor the Sustainable Development Goals: A bottom-up analysis. *Sustain. Sustain Sci* 16 (2021).

[3] H. Chen, M. Vasardani, S. Winter, and M. Tomko. 2018. A Graph Database Model for Knowledge Extracted from Place Descriptions. *ISPRS International Journal of Geo-Information* 7 (2018).

[4] M. Grim-Yefsah and B. Bucher. 2021. Towards Improving Knowledge Capitalization System for Sport Events Legacy. *Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KMIS* (2021).

[5] M. Harada. 2005. Hosting a mega-sports event and its impact upon city development challenges of the city of Osaka. Comparing Sports Policy, Sports Investment and Regional Development Initiatives in the Hosting of Sports Events in East Asia and Europe. (2005).

[6] K. Kurowska, R. Marks-Bielska, S. Bielski, A. Aleknavičius, and C. Kowalczyk. 2021. Geographic Information Systems and the Sustainable Development of Rural Areas. *Land* 10 (2021).

[7] J. Mali, S. Ahvar, F. Atigui, A. Azough, and N. Travers. 2022. A Global Model-Driven Denormalization. *Lecture Notes in Business Information Processing* 446 (2022).

[8] United Nations. 2015. Transforming our World: The 2030 Agenda for Sustainable Development. (2015).

[9] H. Preuss. 2019. Event legacy framework and measurement, International Journal Sport Policy Politics. *International Journal of Sport Policy and Politics* 11, 1 (2019), 103–118.

<footnote n="1">¹IRIS - Îlots Regroupés pour l'Information Statistique</footnote>
# Apprentissage Distribué à Grande Echelle des Embeddings d'Ontologies avec OWL2Vec*

Yuhe Bai

yuhe.bai@lip6.fr

Sorbonne University

Paris, France

## ABSTRACT

OWL2Vec*[1] est un algorithme conçu pour apprendre les embeddings d'ontologies en se basant sur des marches aléatoires afin de générer des séquences de mots, et en utilisant Word2Vec[7] pour calculer les embeddings. Bien que cet algorithme soit capable de produire des embeddings de haute qualité pour des tâches ultérieures, il présente actuellement une limitation en termes de scalabilité, car il traite de grandes ontologies sur une seule machine. Dans cette étude, nous visons à surmonter cette limitation en distribuant l'algorithme. Pour ce faire, nous partitionnons le graphe de connaissances, effectuons des marches aléatoires sur les sous-graphes, calculons les embeddings localement, puis réconcilions les différents espaces d'embeddings.

## KEYWORDS

Algorithme distribué, Apprentissage distribué, Ontologies, Partitionnement de graphes, Marches aléatoires

## 1 INTRODUCTION

La représentation des graphes de connaissances ou *knowledge graphs* (KG) sous forme d'embeddings a suscité un grand intérêt au cours des dernières années [12]. Ces embeddings cherchent à représenter les éléments des KG, tels que les entités et les relations, dans un espace vectoriel qui préserve la structure du graphe. Un certain nombre d'algorithmes d'embeddings pour les KG ont été proposés et se sont avérés performants dans diverses tâches de prédiction.

Cependant, un grand nombre de ces algorithmes se concentrent sur les triplets de faits et sont insuffisants pour traiter les ontologies OWL ou les schémas ontologiques exprimés en OWL. Ces ontologies sont plus complexes que les simples structures de graphes, intégrant des opérateurs logiques tels que la disjonction de classe, la quantification existentielle et universelle et des métadonnées qui comprennent des synonymes, des définitions et des annotations relatives à une classe.

OWL2Vec*[1] est l'une des premières tentatives pour intégrer la sémantique des ontologies OWL, y compris la structure des graphes, les littéraux et les opérateurs logiques. Sa conception générale a été conçue pour gérer de manière flexible diverses sémantiques OWL.

Bien que cet algorithme d'embedding d'ontologies soit capable de produire des embeddings de haute qualité pour diverses tâches prédictives, il manque de capacité pour la formation à grande

échelle sur plusieurs machines. Nous proposons donc un cadre d'apprentissage distribué.

Dans le contexte des algorithmes distribués, il est crucial de maintenir la qualité des embeddings générés. De plus, la communication entre machines peut être un goulot d'étranglement significatif. En résumé, la distribution d'OWL2Vec* présente des défis liés au partitionnement des graphes, aux marches aléatoires sur les sous-graphes, à la gestion des coûts de communication, et à la garantie de haute qualité des embeddings.

## 2 TRAVAUX CONNEXES

De nombreuses méthodes ont été proposées pour apprendre les embeddings de graphes de connaissances, comme TransE[4], DistMult[5], et ComplEx[11]. Concernant les embeddings d'ontologies, des algorithmes comme Onto2Vec[8], OPA2Vec[9], et OWL2Vec*[1] ont été développés, ce dernier offrant une intégration robuste de la sémantique des ontologies OWL. Des frameworks distribués[3] ont également été explorés pour l'apprentissage d'embeddings sur plusieurs machines.

Cependant, les systèmes existants ont des limites pour traiter les ontologies avec des expressions OWL et ont des coûts de communication élevés. Dans notre travail, nous visons à améliorer le passage à l'échelle d'OWL2Vec*[1] avec une version distribuée de l'algorithme, introduisant un processus de partitionnement du graphe et de réconciliation des espaces d'embeddings, tout en réduisant les coûts de communication.

## 3 MÉTHODOLOGIE

Nous nous appuyons sur la méthode proposée par [3] pour distribuer le calcul d'embeddings de graphes. Cette méthode consiste à choisir un petit nombre de noeuds appelés points de repère ou landmarks et qui serviront par la suite à réconcilier les embeddings. Nous adaptons cette méthode pour distribuer le calcul des embeddings de KG définis dans OWL2Vec*[1].

### Etape 1: Partitionnement du graphe de connaissances

La première étape consiste à partitionner le graphe de connaissances. Pour un premier temps, nous utilisons l'algorithme de partitionnement classique graph-cut METIS[6] pour cette tâche. Les noeuds partagés sont utilisés comme landmarks et aideront à l'étape de réconciliation. Nous explorons actuellement d'autres techniques de partitionnement que METIS qui pourraient s'avérer bénéfiques et plus adaptées aux graphes de connaissances.

### Etape 2: Extraction de marches aléatoires et entraînement de Word2vec

Après avoir partitionné le graphe de connaissances, nous extrayons des marches aléatoires de chaque sous-graphe et entraînons Word2vec[7] localement sur chaque partition, y compris les landmarks. Ainsi,

<footnote>© 2023, Copyright is with the authors. Published in the Proceedings of the BDA 2023 Conference (October 23-26, 2023, Montpellier, France). Distribution of this article is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0.</footnote>

<footnote>© 2023, Droits restant aux auteurs. Published in the actes de la conférence BDA 2023 (23-26 octobre 2023, Montpellier, France). Redistribution de cet article autorisée selon les termes de la licence Creative Commons CC-by-nc-nd 4.0.</footnote>
chaque partition a sa propre représentation d'un espace latent cap-
turant les informations spécifiques à cette partition.

Nous explorons actuellement d'autres méthodes que Word2Vec pour le calcul des embeddings. Les modèles de langue pré-entrainés tels que BERT[2] pourraient être capable de représenter, de manière complémentaire à OWL, la sémantique d'un graphe de connaissances.

### Etape 3: Calcul de la matrice de transformation et réconciliation

Les nœuds partagés entre deux partitions auront des embeddings distincts dans leurs espaces latents respectifs. Nous réutilisons la stratégie de réconciliation proposée dans [3]. Cette méthode détermine une "rotation" permettant d'associer un premier espace d'embeddings à un autre : Une matrice de transformation W est calculée à partir des embeddings des landmarks communs aux deux espaces en utilisant une décomposition en valeurs singulières (SVD). Ensuite les embeddings "réconciliés" sont obtenus en multipliant par W les embeddings du premier espace. La Figure 1 illustre les étapes de notre approche.

<figure><img src="image_32.png" /><figcaption>Figure 1: Principales étapes de notre méthode</figcaption></figure>

## 4 RÉSULTATS EXPÉRIMENTAUX

Le tableau 1 présente les résultats obtenus pour deux partitions, sans et avec réconciliation, en utilisant le jeu de données FoodOn pour la prédiction de subsumption de classe (Prédiction d'une classe par-ente d'une classe). On peut observer que la réconciliation améliore d'environ 30% la qualité de la prédiction par rapport à la méthode sans réconciliation, tout en maintenant un apprentissage entière-ment localisé et un coût de communication faible.

<table><caption>Table 1: Résultat sur la tâche de class subsumption prédiction sur FoodOn</caption><thead><tr><th></th><th>MRR</th><th>Hits@1</th><th>Hits@5</th><th>Hits@10</th></tr></thead><tbody><tr><td>No Reconciliation</td><td>12.8</td><td>7.0</td><td>17.3</td><td>23.0</td></tr><tr><td>With Reconciliation</td><td><strong>16.4</strong></td><td><strong>10.1</strong></td><td><strong>22.8</strong></td><td><strong>28.6</strong></td></tr></tbody></table>

Nous poursuivons la validation de notre approche en consid-
érant des jeux de données plus grands, en explorant d'autres tâches
comme la prédiction de liens et le typage d'entités, et en étudiant
l'impact du partitionnement sur la méthode de réconciliation et la
qualité des résultats.

## 5 CONCLUSION

Dans ce travail, nous avons apporté plusieurs contributions :

(1) nous avons distribué l'algorithme OWL2Vec*[1] avec suc-
cès en utilisant l'algorithme de partitionnement de graphes
METIS[6], et réconcilié les embeddings avec l'aide de la dé-
composition en valeurs singulières (SVD).

(2) La réconciliation a montré son efficacité en produisant des embeddings de haute qualité et en réduisant les coûts de communication.

Pour les travaux futurs, nous envisageons d'optimiser notre sélection de landmarks et d'évaluer notre méthode sur d'autres graphes de connaissances à grande échelle comme YAGO[10], pour tester la polyvalence et l'efficacité de notre méthode.

## 6 RECONNAISSANCE

Je tiens à remercier le Centre d'Intelligence Artificielle de la Sor-
bonne (SCAI) pour le financement partiel de cette recherche par le
biais d'une bourse doctorale. Je tiens également à remercier mon
directeur de thèse, Hubert Naacke, ainsi que ma co-encadrante,
Camelia Constantin, pour leurs précieux conseils tout au long de
mes travaux de recherche.

## REFERENCES

[1] Jiaoyan Chen, Pan Hu, Ernesto Jimenez-Ruiz, Ole Magnus Holter, Denvar Antonyrajah, and Ian Horrocks. 2021. Owl2vec*: Embedding of owl ontologies. *Machine Learning* 110, 7 (2021), 1813–1845.

[2] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 [cs.CL].

[3] Chi Thang Duong, Trung Dung Hoang, Hongzhi Yin, Matthias Weidlich, Quoc Viet Hung Nguyen, and Karl Aberer. 2021. Scalable robust graph embedding with Spark. *Proceedings of the VLDB Endowment* 15, 4 (2021), 914–922.

[4] Bordes et al. 2013. Translating embeddings for modeling multi-relational data. *Advances in neural information processing systems* 26 (2013).

[5] Yang et al. 2014. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014).

[6] George Karypis and Vipin Kumar. 1997. METIS: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices. (1997).

[7] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781 [cs.CL].

[8] Fatima Zohra Smaili, Xin Gao, and Robert Hoehndorf. 2018. Onto2vec: joint vector-based representation of biological entities and their ontology-based annotations. *Bioinformatics* 34, 13 (2018), i52–i60.

[9] Fatima Zohra Smaili, Xin Gao, and Robert Hoehndorf. 2019. OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction. *Bioinformatics* 35, 12 (2019), 2133–2140.

[10] Fabian M Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: a core of semantic knowledge. In *Proceedings of the 16th international conference on World Wide Web*. 697–706.

[11] Théo Trouillon, Johannes Welbl, Sebastian Riedel, Eric Gaussier, and Guillaume Bouchard. 2016. Complex Embeddings for Simple Link Prediction. In *Proceedings of The 33rd International Conference on Machine Learning*. 2071–2080.

[12] Quan Wang, Zhendong Mao, Bin Wang, and Li Guo. 2017. Knowledge graph embedding: A survey of approaches and applications. *IEEE Transactions on Knowledge and Data Engineering* 29, 12 (2017), 2724–2743.
# Entropy Maximisation For Diverse Recommendations

Jonathan Colin

jonathan.colin@universite-paris-saclay.fr

Paris Saclay University

Gif-sur-Yvette, France

## ABSTRACT

The popularity of online social networks and the interactions they allow has brought great benefits in terms of ease of communication between humans. On the other hand, it has been found that they may also be a disruptive force for society, notably through the spread of fake news or the creation of filter bubbles. Indeed, in order to maximize user engagement, recommender systems tend to overrate trendy content or content that is similar to a user's established taste; this overshadows any form of new, niche, or diverse content. This is a critical issue as these processes end up increasing opinion polarization. In this paper, in order to alleviate this issue, we propose a new semantic for diverse link recommendations in online social networks based on increasing the degree of information entropy. We perform a comparative analysis of our proposed

semantic to baseline additional semantics for recommendation and find that traditional recommender systems tend to reduce information entropy. At the same time, experimental results show that the proposed semantic manages to promote diverse content.

## KEYWORDS

social networks, echo chambers, diversity, optimization, recommender systems

© 2023, Copyright is with the authors. Published in the Proceedings of the BDA 2023 Conference (October 23-26, 2023, Montpellier, France). Distribution of this article is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0.

© 2023, Droits restant aux auteurs. Published dans les actes de la conférence BDA 2023 (23-26 octobre 2023, Montpellier, France). Redistribution de cet article autorisée selon les termes de la licence Creative Commons CC-by-nc-nd 4.0.
# 8 Prix BDA 2023

## 8.1 Prix des articles de recherche

BDA a la particularité de proposer deux catégories d'articles : les articles originaux non publiés et les articles publiés récemment dans une conférence internationale de renom. Cette dernière catégorie permet de diffuser largement les travaux faisant la renommée internationale de notre communauté nationale en gestion de données.

### Lauréats du prix des articles de recherche

Scalable Reasoning on Document Stores via Instance-Aware Query Rewriting, Olivier Rodriguez, Federico Ulliana, Marie-Laure Mugnier

GPC : A Pattern Calculus for Property Graphs, Nadime Francis, Amélie DR Gheerbrant, Paolo Gugliardo, Leonid Libkin, Victor Marsault, Wim Martens, Filip Murlak, Liat Peterfreund, Alexandra Rogova, Domagoj Vrgoc

## 8.2 Prix des démonstrations

### Lauréat du prix des démonstrations

Headwork : Powering the Crowd with Tuple Artifacts, David Gross-Amblard, Constance Thierry

## 8.3 Prix des thèses en gestion de données

### Lauréats du prix des thèses

Prix de thèse : Quentin Manière pour sa thèse intitulée
*Counting Queries in Ontology-Based Data Access.*

encadrants : Meghyn Bienvenu et Michaël Thomazo

Accessit au Prix de Thèse : Daniel Rosendo pour sa thèse intitulée
*Methodologies for Reproducible Analysis of Workflows on the Edge-to-Cloud Continuum*

encadrants : Gabriel Antoniu, Alexandru Costan et Patrick Valduriez