7.1.1 ConnectionLens

• Keywords:
  Data management, Big data, Information extraction, Semantic Web
• Functional Description:
  ConnectionLens treats a set of heterogeneous, independently authored data sources as a single virtual graph, whereas nodes represent fine-granularity data items (relational tuples, attributes, key-value pairs, RDF, JSON or XML nodes…) and edges correspond either to structural connections (e.g., a tuple is in a database, an attribute is in a tuple, a JSON node has a parent…) or to similarity (sameAs) links. To further enrich the content journalists work with, we also apply entity extraction which enables to detect the people, organizations etc. mentioned in text, whether full-text or text snippets found e.g. in RDF or XML. ConnectionLens is thus capable of finding and exploiting connections present across heterogeneous data sources without requiring the user to specify any join predicate.
• URL:
  https://­team.­inria.­fr/­cedar/­connectionlens/
• Publications:
  hal-02934277, hal-02904797, hal-01841009
• Authors:
  Tayeb Merabti, Helena Galhardas, Julien Leblay, Ioana Manolescu, Oana-Denisa Balalau, Catarina Pinto Conceicao
• Contact:
  Manolescu Ioana

7.1.2 ConnectionLensInMem

• Keywords:
  Data management, Graph processing
• Functional Description:
  In-memory graph-based keyword search. It works in collaboration with ConnectionLens and it focuses on parallelization of the query execution. The software includes a module to export a graph from ConnectionLens PostgreSQL warehouse which can then be loaded in the main memory for querying.
• Contact:
  Angelos Anadiotis

7.1.3 Butterfly

• Keywords:
  Data management, Databases, Graph processing
• Functional Description:
  Integrated system for data science workload processing. Butterfly includes operators for relational and graph processing, as well as different data layouts and execution models.
• Contact:
  Angelos Anadiotis

7.1.4 GraphCite

• Name:
  GraphCite: Citation Intent Classification in Scientific Publications via Graph Embeddings
• Keywords:
  Graph embedding, Citation intent prediction
• Functional Description:
  Citations are crucial in scientific works as they help position a new publication. Each citation carries a particular intent, for example, to highlight the importance of a problem or to compare against results provided by another method. The authors’ intent when making a new citation has been studied to understand the evolution of a field over time or to make recommendations for further citations. In this software, we address the task of citation intent prediction from a new perspective. In addition to textual clues present in the citation phrase, we also consider the citation graph, leveraging high-level information of citation patterns.
• Contact:
  Oana-Denisa Balalau

7.1.5 Abstra

• Name:
  Abstra: Toward Generic Abstractions for Data of Any Model
• Keywords:
  Heterogeneous Data, Data Exploration, Data analysis, Databases, LOD - Linked open data
• Functional Description:
  Abstra computes a description meant for humans, based on the idea that, regardless of the syntax or the data model, any dataset holds some collections of entities/records, that are possibly linked with relationships. Abstra relies on a common graph representation of any incoming dataset, it leverages Information Extraction to detect what the dataset is about, and relies on an original algorithm for selecting the core entity collections and their relations. Abstractions are shown both as HTML text and a lightweight Entity-Relationship diagram. A GUI also allows to tune the abstraction parameters and explore the dataset.
• URL:
  https://­team.­inria.­fr/­cedar/­projects/­abstra/
• Contact:
  Nelly Barret

7.1.6 StatCheck

• Name:
  Fact-checking Multidimensional Statistic Claims in French
• Keywords:
  Machine learning, Databases, Natural language processing, Software engineering
• Scientific Description:
  To strengthen public trust and counter disinformation, computational fact-checking, leveraging digital data sources, attracts interest from the journalists and the computer science community. A particular class of interesting data sources comprises statistics, that is, numerical data compiled mostly by governments, administrations, and international organizations. Statistics are often multidimensional datasets, where multiple dimensions characterize one value, and the dimensions may be organized in hierarchies. This paper describes STATCHECK, a statistic fact-checking system jointly developed by the authors, which are either computer science researchers or fact-checking journalists working for a French-language media with a daily audience of more than 15 millions (aud, 2022). The technical novelty of STATCHECK is twofold: (i) we focus on multidimensional, complex-structure statistics, which have received little attention so far, despite their practical importance, and (ii) novel statistical claim extraction modules for French, an area where few resources exist. We validate the efficiency and quality of our system on large statistic datasets (hundreds of millions of facts), including the complete INSEE (French) and Eurostat (European Union) datasets, as well as French presidential election debates.
• Functional Description:
  StatCheck firstly allows the collection of data for its operation. Two types of data are collected: statistical tables and posts from social networks: - Acquisition of statistical files on the site of referent organisations (INSEE, Eurostat) - Extraction of statistical tables from these files, and storage of the extracted tables - Acquisition of political tweets from a list of accounts The application allows the detection, extraction and search of statistical facts: - Detection and extraction of statistical facts from Twitter posts (e.g. "Unemployment rate increased by 30% in 2023) - Search for statistical facts in our database. Display of the twenty most relevant statistical tables for a statistical fact - Automatic transcription of audio files to detect and extract transcripts of statistical facts.
• Release Contributions:
  - Redesign of the user interface - Modification of the software architecture - Addition of audio transcription
• URL:
  https://­cedar-rf.­saclay.­inria.­fr/
• Publications:
  hal-01496700, hal-01745768, hal-02121389, hal-01915148, hal-03767992, hal-03791175
• Contact:
  Ioana Manolescu
• Participants:
  Tien Duc Cao, Ioana Manolescu, Xavier Tannier, Oana-Denisa Balalau, Simon Ebel, Theo Galizzi

8.1.1 Graph integration of heterogeneous data sources for data journalism

Work carried within the ANR AI Chair SourcesSay project has focused on developing a platform for integrating arbitrary heterogeneous data into a graph, then exploring and querying that graph in a simple, intuitive manner through keyword search 11. The main technical challenges are: (i) how to interconnect structured and semi-structured data sources? We address this through information extraction (when an entity appears in two data sources or two places in the same graph, we only create one node, thus interlinking the two locations) and through similarity comparisons; (ii) how to find all connections between nodes matching specific search criteria, or certain keywords? The question is particularly challenging in our context since ConnectionLens graphs can be pretty large, and query answers can traverse edges in both directions.

In this context, the following new contributions have been brought:

1. Integrating connection search in graph queries. When graphs are very heterogeneous and/or users are unfamiliar with their structure, they may need to find how two or more groups of nodes are connected in a graph, even when users cannot describe the connections. This is only partially supported by existing query languages, which allow searching for paths, but not for trees connecting three or more node groups. Prior work on keyword search in databases tackled variants of this problem, related, in its most general form, to the NP-hard Group Steiner Tree problem. We show how to integrate connecting tree patterns (CTPs, in short) with a graph query language such as GPML, SPARQL, or Cypher, leading to Extended Queries. We then study a set of algorithms for evaluating CTPs; we generalize prior keyword search work to be complete, most importantly by (i) considering bidirectional edge traversal, (ii) allowing users to select any score function for ranking CTP results, and (iii) returning all results. Finally, to cope with very large search spaces, we developed efficient pruning techniques and formally established a large set of cases where our best algorithm, MOLESP, is complete even with pruning. Its benefits are validated on synthetic and real-world workloads 21, 27.
2. Shared GAM: generalizing GAM search over graphs for batch keyword query evaluation. GAM is a novel query algorithm for keyword search in graphs. Given a query as a set of search terms, GAM finds links between them within the graph. However, when multiple keyword queries share some of their keywords, GAM runs once per query, leading to duplicate computations for the common keywords and, therefore, inefficiency. In this work, we aim to eliminate this inefficiency by developing a shared version of GAM (we refer to it as SharedGAM) algorithm to execute such keyword queries together by sharing computations.
3. Abstra: toward generic abstractions for data of any model. Abstra is an all-in-one dataset abstraction system that produces an Entity-Relationship schema and a textual description out of any (semi)-structured dataset (e.g., XML documents, JSON documents, RDF graphs, property graphs). Such a system helps users to grasp the content of a dataset they don't know and decide whether it is useful for their needs. Moreover, many actors might benefit from such abstractions, such as scientific researchers, (data) journalists, data providers, and consumers. In a nutshell, Abstra starts by transforming any incoming dataset into a graph. Then, it summarizes it and detects the main entities and their relationships. Main entities are further classified into user-friendly categories. Finally, the abstraction result is presented in the form of an HTML page, showing the textual description of the dataset and the Entity-Relationship schema, both built with the main entities and their relationships found previously. This work has led to a demonstration paper 15, 25.
4. Identifying disguised missing values in heterogeneous, text-rich data Digital data is produced in many data models, ranging from highly structured (typically relational) to semi-structured models (XML, JSON) to various graph formats (RDF, property graphs) or text. Most real-world datasets contain a certain amount of null values, denoting missing, unknown, or inapplicable information. While some data models allow representing nulls by special tokens, so-called disguised missing values (DMVs, in short) are also frequently encountered: these are values that are not syntactically speaking nulls but which do, nevertheless, denote the absence, unavailability, or inapplicability of the information. In this work, we tackle the detection of a particular kind of DMV: texts freely entered by human users. This problem is not tackled by DMV detection methods focused on numeric or categoric data; further, it also escapes DMV detection methods based on value frequency since such free texts are often different from each other, thus most DMVs are unique. We encountered this problem within the ConnectionLens project, where heterogeneous data is integrated into large graphs. We presented two DMV detection methods for our specific problem: (i) leveraging Information Extraction, already applied in ConnectionLens graphs; and (ii) through text embeddings and classification. We detail their performance-precision trade-offs on real-world datasets 26.

ConnectionLens is available online at: https://gitlab.inria.fr/cedar/connection-lens.

8.1.2 Fact-checking Multidimensional Statistic Claims in French

To strengthen public trust and counter disinformation, computational fact-checking, leveraging digital data sources, attracts interest from journalists and the computer science community. A particular class of interesting data sources comprises statistics, that is, numerical data compiled mostly by governments, administrations, and international organizations. Statistics are often multidimensional datasets, where multiple dimensions characterize one value and the dimensions may be organized in hierarchies. To address this challenge we developed STATCHECK, a statistic fact-checking system, in collaboration with RadioFrance. The technical novelty of STATCHECK is twofold: (i) we focus on multidimensional, complex-structure statistics, which have received little attention so far, despite their practical importance; and (ii) novel statistical claim extraction modules for French, an area where few resources exist. We validate the efficiency and quality of our system on large statistic datasets (hundreds of millions of facts), including the complete INSEE (French) and Eurostat (European Union) datasets, as well as French presidential election debates 13, 14.

8.2.1 Argumentation mining

Humans use argumentation daily to evaluate the validity of new ideas, convince an addressee, or solve a difference of opinion. An argument contains a statement to be validated (a proposition also called claim or conclusion), a set of backing propositions (called premises, which should be accepted ideas), and a logical connection between all the pieces of information presented that allows the inference of the conclusion. In this work, we will focus on fallacies: weak arguments that seem convincing, however, their evidence does not prove or disprove the argument's conclusion. Fallacy detection is part of argumentation mining, the area of natural language processing dedicated to extracting, summarizing, and reasoning over human arguments. The task is closely related to propaganda detection, where propaganda consists of a set of manipulative techniques, such as fallacies, used in a political context to enforce an agenda 2. In the past, we have worked on propaganda 2 and fallacy detection 10. We continue this work with a CIFRE PhD that started this year, a collaboration between the Amundi company, Inria and Télécom Paris. This thesis aims to improve fallacy detection in natural language by leveraging both language patterns and additional information, such as common sense knowledge, encyclopedic knowledge and logical rules. To achieve this we will focus on how fallacies can be represented and how we can classify reasoning patterns in argumentation. The interest of Amundi is in how argumentation can be applied to finding examples of greenwashing.

8.2.2 Finding conflicts of opinion on citizen participation platforms

Online citizen participation platforms allow large numbers of contributors to be involved in public decision-making, overcoming limiting factors for their offline counterparts, such as their geographical position. However, for large groups of contributors to collaborate and co-construct joint, well-elaborated proposals, we need to provide tools for users and decision-makers to navigate and understand high volumes of content. To achieve this, we introduce an approach based on natural language processing to detect pairs of contradictory and equivalent proposals in online citizen participation contexts. We apply this approach to two major national citizen consultations, namely the République Numérique and Revenu Universel d'Activité consultations. Our method leverages a Transformer-based classifier, fine-tuned on natural language inference datasets and on two weakly labeled datasets created using data from these consultations. We also address the classification problem on large texts by proposing alternative strategies explicitly designed for texts containing more than one sentence. Finally, we highlight the great potential of our tool in the analysis, synthesis, and recommendation of contributions to citizen participation platforms. This work is currently submitted for publication.

8.2.3 Open information extraction with entity-focused constraints

Open Information Extraction (OIE) is the task of extracting tuples of the form (subject, predicate, object), without any knowledge of the type and lexical form of the predicate, the subject, or the object. In this work, we focus on improving OIE quality by exploiting domain knowledge about the subject and object. More precisely, knowing that the subjects and objects in sentences are oftentimes named entities, we explore how to inject constraints in the extraction through constrained inference and constraint-aware training. Our work leverages the state-of-the-art OpenIE6 platform, which we adapt to our setting. Through a carefully constructed training dataset and constrained training, we obtain a $29.17\%$ F1-score improvement in the CaRB metric and a $24.37\%$ F1-score improvement in the WIRe57 metric. Our technique has important applications, specifically in the construction of vertical knowledge bases supporting investigative journalism, where automatically extracting conflict-of-interest between scientists and funding organizations helps understand the type of relations companies engage with the scientists. This work is under submission.

8.2.4 Citation intent prediction in scientific articles

Citations are crucial in scientific works as they help position a new publication. Each citation carries a particular intent, for example, to highlight the importance of a problem or to compare against results provided by another method. The authors' intent when making a new citation has been studied to understand the evolution of a field over time or to make recommendations for further citations. In this work, we address the task of citation intent prediction from a new perspective. In addition to textual clues present in the citation phrase, we also consider the citation graph, leveraging high-level information of citation patterns. In this novel setting, we perform a thorough experimental evaluation of graph-based models for intent prediction. We show that our model, GraphCite, improves significantly upon models that consider only the citation phrase 16.

8.2.5 Graph-to-text for question generation

The first topic we explored in our work is automatically generating suitable questions for existing knowledge bases. A knowledge graph (KB) is represented by a set of triples, where each triple is composed of a subject, predicate, and object. Our question generation model aims to generate a set of answerable questions from a given knowledge graph KB, where each question corresponds to a subgraph of KB. We are investigating training Transformer networks to generate questions from knowledge graphs. This approach is based on existing datasets that match questions to subgraphs (e.g., SimpleQuestions, GraphQuestions, GrailQA, etc.). The subgraphs in these datasets are usually from existing KBs such as FreeBase and WikiData. An important challenge of this approach is that the neural network usually fails to predict the correct sentences under zero-shot settings, i.e. when it encounters unseen predicates or entities in the test set. This problem is especially acute in the case of zero-shot predicates. We intend to use unsupervised methods for the neural network to learn to understand the unseen predicates and entities to help the question generation under zero-shot settings.

In parallel, we are aligning Question-Answering datasets across similar KBs. We hope that this work will help us have larger training sets for graph-to-text generation of questions. At the core, this problem involves aligning Freebase's entities, classes, and predicates to those of YAGO4. This is especially interesting because (i) even though YAGO has been around for quite some time, there is a dearth of QA datasets on it, and (ii) there are many QA datasets on Freebase, but Google is no longer maintaining Freebase. We have used the paraphrase model of BERT for computing predicate matching with some success. Inspired by previous works, we have also developed a Greedy Matching algorithm for iteratively aligning the two KGs. Going forward, we will now evaluate the Bert model's performance, improve the Greedy Matcher's results, and finally generate a QA dataset on YAGO4.

8.3.1 Towards speeding Up graph-Relational queries in RDBMSs

Graph data is generally stored and processed using two main approaches: (i) extending existing relational database management systems (RDBMSs) with graph capabilities, and (ii) through native graph database management systems (GDBMSs). The advantage of leveraging RDBMSs is to benefit from the maturity of their query optimization and execution. Conversely, native GDBMSs treat complex graph structures as a first-class citizens, which may make them more efficient on complex structural queries. In this work, we consider the processing of graph-relational queries, that is, queries mixing graph and relational operators, on graph data. We take a purely relational approach, reorganizing the graph connectivity information using a novel CSR Optimised Schema (COS). Based on our storage model, incoming queries are reformulated to account for the COS data organization, which can then be optimized and executed by an RDBMS. We have implemented our approach on top of PostgreSQL and we demonstrate that COS improves the performance for many graph-relational queries of the popular Social Network Benchmark 23.

8.3.2 Scalable analytics on multi-streams dynamic graphs summary

Several real-time applications rely on dynamic graphs to model and store data arriving from multiple streams. In addition to the high ingestion rate, the storage and query execution challenges are amplified in contexts where consistency should be considered when storing and querying the data. This Ph.D. thesis addresses the challenges associated with multi-stream dynamic graph analytics. We propose a database design that can provide scalable storage and indexing to support consistent read-only analytical queries (present and historical), in the presence of real-time dynamic graph updates that arrive continuously from multiple streams 22.

8.3.3 Fine-grained modeling and optimization for intelligent resource management in Big Data processing

Big data processing at the production scale presents a highly complex environment for resource optimization (RO), a problem crucial for meeting analytical users' performance goals and budgetary constraints. The RO problem is challenging because it involves a set of decisions (the partition count, placement of parallel instances on machines, and resource allocation to each instance), requires multi-objective optimization (MOO), and is compounded by the scale and complexity of big data systems while having to meet stringent time constraints for scheduling. This project addressed the resource optimization problem for a custom-built big data processing system (MaxCompute) of the Alibaba Cloud. It supports multi-objective resource optimization via fine-grained instance-level modeling and optimization. We proposed a new architecture that breaks RO into simpler problems, new fine-grained predictive models, and novel optimization methods that exploit these models to make effective instance-level RO decisions well under a second 18.

8.4.1 Fast and Explainable Anomaly Detection over High-Dimensional Data Streams

In our increasingly digital and connected society, high-volume data streams have become more and more prevalent and complex. This has made incidents more likely, diverse, and therefore harder to manually anticipate and diagnose for humans. In this project, we aim to assist such anticipation and diagnosis through automated solutions, focusing on deep, unsupervised, and explainable anomaly detection. We conducted multiple comparative studies on the recently proposed Exathlon benchmark, focusing on two main use cases. In the first use case, we aim to detect and explain anomalies in time series recordings from repeated executions of Spark Streaming applications. In this use case, we observed that reconstruction-based and forecasting-based detection methods could generalize well to different execution environments while exhibiting complementary behaviors. In the second use case, we target anomalies in financial transactions coming from the Swift messaging network. In both use cases, our current research focuses on designing new methods that improve detection accuracy and explanation consistency, with a direction being to leverage a few anomaly labels, for instance via contrastive learning approaches.

8.4.2 Accurate, explainable anomaly detection through visually-informative projections

Despite their widespread adoption, anomaly detection (AD) systems thus far have mainly focused on detection power. However, emerging applications such as "Artificial Intelligence for IT operations" (AIOps) point to the need for "explainable anomaly detection" to enhance business operations with proactive, personalized, and dynamic insight and further enable corrective or preventive action to resolve IT performance issues. In our ongoing work, we address explainable AD by proposing a new explainable AD model, which achieves explainability via a set of visually-informative patterns in low-dimensional axis-aligned projections while retaining prediction accuracy. Our model, called VIPAD, builds on a classical explainable classification framework, VIPR, but addresses its fundamental limitations for anomaly detection. Our evaluation using the latest anomaly detection benchmark Exathlon for AIOps shows that VIPAD can approximate the accuracy of random forests, which is not explainable while outperforming other explainable models in both prediction accuracy and quality of explanations.

9.1.1 Horizon Europe

• Oana Goga is the PI – ERC Starting Grant 2022 – 2027 “MOMENTOUS: Measuring and Mitigating Risks of AI-driven Information Targeting” (1,499,952 €).

9.1.2 H2020 projects

• Oana Goga is the local PI for CNRS partner – EU H2020 2021 – 2024 “Trust aWARE: Enhancing Digital Security, Privacy and TRUST in softWARE” (our part: 461,000 €).

9.2.1 ANR

• Oana Goga is the local PI for LIX partner – ANR PRC 2022 – 2026 “FeedingBias: A multi-platform mixed-methods approach to news exposure on social media” (our part: 128,000 €)
• Oana Goga is the local PI for LIX partner – ANR PRCE 2021 – 2025 “PROPEOS: Privacy-oriented Personalization of Online Services” (our part: 202,720 €)
• CQFD (2019-2024) is an ANR project coordinated by F. Ulliana (U. Montpellier), in collaboration with U. Rennes 1 (F. Goasdoué), Inria Lille (P. Bourhis), Institut Mines Télécom (A. Amarilli), Inria Paris (M. Thomazo) and CNRS (M. Bienvenu). Its research aims at investigating efficient data management methods for ontology-based access to heterogeneous databases (polystores).
• SourcesSay (2020-2024) is an AI Chair funded by Agence Nationale de la Recherche and Direction Générale de l’Armement. The project goal is to interconnect data sources of any nature within digital arenas. In an arena, a dataset is stored, analyzed, enriched and connected, graph mining, machine learning, and visualization techniques, to build powerful data analysis tools.

9.2.2 Others

• The goal of the iCODA project is to develop the scientific and technological foundations for knowledge-mediated user-in-the-loop collaborative data analytics on heterogenous informationsources, and to demonstrate the effectiveness of the approach in realistic, high-visibility use-cases. The project stands at the crossroad of multiple research fields—content analysis, data management, knowledge represen- tation, visualization—that span multiple Inria themes, and counts on a club of major press partners to define usage scenarios, provide data and demonstrate achievements. This is a project funded directly by Inria (“Inria Project Lab”), and is in collaboration with GraphIK, ILDA, LINKMEDIA (coordinator), as well as the press partners AFP, Le Monde (Les Décodeurs) and Ouest-France.
• ANRT Project: CIFRE Amundi, advised by O.Balalau and F.Suchanek (Télécom Paris). The goal of this thesis is to improve fallacy detection in natural language, by leveraging both language patterns but also additional information, such as common sense knowledge, encyclopedic knowledge and logical rules. To achieve this we will focus on how fallacies can be represented1 and how we can classify reasoning patterns in argumentation.

10.1.1 Scientific events: selection

10.1.2 Journal

10.1.3 Invited talks

• Angelos Anadiotis presented teamwork on conflicts of interest in the biomedical domain at the University of Cornell Database Seminar.
• Oana Balalau:
  • Keynote "Moving Towards Better Online Argumentation" in workshop Deep Learning pour le traitement automatique des langues, associated with EGC 2022
  • Seminar NoRDF at Télécom Paris on "Argumentation Mining: Challenges and Opportunities in Detecting Fallacious Reasoning."
• Yanlei Diao
  • Distinguished Lecture, Berlin Institute for the Foundations of Learning and Data, September 15, 2022
• Oana Goga:
  • Keynote "Can we safeguard the micro-targeting of political ads? An algorithmic and regulatory perspective" at Colloque Big Data ISTC, Lille, October 2022
  • Seminar at INSA Lyon (DRIM@LIRIS Seminar), Lyon, October 2022
• Ioana Manolescu
  • "Teasing Journalistic Findings out of Heterogeneous Sources: A Data/AI journey" as a keynote at the DASFAA 2022 conference, then in the Colloque Jacques Morgenstern at Sophia Antipolis, on June 2, 2022, then on June 28 at the DEBS 2022 conference.
  • "Que disent les sources ? L’IA et le BigData au service de la détection des fausses nouvelles", seminar for the Digital section of Académie des Technologies
  • Participation to the panel "Quand la tech se met au service de l’information" at the "Médias en Seine" festival, on Nov 22, 2022

10.1.4 Leadership within the scientific community

• Oana Goga: Co-leader of the Action PLATFORM in GDR CIS.

10.1.5 Scientific expertise

• Oana Balalau: reviewed grant application for Viena Science Fund
• Oana Goga:
  • Advisory Boards: Member of the Science Advisory Committee for the NSF-funded Mid-scale RI-1 project (a 15 million US $ project): Observatory for online human and platform behavior (2022).
  • Institute Evaluation Committee: Evaluation of the Max Planck Graduate Center Computer Science (Jan 2023)
  • Award Committee: CNIL-Inria Privacy Protection Award (2022)
  • Grant reviewing: ERC Starting Grant (2022), German National Research Center for Applied Cybersecurity ATHEN (2022).

10.1.6 Research administration

• Ioana Manolescu
  • Joined the Comité Operationnel of Hi!Paris, an Institut Polytechnique de Paris organization fostering research on this topic, in October 2022.
  • Been a member of Inria's Bureau des Comités de Projet from January to October 2022.

10.2.1 Teaching

Until April 2022, A. Anadiotis has been a full-time Assistant Professor at Ecole Polytechnique, where he has been in charge of two courses:

• Master: A. Anadiotis, “Systems for Big Data”, M1, Ecole Polytechnique
• Master: A. Anadiotis, “Systems for Big Data Analytics”, M2, Ecole Polytechnique

O. Balalau is a part-time (33%) assistant professor at Ecole Polytechnique, where she teaches two courses:

• INF473G “Graphe Global Géant”, L3, Ecole Polytechnique
• INF583 “Systems for Big Data”, M1, Ecole Polytechnique

Yanlei Diao: University of Massachusetts Amherst, CMPSCI645, January 24 - March 11, 2022.

I. Manolescu is a part-time (50%) professor at Ecole Polytechnique, where she is in charge of the following:

• INF553 (Database Management Systems), M1, Ecole Polytechnique, 45 ETD;
• INF592 (Internships in Artificial Intelligence and Data Science), together with Jesse Read;
• "Artificial Intelligence and Data Science" year of study (3rd year for Polytechnique students).

I. Manolescu also teaches a course at Institut Mines Télécom:

• TPT DATAIA921 (Architectures for Big Data Management), M2, 18 ETD

Team members also collaborate in teaching courses at Institut Polytechnique de Paris:

• Nelly Barret:
  • labs of INF371 (Mécanismes de la programmation orientée-objet)
  • labs of INF411 (Les bases de la programmation et de l'algorithmique)
• Madhulika Mohanty: TPT DATAIA921 (Architectures for Big Data Management), M2, Institut Mines Télécom, 9h TP.
• Y. Hadad has been a teaching assistant in INF553 Database Management Systems, taught by I. Manolescu.

10.2.2 Supervision

PhD supervision:

• N. Barret, from January to December 2022 (I. Manolescu)
• T. Bouganim from October to December 2022(I. Manolescu, E. Pietriga)
• T. Calamai, from December 2022 (O. Balalau, F.Suchanek)
• S. Chouaki, from December 2022 (O. Goga)
• Qi Fan, from January to December 2022 (Y. Diao)
• Y. Haddad, from January to December 2022 (A. Anadiotis, I. Manolescu)
• G. Khan, from January to December 2022 (A. Anadiotis, I. Manolescu)
• V. Jacob, from January to December 2022 (Y. Diao)
• C. Lyu, from January to December 2022 (Y. Diao, visiting PhD From UMass Amherst)
• V. Sosnovik, from September to December 2022 (O. Goga)
• K. Zhang, from January to December 2022 (O. Balalau, I. Manolescu)

Engineers supervision:

• Simon Ebel (O. Balalau, I. Manolescu)
• Théo Galizzi (O. Balalau, I. Manolescu)
• Tinhinane Medjkoune, at LIG (O. Goga)
• Arnab Singh (Y. Diao)

Intern supervision:

• Antoine Gauquier (M1, IMT Lille), I. Manolescu
• Q. Massonnat (M1, Ecole Polytechnique), O. Balalau and I. Manolescu

10.2.3 Juries

• Oana Balalau: comité de suivi de thèse of Cyril Chhun, Télécom Paris
• Oana Goga: PhD Committee for B. Khalfoun
• Ioana Manolescu: president of the PhD committee of Moussa Kamal Eddine (LIX, Ecole Polytechnique)

10.3.1 Internal or external Inria responsibilities

• Oana Balalau:
  • member of the committee "Moyens calcul Inria"
  • member in the GT comité de centre

10.3.2 Education

O.Balalau, Y.Diao and I. Manolescu reviewed student applications for the Data AI master track at IPP.

10.3.3 Interventions

• Oana Balalau: Participation "Journée après thèse" at Centrale Supelec
• Oana Goga: Participation to podcast La data dans tous ses états
• Ioana Manolescu: Participated in a panel on AI at Centrale Supéléc, organized by a committee of students interested in AI ("Les Automatants")

International journals

• 11 articleA. C.Angelos Christos Anadiotis, O.Oana Balalau, C.Catarina Conceicao, H.Helena Galhardas, M. Y.Mhd Yamen Haddad, I.Ioana Manolescu, T.Tayeb Merabti and J.Jingmao You. Graph integration of structured, semistructured and unstructured data for data journalism.Information Systems104October 2022, 42
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• 12 articleS.Sein Minn. AI-assisted knowledge assessment techniques for adaptive learning environments.Computers and Education: Artificial Intelligence3February 2022
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International peer-reviewed conferences

• 13 inproceedingsO.Oana Balalau, S.Simon Ebel, T.Théo Galizzi, I.Ioana Manolescu, Q.Quentin Massonnat, A.Antoine Deiana, E.Emilie Gautreau, A.Antoine Krempf, T.Thomas Pontillon, G.Gérald Roux and J.Joanna Yakin. Fact-checking Multidimensional Statistic Claims in French.TTO 2022 - Truth and Trust OnlineBoston [Hybrid Event], United StatesOctober 2022
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• 14 inproceedingsO.Oana Balalau, S.Simon Ebel, T.Théo Galizzi, I.Ioana Manolescu, Q.Quentin Massonnat, A.Antoine Deiana, E.Emilie Gautreau, A.Antoine Krempf, T.Thomas Pontillon, G.Gérald Roux and J.Joanna Yakin. Statistical Claim Checking: StatCheck in Action (demonstration).CIKM 2022 - 31st ACM International Conference on Information and Knowledge ManagementAtlanta / Hybrid, United StatesOctober 2022
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• 15 inproceedingsN.Nelly Barret, I.Ioana Manolescu and P.Prajna Upadhyay. Abstra: Toward Generic Abstractions for Data of Any Model.CIKM 2022 - 31st ACM International Conference on Information and Knowledge ManagementAtlanta, Georgia / Hybrid, United StatesOctober 2022
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• 16 inproceedingsD.Dan Berrebbi, N.Nicolas Huynh and O.Oana Balalau. GraphCite: Citation Intent Classification in Scientific Publications via Graph Embeddings.2nd International Workshop on Scientific Knowledge: Representation, Discovery, and AssessmentCompanion Proceedings of the Web Conference 2022 (WWW '22 Companion)Lyon / Virtual, FranceApril 2022
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• 17 inproceedingsE.Eleni Gkiouzepi, A.Athanasios Andreou, O.Oana Goga and P.Patrick Loiseau. Collaborative Ad Transparency: Promises and Limitations.44th IEEE Symposium on Security and PrivacySan Francisco, United States2023
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• 18 inproceedingsC.Chenghao Lyu, Q.Qi Fan, F.Fei Song, A.Arnab Sinha, Y.Yanlei Diao, W.Wei Chen, L.Li Ma, Y.Yihui Feng, Y.Yaliang Li, K.Kai Zeng and J.Jingren Zhou. Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing.VLDB 2022 - 48th International Conference on Very Large Databases1511Sydney, AustraliaSeptember 2022
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• 19 inproceedingsI.Ioana Manolescu. Teasing journalistic findings out of heterogeneous sources: a data/AI journey (invited keynote).International Conference on Distributed Event-Based Systems (DEBS)DEBS '22: The 16th ACM International Conference on Distributed and Event-based SystemsCopenhagen, DenmarkACMAugust 2022, 1-1
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• 20 inproceedingsJ.-J.Jill-Jênn Vie, T.Tomas Rigaux and S.Sein Minn. Privacy-Preserving Synthetic Educational Data Generation.EC-TEL 2022 - 17th European Conference on Technology Enhanced LearningToulouse, FranceSeptember 2022
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National peer-reviewed Conferences

• 21 inproceedingsA. C.Angelos Christos Anadiotis, I.Ioana Manolescu and M.Madhulika Mohanty. Integrating Connection Search in Graph Queries.BDA 2022 - 38ème Conférence sur la Gestion de Données - Principes, Technologies et ApplicationsClermont-Ferrand, FranceOctober 2022
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• 22 inproceedingsM.Muhammad Ghufran Khan. Scalable Analytics on Multi-Streams Dynamic Graphs.BDA conference "Data Management - Principles, Technologies and Applications" 2022Clermont-Ferrand, FranceOctober 2022
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Conferences without proceedings

• 23 inproceedingsA. C.Angelos Christos Anadiotis, F.François Goasdoué, M. Y.Mhd Yamen Haddad and I.Ioana Manolescu. Towards Speeding Up Graph-Relational Queries in RDBMSs.BDA 2022 - 38èmes journées de la conférence BDA « Gestion de Données – Principes, Technologies et ApplicationsClermont-Ferrand, FranceOctober 2022
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• 24 inproceedingsO.Oana Balalau, S.Simon Ebel, T.Théo Galizzi, I.Ioana Manolescu and Q.Quentin Massonnat. Statistical Claim Checking: StatCheck in Action.38ème Conférence sur la Gestion de Données – Principes, Technologies et Applications (BDA 2022)Clermont - Ferrand, FranceOctober 2022
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• 25 inproceedingsN.Nelly Barret, I.Ioana Manolescu and P.Prajna Upadhyay. Abstra: Toward Generic Abstractions for Data of Any Model.BDA 2022 - informal publication onlyClermont-Ferrand, FranceOctober 2022
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Scientific book chapters

• 26 inbookT.Théo Bouganim, H.Helena Galhardas and I.Ioana Manolescu. Efficiently Identifying Disguised Missing Values in Heterogeneous, Text-Rich Data.13410Transactions on Large-Scale Data- and Knowledge-Centered Systems LILecture Notes in Computer ScienceSpringer Berlin HeidelbergOctober 2022, 97-118
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Reports & preprints

• 27 reportA. C.Angelos Christos Anadiotis, I.Ioana Manolescu and M.Madhulika Mohanty. Integrating Connection Search in Graph Queries.Inria Saclay - Île de FranceJanuary 2023
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