6.1.1 AIDES

• Keywords: Data Exploration, Active Learning
• Functional Description: AIDES is a data exploration software. It allows a user to explore a huge (tabular) dataset and discover tuples matching his or her interest. Our system repeatedly proposes the most informative tuples to the user, who must annotate them as “interesting” / “not-interesting”, and as iterations progress an increasingly accurate model of the user’s interest region is built. Our system also focuses on supporting low selectivity, high-dimensional interest regions.
• Contacts: Yanlei Diao, Enhui Huang, Luciano Di Palma

6.1.2 OntoSQL

• Keywords: RDF, Semantic Web, Querying, Databases
• Functional Description: OntoSQL is a tool providing three main functionalities: - Loading RDF graphs (consisting of data triples and possibly a schema or ontology) into a relational database, - Saturating the data based on the ontology. Currently, RDF Schema ontologies are supported. - Querying the loaded data using conjunctive queries. Data can be loaded either from distinct files or from a single file containing them both. The loading process allows to choose between two storage schemas: - One triples table. - One table per role and concept. Querying provides an SQL translation for each conjunctive query according to the storage schema used in the loading process, then the SQL query is evaluated by the underlying relational database.
• URL: https://ontosql.inria.fr/
• Contacts: Ioana Manolescu, Tayeb Merabti
• Participants: Ioana Manolescu, Michaël Thomazo, Tayeb Merabti
• Partner: Université de Rennes 1

6.1.3 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 Balalau, Catarina Pinto Conceicao
• Contact: Manolescu Ioana

6.1.4 INSEE-Search

• Keywords: Document ranking, RDF
• Functional Description: Searching for relevant data cells (or data row/column) given a query in natural language (French)
• Publications: hal-01915148, hal-01745768
• Contacts: Ioana Manolescu, Xavier Tannier, Tien Duc Cao
• Participants: Ioana Manolescu, Xavier Tannier, Tien Duc Cao

6.1.5 RDFQuotient

• Name: Quotient summaries of RDF graphs
• Keywords: RDF, Graph algorithmics, Graph visualization, Graph summaries, Semantic Web
• Functional Description: RDF graphs can be large and heterogeneous, making it hard for users to get acquainted with a new graph and understand whether it may have interesting information. To help users figure it out, we have devised novel equivalence relations among RDF nodes, capable of recognizing them as equivalent (and thus, summarize them together) despite the heterogeneity often exhibited by their incoming and outgoing node properties. From these relations, we derive four novel summaries, called Weak, Strong, Typed Weak and Typed Strong, and show how to obtain from them compact and enticing visualizations.
• Publications: hal-01325900v6, hal-01808737
• Authors: Ioana Manolescu, Pawel Guzewicz, François Goasdoué
• Contact: Manolescu Ioana
• Participants: Ioana Manolescu, Pawel Guzewicz, François Goasdoué
• Partner: Université de Rennes 1

6.1.6 AIDEme

• Keywords: Active Learning, Data Exploration
• Scientific Description:

  AIDEme is a large-scale interactive data exploration system that is cast in a principled active learning (AL) framework: in this context, we consider the data content as a large set of records in a data source, and the user is interested in some of them but not all. In the data exploration process, the system allows the user to label a record as “interesting” or “not interesting” in each iteration, so that it can construct an increasingly-more-accurate model of the user interest. Active learning techniques are employed to select a new record from the unlabeled data source in each iteration for the user to label next in order to improve the model accuracy. Upon convergence, the model is run through the entire data source to retrieve all relevant records.

  A challenge in building such a system is that existing active learning techniques experience slow convergence in learning the user interest when such exploration is performed on large datasets: for example, hundreds of labeled examples are needed to learn a user interest model over 6 attributes, as we showed using a digital sky survey of 1.9 million records. AIDEme employs a set of novel techniques to overcome the slow convergence problem:

  • Factorization: We observe that a user labels a data record, her decision making process often can be broken into a set of smaller questions, and the answers to these questions can be combined to derive the final answer. This insight, formally modeled as a factorization structure, allows us to design new active learning algorithms, e.g., factorized version space algorithms [2], that break the learning problem into subproblems in a set of subspaces and perform active learning in each subspace, thereby significantly expediting convergence.

  • Optimization based on class distribution: Another interesting observation is that when projecting the data space for exploration onto a subset of dimensions, the user interest pattern projected onto such a subspace often entails a convex object. When such a subspatial convex property holds, we introduce a new “dual-space model” (DSM) that builds not only a classification model from labeled examples, but also a polytope model of the data space that offers a more direct description of the areas known to be positive, areas known to be negative, and areas with unknown labels. We use both the classification model and the polytope model to predict unlabeled examples and choose the best example to label next. • Formal results on convergence: We further provide theoretical results on the convergence of our proposed techniques. Some of them can be used to detect convergence and terminate the exploration process. • Scaling to large datasets: In many applications the dataset may be too large to fit in memory. In this case, we introduce subsampling procedures and provide provable results that guarantee the performance of the model learned from the sample over the entire data source.

• Functional Description: There is an increasing gap between fast growth of data and limited human ability to comprehend data. Consequently, there has been a growing demand for analytics tools that can bridge this gap and help the user retrieve high-value content from data. We introduce AIDEme, a scalable interactive data exploration system for efficiently learning a user interest pattern over a large dataset. The system is cast in a principled active learning (AL) framework, which iteratively presents strategically selected records for user labeling, thereby building an increasingly-more-accurate model of the user interest. However, a challenge in building such a system is that existing active learning techniques experience slow convergence when learning the user interest on large datasets. To overcome the problem, AIDEme explores properties of the user labeling process and the class distribution of observed data to design new active learning algorithms, which come with provable results on model accuracy, convergence, and approximation, and have evaluation results showing much improved convergence over existing AL methods while maintaining interactive speed.
• Release Contributions: Project code can be found over: https://gitlab.inria.fr/ldipalma/aideme
• URL: http://www.lix.polytechnique.fr/aideme
• Contacts: Yanlei Diao, Luciano Di Palma, Enhui Huang
• Participants: Luciano Di Palma, Enhui Huang

6.1.7 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

7.1.1 Efficient query answering over semantic graphs

In this area, the end of the PhD thesis of M. Buron has lead to two main contributions.

The first is related to the performance of query answering in RDF databases. This strongly depends on the data layout, that is, the way data is split in persistent data structures. In 15, we consider answering Basic Graph Pattern Queries (BGPQs), and in particular those with variables (also) in class and property positions, in the presence of RDFS ontologies, both through data saturation and query reformulation. We show that such demanding queries often lead to inefficient query answering on two popular storage layouts, so-called T and CP. We present novel query answering algorithms on the TCP layout, which combines T and CP. In exchange to occupying more storage space, e.g. on an inexpensive disk, TCP avoids the bad or even catastrophic performance that T and/or CP sometimes exhibit.

The second and main contribution concerns the efficient integration of heterogeneous, structured databases, as semantic graphs. The proliferation of heterogeneous data sources in many application contexts brings an urgent need for expressive and efficient data integration mechanisms. There are strong advantages to using RDF graphs as the integration format: being schemaless, they allow for flexible integration of data from heterogeneous sources; RDF graphs can be interpreted with the help of an ontology, describing application semantics; last but not least, RDF enables joint querying of the data and the ontology.

To address this need, in 17, we formalize RDF Integration Systems (RIS), Ontology Based-Data Access mediators, that go beyond the state of the art in the ability to expose, integrate and flexibly query data from heterogeneous sources through GLAV (global-local-as-view) mappings. We devise several query answering strategies, based on an innovative integration of LAV view-based rewriting and a form of mapping saturation. Our experiments show that one of these strategies brings strong performance advantages, resulting from a balanced use of mapping saturation and query reformulation. A demonstration of the system implementing these techniques has been shown at the VLDB 2020 conference 16.

7.1.2 Quotient summaries of RDF graphs

To help users get familiar with large RDF graphs, we can use RDF summarization techniques. We study quotient summaries of RDF graphs, that is, graph summaries derived from a notion of equivalence among RDF graph nodes. In our recently published work 11, we make the following contributions: (i) four novel summaries, which are often small and easy-to-comprehend, in the style of E-R diagrams; (ii) efficient (amortized linear-time) algorithms for computing these summaries either from scratch or incrementally, reflecting additions to the graph; (iii) the first formal study of the interplay between RDF graph saturation in the presence of an RDFS ontology, and summarization; we provide a sufficient condition for a highly efficient shortcut method to build the quotient summary of a graph without saturating it; (iv) formal results establishing the shortcut conditions for some of our summaries and others from the literature; (v) experimental validations of our claim within a tool available online.

This research has also been featured in an invited keynote 19.

7.2.1 Graph integration of heterogeneous data sources for data journalism

Work carried within the ANR ContentCheck and especially the ANR AI Chair SourcesSay projects 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. The main technical challenges are: (i) how to interconnect structured and semistructured data sources? We address this th ough 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 certain search criteria, or certain keywords? The question is particularly challenging in our context since ConnectionLens graphs can be quite large and query answers can traverse edges in both directions.

Intense work has been invested in the ConnectionLens prototype, in particular thanks to the recruitment of T. Merabti as an AI engineer within the team 27.

• In the first part of the year, work done by Irène Burger (M1 Polytechnique student) has set the first steps toward simplifying ConnectionLens graphs through a mechanism called abstraction 14. That work has been continued during the fall through the pre-doctoral contract of Nelly Barret.
• During the summer, the M2 interns Jingmao You and Youssr Youssef contributed to the development of two new modules for ConnectionLens: one for disambiguating named entities recognized in the text, by attaching them to known entities from the YAGO knowledge base, and the other for converting PDF documents into semistructured JSON (and possibly RDF) data, which allows their ingestion into ConnectionLens. Together with improvements brought to our Named Entity Recognition module and a general description of ConnectionLens graph construction, these modules were described in 30.
• As part of the internship of Mhd Yamen Haddad, we have revisited the keyword search algorithm of ConnectionLens, called GAM. We have notably introduced a new technique, called Grow2Representative, which enables to find answers for keyword queries that span over multiple datasets, yet is much more efficient than the previous technique 27 proposed for the task. The new algorithm has lead to the publication of 12.
• Finally, following the invited keynote of Ioana Manolescu at the ADBIS 2020 conference, an invited paper to a special issue of Elsevier Information Systems has been submitted 26, consolidating and unifying 30 and 12 and including larger scale experiments.

During the spring-summer of 2020, during the M1 internship of Jérémie Feitz (Polytechnique), co-supervised by E. Pietriga from the ILDA team of Inria Saclay, a new interactive user interface has been developed for querying and exploring ConnectionLens graphs. Figure 1 illustrates it on an example. This graph originates in four data sources (the four red nodes); they interconnect on common extracted entities (green and yellow nodes).

The main novelty of this interface with respect to the previous one is to support interactive exploration of the graph: users are able to explore the neighbors of a node present in the interface, interactively selecting these neighbors depending on their type etc.

This research has also lead to two invited keynotes 20, 24.

7.2.2 Novel fact-checking architectures and algorithms

Among existing sources of Open Data, statistical databases published by national and international organizations such as the International Monetary Fund, the United Nations, OECD etc. stand out for their high quality and valuable insights. However, technical means to interact easily with such sources are currently lacking.

In the past, we had worked to improve the accessibility and ease of use of statistic databases published by INSEE, the leading French statistic institute.

In 2020, during the internship of Guillaume Thiry (M1 Polytechnique), co-supervised with Leo Liberti (LIX), we have considered a new trove of statistic data, namely those structured according to the SDMX standard (https://sdmx.org). SDMX is a standard to facilitate the exchange of statistical data and metadata using modern information technology. Several versions of the SDMX standard (ISO IS 17369) exist since 2004. 23 presents an effort to build an interactive Question Answering system for accessing statistical databases structured according to the SDMX standard. We describe the system architectures, its main technical choices, and present a preliminary evaluation. The system is available online (https://github.com/guillaume-thiry/OECD-Chatbot).

7.2.3 Argumentation Mining in Online Forums

In this area, we first studied propaganda dissemination in online forums and then we focused on a particular type of propaganda, fallacies. Political discussions revolve around ideological conflicts that often split the audience into two opposing parties. Both parties try to win the argument by bringing forward information. However, often this information is misleading, and its dissemination employs propaganda techniques. We analyzed the impact of propaganda on six major political forums on Reddit that target a diverse audience in two countries, the US and the UK. We focused on three research questions: who is posting propaganda? how does propaganda differ across the political spectrum? and how is propaganda received on political forums? This work was accepted for publication and will appear in 2021. Our second project was on fallacious arguments. Fallacies are generally persuasive arguments that provide insufficient or incorrect evidence that does not support the claim. We looked at the most frequent fallacies that users make on Reddit, and we presented them using the pragma-dialectical theory of argumentation. We constructed a new annotated dataset of fallacies, using user comments containing fallacy mentions as noisy labels, and cleaning the data via crowdsourcing. Finally, we studied the task of classifying fallacies using neural models. This work is under review.

7.2.4 Machine Learning for Graph Data

In this area, we worked on several topics. The first one is on how to compute subgraph embeddings in heterogeneous graphs. State-of-the-art research is focused mostly on node embeddings, with little effort dedicated to the closely related task of computing subgraph embeddings. Subgraph embeddings have many applications, such as community detection, cascade prediction, and question answering. We proposed a subgraph to subgraph proximity measure as a building block for a subgraph embedding framework in this work. Experiments on real-world datasets showed that our approach, outperformed state-of-the-art methods on several important data mining tasks. This work was published as a full paper at PAKDD 2020 13. The second topic was on how to rank heterogeneous subgraphs. We focused on learning methods to rank paths and then subgraphs in heterogeneous graphs. We have initially investigated unsupervised ranking methods, such as methods based on the average personalized PageRank of nodes. We then moved to the supervised learning to rank approach. In this approach, we don't rank paths/subgraphs but meta paths/meta subgraphs. A meta path is a class of paths, where we keep the edge labels, but we replace nodes with their type. For example, Jane livesIn Paris and Jack livesIn London both belong to the metapath Person livesIn Location. In order to find the interesting meta paths, we investigated which meta paths could be used to answer popular web questions. This is ongoing work.

The last topic is on citation intent in citation networks. Many metrics have been proposed over the years to understand influential authors and influential articles. However, recent advancements in NLP have relaunched the discussion on what makes a paper influential and how a scientific field evolves over the year. In particular, recent works have looked at the intent of authors when citing other papers, highlighting six functions of a citation: citation of background work - an article relevant for the field, a motivation work which illustrates the need for the current work, an article containing a method used in the current paper, a state-of-the-art competitor, an article which the work extends, or a paper that could be an inspiration for future work. In our work we predict the intent of a citation. We depart from previous literature that took into consideration for the task only the linguistic information in an article. We incorporate more context by representing articles and citations as a heterogeneous graph, with node and edge types and labels, and predict new citations as new links in our graph. This is ongoing work.

7.3.1 Semantic graph exploration through interesting aggregates

As large Open Data are increasingly shared as RDF graphs today, there is a growing demand to help users discover the most interesting facets of a graph, which are often hard to grasp without automatic tools. We consider the problem of automatically identifying the k most interesting aggregate queries that can be evaluated on an RDF graph, given an integer k and a user-specified interestingness function. Our problem departs from analytics in relational data warehouses in that (i) in an RDF graph we are not given but we must identify the facts, dimensions, and measures of candidate aggregates; (ii) the classical approach to efficiently evaluating multiple aggregates breaks in the face of multi-valued dimensions in RDF data. In this work, we propose an extensible end-to-end framework that enables the identification and evaluation of interesting aggregates based on a new RDF-compatible one-pass algorithm for efficiently evaluating a lattice of aggregates and a novel early-stop technique (with probabilistic guarantees) that can prune uninteresting aggregates. Experiments using both real and synthetic graphs demonstrate the ability of our framework to find interesting aggregates in a large search space, the efficiency of our algorithms (with up to 2.9 times speedup over a similar pipeline based on existing algorithms), and scalability as the data size and complexity grow.

7.3.2 A factorized version space algorithm for interactive database exploration

In a recent trend known as “Machine Learning for Everyone”, IT companies are delivering cloud platforms to help every data user to develop machine learning models for their data sets with minimum effort. A key question, however, is how to obtain a high-quality training data set for model development with minimum user effort. In other words, at the center of the new IT trend lies a critical “training data” problem. While industry solutions to this problem are limited to manual labeling or crowdsourcing, recent research on interactive data exploration (IDE) for model development bridges the gap between the data exploration and machine learning communities, and brings active learning-based data exploration to bear on the new process of model learning. In this setting, active learning is applied to select a small sequence of data instances for the user to label in order to derive an accurate model, while at the same time, offering interactive performance in presenting the next data instance for the user to review and label.

Existing active learning techniques, however, often fail to provide satisfactory performance when such models need to be built over large data sets. Not only such models often require hundreds of labeled data instances in order to reach high accuracy (slow convergence), but retrieving the next instance to label can be time consuming (inefficiency), making it incompatible with the interactive nature of the human exploration process. To address the slow convergence and inefficiency issues, we have developed two main ideas: First, we introduce a novel version space based active learning algorithm for kernel classifiers, which not only has strong theoretical guarantees on convergence, but also allows for an efficient implementation in time and space. Second, by leveraging additional insights obtained in the user exploration and labeling process, we explore a new opportunity to factorize an active learner so that active learning can be performed in a set of low-dimensional subspaces, which further expedites convergence and reduces the user labeling effort.

More specifically, we have developed the following contributions:

1. A new theoretical framework for version space (VS) algorithms over kernel classifiers: We developed a new theoretical framework that allows for an efficient implementation of the Generalized Binary Search strategy over kernel classifiers, offering both strong theoretical guarantees on performance and an efficient implementation in time and space. We also proved generalization error bounds on accuracy and F-score, enabling our techniques to run over a sample from the original large data set with minimal performance loss.
2. Implementation and Optimizations: Based on our theoretical results, we devised an optimized VS algorithm called OptVS, which uses the hit-and-run algorithm for sampling the version space. However, hit-and-run may require thousands of iterations to output a high-quality sample, which can incur a high time cost. To reduce the cost, we develop a range of sampling optimizations to improve both sample quality and running time. In particular, we provide a highly efficient version of the rounding technique for improving the sample quality from the version space.
3. A Factorized Version Space Algorithm: Additionally, we developed a new algorithm that leverages the factorization structure provided by the user to create low-dimensional subspaces, and factorizes the version space accordingly to perform active learning in the subspaces. Compared to recent work that also used factorization for active learning, our work explores it in the new setting of VS algorithms and eliminates the strong assumptions made in prior work such as convexity of user interest patterns, resulting in significant performance improvement while increasing the applicability in real-world problems. We also managed to prove theoretical results on the optimality of our factorized VS algorithm.

Using real-world data sets and a large suite of user interest patterns, we have empirically observed that our optimized version space (VS) algorithms outperform existing VS algorithms, as well as DSM, a factorization-aware algorithm, often by a wide margin while maintaining interactive speed.

The results of our work are included in 28.

7.3.3 Learning with label noise

In active learning based data exploration, theory of active learning is applied to select a small sequence of data instances for the user to label in order to derive an accurate model, while at the same time, offering interactive performance in presenting the next data instance for the user to review and label. Several algorithms that have been proposed in the literature to address the slow convergence and inefficiency problems, making the assumption that the user-provided labels are uncorrupted. However, practical experience shows that users may mislabel some examples. Given limited labeled examples in the interactive data exploration scenario, we improve the robustness of learning algorithms in the presence of label noise by (i) applying advanced methods to collect distilled examples out of noisy examples automatically, (ii) leveraging the polytope-based model learnt on distilled examples to further filter noisy labels, and (iii) developing new sample acquisition strategies that are less sensitive to label noise. Evaluation results using real-world datasets and user interest patterns show that our proposed algorithm is far more robust than alternative algorithms and it achieves desired accuracy and efficiency under different noise levels.

7.4.1 Scalable storage for polystores

Big data applications increasingly involve diverse datasets, conforming to different data models. Such datasets are routinely hosted in heterogeneous stores, each capable of handling one or a few data models, and each efficient for some, but not all, kinds of data processing. Systems capable of exploiting disparate data in this fashion are usually termed polystores. A current limitation of polystores is that applications are written taking into account which part of the data is stored in which store and how. This fails to take advantage of (i) possible redundancy, when the same data may be accessible (with different performance) from distinct data stores; (ii) previous query results (in the style of materialized views), which may be available in the stores.

The Estocada system has been developed in collaboration with A. Deutsch and R. Al-Otaibi from UCSD. It proposes a novel approach that can be used in a polystore setting to transparently enable each query to benefit from the best combination of stored data and available processing capabilities. The system leverages recent advances in the area of view-based query rewriting under constraints, which we use to describe the various data models and stored data. In 2020, the system has been further enhanced with the ability to support view-based rewriting and algebraic optimizations for matrix data, at the core of modern Machine Learning data-intensive computational pipelines 9.

7.4.2 Boosting Cloud Data Analytics using Multi-Objective Optimization

In our work, we present a data analytics optimizer together with a principled multi-objective optimization approach. This approach computes a Pareto optimal set of job configurations to reveal tradeoffs between different user objectives, recommends a new job configuration that best explores such tradeoffs, and employs novel optimizations to enable such recommendations within a few seconds. Using benchmark workloads, our experimental results show that our MOO techniques outperform existing MOO methods in speed and coverage of the Pareto frontier, as well as simpler optimization methods, and can indeed recommend configurations that lead to better performance or explore interesting tradeoffs.

This work has been accepted to the proceedings of the ICDE 2021 for inclusion as a full paper 22.

7.4.3 Workload tuning using recommender systems

Spark is a widely popular massively parallel processing system which is used to execute various types of data analysis workloads. Accordingly, tuning its performance is a hard problem. In this research thread, we have casted the problem of tuning Spark workloads into a recommender systems framework.

In  29, we have introduced a representation learning architectures from three families of techniques: (i) encoder/decoder architectures; (ii) siamese neural networks; and (iii) a new family that combines the first two in hybrid architectures. These representation learning based techniques explicitly extract encodings from runtime traces before feeding them to a neural network dedicated for the end regression task on the runtime latency. We have covered deterministic and generative auto-encoders and proposed extensions of them in order to satisfy different desired encoding properties. We have also explained why the siamese neural networks are particularly interesting when a job is admitted with an arbitrary configuration and we have trained these architectures using two types of losses: a triplet loss and a soft nearest neighbor loss.

We have extended a previous benchmark of streaming workloads and sampled traces from these workloads as well as workloads from the TPCx-BB benchmark. We have provided comparative results between different modeling techniques and provided end-to-end comparative results with related work, which demonstrate the efficiency of our approach.

Our project is available online on Github: https://github.com/udao-modeling/code.

7.4.4 Elastic resource management in relational database systems

We have considered two problems in this setting. First, the scale-up elasticity of an online transaction processing engine (OLTP), which is required in order to allow the engine to adapt to workload variations, such as spikes in the incoming traffic, and to create new buiseness opportunities for cloud-hosted OLTP engines. The case of OLTP elasticity is particular because transactions require a stateful concurrency control protocol to coordinate operations on the database records. Therefore, elastic scale-out apporaches bring significant overheads for maintaining the protocol state, effectively reflected as additional transactional latency. We devised a novel system design which spans vertically the software virtualization stack and enables elastic scale-up of stateful engines, such as OLTP. Our results have been presented in 10.

Second, we considered the case of hybrid transactional and analytical processing engines, where a single engine needs to combine transactional (short, write-intensive queries) with analytical (long, read-only queries) workloads. We analyzed the system design space and modeled it using a set of states. We devised an adaptive algorithm which migrates across states based on the workload, and the resource availability. Our results have been presented in 21.

8.1.1 Collaborations with major European organizations

Angelos Anadiotis holds a visiting professor position at EPFL.

8.2.1 ANR

• AIDE (“A New Database Service for Interactive Exploration on Big Data”) is an ANR “Young Researcher” project led by Y. Diao, started at the end of 2016.
• ContentCheck (2015-2020) is an ANR project led by I. Manolescu, in collaboration with U. Rennes 1 (F. Goasdoué), INSA Lyon (P. Lamarre), the LIMSI lab from U. Paris Sud, and the Le Monde newspaper, in particular their fact-checking team Les Décodeurs. Its aim is to investigate content management models and tools for journalistic fact-checking.
• CQFD (2019-2023) 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.

8.2.2 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.

9.1.1 Scientific events: selection

9.1.2 Journal

9.1.3 Invited talks

I. Manolescu has given invited keynote talks at:

• DOLAP 2020 (22nd International Workshop On Design, Optimization, Languages and Analytical Processing of Big Data) 19
• ADBIS 2020 (24th European Conference on Advances in Databases and Information Systems) 20
• DATA 2020 (9th International Conference on Data Science, Technology and Applications) 24

9.1.4 Leadership within the scientific community

Y. Diao and I. Manolescu are members of the Proceedings of Very Large Databases Endowment Board.

I. Manolescu is a member of the steering committee of the Bases de Données Avancées conference.

9.1.5 Scientific expertise

Oana Balalau has served as a reviewer for ANR Projects in the Artificial Intelligence Area.

9.1.6 Research administration

Ioana Manolescu is a member of the Bureau du Comité de Projets of Inria Saclay–Île-de-France. She is also responsible of the "Data Analytics and Machine Learning (DAML)" axis of the LIX, the Computer Science Laboratory of Ecole Polytechnique. The axis includes 3 teams: CEDAR, COMETE (also joint with Inria SIF) and DASCIM (LIX-only team).

Ioana Manolescu is the scientific director of LabIA, a collaboration between Inria and the DINUM focused on applying Artificial Intelligence techniques to address concrete problems encountered by public administrations. Oana Balalau is also devoting part of her activity to LabIA research projects.

9.2.1 Teaching

Angelos Anadiotis is full-time Assistant Professor at Ecole Polytechnique, where he is 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

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

• Master: I. Manolescu, “Database Management Systems”, 45h, M1, École Polytechnique
• Master: I. Manolescu, "Research Internship in Data Science", 30h, M1, Ecole Polytechnique

I. Manolescu is also co-responsible of the "Data Science" M1 program of Ecole Polytechnique/Institut Polytechnique de Paris (http://www.enseignement.polytechnique.fr/informatique/3A/3A.php#Data).

Team members also collaborate in teaching an M2 course of Institut Polytechnique de Paris:

• Master: A. Anadiotis, P. Guzewicz, I. Manolescu, “Architectures for Massive Data Management”, 32h, M2, Institut Polytechnique de Paris

In 2020, M. Buron and P. Guzewicz have been serving as Teaching Assistants at Ecole Polytechnique, in respectively "Data Vizualization" (taught by E. Pietriga) and "Machine Learning" (taught by J. Read).

9.2.2 Supervision

• PhD: Maxime Buron: "Raisonnement efficace sur des grands graphes hétérogènes" 25, 07/10/2020, François Goasdoué (U. Rennes 1), Ioana Manolescu and Marie-Laure Mugnier (GraphIK Inria team in Montpellier)
• PhD in progress: Ludivine Duroyon: “Data management models, algorithms & tools for fact-checking", since October 2017, François Goasdoué and Ioana Manolescu (Ludivine is in the Shaman team of U. Rennes 1 and IRISA, in Lannion)
• PhD in progress: Paweł Guzewicz: “Expressive and efficient analytics for RDF graphs”, since October 2018, Yanlei Diao and Ioana Manolescu
• PhD in progress: Qi Fan: “Multi-Objective Optimization for Data Analytics in the Cloud”, since December 2019, Yanlei Diao
• PhD in progress: Enhui Huang: “Interactive Data Exploration at Scale”, since October 2016, Yanlei Diao and Anna Liu (U. Massachussets at Amherst, USA)
• PhD in progress: Vincent Jacob: “Explainable Anomaly Detection in High-Volume Stream Analytics”, since December 2019, Yanlei Diao
• PhD in progress: Luciano di Palma, “New sampling algorithms and optimizations for interactive exploration in Big Data", since October 2017, Yanlei Diao and Anna Liu (U. Massachussets at Amherst, USA)
• PhD in progress: Khaled Zaouk: “Performance Modeling and Multi-Objective Optimization for Data Analytics in the Cloud", since October 2017, Yanlei Diao
• PhD in progress: Fanzhi Zhu: “Operator Fusion for Large Scale Complex Data Analytics Pipelines”, since July 2020, Yanlei Diao and Angelos Anadiotis

9.2.3 Juries

Angelos Anadiotis has been evaluator for PhD of Giuseppe Massimiliano Milotta who did his PhD at the Universita Mediterranea Degli Studi di Reggio Calabria.

Oana Balalau has been an examinator for PhD of Alexis Galland on ìDeep learning techniques for graph embedding at different scalesî, defended on 17 December 2020.

Ioana Manolescu has been a member of the Engineer Diploma committee of Catarina Conceiçao, at the Instituto Superior Technico, in Lisbon, Portugal, on October 16, 2020.

9.3.1 Articles and contents

• Ioana Manolescu has been featured in a short film part of a "Women in Data Science" (WiDS) series organized as a TedX talk series and aimed at increasing the presence of female students and young professionals in Data Science (https://www.youtube.com/watch?v=KT8vmQDke4A)

9.3.2 Interventions

• Ioana Manolescu discussed fact-checking in Forum des Médias Jeunes organized par Radio Campus Lorraine, Dec 17, 2020
• Ioana Manolescu discussed accessibility and exploration of statistic data in StatsBot webinar, based on 23, next to 2020 Workshop on the Modernisation of Official Statistics organized by The United Nations Economic Commission for Europe (UNECE), on Nov 20, 2020
• Ioana Manolescu presented ContentCheck work on fact-checking at a Journée d’Etude “Répondre aux Fake News” at the University of Cergy, to an audience of faculty and students in political sciences and the media, Nov 5, 2020
• Angelos Anadiotis, Oana Balalau, Helena Galhardas, Ioana Manolescu and Youssr Youssef attended the DataHarvest Investigative Journalism Conference on Oct 24, 2020, where, at the invitation of Stéphane Horel (Le Monde), we discussed SourcesSay applications to computational journalism.

International journals

• 9 articleR. Alotaibi, B. Cautis, A. Deutsch, M. Latrache, IoanaI. Manolescu and Y. Yang. ESTOCADA: Towards Scalable Polystore SystemsProceedings of the VLDB Endowment (PVLDB)1312August 2020, 2949-2952
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• 10 articleAngelos-ChristosA.-C. Anadiotis, RajaR. Appuswamy, AnastasiaA. Ailamaki, IlanI. Bronshtein, HillelH. Avni, DavidD. Dominguez-Sal, ShayS. Goikhman and EliezerE. Levy. A system design for elastically scaling transaction processing engines in virtualized serversProceedings of the VLDB Endowment (PVLDB)1312August 2020, 3085-3098
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• 11 articleFrançoisF. Goasdoué, PawelP. Guzewicz and IoanaI. Manolescu. RDF graph summarization for first-sight structure discoveryThe VLDB Journal295April 2020, 1191-1218
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International peer-reviewed conferences

• 12 inproceedings Angelos ChristosA. Anadiotis, Mhd YamenM. Haddad and IoanaI. Manolescu. Graph-based keyword search in heterogeneous data sources 36ème Conférence sur la Gestion de Données – Principes, Technologies et Applications (informal publication only); 36ème Conférence sur la Gestion de Données – Principes, Technologies et Applications (informal publication only) BDA 2020 - 36ème Conférence sur la Gestion de Données – Principes, Technologies et Applications Online, France October 2020
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• 13 inproceedingsOanaO. Balalau and SagarS. Goyal. SubRank: Subgraph Embeddings via a Subgraph Proximity MeasurePAKDD 2020 - Pacific-Asia Conference on Knowledge Discovery and Data MiningSingapore, Singaporehttps://pakdd2020.org/May 2020, 487-498
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• 14 inproceedings IrèneI. Burger, IoanaI. Manolescu, EmmanuelE. Pietriga and Fabian MF. Suchanek. Toward Visual Interactive Exploration of Heterogeneous Graphs SEAdata 2020 - Workshop on Searching, Exploring and Analyzing Heterogeneous Data in conjunction with EDBT/ICDT Copenhagen, Denmark https://mott.in/news/seadata-19/ March 2020
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• 15 inproceedings MaximeM. Buron, FrançoisF. Goasdoué, IoanaI. Manolescu, TayebT. Merabti and Marie-LaureM.-L. Mugnier. Revisiting RDF storage layouts for efficient query answering SSWS 2020 - 13th International Workshop on Scalable Semantic Web Knowledge Base Systems Athène, Greece inria saclay August 2020
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• 16 inproceedings MaximeM. Buron, FrançoisF. Goasdoué, IoanaI. Manolescu and Marie-LaureM.-L. Mugnier. Obi-Wan: Ontology-Based RDF Integration of Heterogeneous Data VLDB 2020 - 46th International Conference on Very Large Data Bases Tokyo, Japan August 2020
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• 17 inproceedings MaximeM. Buron, FrançoisF. Goasdoué, IoanaI. Manolescu and Marie-LaureM.-L. Mugnier. Ontology-Based RDF Integration of Heterogeneous Data EDBT/ICDT 2020 - 23rd International Conference on Extending Database Technology Copenhagen, Denmark March 2020
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• 18 inproceedings AzinA. Ghazimatin, OanaO. Balalau, RishirajR. Saha and GerhardG. Weikum. PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems WSDM 2020 - 13th ACM International Conference on Web Search and Data Mining Houston, Texas, United States February 2020
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• 19 inproceedings IoanaI. Manolescu. Exploring RDF Graphs through Summarization and Analytic Query Discovery DOLAP 2020 - 22nd International Workshop On Design, Optimization, Languages and Analytical Processing of Big Data Copenhagen, Denmark https://www.essi.upc.edu/dtim/DOLAP/index.html March 2020
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• 20 inproceedingsIoanaI. Manolescu. Integrating (Very) Heterogeneous Data Sources: A Structured and an Unstructured PerspectiveADBIS 2020 - 24th European Conference on Advances in Databases and Information SystemsLyon, FranceAugust 2020, 15-20
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• 21 inproceedingsAunnA. Raza, PeriklisP. Chrysogelos, Angelos ChristosA. Anadiotis and AnastasiaA. Ailamaki. Adaptive HTAP through Elastic Resource SchedulingSIGMOD/PODS '20: International Conference on Management of DataPortland OR USA, United StatesJune 2020, 2043-2054
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• 22 inproceedings FeiF. Song, KhaledK. Zaouk, ChenghaoC. Lyu, ArnabA. Sinha, QiQ. Fan, YanleiY. Diao and PrashantP. Shenoy. Spark-based Cloud Data Analytics using Multi-Objective Optimization ICDE Chania, Greece Ecole Polytechnique (Palaiseau, France); CEDAR 2021
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• 23 inproceedings GuillaumeG. Thiry, IoanaI. Manolescu and LeoL. Liberti. A Question Answering System For Interacting with SDMX Databases NLIWOD 2020 - 6th Natural Language Interfaces for the Web of Data / Workshop (in conjunction with ISWC) Heraklion, Greece https://2020.nliwod.org/ November 2020
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Conferences without proceedings

• 24 inproceedings IoanaI. Manolescu. From Data to the Press: Data Management for Journalism and Fact-Checking DATA 2020 - 9th International Conference on Data Science, Technology and Applications Paris / Virtuel, France http://www.dataconference.org/KeynoteSpeakers.aspx#2 July 2020
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Doctoral dissertations and habilitation theses

• 25 thesis MaximeM. Buron. Efficient reasoning on large and heterogeneous graphs École Polytechnique October 2020
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Reports & preprints

• 26 misc Angelos ChristosA. Anadiotis, OanaO. Balalau, CatarinaC. Conceicao, HelenaH. Galhardas, Mhd YamenM. Haddad, IoanaI. Manolescu, TayebT. Merabti and JingmaoJ. You. Graph integration of structured, semistructured and unstructured data for data journalism February 2021
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• 27 misc FelipeF. Cordeiro, HelenaH. Galhardas, JulienJ. Leblay, IoanaI. Manolescu and TayebT. Merabti. Keyword Search in Heterogeneous Data Sources April 2020
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• 28 misc Luciano DiL. Palma, YanleiY. Diao and AnnaA. Liu. Efficient Version Space Algorithms for "Human-in-the-Loop" Model Development December 2020
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• 29 misc KhaledK. Zaouk, FeiF. Song, ChenghaoC. Lyu and YanleiY. Diao. Neural-based Modeling for Performance Tuning of Spark Data Analytics January 2021
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Other scientific publications

• 30 misc OanaO. Balalau, CatarinaC. Conceição, HelenaH. Galhardas, IoanaI. Manolescu, TayebT. Merabti, JingmaoJ. You and YoussrY. Youssef. Graph integration of structured, semistructured and unstructured data for data journalism October 2020
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