2023Activity reportProject-TeamVALDA

2.1 Objectives

Valda's focus is on both foundational and systems aspects of complex data management, especially human-centric data. The data we are interested in is typically heterogeneous, massively distributed, rapidly evolving, intensional, and often subjective, possibly erroneous, imprecise, incomplete. In this setting, Valda is in particular concerned with the optimization of complex resources such as computer time and space, communication, monetary, and privacy budgets. The goal is to extract value from data, beyond simple query answering.

Data management 37, 46 is now an old, well-established field, for which many scientific results and techniques have been accumulated since the sixties. Originally, most works dealt with static, homogeneous, and precise data. Later, works were devoted to heterogeneous data 3538, and possibly distributed 70 but at a small scale.

However, these classical techniques are poorly adapted to handle the new challenges of data management. Consider human-centric data, which is either produced by humans, e.g., emails, chats, recommendations, or produced by systems when dealing with humans, e.g., geolocation, business transactions, results of data analysis. When dealing with such data, and to accomplish any task to extract value from such data, we rapidly encounter the following facets:

• Heterogeneity: data may come in many different structures such as unstructured text, graphs, data streams, complex aggregates, etc., using many different schemas or ontologies.
• Massive distribution: data may come from a large number of autonomous sources distributed over the web, with complex access patterns.
• Rapid evolution: many sources may be producing data in real time, even if little of it is perhaps relevant to the specific application. Typically, recent data is of particular interest and changes have to be monitored.
• Intensionality1: in a classical database, all the data is available. In modern applications, the data is more and more available only intensionally, possibly at some cost, with the difficulty to discover which source can contribute towards a particular goal, and this with some uncertainty.
• Confidentiality and security: some personal data is critical and need to remain confidential. Applications manipulating personal data must take this into account and must be secure against linking.
• Uncertainty: modern data, and in particular human-centric data, typically includes errors, contradictions, imprecision, incompleteness, which complicates reasoning. Furthermore, the subjective nature of the data, with opinions, sentiments, or biases, also makes reasoning harder since one has, for instance, to consider different agents with distinct, possibly contradicting knowledge.

These problems have already been studied individually and have led to techniques such as query rewriting59 or distributed query optimization65.

Among all these aspects, intensionality is perhaps the one that has least been studied, so we pay particular attention to it. Consider a user's query, taken in a very broad sense: it may be a classical database query, some information retrieval search, a clustering or classification task, or some more advanced knowledge extraction request. Because of intensionality of data, solving such a query is a typically dynamic task: each time new data is obtained, the partial knowledge a system has of the world is revised, and query plans need to be updated, as in adaptive query processing 52 or aggregated search 78. The system then needs to decide, based on this partial knowledge, of the best next access to perform. This is reminiscent of the central problem of reinforcement learning 76 (train an agent to accomplish a task in a partially known world based on rewards obtained) and of active learning 72 (decide which action to perform next in order to optimize a learning strategy) and we intend to explore this connection further.

Uncertainty of the data interacts with its intensionality: efforts are required to obtain more precise, more complete, sounder results, which yields a trade-off between processing cost and data quality.

Other aspects, such as heterogeneity and massive distribution, are of major importance as well. A standard data management task, such as query answering, information retrieval, or clustering, may become much more challenging when taking into account the fact that data is not available in a central location, or in a common format. We aim to take these aspects into account, to be able to apply our research to real-world applications.

2.2 The Issues

We intend to tackle hard technical issues such as query answering, data integration, data monitoring, verification of data-centric systems, truth finding, knowledge extraction, data analytics, that take a different flavor in this modern context. In particular, we are interested in designing strategies to minimize data access cost towards a specific goal, possibly a massive data analysis task. That cost may be in terms of communication (accessing data in distributed systems, on the Web), of computational resources (when data is produced by complex tools such as information extraction, machine learning systems, or complex query processing), of monetary budget (paid-for application programming interfaces, crowdsourcing platforms), or of a privacy budget (as in the standard framework of differential privacy).

A number of data management tasks in Valda are inherently intractable. In addition to properly characterizing this intractability in terms of complexity theory, we intend to develop solutions for solving these tasks in practice, based on approximation strategies, randomized algorithms, enumeration algorithms with constant delay, or identification of restricted forms of data instances lowering the complexity of the task.

3.1 Scientific Foundations

We now detail some of the scientific foundations of our research on complex data management. This is the occasion to review connections between data management, especially on complex data as is the focus of Valda, with related research areas.

3.2 Research Directions

At the beginning of the Valda team, the project was to focus on the following directions:

• foundational aspects of data management, in particular related to query enumeration and reasoning on data, especially regarding security issues;
• implementation of provenance and uncertainty management, real-world applications, other aspects of uncertainty and incompleteness, in particular dynamic;
• development of personal information management systems, integration of machine learning techniques.

We believe the first two directions have been followed in a satisfactory manner. The focus on personal information management has not been kept for various organizational reasons, however, but the third axis of the project is reoriented to more general aspects of Web data management.

New permanent arrivals in the group since its creation have impacted its research directions in the following manner:

• Camille Bourgaux and Michaël Thomazo are both specialists of knowledge representation and formal aspects of knowledge bases, which is an expertise that did not exist in the group. They are also both interested in, and have been working on aspects related to connecting their research with database theory, and investigating aspects of uncertainty and incompleteness in their research. This leads to more work on knowledge representation and symbolic AI aspects, while keeping the focus of Valda on foundations of data management and uncertainty.
• Leonid Libkin is a specialist of database theory, of incomplete data management, and has a line of current research on graph data management. His profile fits very well with the original orientation of the Valda project.
• Paul Boniol is reinforcing the team activity on systems aspects.

We intend to keep producing leading research on the foundations of data management. Generally speaking, the goal is to investigate the borders of feasibility of various tasks. For instance, what are the assumptions on data that allow for computable problems? When is it not possible at all? When can we hope for efficient query answering, when is it hopeless? This is a problem of theoretical nature which is necessary for understanding the limit of the methods and driving research towards the scenarios where positive results may be obtainable. Only when we have understood the limitation of different methods and have many examples where this is possible, we can hope to design a solid foundation that allowing for a good trade-off between what can be done (needs from the users) and what can be achieved (limitation from the system).

Similarly, we will continue our work, both foundational and practical, on various aspects of provenance and uncertainty management. One overall long-term goal is to reach a full understanding of the interactions between query evaluation or other broader data management tasks and uncertain and annotated data models. We would in particular want to go towards a full classification of tractable (typically polynomial-time) and intractable (typically NP-hard for decision problems, or #P-hard for probability evaluation) tasks, extending and connecting the query-based dichotomy 51 on probabilistic query evaluation with the instance-based one of 39, 40. Another long-term goal is to consider more dynamic scenarios than what has been considered so far in the uncertain data management literature: when following a workflow, or when interacting with intensional data sources, how to properly represent and update uncertainty annotations that are associated with data. This is critical for many complex data management scenarios where one has to maintain a probabilistic current knowledge of the world, while obtaining new knowledge by posing queries and accessing data sources. Such intensional tasks requires minimizing jointly data uncertainty and cost to data access.

As application area, in addition to the historical focus on personal information management which is now less stressed, we target Web data (Web pages, the semantic Web, social networks, the deep Web, crowdsourcing platforms, etc.).

We aim at keeping a delicate balance between theoretical, foundational research, and systems research, including development and implementation. This is a difficult balance to find, especially since most Valda researchers have a tendency to favor theoretical work, but we believe it is also one of the strengths of the team.

4.1 Personal Information Management Systems

We recall that Valda's focus is on human-centric data, i.e., data produced by humans, explicitly or implicitly, or more generally containing information about humans. Quite naturally, we have used as a privileged application area to validate Valda’s results that of personal information management systems (Pims for short) 33.

A Pims is a system that allows a user to integrate her own data, e.g., emails and other kinds of messages, calendar, contacts, web search, social network, travel information, work projects, etc. Such information is commonly spread across different services. The goal is to give back to a user the control on her information, allowing her to formulate queries such as “What kind of interaction did I have recently with Alice B.?”, “Where were my last ten business trips, and who helped me plan them?”. The system has to orchestrate queries to the various services (which means knowing the existence of these services, and how to interact with them), integrate information from them (which means having data models for this information and its representation in the services), e.g., align a GPS location of the user to a business address or place mentioned in an email, or an event in a calendar to some event in a Web search. This information must be accessed intensionally: for instance, costly information extraction tools should only be run on emails which seem relevant, perhaps identified by a less costly cursory analysis (this means, in turn, obtaining a cost model for access to the different services). Impacted people can be found by examining events in the user's calendar and determining who is likely to attend them, perhaps based on email exchanges or former events' participant lists. Of course, uncertainty has to be maintained along the entire process, and provenance information is needed to explain query results to the user (e.g., indicate which meetings and trips are relevant to each person of the output). Knowledge about services, their data models, their costs, need either to be provided by the system designer, or to be automatically learned from interaction with these services, as in 71.

One motivation for that choice is that Pims concentrate many of the problems we intend to investigate: heterogeneity (various sources, each with a different structure), massive distribution (information spread out over the Web, in numerous sources), rapid evolution (new data regularly added), intensionality (knowledge from Wikidata, OpenStreetMap...), confidentiality and security (mostly private data), and uncertainty (very variable quality). Though the data is distributed, its size is relatively modest; other applications may be considered for works focusing on processing data at large scale, which is a potential research direction within Valda, though not our main focus. Another strong motivation for the choice of Pims as application domain is the importance of this application from a societal viewpoint.

A Pims is essentially a system built on top of a user's personal knowledge base; such knowledge bases are reminiscent of those found in the Semantic Web, e.g., linked open data. Some issues, such as ontology alignment 74 exist in both scenarios. However, there are some fundamental differences in building personal knowledge bases vs collecting information from the Semantic Web: first, the scope is quite smaller, as one is only interested in knowledge related to a given individual; second, a small proportion of the data is already present in the form of semantic information, most needs to be extracted and annotated through appropriate wrappers and enrichers; third, though the linked open data is meant to be read-only, the only update possible to a user being adding new triples, a personal knowledge base is very much something that a user needs to be able to edit, and propagating updates from the knowledge base to original data sources is a challenge in itself.

4.2 Web Data

The choice of Pims is not exclusive. We also consider other application areas as well. In particular, we have worked in the past and have a strong expertise on Web data 38 in a broad sense: semi-structured, structured, or unstructured content extracted from Web databases 71; knowledge bases from the Semantic Web 74; social networks 67; Web archives and Web crawls 54; Web applications and deep Web databases 47; crowdsourcing platforms 41. We intend to continue using Web data as a natural application domain for the research within Valda when relevant. For instance 45, deep Web databases are a natural application scenario for intensional data management issues: determining if a deep Web database contains some information requires optimizing the number of costly requests to that database.

A common aspect of both personal information and Web data is that their exploitation raises ethical considerations. Thus, a user needs to remain fully in control of the usage that is made of her personal information; a search engine or recommender system that ranks Web content for display to a specific user needs to do so in an unbiased, justifiable, manner. These ethical constraints sometimes forbid some technically solutions that may be technically useful, such as sharing a model learned from the personal data of a user to another user, or using blackboxes to rank query result. We fully intend to consider these ethical considerations within Valda. One of the main goals of a Pims is indeed to empower the user with a full control on the use of this data.

5.1 Awards

• Leonid Libkin received the ICDT test-of-time award for his ICDT 2013 paper 68
• Leonid Libkin received the SIGMOD 2023 Industry Track best paper award 14
• Leonid Libkin and Alexandra Rogova received the test-of-time award at the French BDA 2023 conference for their PODS 2023 paper 20
• As part of the group working on the SQL/PGQ standard, Leonid Libkin received the INCITS 2023 Team award

6.1 New software

6.2 New platforms

6.3 Open data

• A number of machine learning models, trained in the context of various works related to the TheoremKB project, were released on HuggingFace. Contacts:Shrey Mishra , Pierre Senellart
• Instructions to obtain the dataset and reproduce the experiments from 30 are provided in a companion repository on Github. Contacts:Antoine Gauquier , Pierre Senellart
• Instructions to obtain the dataset and reproduce the experiments from 22 are provided in a companion repository on Github. Contacts:Shufan Jiang , Pierre Senellart

7.1 Querying property graphs

GQL (Graph Query Language) is being developed as a new ISO standard for graph query languages to play the same role for graph databases as SQL plays for relational. In parallel, an extension of SQL for querying property graphs, SQL/PGQ, is added to the SQL standard; it shares the graph pattern matching functionality with GQL. Both standards (not yet published) are hard-to-understand specifications of hundreds of pages. The goal of 13 is to present a digest of the language that is easy for the research community to understand, and thus to initiate research on these future standards for querying graphs. The paper concentrates on pattern matching features shared by GQL and SQL/PGQ, as well as querying facilities of GQL.

Indeed, the development of practical query languages for graph databases runs well ahead of the underlying theory. The main component of both GQL and SQL/PGQ is the pattern matching facility, which is shared by the two standards. In many aspects, it goes well beyond RPQs, CRPQs, and similar queries on which the research community has focused for years. Our main contribution in 20 is to distill the lengthy standard specification into a simple Graph Pattern Calculus (GPC) that reflects all the key pattern matching features of GQL and SQL/PGQ, and at the same time lends itself to rigorous theoretical investigation. We describe the syntax and semantics of GPC, along with the typing rules that ensure its expressions are well-defined, and state some basic properties of the language. With this paper we provide the community a tool to embark on a study of query languages that will soon be widely adopted by industry.

Despite documented demand, schema support is limited both in existing systems and in the first version of the GQL Standard. It is anticipated that the second version of the GQL Standard will include a rich DDL. Aiming to inspire the development of GQL and enhance the capabilities of graph database systems, we propose in 14 PG-Schema, a simple yet powerful formalism for specifying property graph schemas. It features PG-Schema with flexible type definitions supporting multi-inheritance, as well as expressive constraints based on the recently proposed PG-Keys formalism. We provide the formal syntax and semantics of PG-Schema, which meet principled design requirements grounded in contemporary property graph management scenarios, and offer a detailed comparison of its features with those of existing schema languages and graph database systems.

7.2 Incomplete and uncertain information

In 15, we revisit the problem of repairing and querying inconsistent databases equipped with universal constraints. We adopt symmetric difference repairs, in which both deletions and additions of facts can be used to restore consistency, and suppose that preferred repair actions are specified via a binary priority relation over (negated) facts. Our first contribution is to show how existing notions of optimal repairs, defined for simpler denial constraints and repairs solely based on fact deletion, can be suitably extended to our richer setting. We next study the computational properties of the resulting repair notions, in particular, the data complexity of repair checking and inconsistency-tolerant query answering. Finally, we clarify the relationship between optimal repairs of prioritized databases and repair notions introduced in the framework of active integrity constraints. In particular, we show that Pareto-optimal repairs in our setting correspond to founded, grounded and justified repairs w.r.t. the active integrity constraints obtained by translating the prioritized database. Our study also yields useful insights into the behavior of active integrity constraints.

In 19, we consider the dichotomy conjecture for consistent query answering under primary key constraints stating that for every fixed Boolean conjunctive query q, testing whether it is certain over all repairs of a given inconsistent database is either polynomial time or coNP-complete. This conjecture has been verified for self-join-free and path queries. We propose a simple inflationary fixpoint algorithm for consistent query answering which, for a given database, naively computes a set $\Delta$ of subsets of database repairs with at most $k$ facts, where $k$ is the size of the query $q$. The algorithm runs in polynomial time and can be formally defined as: 1. Initialize $\Delta$ with all sets $S$ of at most $k$ facts such that $S$ satisfies $q$. 2. Add any set $S$ of at most $k$ facts to $\Delta$ if there exists a block $B$ (ie, a maximal set of facts sharing the same key) such that for every fact $a$ of $B$ there is a set $S^{\text{'}}\in \Delta$ contained in $(S\cup \{a\left\}\right)$. The algorithm answers “$q$ is certain” iff $\Delta$ eventually contains the empty set. The algorithm correctly computes certain answers when the query $q$ falls in the polynomial time cases for self-join-free queries and path queries. For arbitrary queries, the algorithm is an under-approximation: The query is guaranteed to be certain if the algorithm claims so. However, there are polynomial time certain queries (with self-joins) which are not identified as such by the algorithm.

The design of SQL is based on a three-valued logic (3VL), rather than the familiar two-valued Boolean logic (2VL). In addition totrue andfalse, 3VL addsunknown to handle nulls. Viewed as indispensable for SQL expressiveness, it is often criticized for unintuitive behavior of queries and for being a source of programmer mistakes. In 23, we show that, contrary to the widely held view, SQL could have been designed based on 2VL, without any loss of expressiveness. Similarly to SQL's WHERE clause, which only keeps true tuples, we conflate false and unknown for conditions involving nulls to obtain an equally expressive 2VL-based version of SQL. This applies to the core of the 1999 SQL Standard. Queries written under the 2VL semantics can be efficiently translated into the 3VL SQL and thus executed on any existing RDBMS. We show that 2VL enables additional optimizations. To gauge its applicability, we establish criteria under which 2VL and 3VL semantics coincide, and analyze common benchmarks such as TPC-H and TPC-DS to show that most of their queries are such. For queries that behave differently under 2VL and 3VL, we undertake a user study to show a consistent preference for the 2VL semantics.

Queries with aggregation and arithmetic operations, as well as incomplete data, are common in real-world database, but we lack a good understanding of how they should interact. On the one hand, systems based on SQL provide ad-hoc rules for numerical nulls, on the other, theoretical research largely concentrates on the standard notions of certain and possible answers. In the presence of numerical attributes and aggregates, however, these answers are often meaningless, returning either too little or too much. Our goal in 17 is to define a principled framework for databases with numerical nulls and answering queries with arithmetic and aggregations over them. Towards this goal, we assume that missing values in numerical attributes are given by probability distributions associated with marked nulls. This yields a model of probabilistic bag databases in which tuples are not necessarily independent, since nulls can repeat. We provide a general compositional framework for query answering, and then concentrate on queries that resemble standard SQL with arithmetic and aggregation. We show that these queries are measurable, and that their outputs have a finite representation. Moreover, since the classical forms of answers provide little information in the numerical setting, we look at the probability that numerical values in output tuples belong to specific intervals. Even though their exact computation is intractable, we show efficient approximation algorithms to compute such probabilities.

Federated knowledge discovery and data mining are challenged to assess the trustworthiness of data originating from autonomous sources while protecting confidentiality and privacy. Truth-finding algorithms help corroborate data from disagreeing sources. For each query it receives, a truth-finding algorithm predicts a truth value of the answer, possibly updating the trustworthiness factor of each source. Few works, however, address the issues of confidentiality and privacy. In 24, 28, we devise and present a secure secret-sharing-based multi-party computation protocol for pseudo-equality tests that are used in truth-finding algorithms to compute additions depending on a condition. The protocol guarantees confidentiality of the data and privacy of the sources. We also present a variants of a truth-finding algorithm that would make the computation faster when executed using secure multi-party computation. We empirically evaluate the performance of the proposed protocol on a state-of-the-art truth-finding algorithm, 3-Estimates, and compare it with that of the baseline plain algorithm. The results confirm that the secret-sharing-based secure multi-party algorithms are as accurate as the corresponding baselines but for proposed numerical approximations that significantly reduce the efficiency loss incurred.

7.3 Information extraction and natural language processing

In 21, we consider the problem of automatically inferring the () document class used to write a scientific article from its PDF representation. Applications include improving the performance of information extraction techniques that rely on the style used in each document class, or determining the publisher of a given scientific article. We introduce two approaches: a simple classifier based on hand-coded document style features, as well as a CNN-based classifier taking as input the bitmap representation of the first page of the PDF article. We experiment on a dataset of around 100k articles from arXiv, where labels come from the source LaTeX document associated to each article. Results show the CNN approach significantly outperforms that based on simple document style features, reaching over 90% average F1-score on a task to distinguish among several dozens of the most common document classes.

We consider in 22 automatically identifying the defined term within a mathematical definition from the text of an academic article. Inspired by the development of transformer-based natural language processing applications, we pose the problem as (a) a token-level classification task using fine-tuned pre-trained transformers; and (b) a question-answering task using a generalist large language model (GPT). We also propose a rule-based approach to build a labeled dataset from the source of papers. Experimental results show that it is possible to reach high levels of precision and recall using either recent (and expensive) GPT 4 or simpler pre-trained models fine-tuned on our task.

The Bidirectional Encoder Representations from Transformers (BERT) architecture offers a cutting-edge approach to Natural Language Processing. It involves two steps: 1) pre-training a language model to extract contextualized features and 2) fine-tuning for specific downstream tasks. Although pre-trained language models (PLMs) have been successful in various text-mining applications, challenges remain, particularly in areas with limited labeled data such as plant health hazard detection from individuals' observations. To address this challenge, we propose in 12 to combine GAN-BERT, a model that extends the fine-tuning process with unlabeled data through a Generative Adversarial Network (GAN), with ChouBERT, a domain-specific PLM. Our results show that GAN-BERT outperforms traditional fine-tuning in multiple text classification tasks. In this paper, we examine the impact of further pre-training on the GAN-BERT model. We experiment with different hyper parameters to determine the best combination of models and fine-tuning parameters. Our findings suggest that the combination of GAN and ChouBERT can enhance the generalizability of the text classifier but may also lead to increased instability during training. Finally, we provide recommendations to mitigate these instabilities.

7.4 Database theory

Even though query evaluation is a fundamental task in databases, known classifications of conjunctive queries by their fine-grained complexity only apply to queries without self-joins. We study in 16 how self-joins affect enumeration complexity, with the aim of building upon the known results to achieve general classifications. We do this by examining the extension of two known dichotomies: one with respect to linear delay, and one with respect to constant delay after linear preprocessing. As this turns out to be an intricate investigation, this paper is structured as an example-driven discussion that initiates this analysis. We show enumeration algorithms that rely on self-joins to efficiently evaluate queries that otherwise cannot be answered with the same guarantees. Due to these additional tractable cases, the hardness proofs are more complex than the self-join-free case. We show how to harness a known tagging technique to prove hardness of queries with self-joins. Our study offers sufficient conditions and necessary conditions for tractability and settles the cases of queries of low arity and queries with cyclic cores. Nevertheless, many cases remain open.

In 11, we study the question of when we can provide direct access to the $k$-th answer to a Conjunctive Query (CQ) according to a specified order over the answers in time logarithmic in the size of the database, following a preprocessing step that constructs a data structure in time quasilinear in database size. Specifically, we embark on the challenge of identifying the tractable answer orderings , that is, those orders that allow for such complexity guarantees. To better understand the computational challenge at hand, we also investigate the more modest task of providing access to only a single answer (i.e., finding the answer at a given position), a task that we refer to as the selection problem , and ask when it can be performed in quasilinear time. We also explore the question of when selection is indeed easier than ranked direct access. We begin with lexicographic orders . For each of the two problems, we give a decidable characterization (under conventional complexity assumptions) of the class of tractable lexicographic orders for every CQ without self-joins. We then continue to the more general orders by the sum of attribute weights and establish the corresponding decidable characterizations, for each of the two problems, of the tractable CQs without self-joins. Finally, we explore the question of when the satisfaction of Functional Dependencies (FDs) can be utilized for tractability and establish the corresponding generalizations of our characterizations for every set of unary FDs.

Existential rules are an expressive knowledge representation language mainly developed to query data. In the literature, they are often supposed to be in some normal form that simplifies technical developments. For instance, a common assumption is that rule heads are atomic, i.e., restricted to a single atom. Such assumptions are considered to be made without loss of generality as long as all sets of rules can be normalised while preserving entailment. However, an important question is whether the properties that ensure the decidability of reasoning are preserved as well. We provide in 25 a systematic study of the impact of these procedures on the different chase variants with respect to chase (non-)termination and FO-rewritability. This also leads us to study open problems related to chase termination of independent interest.

7.5 Theoretical computer science beyond databases

In 18, we show how to efficiently solve energy Büchi problems in finite weighted automata and in one-clock weighted timed automata. Solving the former problem is our main contribution and is handled by a modified version of Bellman-Ford interleaved with Couvreur's algorithm. The latter problem is handled via a reduction to the former relying on the corner-point abstraction. All our algorithms are freely available and implemented in a tool based on the open-source platforms TChecker and Spot.

8.1 Standardization activities

Leonid Libkin is involved in the standardization process of the GQL and SQL query languages. In particular, he is a chair of the LDBC working group on semantics of GQL, and a member of ISO/IEC JTC1 SC32 WG3 (SQL committee). He is also a member of INCITS, the US InterNational Committee for Information Technology Standards.

Participants: Leonid Libkin.

9.1 International initiatives

9.2 International research visitors

9.3 European initiatives

9.4 National initiatives

10.1 Promoting scientific activities

10.2 Teaching - Supervision - Juries

10.3 Popularization

11.1 Major publications

• 1 articleM.Michael Benedikt, P.Pierre Bourhis, G.Georg Gottlob and P.Pierre Senellart. Monadic Datalog, Tree Validity, and Limited Access Containment.ACM Transactions on Computational Logic2112020, 6:1-6:45HALDOI
• 2 inproceedingsM.Meghyn Bienvenu, Q.Quentin Manière and M.Michaël Thomazo. Answering Counting Queries over DL-Lite Ontologies.IJCAI 2020 - Twenty-Ninth International Joint Conference on Artificial IntelligenceProceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020.Reportée de juillet 2020 à janvier 2021 en raison de la COVIDYokohama, JapanJuly 2020HAL
• 3 inproceedingsC.Camille Bourgaux, P.Pierre Bourhis, L.Liat Peterfreund and M.Michaël Thomazo. Revisiting Semiring Provenance for Datalog.KR 2022 - 19th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 19th International Conference on Principles of Knowledge Representation and ReasoningHaifa, IsraelJuly 2022, 91–101HALDOI
• 4 inproceedingsC.Camille Bourgaux, D.David Carral, M.Markus Krötzsch, S.Sebastian Rudolph and M.Michaël Thomazo. Capturing Homomorphism-Closed Decidable Queries with Existential Rules.KR 2021 - 18th International Conference on Principles of Knowledge Representation and ReasoningVirtual, VietnamNovember 2021, 141--150HAL
• 5 inproceedingsM.Maxime Buron, M.-L.Marie-Laure Mugnier and M.Michaël Thomazo. Parallelisable Existential Rules: a Story of Pieces.KR 2021 - 18th International Conference on Principles of Knowledge Representation and ReasoningVirtual, VietnamNovember 2021HAL
• 6 inproceedingsM.Marco Console, P.Paolo Guagliardo, L.Leonid Libkin and E.Etienne Toussaint. Coping with Incomplete Data: Recent Advances.SIGMOD/PODS 2020 - International Conference on Management of DataPortland / Virtual, United StatesACMJune 2020, 33-47HALDOI
• 7 articleN.Nathan Grosshans, P.Pierre Mckenzie and L.Luc Segoufin. Tameness and the power of programs over monoids in DA.Logical Methods in Computer Science183August 2022, 14:1–14:34HALDOI
• 8 articleN.Nicole Schweikardt, L.Luc Segoufin and A.Alexandre Vigny. Enumeration for FO Queries over Nowhere Dense Graphs.Journal of the ACM (JACM)693June 2022, 1-37HALDOI
• 9 articleP.Pierre Senellart, L.Louis Jachiet, S.Silviu Maniu and Y.Yann Ramusat. ProvSQL: Provenance and Probability Management in PostgreSQL.Proceedings of the VLDB Endowment (PVLDB)1112August 2018, 2034-2037HALDOI
• 10 articleE.Etienne Toussaint, P.Paolo Guagliardo, L.Leonid Libkin and J.Juan Sequeda. Troubles with nulls, views from the users.Proceedings of the VLDB Endowment (PVLDB)1511July 2022, 2613-2625HALDOI

11.2 Publications of the year

11.3 Cited publications

• 33 articleS.Serge Abiteboul, B.Benjamin André and D.Daniel Kaplan. Managing your digital life.Commun. ACM5852015, 32-35URL: http://doi.acm.org/10.1145/2670528DOIback to text
• 34 articleS.Serge Abiteboul, P.Pierre Bourhis and V.Victor Vianu. Comparing workflow specification languages: A matter of views.ACM Trans. Database Syst.3722012, 10:1-10:59URL: http://doi.acm.org/10.1145/2188349.2188352DOIback to text
• 35 bookS.Serge Abiteboul, P.Peter Buneman and D.Dan Suciu. Data on the Web: From Relations to Semistructured Data and XML.Morgan Kaufmann1999back to text
• 36 inproceedingsS.Serge Abiteboul, L.Laurent Herr and J. V.Jan Van den Bussche. Temporal Versus First-Order Logic to Query Temporal Databases.Proceedings of the Fifteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 3-5, 1996, Montreal, Canada1996, 49-57URL: http://doi.acm.org/10.1145/237661.237674DOIback to text
• 37 bookS.Serge Abiteboul, R.Richard Hull and V.Victor Vianu. Foundations of Databases.Addison-Wesley1995, URL: http://webdam.inria.fr/Alice/back to textback to textback to textback to text
• 38 bookS.Serge Abiteboul, I.Ioana Manolescu, P.Philippe Rigaux, M.-C.Marie-Christine Rousset and P.Pierre Senellart. Web Data Management.Cambridge University Press2011, URL: http://webdam.inria.fr/Jorgeback to textback to text
• 39 inproceedingsA.Antoine Amarilli, P.Pierre Bourhis and P.Pierre Senellart. Provenance Circuits for Trees and Treelike Instances.Automata, Languages, and Programming - 42nd International Colloquium, ICALP 2015, Kyoto, Japan, July 6-10, 2015, Proceedings, Part II2015, 56-68URL: https://doi.org/10.1007/978-3-662-47666-6_5DOIback to text
• 40 inproceedingsA.Antoine Amarilli, P.Pierre Bourhis and P.Pierre Senellart. Tractable Lineages on Treelike Instances: Limits and Extensions.Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, PODS 2016, San Francisco, CA, USA, June 26 - July 01, 20162016, 355-370URL: http://doi.acm.org/10.1145/2902251.2902301DOIback to textback to text
• 41 articleY.Yael Amsterdamer, Y.Yael Grossman, T.Tova Milo and P.Pierre Senellart. CrowdMiner: Mining association rules from the crowd.PVLDB6122013, 1250-1253URL: http://www.vldb.org/pvldb/vol6/p1250-amsterdamer.pdfback to textback to text
• 42 inproceedingsP. B.Pablo Barceló Baeza. Querying graph databases.Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2013, New York, NY, USA - June 22 - 27, 20132013, 175-188URL: http://doi.acm.org/10.1145/2463664.2465216DOIback to text
• 43 articleD.Daniel Barbará, H.Hector Garcia-Molina and D.Daryl Porter. The Management of Probabilistic Data.IEEE Trans. Knowl. Data Eng.451992, 487-502URL: https://doi.org/10.1109/69.166990DOIback to text
• 44 articleD.Debabrota Basu, Q.Qian Lin, W.Weidong Chen, H. T.Hoang Tam Vo, Z.Zihong Yuan, P.Pierre Senellart and S.Stéphane Bressan. Regularized Cost-Model Oblivious Database Tuning with Reinforcement Learning.T. Large-Scale Data- and Knowledge-Centered Systems282016, 96-132URL: https://doi.org/10.1007/978-3-662-53455-7_5DOIback to text
• 45 inproceedingsM.Michael Benedikt, G.Georg Gottlob and P.Pierre Senellart. Determining relevance of accesses at runtime.Proceedings of the 30th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2011, June 12-16, 2011, Athens, Greece2011, 211-222URL: http://doi.acm.org/10.1145/1989284.1989309DOIback to text
• 46 incollectionM.Michael Benedikt and P.Pierre Senellart. Databases.Computer Science, The Hardware, Software and Heart of ItSpringer2011, 169-229URL: https://doi.org/10.1007/978-1-4614-1168-0_10DOIback to textback to text
• 47 inproceedingsM.Meghyn Bienvenu, D.Daniel Deutch, D.Davide Martinenghi, P.Pierre Senellart and F. M.Fabian M. Suchanek. Dealing with the Deep Web and all its Quirks.Proceedings of the Second International Workshop on Searching and Integrating New Web Data Sources, Istanbul, Turkey, August 31, 20122012, 21-24URL: http://ceur-ws.org/Vol-884/VLDS2012_p21_Bienvenu.pdfback to text
• 48 inproceedingsM.Miko\laj Bojańczyk, L.Luc Segoufin and S.Szymon Toruńczyk. Verification of database-driven systems via amalgamation.Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2013, New York, NY, USA - June 22 - 27, 20132013, 63-74URL: http://doi.acm.org/10.1145/2463664.2465228DOIback to text
• 49 inproceedingsP.Peter Buneman, S.Sanjeev Khanna and W.-C.Wang-Chiew Tan. Why and Where: A Characterization of Data Provenance.Database Theory - ICDT 2001, 8th International Conference, London, UK, January 4-6, 2001, Proceedings.2001, 316-330URL: https://doi.org/10.1007/3-540-44503-X_20DOIback to text
• 50 articleB.Bruno Courcelle. The Monadic Second-Order Logic of Graphs. I. Recognizable Sets of Finite Graphs.Inf. Comput.8511990, 12-75URL: https://doi.org/10.1016/0890-5401(90)90043-HDOIback to text
• 51 articleN. N.Nilesh N. Dalvi and D.Dan Suciu. The dichotomy of probabilistic inference for unions of conjunctive queries.J. ACM5962012, 30:1-30:87URL: http://doi.acm.org/10.1145/2395116.2395119DOIback to text
• 52 articleA.Amol Deshpande, Z. G.Zachary G. Ives and V.Vijayshankar Raman. Adaptive Query Processing.Foundations and Trends in Databases112007, 1-140URL: https://doi.org/10.1561/1900000001DOIback to text
• 53 inproceedingsP.Pinar Donmez and J. G.Jaime G. Carbonell. Proactive learning: cost-sensitive active learning with multiple imperfect oracles.Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, Napa Valley, California, USA, October 26-30, 20082008, 619-628URL: http://doi.acm.org/10.1145/1458082.1458165DOIback to text
• 54 inproceedingsM.Muhammad Faheem and P.Pierre Senellart. Adaptive Web Crawling Through Structure-Based Link Classification.Digital Libraries: Providing Quality Information - 17th International Conference on Asia-Pacific Digital Libraries, ICADL 2015, Seoul, Korea, December 9-12, 2015, Proceedings2015, 39-51URL: https://doi.org/10.1007/978-3-319-27974-9_5DOIback to text
• 55 bookL.Lise Getoor. Introduction to statistical relational learning.MIT Press2007back to text
• 56 inproceedingsG.Georges Gouriten, S.Silviu Maniu and P.Pierre Senellart. Scalable, generic, and adaptive systems for focused crawling.25th ACM Conference on Hypertext and Social Media, HT '14, Santiago, Chile, September 1-4, 20142014, 35-45URL: http://doi.acm.org/10.1145/2631775.2631795DOIback to text
• 57 inproceedingsT. J.Todd J. Green, G.Gregory Karvounarakis and V.Val Tannen. Provenance semirings.Proceedings of the Twenty-Sixth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 11-13, 2007, Beijing, China2007, 31-40URL: http://doi.acm.org/10.1145/1265530.1265535DOIback to text
• 58 articleT. J.Todd J. Green and V.Val Tannen. Models for Incomplete and Probabilistic Information.IEEE Data Eng. Bull.2912006, 17-24URL: http://sites.computer.org/debull/A06mar/green.psback to text
• 59 articleA. Y.Alon Y. Halevy. Answering queries using views: A survey.VLDB J.1042001, 270-294URL: https://doi.org/10.1007/s007780100054DOIback to text
• 60 articleM. A.Marti A. Hearst, S. T.Susan T Dumais, E.Edgar Osuna, J.John Platt and B.Bernhard Scholkopf. Support vector machines.IEEE Intelligent Systems1341998, 18-28URL: https://doi.org/10.1109/5254.708428DOIback to text
• 61 articleT.Tomasz Imielinski and W.Witold Lipski Jr.. Incomplete Information in Relational Databases.J. ACM3141984, 761-791URL: http://doi.acm.org/10.1145/1634.1886DOIback to text
• 62 articleF.Florent Jacquemard, L.Luc Segoufin and J.Jerémie Dimino. FO2(<, +1, $$) on data trees, data tree automata and branching vector addition systems.Logical Methods in Computer Science1222016, URL: https://doi.org/10.2168/LMCS-12(2:3)2016DOIback to text
• 63 incollectionB.Benny Kimelfeld and P.Pierre Senellart. Probabilistic XML: Models and Complexity.Advances in Probabilistic Databases for Uncertain Information ManagementSpringer2013, 39-66URL: https://doi.org/10.1007/978-3-642-37509-5_3DOIback to text
• 64 articleA. C.Anthony C. Klug. Equivalence of Relational Algebra and Relational Calculus Query Languages Having Aggregate Functions.J. ACM2931982, 699-717URL: http://doi.acm.org/10.1145/322326.322332DOIback to text
• 65 articleD.Donald Kossmann. The State of the art in distributed query processing.ACM Comput. Surv.3242000, 422-469URL: http://doi.acm.org/10.1145/371578.371598DOIback to text
• 66 inproceedingsJ. D.John D. Lafferty, A.Andrew McCallum and F. C.Fernando C. N. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 20012001, 282-289back to text
• 67 inproceedingsS.Siyu Lei, S.Silviu Maniu, L.Luyi Mo, R.Reynold Cheng and P.Pierre Senellart. Online Influence Maximization.Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, August 10-13, 20152015, 645-654URL: http://doi.acm.org/10.1145/2783258.2783271DOIback to text
• 68 inproceedingsL.Leonid Libkin, W.Wim Martens and D.Domagoj Vrgoc. Querying graph databases with XPath.Joint 2013 EDBT/ICDT Conferences, ICDT '13 Proceedings, Genoa, Italy, March 18-22, 2013ACM2013, 129--140URL: https://doi.org/10.1145/2448496.2448513DOIback to text
• 69 articleF.Frank Neven. Automata Theory for XML Researchers.SIGMOD Record3132002, 39-46URL: http://doi.acm.org/10.1145/601858.601869DOIback to text
• 70 bookM. T.M. Tamer Özsu and P.Patrick Valduriez. Principles of Distributed Database Systems, Third Edition.Springer2011, URL: https://doi.org/10.1007/978-1-4419-8834-8DOIback to text
• 71 inproceedingsP.Pierre Senellart, A.Avin Mittal, D.Daniel Muschick, R.Rémi Gilleron and M.Marc Tommasi. Automatic wrapper induction from hidden-web sources with domain knowledge.10th ACM International Workshop on Web Information and Data Management (WIDM 2008), Napa Valley, California, USA, October 30, 20082008, 9-16URL: http://doi.acm.org/10.1145/1458502.1458505DOIback to textback to textback to text
• 72 bookB.Burr Settles. Active Learning.Synthesis Lectures on Artificial Intelligence and Machine LearningMorgan & Claypool Publishers2012, URL: https://doi.org/10.2200/S00429ED1V01Y201207AIM018DOIback to textback to text
• 73 inproceedingsB.Burr Settles, M.Mark Craven and L.Lewis Friedland. Active learning with real annotation costs.NIPS 2008 Workshop on Cost-Sensitive Learning2008, URL: http://burrsettles.com/pub/settles.nips08ws.pdfback to text
• 74 articleF. M.Fabian M. Suchanek, S.Serge Abiteboul and P.Pierre Senellart. PARIS: Probabilistic Alignment of Relations, Instances, and Schema.PVLDB532011, 157-168URL: http://www.vldb.org/pvldb/vol5/p157_fabianmsuchanek_vldb2012.pdfback to textback to text
• 75 bookD.Dan Suciu, D.Dan Olteanu, C.Christopher Ré and C.Christoph Koch. Probabilistic Databases.Synthesis Lectures on Data ManagementMorgan & Claypool Publishers2011, URL: https://doi.org/10.2200/S00362ED1V01Y201105DTM016DOIback to text
• 76 bookR. S.Richard S. Sutton and A. G.Andrew G. Barto. Reinforcement learning - an introduction.Adaptive computation and machine learningMIT Press1998, URL: http://www.worldcat.org/oclc/37293240back to textback to text
• 77 inproceedingsM. Y.Moshe Y. Vardi. The Complexity of Relational Query Languages (Extended Abstract).Proceedings of the 14th Annual ACM Symposium on Theory of Computing, May 5-7, 1982, San Francisco, California, USA1982, 137-146URL: http://doi.acm.org/10.1145/800070.802186DOIback to text
• 78 inproceedingsK.Ke Zhou, M.Mounia Lalmas, T.Tetsuya Sakai, R.Ronan Cummins and J. M.Joemon M. Jose. On the reliability and intuitiveness of aggregated search metrics.22nd ACM International Conference on Information and Knowledge Management, CIKM'13, San Francisco, CA, USA, October 27 - November 1, 20132013, 689-698URL: http://doi.acm.org/10.1145/2505515.2505691DOIback to text