7.1.1 MedAnalytics

• Keywords:
  Big data, Predictive analytics, Distributed systems
• Functional Description:
  We implemented a method for the automatic detection of at-risk profiles based on a fine-grained analysis of prescription data at the time of admission. The system relies on an optimized distributed architecture adapted for processing very large volumes of medical records and clinical data. We conducted practical experiments with real data of millions of patients and hundreds of hospitals. We demonstrated how the various perspectives of big data improve the detection of at-risk patients, making it possible to construct predictive models that benefit from volume and variety. Parts of this prototype implementation are described in the publications DSAA'18, Big Data'18, CHIL'21, UAI'21.
• Publications:
  hal-01517087, hal-01877742, hal-03124966, hal-03125018, hal-03160473, hal-03066941, hal-03266004
• Contact:
  Pierre Genevès
• Partner:
  CHU Grenoble

7.1.2 MuIR

• Name:
  Mu Intermediate Representation
• Keywords:
  Optimizing compiler, Querying
• Functional Description:
  This is a prototype of an intermediate language representation, i.e. an implementation of algebraic terms, rewrite rules, query plans, cost model, query optimizer, and query evaluators. This includes a distributed evaluator of algebraic terms using Apache Spark. Concepts of this implementation have been described in the SIGMOD'20 and CIKM'20 publications, among others, and the distributed evaluator and query optimizers are described in 2021 preprints.
• Publications:
  hal-01673025, hal-03295445, hal-03004218, hal-03517826
• Contact:
  Pierre Genevès

Distributed Evaluation of Graph Queries using Recursive Relational Algebra.

We have investigated the distributed evaluation of $\mu$-RA queries. We present a system called Dist-$\mu$-RA for the distributed evaluation of recursive graph queries. Dist-$\mu$-RA builds on the recursive relational algebra and extends it with evaluation plans suited for the distributed setting. The goal is to offer expressivity for high-level queries while providing efficiency at scale and reducing communication costs. Experimental results on both real and synthetic graphs show the effectiveness of the proposed approach compared to existing systems 4.

An Algebra with a Fixpoint Operator for Distributed Data Collections.

Big data programming frameworks are becoming increasingly important for the development of applications, for which performance and scalability are critical. In those complex frameworks, optimizing code by hand is hard and time-consuming, making automated optimization particularly necessary. In order to automate optimization, a prerequisite is to find suitable abstractions to represent programs; for instance, algebras based on monads or monoids to represent distributed data collections. Currently, however, such algebras do not represent recursive programs in a way which allows analyzing or rewriting them. We extend a monoid algebra with a fixpoint operator for representing recursion as a first class citizen and show how it allows new optimizations. The fixpoint operator is suitable for modeling recursive computations with distributed data collections. We show that under reasonable conditions this fixpoint can be evaluated by parallel loops with one final merge rather than by a global loop requiring network overhead after each iteration. We also propose several rewrite rules, showing when and how filters can be pushed through recursive terms, and how to filter inside a fixpoint before a join. Experiments with the Spark platform illustrate performance gains brought by these systematic optimizations 5, 3.

Efficient Enumeration of Recursive Plans in Transformation-based Query Optimizers.

Query optimizers built on the transformation-based Volcano/Cascades framework are used in many database systems. Transformations proposed earlier on the logical query dag (LQDAG) data structure, which is key in such a framework, focus only on recursion-free queries. We propose the recursive logical query dag (RLQDAG) which extends the LQDAG with the ability to capture and transform recursive queries, leveraging recent developments in recursive relational algebra. Specifically, this extension includes: (i) the ability of capturing and transforming sets of recursive relational terms thanks to (ii) annotated equivalence nodes used for guiding transformations that are more complex in the presence of recursion; and (iii) RLQDAG rewrite rules that transform sets of subterms in a grouped manner, instead of transforming individual terms in a sequential manner; and that (iv) incrementally update the necessary annotations. Core concepts of the RLQDAG are formalized using a syntax and formal semantics with a particular focus on subterm sharing and recursion. The result is a clean generalization of the LQDAG transformation-based approach, enabling more efficient explorations of plan spaces for recursive queries. An implementation of the proposed approach shows significant performance gains compared to the state-of-the-art 6.

Exploring Property Graphs with Recursive Path Patterns.

We demonstrate a system for recursive query answering over property graphs. The novelty of the system resides in its ability to optimize and efficiently answer recursive path patterns in queries for property graphs. The system is based on a complete implementation of the $\mu$-recursive relational algebra 1. It also includes parsers and compilers adapted for property graphs so that one can formulate, optimize and answer queries that navigate recursively along paths in property graphs. We demonstrate the system on three real datasets, including the exploration of chains of drug interactions 7.

Provenance-aware Discovery of Functional Dependencies on Integrated Views.

The automatic discovery of functional dependencies(FDs) has been widely studied as one of the hardest problems in data profiling. Existing approaches have focused on making the FD computation efficient while inspecting single relations at a time. In this paper, for the first time we address the problem of inferring FDs for multiple relations as they occur in integrated views by solely using the functional dependencies of the base relations of the view itself. To this purpose, we leverage logical inference and selective mining and show that we can discover most of the exact FDs from the base relations and avoid the full computation of the FDs for the integrated view itself, while at the same time preserving the lineage of FDs of base relations. We propose algorithms to speedup the inferred FD discovery process and mine FDs on-the-fly only from necessary data partitions. We present InFine(INferred FunctIoNal dEpendency), an end-to-end solution to discover inferred FDs on integrated views by leveraging provenance information of base relations. Our experiments on a range of real-world and synthetic datasets demonstrate the benefits of our method over existing FD discovery methods that need to rerun the discovery process on the view from scratch and cannot exploit lineage information on the FDs. We show that InFine outperforms traditional methods necessitating the full integrated view computation by one to two order of magnitude in terms of runtime. It is also the most memory efficient method while preserving FD provenance information using mainly inference from base table with negligible execution time.

These results were presented at the ICDE 2022 conference 2.

On the Replicability of Knowledge Enhanced Neural Networks in a Graph Neural Network Framework.

In order to extend Knowledge Enhanced Neural Networks, we investigate the replicability of the approach and present a re-implementation of Knowledge Enhanced Neural Networks based on a Graph Neural Network framework (PyTorch Geometric). Knowledge Enhanced Neural Networks integrate prior knowledge in the form of logical formulas into an Artificial Neural Network by adding additional Knowledge Enhancement layers. The obtained results show that the model outperforms pure neural models as well as Neural-Symbolic models. Our long term goal is to be able to address more complex and large-scale knowledge graphs and to benefit from the wide range of functionalities available in PyTorch Geometric. To ensure that our implementation produces the same results, we replicate the original transductive experiments and explain the various challenges and the steps that we went through to reach that goal 8.

10.1.1 ANR

11.1.1 Scientific events: organisation

• P. Genevès organized the MFML Axis Workshop 2022, a yearly workshop on Formal Methods Model and Languages organized of the LIG laboratory.

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Research administration

• N. Layaïda is president of the Hiring committee CRCN-ISFP of the Centre Inria de l'Université de Rennes 2023. The Inria Rennes hiring commission is in charge of hiring permanent researchers CRCN-ISFP of the Research Center.
• N. Layaïda was a member of Professor hiring committee at Université de Lyon 1. 2022. Hiring committee member for a Professor position for the LIRIS Laboratory in the Database Group.
• N. Layaïda is a member of the scientific committee of the LabEx PERSYVAL-lab (Pervasive Systems and Algorithms).
• N. Layaïda is a member of the Scientific Board of Digital League, the digital cluster of Auvergne-Rhône-Alpes.
• P. Genevès is member of the board of the DeepCare MIAI Chair
• P. Genevès is co-responsible of the Doctoral School MSTII that organizes the PhD program in Computer Science at the University Grenoble Alpes.
• P. Genevès is member of the board of the LIG laboratory, responsible for the “Formal Methods, Models and Languages” research axis regrouping 4 research teams working in this field.

11.2.1 Teaching

• Master: P. Genevès is co-responsible and teacher of the M2-level course ‘Fundamentals of Data Processing and Distributed Knowledge’ of the MOSIG program at UGA (36h)
• Master: P. Genevès is co-responsible and teacher of the M2-level course ‘Accès à l'information: du web des données au web sémantique’ in the ENSIMAG ISI 3A program at Grenoble-INP (30h)
• Master : N. Gesbert, academic tutorship of an apprentice, 6 h eq TD, M1, Grenoble INP
• Master : N. Gesbert, ‘Construction d’applications Web’, 27 h eq TD, M1, Grenoble INP
• Master : N. Gesbert, ‘Principes des systèmes de gestion des bases de données’, 58 h eq TD, M1, Grenoble INP
• Master : N. Gesbert, ‘Introduction to lambda-calculus’, 4 h eq TD, M2, UGA-Grenoble INP (MOSIG)
• Licence : N. Gesbert, ‘Logique pour l’informatique’, 45 h eq TD, L3, Grenoble INP
• N. Gesbert is in charge of the L3-level course ‘logique pour l’informatique’ and of the M1-level course ‘Principes des systèmes de gestion de bases de données (SEOC)’.
• Master : U. Comignani is co-responsible of the "BigData" master, co-accredited between Grenoble Ecole de Management and Grenoble INP
• Master : U. Comignani is in charge of the 'Projets fil rouges', 10 h eq TD, MS BigData, Grenoble INP
• Master : U. Comignani, ‘Principes des systèmes de gestion de bases de données’, 99.5 h eq TD, M1, Grenoble INP
• Master : U. Comignani is in charge of the ‘Projet BD’, 64 h eq TD, M1, Grenoble INP
• Master : U. Comignani, ‘Stockage et traitement de données à grande échelle’, 28.5 h eq TD, M2, Grenoble INP
• Master : U. Comignani, academic tutorship of an apprentice, 10 h eq TD, M1, Grenoble INP

11.2.2 Supervision

• PhD in progress: Luisa Werner, Neural Symbolic Integration, PhD started in October 2020, co-supervised by Nabil Layaïda and Pierre Genevès.
• PhD thesis of Amela Fejza, On the Optimization of Recursive Plan Enumeration with an Application to Property Graph Queries, supervised by Pierre Genevès, defended on January 11, 2023.
• PhD thesis of Sarah Chlyah, On Algebraic Foundations for the Optimization of Iterative Programming with Distributed Data Collections, co-supervised by Pierre Genevès and Nabil Layaïda, defended on May 5, 2022.

11.2.3 Juries

• N. Layaïda has been reviewer (Rapporteur) for the thesis of Issam GHABRI. Efficacité énergétique des phases de conception et d'exploitation des entrepôts de données. PhD in computer science. Ecole Nationale Supérieure de Mécanique et d'Aérotechnique. 29 December 2022.
• N. Layaïda has been reviewer (Rapporteur) for the thesis of Théo Ducros, Reasoning in Description Logics Augmented with Refreshing Variables. PhD in computer science from Université Clermont Auvergne. 27 September 2022.

11.3.1 Interventions

• Luisa Werner gave a presentation on “Neural symbolic integration of knowledge extraction and reasoning on graph data” at the MIAI days, for which she received the best PhD presentation award.
• Sarah Chlyah gave a presentation on “Neurosymbolic integration” for the Atelier d’Innovation at Centre Inria de l'Université de Grenoble Alpes.
• Sarah Chlyah gave a presentation on “Distributed evaluation of recursive algebra” for the service d'expérimentation et développement of the Centre Inria de l'Université de Grenoble Alpes.

International peer-reviewed conferences

• 2 inproceedingsU.Ugo Comignani, L.Laure Berti-Équille, N.Noël Novelli and A.Angela Bonifati. Provenance-aware Discovery of Functional Dependencies on Integrated Views.ICDE 2022 - 38th IEEE International Conference on Data EngineeringKuala Lumpur, MalaysiaIEEEMay 2022, 1-15
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Doctoral dissertations and habilitation theses

• 3 thesisS.Sarah Chlyah. On Algebraic Foundations for the Optimization of Iterative Programming with Distributed Data Collections.Université Grenoble Alpes [2020-....]May 2022
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Reports & preprints

• 4 miscS.Sarah Chlyah, P.Pierre Genevès and N.Nabil Layaïda. Distributed Evaluation of Graph Queries using Recursive Relational Algebra.October 2022
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• 5 miscS.Sarah Chlyah, N.Nils Gesbert, P.Pierre Genevès and N.Nabil Layaïda. On the Optimization of Iterative Programming with Distributed Data Collections.May 2022
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• 6 miscA.Amela Fejza, P.Pierre Genevès and N.Nabil Layaïda. Efficient Enumeration of Recursive Plans in Transformation-based Query Optimizers.October 2022
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• 7 miscA.Amela Fejza, P.Pierre Genevès and N.Nabil Layaïda. Exploring Labeled Graphs with Recursive Path Patterns.October 2022
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• 8 miscL.Luisa Werner, N.Nabil Layaïda and P.Pierre Genèves. On the Replicability of Knowledge Enhanced Neural Networks in a Graph Neural Network Framework.June 2022
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