Graph Summarization

The continuous and rapid growth of highly interconnected datasets, which are both voluminous and complex, calls for the development of adequate processing and analytical techniques. One method for condensing and simplifying such datasets is graph summarization. It denotes a series of application-specific algorithms designed to transform graphs into more compact representations while preserving structural patterns, query answers, or specific property distributions. As this problem is common to several areas studying graph topologies, different approaches, such as clustering, compression, sampling, or influence detection, have been proposed, primarily based on statistical and optimization methods. The focus of our chapter is to pinpoint the main graph summarization methods, but especially to focus on the most recent approaches and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie

PG-Keys: Keys for Property Graphs

International audienceWe report on a community effort between industry and academia to shape the future of property graph constraints. The standardization for a property graph query language is currently underway through the ISO Graph Query Language (GQL) project. Our position is that this project should pay close attention to schemas and constraints, and should focus next on key constraints. The main purposes of keys are enforcing data integrity and allowing the referencing and identifying of objects. Motivated by use cases from our industry partners, we argue that key constraints should be able to have different modes, which are combinations of basic restriction that require the key to be exclusive, mandatory, and singleton. Moreover, keys should be applicable to nodes, edges, and properties since these all can represent valid real-life entities. Our result is PG-Keys, a flexible and powerful framework for defining key constraints, which fulfills the above goals. PG-Keys is a design by the Linked Data Benchmark Council's Property Graph Schema Working Group, consisting of members from industry, academia, and ISO GQL standards group, intending to bring the best of all worlds to property graph practitioners. PG-Keys aims to guide the evolution of the standardization efforts towards making systems more useful, powerful, and expressive. CCS CONCEPTS • Information systems → Integrity checking; • Theory of computation → Data modeling; Database constraints theory

PG-Schema: Schemas for Property Graphs

Property graphs have reached a high level of maturity, witnessed by multiple robust graph database systems as well as the ongoing ISO standardization effort aiming at creating a new standard Graph Query Language (GQL). Yet, 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 PG-Schema, a simple yet powerful formalism for specifying property graph schemas. It features PG-Types 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.Comment: 25 page

33èmes Journées Francophones des Langages Applicatifs

International audienceLes 33èmes Journées Francophones des Langages Applicatifs (JFLA) se sont tenues à Saint-Médard-d'Excideuil, plus précisément Domaine d'Essendiéras (Périgord), du mardi 28 juin 2022 au vendredi 1er juillet 2022.Les JFLA réunissent concepteurs, utilisateurs et théoriciens ; elles ont pour ambition de couvrir les domaines des langages applicatifs, de la preuve formelle, de la vérification de programmes, et des objets mathématiques qui sous-tendent ces outils. Ces domaines doivent être pris au sens large : nous souhaitons promouvoir les ponts entre les différentes thématiques.- Langages fonctionnels et applicatifs : sémantique, compilation, optimisation, typage, mesures, extensions par d'autres paradigmes.- Assistants de preuve : implémentation, nouvelles tactiques, développements présentant un intérêt technique ou méthodologique.- Logique, correspondance de Curry-Howard, réalisabilité, extraction de programmes, modèles.- Spécification, prototypage, développements formels d'algorithmes.- Vérification de programmes ou de modèles, méthode déductive, interprétation abstraite, raffinement.- Utilisation industrielle des langages fonctionnels et applicatifs, ou des méthodes issues des preuves formelles, outils pour le web.Les articles soumis aux JFLA sont relus par au moins deux personnes s'ils sont acceptés, trois personnes s'ils sont rejetés. Les critiques des relecteurs sont toujours bienveillantes et la plupart du temps encourageantes et constructives, même en cas de rejet

The Future is Big Graphs! A Community View on Graph Processing Systems

Graphs are by nature unifying abstractions that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users and consumers of graph instances and graph workloads understand these abstractions, future problems will require new abstractions and systems. What needs to happen in the next decade for big graph processing to continue to succeed?Comment: 12 pages, 3 figures, collaboration between the large-scale systems and data management communities, work started at the Dagstuhl Seminar 19491 on Big Graph Processing Systems, to be published in the Communications of the AC

Graph Queries: From Theory to Practice

International audienceWe review various graph query language fragments that are both theoretically tractable and practically relevant. We focus on the most expressive one that retains these properties and use it as a stepping stone to examine the underpinnings of graph query evaluation along graph view maintenance. Further broadening the scope of the discussion, we then consider alternative processing techniques for graph queries, based on graph summarization and path query learning. We conclude by pinpointing the open research directions in this emerging area

A Coq Formalization of the Relational Data Model

International audienceIn this article, we propose a coq formalization of the relational data model which underlies relational database systems. More precisely, we present and formalize the data definition part of the model including integrity constraints. We model two different query language formalisms: relational algebra and conjunctive queries. The former is the basis of We also present logical query optimization and prove the main ''database theorems'': algebraic equivalences, the homomorphism theorem and conjunctive query minimization

Graph Summarization

To appear in the Encyclopedia of Big Data TechnologiesThe continuous and rapid growth of highly interconnected datasets, which are both voluminous and complex, calls for the development of adequate processing and analytical techniques. One method for condensing and simplifying such datasets is graph summarization. It denotes a series of application-specific algorithms designed to transform graphs into more compact representations while preserving structural patterns, query answers, or specific property distributions. As this problem is common to several areas studying graph topologies, different approaches, such as clustering, compression, sampling, or influence detection, have been proposed, primarily based on statistical and optimization methods. The focus of our chapter is to pinpoint the main graph summarization methods, but especially to focus on the most recent approaches and novel research trends on this topic, not yet covered by previous surveys