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5 results on '"Property graph"'

1. Graph Pattern Matching in GQL and SQL/PGQ

Author

Alin Deutsch, Nadime Francis, Alastair Green, Keith Hare, Bei Li, Leonid Libkin, Tobias Lindaaker, Victor Marsault, Wim Martens, Jan Michels, Filip Murlak, Stefan Plantikow, Petra Selmer, Oskar van Rest, Hannes Voigt, Domagoj Vrgoč, Mingxi Wu, Fred Zemke, Department of Computer Science and Engineering [Univ California San Diego] (CSE - UC San Diego), University of California [San Diego] (UC San Diego), University of California (UC)-University of California (UC), Laboratoire d'Informatique Gaspard-Monge (LIGM), École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel, Neo4j, JCC Consulting Inc, Shenzhen Univerisity [Shenzhen], Value from Data (VALDA ), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), University of Edinburgh, University of Bayreuth, Oracle, University of Warsaw (UW), Pontificia Universidad Católica de Chile (UC), TigerGraph, The work of the FSWG is supported by grants from Neo4j held at the University of Edinburgh and ENS-Paris. The academic group also gratefully acknowledges support of the following research grants: EPSRC grants N023056 and S003800 (Libkin), DFG grants 369116833 and 431183758 (Martens), NCN grant 2018/30/E/ST6/00042 (Murlak), ANID – Millennium Science Initiative Program –Code ICN17_002 (Vrgoč)., ANR-18-CE40-0031,QUID,Calcul efficace de requêtes sur des données incomplètes ou incohérentes(2018), ANR-21-CE48-0015,VeriGraph,Requêtes et transformations vérifiables pour les graphes(2021), Ives, Zachary, Bonifati, Angela, El Abbadi, Amr, Paparrizos, John, Taft, Rebecca, Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS), Shenzhen University (Shenzhen University), Laboratory for the Foundations of Computer Science [Edinburgh] (LFCS), and Université Paris sciences et lettres (PSL)

Subjects
FOS: Computer and information sciences, SQL, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS], Query Language, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.1: Logical Design/H.2.1.0: Data models, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], Databases (cs.DB), [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], Graph database, Standardization, H.2.3, Computer Science - Databases, [INFO.INFO-FL]Computer Science [cs]/Formal Languages and Automata Theory [cs.FL], Property graph, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.3: Languages/H.2.3.3: Query languages, Pattern matching, Query language, GQL
Abstract

International audience; As graph databases become widespread, JTC1 -- the committee in joint charge of information technology standards for the International Organization for Standardization (ISO), and International Electrotechnical Commission (IEC) -- has approved a project to create GQL, a standard property graph query language. This complements a project to extend SQL with a new part, SQL/PGQ, which specifies how to define graph views over an SQL tabular schema, and to run read-only queries against them. Both projects have been assigned to the ISO/IEC JTC1 SC32 working group for Database Languages, WG3, which continues to maintain and enhance SQL as a whole. This common responsibility helps enforce a policy that the identical core of both PGQ and GQL is a graph pattern matching sub-language, here termed GPML. The WG3 design process is also analyzed by an academic working group, part of the Linked Data Benchmark Council (LDBC), whose task is to produce a formal semantics of these graph data languages, which complements their standard specifications. This paper, written by members of WG3 and LDBC, presents the key elements of the GPML of SQL/PGQ and GQL in advance of the publication of these new standards.

Published
2022
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2. A Tracing Based Model to Identify Bottlenecks in Physically Distributed Applications

Author

Clement Casse, Pascal Berthou, Philippe Owezarski, Sebastien Josset, Équipe Services et Architectures pour Réseaux Avancés (LAAS-SARA), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), and Orange Labs

Subjects
Distributed Tracing, Multi-zones Cloud, Hierarchical Model, Property Graph, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Edge-IoT, Graph Rewriting
Abstract

International audience; The Cloud computing paradigm has become the new industry standard way of designing large scale applications. Over the past years, we observe an increased adoption of this technology on numerous IoT-Edge applications. And while this technology comes with its promises and benefits, considering almost infinite scalability, it also comes along with its drawbacks and challenges. Detecting partial failures or bottlenecks are new obstacles that arose with the adoption of Cloud Applications. Distributed Tracing now allows developers to gain insight on the composition of services within a distributed Application. Today we observe an increased adoption of this technology on numerous cloud-native architectures. The project OpenTelemetry proposes a specification for traces that normalizes this new monitoring data format. In this publication we present an approach that leverages these traces to identify bottlenecks at the scale of a physically distributed application. We propose an extension of our model that builds a hierarchical property graph to exhibit bottlenecks in an application that follows the layered Cloud-IoT network model. Based on OpenTelemetry traces we can maintain a model at runtime of the whole application and compute bottlenecks. Their identification relies on the scores provided by centrality algorithms.

Published
2022
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3. Approximate Querying on Property Graphs

Author

Romain Vuillemot, Stefania Dumbrava, Angela Bonifati, Amaia Nazabal Ruiz Diaz, Méthodes et modèles pour les réseaux (METHODES-SAMOVAR), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2), Types and Reasoning for the Web (TYREX), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), École Centrale de Lyon (ECL), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Theoretical computer science, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer science, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.2: Information Storage, Graph summarization, 02 engineering and technology, approximate query answering, label-reachability queries, Graph, Data modeling, Compact space, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Rdf graph, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], property graph, Computer Science::Databases, ComputingMilieux_MISCELLANEOUS, MathematicsofComputing_DISCRETEMATHEMATICS, graph summary, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrieval/H.3.3.0: Clustering
Abstract

Property graphs are becoming widespread when modeling data with complex structural characteristics and enhancing edges and nodes with a list of properties. In this paper, we focus on the approximate evaluation of counting queries involving recursive paths on property graphs. As such queries are already difficult to evaluate over pure RDF graphs, they require an ad-hoc graph summary for their approximate evaluation on property graphs. We prove the intractability of the optimal graph summarization problem, under our algorithm’s conditions. We design and implement a novel property graph summary suitable for the above queries, along with an approximate query evaluation module. Finally, we show the compactness of the obtained summaries as well as the accuracy of answering counting recursive queries on them.

Published
2019
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4. GRAM: A GPU-Based Property Graph Traversal and Query for HPC Rich Metadata Management

Author

Dong Dai, Hong Huang, Hai Jin, Yong Chen, Peng Zhao, Xuanhua Shi, Li Wenke, Services Computing Technology and System Lab [HUST], Huazhong University of Science and Technology [Wuhan] (HUST), Texas Tech University [Lubbock] (TTU), Feng Zhang, Jidong Zhai, Marc Snir, Hai Jin, Hironori Kasahara, Mateo Valero, TC 10, and WG 10.3

Subjects
020203 distributed computing, Theoretical computer science, Computer science, Graph query, GPU, 020207 software engineering, Scenario, 02 engineering and technology, Graph traversal, External Data Representation, Computer Science::Digital Libraries, Metadata, Property graph, Data file, Metadata management, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Rich metadata management, Gram
Abstract

International audience; In HPC systems, rich metadata are defined to describe rich information about data files, like the executions that lead to the data files, the environment variables, and the parameters of all executions, etc. Recent studies have shown the feasibility of using property graph to model rich metadata and utilizing graph traversal to query rich metadata stored in the property graph. We propose to utilize GPU to process the rich metadata graphs. There are generally two challenges to utilize GPU for metadata graph query. First, there is no proper data representation for the metadata graph on GPU yet. Second, there is no optimization techniques specifically for metadata graph traversal on GPU neither. In order to tackle these challenges, we propose GRAM, a GPU-based property graph traversal and query framework. GRAM uses GPU to express metadata graph in Compressed Sparse Row (CSR) format, and uses Structure of Arrays (SoA) layout to store properties. In addition, we propose two new optimizations, parallel filtering and basic operations merging, to accelerate the metadata graph traversal. Our evaluation results show that GRAM can be effectively applied to user scenarios in HPC systems, and the performance of metadata management is greatly improved.

Published
2018
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5. Model-Driven reverse engineering of NoSQL property graph databases

Author

Comyn-Wattiau, Isabelle, Akoka, Jacky, Essec Business School, Département Systèmes d'Information (DSI), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom Business School (IMT-BS), Institut Mines-Télécom [Paris] (IMT)-Télécom Ecole de Management (TEM), Centre d'études et de recherche en informatique et communications (CEDRIC), and Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Property graph, Model-Driven Approach, InformationSystems_DATABASEMANAGEMENT, NoSQL graph database, Reverse engineering, Neo4j
Abstract

International audience; - Most NoSQL databases are schemaless. Although they offer some flexibility, they do not have any knowledge of the database schema, losing the benefits provided by these schemas. It is generally accepted that data modelling can have an impact on performance, consistency, usability, and maintainability. We argue that NoSQL databases need data models that ensure the proper storage and the relevant querying of the data. This paper seeks to present and illustrate an MDA-based approach, allowing us to achieve a reverse engineering of NoSQL property graph databases into an Extended Entity-Relationship schema. The approach is applied to the case of Neo4j graph database. We present an illustrative scenario and evaluate the reverse engineering approach.

Published
2017
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