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The data produced by various services should be stored and managed in an appropriate format for gaining valuable knowledge conveniently. This leads to the emergence of various data models, including relational, semi-structured, and graph models, and so on. Considering the fact that the mature relational databases established on relational data models are still predominant in today's market, it has fueled interest in storing and processing semi-structured data and graph data in relational databases so that mature and powerful relational databases' capabilities can all be applied to these various data. In this survey, we review existing methods on mapping semi-structured data and graph data into relational tables, analyze their major features, and give a detailed classification of those methods. We also summarize the merits and demerits of each method, introduce open research challenges, and present future research directions. With this comprehensive investigation of existing methods and open problems, we hope this survey can motivate new mapping approaches through drawing lessons from eachmodel's mapping strategies, aswell as a newresearch topic - mapping multi-model data into relational tables.

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.

In property graph query languages, nothing other than the properties of the current edge and those of its two vertex endpoints can be used to decide if an otherwise qualifying edge should be added to extend the current variable-length path. This, or the even stricter “thou can’t see anything” commandment in SPARQL arbitrary-length property path, is an impediment to writing queries that require that the inclusion (or not) of an edge be contingent upon properties of its neighborhood. An example of such a query requirement, supported in SQL, would be finding gender-alternating foaf:knows paths (in RDF data), or finding (in a property graph) money transfer trails carrying the same amount of money throughout. Proposed fully optional constructs for neighborhood-aware path traversal that eliminate these restrictions without requiring any changes to the underlying graph data model may facilitate expedited widespread adoption of graphs for representing enterprise data., This presentation discusses the motivation and current state of the art for Neighborhood-Aware Path Traversal (NAPT) in graph query languages. It proposes an approach that is rooted at introducing notations for references to neighborhood elements of the current edge being processed. Ideas are explained for new constructs that enable specification of constraints involving the properties of the neighborhood elements of an edge so that only those edges that satisfy the constraints are added to the path being created. Additional constructs are proposed for retrieval of path (aggregates), aggregate-based filter, and returning top-K paths based on specified criteria.

This presentation discusses the motivation and current state of the art for Neighborhood-Aware Path Traversal in graph query languages. It proposes an approach that is rooted at introducing notations for referencesto neighborhood elements of the current edge being processed. Ideas are explained for new constructs that enable specification of constraints involving the properties of the neighborhood elements of an edge so thatonly those edges that satisfy the constraints are added to thepath being created. Additional constructs are proposed for retrieval of path (aggregates), aggregate-based filter, and returning top-K paths based on specified criteria.

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.

Today's space of graph database solutions is characterized by two main technology stacks that have evolved separate from one another: on one hand, there are systems that focus on supporting the RDF family of standards; on the other hand, there is the Property Graph category of systems. As a basis for bringing these stacks together and, in particular, to facilitate data exchange between the different types of systems, different direct mappings between the underlying graph data models have been introduced in the literature. While fundamental properties are well-documented for most of these mappings, the same cannot be said about the practical implications of choosing one mapping over another. Our research aims to contribute towards closing this gap. In this paper we report on a preliminary study for which we have selected two direct mappings from (Labeled) Property Graphs to RDF, where one of them uses features of the RDF-star extension to RDF. We compare these mappings in terms of the query performance achieved by two popular commercial RDF stores, GraphDB and Stardog, in which the converted data is imported. While we find that, for both of these systems, none of the mappings is a clear winner in terms of guaranteeing better query performance, we also identify types of queries that are problematic for the systems when using one mapping but not the other. This work was funded by the Swedish Research Council (Vetenskapsrådet, project reg. no. 2019-05655) and by the CENIIT program at Linköping University (project no. 17.05).The software related to this work can be found in the following repository on Github: https://github.com/LiUSemWeb/GRADES2022-paper

Knowledge Graphs (KG) have been widely popular for presenting real-world entities as nodes and edges according to semantic web rules and regulations. Many organizations and industries have used Knowledge Graphs (KGs) for publishing their datasets according to Linked Open Data (LOD) principles. Many Graph data models, e.g., RDF, Property Graph, and Domain Graph data model, have been used to model real-world entities as Knowledge Graphs (KGs). However, each data model has represented the knowledge differently, which sometimes affects the performance of the Knowledge Graph, especially in data storage and retrieval. The selection of an exemplary graph data model for representing Knowledge Graphs (KGs) plays a vital role in extracting and integrating data from various sources. Wikidata (Vrandečić & Krötzsch, 2014) is a Knowledge Graph representing real-world entities and connecting them to Wikipedia articles. Wikidata entities are defined by the Pages, describing the information as statements. Each statement has some additional information, e.g., qualifiers and references. Wikidata is one of the most extensive Knowledge Graphs where the data is updated daily. Hence, the representation of Wikidata entities in the different graph data models is challenging and costly in terms of data storage and data retrieval. So, the thesis represents the Wikidata in three graph data models, e.g., RDF, Property Graph, and Domain Graph, and does a qualitative analysis of three graph data models by conducting comparison and describe their advantages and disadvantages. The RDF data model represents Wikidata as triples (subject-predicate-object) and uses RDF reification to model Wikidata complex statements. The property Graph data model uses node and edge labels to represent Wikidata entities and model the complex statement as edge attributes and a compact data model. The Domain Graph data model uses the edges as nodes to model Wikidata statements. The RDF reification lacks internal structure and generates many redundant triples, which increases the data storage and reduces the query response time. The Property Graph data model needs to be fully represented Wikidata statements as edge attributes. However, the Domain Graph data model facilitates the edges as nodes, fully represents Wikidata statements, and provides better storage and query response time than RDF and PG. In addition, the thesis represents the general qualitative analysis between three graph data models (RDF, Property Graph, and Domain Graph), which helps the readers to select the best graph data model for modeling Knowledge Graphs (KGs).

International audience; Cloud-Applications are the new industry standard way of designing Web-Applications. With Cloud Computing, Applications are usually designed as microservices, and developers can take advantage of thousands of such existing microservices, involving several hundred of cross-component communications on different physical resources. Microservices orchestration (as Kubernetes) is an automatic process, which manages each component lifecycle, and notably their allocation on the different resources of the cloud infrastructure. Whereas such automatic cloud technologies ease development and deployment, they nevertheless obscure debugging and performance analysis. In order to gain insight on the composition of services, distributed tracing recently emerged as a way to get the decomposition of the activity of each component within a cloud infrastructure. This paper aims at providing methodologies and tools (leveraging state-of-the-art tracing) for getting a wider view of application behaviours, especially focusing on application performance assessment. In this paper, we focus on using distributed traces and allocation information from microservices to model their dependencies as a hierarchical property graph. By applying graph rewriting operations, we managed to project and filter communications observed between microservices at higher abstraction layers like the machine nodes, the zones or regions. Finally, in this paper we propose an implementation of the model running on a microservices shopping application deployed on a Zonal Kubernetes cluster monitored by OpenTelemetry traces. We propose using the flow hierarchy metric on the graph model to pinpoint cycles that reveal inefficient resource composition inducing possible performance issues and economic waste.

Converting property graphs to RDF graphs allows to enhance the interoperability of knowledge graphs. But existing tools perform the same conversion for every graph, regardless of its content. In this paper, we propose PREC, a user-configured conversion of property graphs to RDF graphs to better capture the semantics of the content., 6 pages, 1 figure, 1st Workshop on Squaring the Circles on Graphs at SEMANTiCS 2021

Data vocabularies facilitate the organization and retrieval of knowledge. As the volumes of data being generated in the internet age is exploding, the need for structuring data such that automated organization can occur is becoming even more crucial to manage this data. A popular method of searching this data in the context of a vocabulary is through the use of graph theory. This paper describes the scenario where, through the use of a data vocabulary for describing automotive data based on labeled property graphs, data is modified such that it is stored in a knowledge graph. The difficulties that were encountered in this effort and how they were overcome are also discussed.

RDF triplestores and property graph databases are two approaches for data management which are based on modeling, storing and querying graph-like data. In spite of such common principle, they present special features that complicate the task of database interoperability. While there exist some methods to transform RDF graphs into property graphs, and vice versa, they lack compatibility and a solid formal foundation. This paper presents three direct mappings (schema-dependent and schema-independent) for transforming an RDF database into a property graph database, including data and schema. We show that two of the proposed mappings satisfy the properties of semantics preservation and information preservation. The existence of both mappings allows us to conclude that the property graph data model subsumes the information capacity of the RDF data model.

Summarization: The General Data Protection Regulation (GDPR) was created for regulating how organizations that collect personal data process and protect it. In cases of digital handling of personal data, GDPR compliance must be proven by analyzing the actions that a system applies in order to gather, process and safeguard the data. We advocate that compliance must be considered in the design phase of the system, by analyzing the dependencies between system entities (e.g. personal data, users etc.) and the processes enacted upon them. Then, it is possible to generate a series of data reports that can be assessed by regulators who inspect the system for GDPR compliance. However, there can not be a universal methodology that covers all application domains and systems. To show proof of concept, we apply the methodology to a remote patient monitoring service that runs in the cloud. Παρουσιάστηκε στο: 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) The new era of data economics, based on data sets with enormous volume, wide variety of formats, and rapid speed in data production and utilization, has brought the need for new data management structures and methods. Then come Not Only SQL (NoSQL) databases, which provide a new way of storing and retrieving data with features that go beyond relational database management systems. NoSQL databases are divided into four major models: key -value, column-oriented, document-oriented and graph-oriented. They are able to scalably handle the storage and processing of gigantic volumes of data with a flexible format, where manipulations are not exclusively performed through the SQL language. These data sets, many of which, modeled as large graphs, to - a challenge for industry and academia, which has been increasingly committed to research and innovation in the area. Thus, there is a natural increase in demand for performance analysis systems for Graph Oriented NoSQL Databases. This implies the need for new benchmarks capable of testing these new technologies and that guide users to identify tools that best adapt to their applications. Many studies approach the proposal of benchmarks for an analysis of Graph Oriented NoSQL Database systems. However, no study studies the comparison of such benchmarks. Thus, this Master's research work aimed to carry out a comparative analysis of benchmarks for performance evaluation of Graph Oriented NoSQL Databases, with specific emphasis on the property graph model, and thus highlight as main differences between these, in addition to identifying strengths and limitations of each benchmark. Thus, it becomes possible to identify the most appropriate benchmark to analyze the performance of Property Graph-Oriented NoSQL Database systems according to a set of requirements of a specific domain. Application. Among the several existing benchmarks, it was investigated to compare the LDBC SNB, XGDBench, HPC-SGAB, TGDB and Cyclone benchmarks. In general terms, the LDBC-SNB benchmark stood out in relation to the other benchmarks, especially when it comes to query types and performance measures, and because of its tester support for several graph-oriented NoSQL DBMS's. A nova era da economia de dados, baseada em conjuntos de dados com enorme volume, grande variedade de formatos e rápida velocidade na produção e utilização de dados, trouxe a necessidade de novas estruturas e métodos de gerenciamento de dados. Surgem então os bancos de dados Not Only SQL (NoSQL), que fornecem uma nova forma de armazenamento e recuperação dedados com capacidades que vão além dos sistemas gerenciadores de bancos de dados relacionais.Bancos de dados NoSQL são divididos em quatro grandes modelos: chave-valor, orientados à colunas, orientados a documentos e orientados a grafos. São capazes de lidar de forma escalável com o armazenamento e o processamento de gigantescos volumes de dados com formato flexível, onde as manipulações não são exclusivamente realizadas por meio da linguagem SQL. Esses conjuntos de dados, muitos dos quais, modelados como grandes grafos, tornaram-se um desafio para a indústria e a academia, que vem se empenhando cada vez mais em pesquisa e inovação na área. Assim, naturalmente ocorre o aumento da demanda por sistemas de análise de desempenho de Bancos de Dados NoSQL Orientados a Grafos. Isso implica na necessidade de novos benchmarks capazes de testar essas novas tecnologias e que orientemos usuários a identificar as ferramentas que melhor se adéquem às suas aplicações. Muitos estudos têm abordado a proposta de benchmarks para a análise dos sistemas de Bancos de Dados NoSQL Orientados a Grafos. Porém, nenhum estudo descreve a comparação detalhada desses benchmarks. Dessa forma, este trabalho de pesquisa de Mestrado teve por objetivo realizar uma análise comparativa de benchmarks para avaliação de desempenho de Bancos de Dados NoSQL Orientados a Grafos, com ênfase especificamente no modelo de grafos de propriedade, e assim destacar as principais diferenças entre esses, além de identificar pontos positivos e limitações de cada benchmark. Assim, torna-se possível identificar o benchmark mais adequado para analisar o desempenho de sistemas de Bancos de Dados NoSQL Orientados a Grafos de Propriedades em função de um conjunto de requisitos de um domínio específico de aplicação. Dentre os diversos benchmarks existentes, foi investigada a relevância dos benchmarks LDBC SNB, XGDBench, HPC, TGDB e Cyclone. Em termos gerais, o benchmark LDBC-SNB se sobressaiu em relação aos outros benchmarks, principalmente quando se trata dos tipos de consultas e medidas de desempenho e por prover suporte a diversos SGBD’s NoSQL orientados a grafos. CAPES/DS (01/08/2018 a 17/07/2020)

Modeling your data as a graph has a significant advantage: The schema does not need to be explicitly defined or specified ahead of time. Thus, you can add data to your graph without being constrained by any schema. One of the less recognized problems with data addition to a graph, however, is the potential for loss of backward compatibility with regard to queries designed before the changes are made to the data. Use of RDF Quads (W3C RDF1.1 Recommendation 25-FEB-2014) as your graph data model would allow schema evolution caused by data addition to your graph to preserve backward compatibility of all your pre-existing queries. The tutorial also includes discussions on the following topics: new way of distinguishing among the various types of graphs: normal (discrete math) graph, RDF triples, Property Graph, RDF Quads. the new RDF# proposal for extension to RDF and SPARQL that allows explicit naming of RDF triples review of W3C SPARQL Query language and W3C SPARQL Update (used for DML) comparison between two popular graph query languages: SPARQL and PGQL use of RDF data for graph analytics review of W3C R2RML (RDB to RDF Mapping Language) methodology for creating relational-to-RDF (R2RML) mapping for a given relational schema. All of these discussions are complemented with live demo on a freely downloadable VM running Oracle 19c. &nbsp

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.

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.

—This paper proposes N O X PERANTO , a novelcrowdsourcing approach to address querying over datacollections managed by polyglot persistence settings. The maincontribution of N O X PERANTO is the ability to solve complexqueries involving different data stores by exploiting queriesfrom expert users (i.e. a crowd of database administrators, dataengineers, domain experts, etc.), assuming that these users cansubmit meaningful queries. N O X PERANTO exploits the resultsof “meaningful queries” in order to facilitate the forthcomingquery answering processes. In particular, queries results areused to: (i) help non-expert users in using the multi-databaseenvironment and (ii) improve performances of the multi-databaseenvironment, which not only uses disk and memory resources,but heavily rely on network bandwidth. N O X PERANTO employsa layer to keep track of the information produced by the crowdmodeled as a Property Graph and managed in a Graph DatabaseManagement System (GDBMS).Index Terms—Polyglot persistence, crowdsourcing, multi-databases, big data, property graph, graph databases.

Cloud computing has given rise to on-demand service provisioning and massive outsourcing of IT infrastructures and applications to virtual, commoditized ones. Despite the broad Service Level Agreement (SLA) usage in scientific settings, their role in cloud markets is peripheral and misinterpreted. The paper introduces a SLA graph data model that supports automated SLA formalization and data management through a property digraph. The data model is described as a directed graph (digraph). We elaborate on node and edge properties that indicate dependencies in the SLA data management flow. We sketch a realistic scenario of cloud data service provisioning to extract attributes that characterize the data service. The SLA graph model and data service attributes are used to demonstrate the formalization of a SLA template that is managed as a property graph. The graph structure enables the manipulation of SLA information in a modular, extensible way that considers the data flow and all inclusive data dependencies.

This article is a collective position paper from the Wimmics research team, expressing our vision of how Web graph data technologies should evolve in the future in order to ensure a high-level of interoperability between the many types of applications that produce and consume graph data. Wimmics stands for Web-Instrumented Man-Machine Interactions, Communities, and Semantics. We are a joint research team between INRIA Sophia Antipolis-M{\'e}diterran{\'e}e and I3S (CNRS and Universit{\'e} C{\^o}te d'Azur). Our challenge is to bridge formal semantics and social semantics on the web. Our research areas are graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The application of our research is supporting and fostering interactions in online communities and management of their resources. In this position paper, we emphasize the need to extend the semantic Web standard stack to address and fulfill new graph data needs, as well as the importance of remaining compatible with existing recommendations, in particular the RDF stack, to avoid the painful duplication of models, languages, frameworks, etc. The following sections group motivations for different directions of work and collect reasons for the creation of a working group on RDF 2.0 and other recommendations of the RDF family., Comment: W3C Workshop on Web Standardization for Graph Data - Creating Bridges: RDF, Property Graph and SQL, Mar 2019, Berlin, France. 2019, https://www.w3.org/Data/events/data-ws-2019/index.html