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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.

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.

During the last decades, there has been significant changes in science that have provoked a big increase in the number of articles published every year. This increment implies a new difficulty for scientists, who have to do an extra effort for selecting literature relevant for their activity. In this work, we present a pipeline for the generation of scientific literature knowledge graphs in the agriculture domain. The pipeline combines Semantic Web and natural language processing technologies, which make data understandable by computer agents, empowering the development of final user applications for literature searches. This workflow consists of (1) RDF generation, including metadata and contents, (2) semantic annotation of the content, and (3) property graph population by adding domain knowledge from ontologies, in addition to the previously generated RDF data describing the articles. This pipeline was applied to a set of 127 agriculture articles, generating a knowledge graph implemented in Neo4j, publicly available on Docker. The potential of our model is illustrated through a series of queries and use cases, which not only include queries about authors or references but also deal with article similarity or clustering based on semantic annotation, which is facilitated by the inclusion of domain ontologies in the graph.

—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.