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GQL (Graph Query Language) is being developed as a new ISO standard for graph query languages to play the same role for graph databases as SQL plays for relational. In parallel, an extension of SQL for querying property graphs, SQL/PGQ, is added to the SQL standard; it shares the graph pattern matching functionality with GQL. Both standards (not yet published) are hard-to-understand specifications of hundreds of pages. The goal of this paper is to present a digest of the language that is easy for the research community to understand, and thus to initiate research on these future standards for querying graphs. The paper concentrates on pattern matching features shared by GQL and SQL/PGQ, as well as querying facilities of GQL.

Modern information systems rely on data analytics and use various data sources to steer information processing and process execution activities. Capability-driven development is a method for the design and delivery of this kind of information systems. This chapter elaborates a method extension for capability-based modeling of data ecosystems for the purpose of ensuring their resilience. The ecosystem perspective is adopted because there is a need to understand the interactions among the various parties involved in capability delivery. The ecosystem model allows to analyze the impact on reliability and other properties of data providers on capability delivery resilience. The meta-model is elaborated together with a set of rules for analyzing the ecosystem model. The model is perceived as a property graph, and the network theory is used for the analysis. The ADOxx meta-modeling platform is used to implement the modeling tool, which is integrated with a graph database, where the model analysis is performed. The method and the tool are demonstrated using an example of a winter road maintenance ecosystem.

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

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

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

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

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

Nowadays Knowledge Graphs are a common way to integrate and manage all the information that an organization owns. This involve also sensitive domains like security, so the management of access control on these graphs became crucial. Due to their dimension, Knowledge graphs are often stored using NoSQL solutions, that have very poor support to access control. In this paper a distributed and secure Knowledge graph management system is presented. The system supports both open and closed access control and its architecture guarantees the management of very large knowledge graph.

Táto práca sa zameriava na možnosti distribuovaného spracovania rozsiahlych dát na platforme Java s využitím grafových databáz. Analyzuje niekoľko distribúcii grafových databáz a spôsob ich prepojenia so systémom pre distribuované spracovanie dát, Apache Hadoop. Pre testovanie efektivity jednotlivých databázových riešení je výsledkom práce aplikácia, ktorá sťahuje dáta zo sociálnych sietí Twitter a Facebook. Tieto dáta je potom schopná zapísať a analyzovať pomocou dvoch rôznych databázových frameworkov. Jedná sa o frameworky Halyard a HGraphDB., This thesis is focused on the distributed Big Data processing on the Java platform, together with graph databases. It analyses several graph database distributions and the possibilities to connect them to the Apache Hadoop system for distributed data processing. For the purpose of testing database solutions effectiveness, the thesis outcome is an application, which is downloading data from social networks Twitter and Facebook. It is able to write and analyse data with two different database frameworks which are Halyard and HGraphDB.

Táto práca sa zameriava na možnosti distribuovaného spracovania rozsiahlych dát na platforme Java s využitím grafových databáz. Analyzuje niekoľko distribúcii grafových databáz a spôsob ich prepojenia so systémom pre distribuované spracovanie dát, Apache Hadoop. Pre testovanie efektivity jednotlivých databázových riešení je výsledkom práce aplikácia, ktorá sťahuje dáta zo sociálnych sietí Twitter a Facebook. Tieto dáta je potom schopná zapísať a analyzovať pomocou dvoch rôznych databázových frameworkov. Jedná sa o frameworky Halyard a HGraphDB., This thesis is focused on the distributed Big Data processing on the Java platform, together with graph databases. It analyses several graph database distributions and the possibilities to connect them to the Apache Hadoop system for distributed data processing. For the purpose of testing database solutions effectiveness, the thesis outcome is an application, which is downloading data from social networks Twitter and Facebook. It is able to write and analyse data with two different database frameworks which are Halyard and HGraphDB.

Táto práca sa zameriava na možnosti distribuovaného spracovania rozsiahlych dát na platforme Java s využitím grafových databáz. Analyzuje niekoľko distribúcii grafových databáz a spôsob ich prepojenia so systémom pre distribuované spracovanie dát, Apache Hadoop. Pre testovanie efektivity jednotlivých databázových riešení je výsledkom práce aplikácia, ktorá sťahuje dáta zo sociálnych sietí Twitter a Facebook. Tieto dáta je potom schopná zapísať a analyzovať pomocou dvoch rôznych databázových frameworkov. Jedná sa o frameworky Halyard a HGraphDB., This thesis is focused on the distributed Big Data processing on the Java platform, together with graph databases. It analyses several graph database distributions and the possibilities to connect them to the Apache Hadoop system for distributed data processing. For the purpose of testing database solutions effectiveness, the thesis outcome is an application, which is downloading data from social networks Twitter and Facebook. It is able to write and analyse data with two different database frameworks which are Halyard and HGraphDB.

Táto práca sa zameriava na možnosti distribuovaného spracovania rozsiahlych dát na platforme Java s využitím grafových databáz. Analyzuje niekoľko distribúcii grafových databáz a spôsob ich prepojenia so systémom pre distribuované spracovanie dát, Apache Hadoop. Pre testovanie efektivity jednotlivých databázových riešení je výsledkom práce aplikácia, ktorá sťahuje dáta zo sociálnych sietí Twitter a Facebook. Tieto dáta je potom schopná zapísať a analyzovať pomocou dvoch rôznych databázových frameworkov. Jedná sa o frameworky Halyard a HGraphDB., This thesis is focused on the distributed Big Data processing on the Java platform, together with graph databases. It analyses several graph database distributions and the possibilities to connect them to the Apache Hadoop system for distributed data processing. For the purpose of testing database solutions effectiveness, the thesis outcome is an application, which is downloading data from social networks Twitter and Facebook. It is able to write and analyse data with two different database frameworks which are Halyard and HGraphDB.

Táto práca sa zameriava na možnosti distribuovaného spracovania rozsiahlych dát na platforme Java s využitím grafových databáz. Analyzuje niekoľko distribúcii grafových databáz a spôsob ich prepojenia so systémom pre distribuované spracovanie dát, Apache Hadoop. Pre testovanie efektivity jednotlivých databázových riešení je výsledkom práce aplikácia, ktorá sťahuje dáta zo sociálnych sietí Twitter a Facebook. Tieto dáta je potom schopná zapísať a analyzovať pomocou dvoch rôznych databázových frameworkov. Jedná sa o frameworky Halyard a HGraphDB., This thesis is focused on the distributed Big Data processing on the Java platform, together with graph databases. It analyses several graph database distributions and the possibilities to connect them to the Apache Hadoop system for distributed data processing. For the purpose of testing database solutions effectiveness, the thesis outcome is an application, which is downloading data from social networks Twitter and Facebook. It is able to write and analyse data with two different database frameworks which are Halyard and HGraphDB.