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Ivannikov Memorial Workshop (IVMEM)

作者： Anikin, Dmitry Borisenko, Oleg Nedumov, Yaroslav RAS Higher Sch Econ Ivannikov Inst Syst Programming Moscow Russia Plekhanov Russian Univ Econ RAS Ivannikov Inst Syst Programming Moscow Russia RAS Ivannikov Inst Syst Programming Moscow Russia

ISBN: (纸本)9781728146232

There are two main approaches to graph databases: based on RDF model and based on labeled property graph model. RDF is well known and studied, but modern graph dataabases with labeled property graph model are studied much lesser. In this paper we evaluated several possible solutions for storing and querying graph data using Gremlin - general purpose graph query language from Apache TinkerPop. We used LDBC graphalytics framework and compared NoSQL-based setups with SQL-based setups. We evaluated Janusgraph on HBase both on single machine and cluster and SQLG on top of PostgreSQL and H2. We used datasets from the different domains and of different sizes up to tens of millions vertices and edges. Evaluation results show that for the used workload SQLG with PostgreSQL is about ten times faster than Janusgraph on HBase and SQLG with H2 performance is in between.

关键词： graph databases labeled property graph post-gresql hbase TinkerPop

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Towards Generating labeled property graphs for Comprehending C#-based Software Projects 7

Towards Generating Labeled Property Graphs for Comprehending...

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37th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Heidrich, David Schreiber, Andreas Oberdoerfer, Sebastian German Aerosp Ctr DLR Wessling Germany German Aerosp Ctr DLR Cologne Germany Univ Wurzburg Wurzburg Germany

ISBN: (纸本)9781450394758

C# is the most widely used programming language among XR developers. However, only a limited number of graph-based data acquisition tools exist for C# software. XR development commonly relies on reusing existing software components to accelerate development. graph-based visualization tools can facilitate this comprehension process, e.g., by providing an overview of relationships between components. This work describes a new tool called Src2Neo that generates labeled property graphs of C#-based software projects. The stored graph follows a simple C# naming scheme and - contrary to other solutions - maps each software entity to exactly one node. The resulting graph facilitates the comprehension process by providing an easy to read representation of software components. Additionally, the generated graphs can act as a data basis for more advanced software visualizations without the need for complex data requests.

关键词： software visualization software comprehension labeled property graph c# game engines

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Demystifying graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and graph Queries

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ACM COMPUTING SURVEYS 2024年第2期56卷 1-40页

作者： Besta, Maciej Gerstenberger, Robert Peter, Emanuel Fischer, Marc Podstawski, Michal Barthels, Claude Alonso, Gustavo Hoefler, Torsten Swiss Fed Inst Technol Inst Comp Platforms OAT S 17Andreasstr 5 CH-8092 Zurich Switzerland Swiss Fed Inst Technol Inst Comp Platforms Dep Comp Sci STF J 420Stampfenbachstr 114 CH-8092 Zurich Switzerland PRODYNA Schweiz AG Grosspeteranlage 29 CH-4052 Basel Switzerland Future Proc Bojkowska 37a PL-44100 Gliwice Poland

Numerous irregular graph datasets, for example social networks or web graphs, may contain even trillions of edges. Often, their structure changes over time and they have domain-specific rich data associated with vertices and edges. graph database systems such as Neo4j enable storing, processing, and analyzing such large, evolving, and rich datasets. Due to the sheer size and irregularity of such datasets, these systems face unique design challenges. To facilitate the understanding of this emerging domain, we present the first survey and taxonomy of graph database systems. We focus on identifying and analyzing fundamental categories of these systems (e.g., document stores, tuple stores, native graph database systems, or object-oriented systems), the associated graph models (e.g., Resource Description Framework or labeled property graph), data organization techniques (e.g., storing graph data in indexing structures or dividing data into records), and different aspects of data distribution and query execution (e.g., support for sharding and Atomicity, Consistency, Isolation, Durability). Fifty-one graph database systems are presented and compared, including Neo4j, OrientDB, and Virtuoso. We outline graph database queries and relationships with associated domains (NoSQL stores, graph streaming, and dynamic graph algorithms). Finally, we outline future research and engineering challenges related to graph databases.

关键词： graphs graph Databases NoSQL Stores graph Database Management Systems graph Models Data Layout graph Queries graph Transactions graph Representations RDF labeled property graph Triple Stores Key-Value Stores RDBMS Wide-Column Stores Document Stores

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Towards Generating labeled property graphs for Comprehending C#-based Software Projects 22

Towards Generating Labeled Property Graphs for Comprehending...

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Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

作者： David Heidrich Andreas Schreiber Sebastian Oberdörfer German Aerospace Center (DLR) Germany University of Würzburg Germany

ISBN: (纸本)9781450394758

C# is the most widely used programming language among XR developers. However, only a limited number of graph-based data acquisition tools exist for C# software. XR development commonly relies on reusing existing software components to accelerate development. graph-based visualization tools can facilitate this comprehension process, e.g., by providing an overview of relationships between components. This work describes a new tool called Src2Neo that generates labeled property graphs of C#-based software projects. The stored graph follows a simple C# naming scheme and — contrary to other solutions — maps each software entity to exactly one node. The resulting graph facilitates the comprehension process by providing an easy to read representation of software components. Additionally, the generated graphs can act as a data basis for more advanced software visualizations without the need for complex data requests.

关键词： game engines labeled property graph software comprehension c# software visualization

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An Empirical Study on Recent graph Database Systems 13th

An Empirical Study on Recent Graph Database Systems

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13th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Wang, Ran Yang, Zhengyi Zhang, Wenjie Lin, Xuemin East China Normal Univ Shanghai Peoples R China Univ New South Wales Sydney NSW Australia

ISBN: (纸本)9783030551308;9783030551292

graphs are widely used to model the intricate relationships among objects in a wide range of applications. The advance in graph data has brought significant value to artificial intelligence technologies. Recently, a number of graph database systems have been developed. In this paper, we present a comprehensive overview and empirical investigation on existing property graph database systems such as Neo4j, Agensgraph, Tigergraph and Lightgraph (Lightgraph has recently renamed to Tugraph.). These systems support declarative graph query languages. Our empirical studies are conducted in a single-machine environment against on the LDBC social network benchmark, consisting of three different large-scale datasets and a set of benchmark queries. This is the first empirical study to compare these graph database systems by evaluating data bulk importing and processing simple and complex queries. Experimental results provide insightful observations of various graph data systems and indicate that Agensgraph works well on SQL based workload and simple update queries, Tigergraph is powerful on complex business intelligence queries, Neo4j is user-friendly and suitable for small queries, and Lightgraph is a more balanced product achieving good performance on different queries. The related code, scripts and data of this paper are available online (https://***-database/graphDB-Benchmark).

关键词： graph database systems labeled property graph LDBC benchmark

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Propagating Threat Scores With a TLS Ecosystem graph Model Derived by Active Measurements 8

Propagating Threat Scores With a TLS Ecosystem Graph Model D...

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8th Network Traffic Measurement and Analysis Conference (TMA)

作者： Sosnowski, Markus Sattler, Patrick Zirngibl, Johannes Betzer, Tim Carle, Georg Tech Univ Munich TUM Sch Computat Informat & Technol Munich Germany Tech Univ Munich Dept Comp Engn Chair Network Architectures & Serv Munich Germany

ISBN: (纸本)9798350378887;9783903176645

The Internet is shaped by independent actors and heterogeneous deployments. With the wide adoption of Transport Layer Security (TLS), a whole ecosystem of intertwined entities emerged. Acquiring a comprehensive view allows searching for previously unknown malicious entities and providing valuable cyber-threat intelligence. Actively collected Internet-wide Domain Name System (DNS) and TLS meta-data can provide the basis for such large-scale analyses. However, in order to efficiently navigate the vast volumes of data, an effective methodology is required. This work proposes a graph model of the TLS ecosystem that utilizes the relationships between servers, domains, and certificates. A Probabilistic Threat Propagation (PTP) algorithm is then used to propagate a threat score from existing blocklists to related nodes. We conducted a one-year-long measurement study of 13 monthly active Internet-wide DNS and TLS measurements to evaluate the methodology. The latest measurement found four highly suspicious clusters among the nodes with high threat scores. External threat intelligence services were used to confirm a high rate of maliciousness in the rest of the newly found servers. With the help of optimized thresholds, we identified 557 domains and 11 IP addresses throughout the last year before they were known to be malicious. Up to 40% of the identified nodes appeared on average three months later on the input blocklist. This work proposes a versatile graph model to analyze the TLS ecosystem and a PTP analysis to help security researchers focus on suspicious subsets of the Internet when searching for unknown threats.

关键词： TLS DNS Probabilistic Threat Propagation labeled property graph Blocklists Internet-wide Measurements

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graph-based Data for Accessible Indoor Navigation 23

Graph-based Data for Accessible Indoor Navigation

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23rd IEEE International Conference on Intelligent Engineering Systems (INES)

作者： Simon-Nagy, Gabriella Chalhoub, Nidal Fleiner, Rita Obuda Univ John von Neumann Fac Informat Budapest Hungary

ISBN: (纸本)9781728112138

In the paper the optimal representation of data based on iLOC and iACC ontologies in labeled property graph data model is explored. We describe and analyse the process where RDF-type data is transformed into Neo4j. The focus is on a specific subtask of the transformation, namely how instance-of a subclass type relationships can be expressed in labeled property graphs. Different solutions of the problem are described and analysed from the perspective of path traversal effectiveness.

关键词： RDF labeled property graph ontology data modelling Neo4j

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Forensic Analysis of Heterogeneous Social Media Data 11

Forensic Analysis of Heterogeneous Social Media Data

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11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) / 11th International Conference on Knowledge Engineering and Ontology Development (KEOD)

作者： Nikolaidou, Aikaterini Lazaridis, Michalis Semertzidis, Theodoros Axenopoulos, Apostolos Daras, Petros CERTH Informat Technol Inst Thessaloniki Greece

ISBN: (纸本)9789897583827

It is a challenge to aggregate and analyze data from heterogeneous social media sources not only for businesses and organizations but also for Law Enforcement Agencies. The latter's core objectives are to monitor criminal and terrorist related activities and to identify the "key players" in various networks. In this paper, a framework for homogenizing and exploiting data from multiple sources is presented. Moreover, as part of the framework, an ontology that reflects today's social media perceptions is introduced. Data from multiple sources is transformed into a labeled property graph and stored in a graph database in a homogenized way based on the proposed ontology. The result is a cross-source analysis system where end-users can explore different scenarios and draw conclusions through a library of predefined query placeholders that focus on forensic investigation. The framework is evaluated on the Stormfront dataset, a radical right, web community. Finally, the benefits of applying the proposed framework to discover and visualize the relationships between the Stormfront profiles are presented.

关键词： Social Media Analytics Forensic Platform Heterogeneous Social Media Data Ontology labeled property graph

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Extending the Intelligent Digital Twin with a context modeling service: A decision support use case 55

Extending the Intelligent Digital Twin with a context modeli...

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55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022

作者： Sahlab, Nada Braun, Dominik Köhler, Christian Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart70569 Germany

Considering the surrounding context of manufacturing systems at runtime enhances their adaptability, which is advantageous given the increasing demand for flexible production. Context-awareness presents a viable approach to continuously model the surrounding context and relate it to the system's operation. As the surrounding context is both acquirable from heterogeneous data sources and is dynamic in nature, a coherent and evolving context model is necessary. The context modeling approach needs to be both: system-centric to enable re-usability of the model by different applications as well as continuously and efficiently extendable to continuously represent the environment. Some approaches to model context within the manufacturing domain exist, but are mostly tailored to specific applications or do not consider an extension at runtime. To include the surrounding context of a system effectively and adapt it accordingly, a relation between the modeled context and the system during runtime is necessary. The intelligent Digital Twin provides essential interdisciplinary models of a physical system, which can be used for its analysis and monitoring. Adding a further service in the form of a model to represent the surrounding context enhances the intelligent Digital Twin to be used by various applications, e.g., for reconfiguration or decision-support. In our previous work, we have presented a tier-based model to realize context-awareness for intelligent Digital Twins. The presented tier model has different levels corresponding to different context scopes. In this contribution, we present an approach to realize an extendible and evolving tier-based context model with a generic ontology and model transformation to a labeled property graph with focus on the external context. To show the applicability and added value of the context model, a use case is presented, which addresses an intelligent warehouse's Digital Twin and its context-model supported decision-making. © 2022 Th

关键词： context-awareness cyber-physical systems Digital Twin intelligent warehouse labeled property graph ontology

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Process Mining on Event graph Databases

Process Mining on Event Graph Databases

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作者： Turkyilmaz, Pinar Eindhoven University of Technology

学位级别：硕士

graph databases allow storing the multi-dimensional event data in a for- mat that makes it easy to follow the traces. property graphs can store case identifiers in nodes. Moreover, the information between each event, between each case and between cases and events can be stored under relationships. Therefore, behavioral sequential information of the events, which are traces, can be represented as paths. Hence, the results become more reliable and easily interpretable. There are two different schemas previously implemented for event logs in property graphs, however there is no process mining analysis executed on a graph structure. Therefore, a research is essential on a process mining analysis on graph databases. In this thesis, an ITIL process is used for an impact analysis of the changes to IT components. We discuss the requirements of a data model and the structure should satisfy. Moreover, we propose a new schema where the analysis will be conducted on and we com- pare the results with the classical process mining techniques gives. Finally, we visualize our findings to grasp the efficiency of graph structure and gain more elaborated insights than a classical process mining analysis gives.

关键词： Process Mining graph Databases Data Analysis labeled property graph Multi-Dimensional Event Data ITIL

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