Graph databases and their application to the Italian Business Register for efficient search of relationships among companies

We studied and tested three of the major graph databases, and we compared them with a relational database. We worked on a dataset representing equity participations among companies, and we found out that the strong points of graph databases are: the purposely designed storage techniques; and their query languages. The main performance increments have been obtained when heavy graph situations are queried; for simpler situations and queries, a relational database performs equally wellope

Comparison of Graph Databases and Relational Databases When Handling Large-Scale Social Data

Over the past few years, with the rapid development of mobile technology, more people use mobile social applications, such as Facebook, Twitter and Weibo, in their daily lives, and there is an increasing amount of social data. Thus, finding a suitable storage approach to store and process the social data, especially for the large-scale social data, should be important for the social network companies. Traditionally, a relational database, which represents data in terms of tables, is widely used in the legacy applications. However, a graph database, which is a kind of NoSQL databases, is in a rapid development to handle the growing amount of unstructured or semi-structured data. The two kinds of storage approaches have their own advantages. For example, a relational database should be a more mature storage approach, and a graph database can handle graph-like data in an easier way. In this research, a comparison of capabilities for storing and processing large-scale social data between relational databases and graph databases is applied. Two kinds of analysis, the quantitative research analysis of storage cost and executing time and the qualitative analysis of five criteria, including maturity, ease of programming, flexibility, security and data visualization, are taken into the comparison to evaluate the performance of relational databases and graph databases when handling large-scale social data. Also, a simple mobile social application is developed for experiments. The comparison is used to figure out which kind of database is more suitable for handling large-scale social data, and it can compare more graph database models with real-world social data sets in the future research

Graph Processing in Main-Memory Column Stores

Evermore, novel and traditional business applications leverage the advantages of a graph data model, such as the offered schema flexibility and an explicit representation of relationships between entities. As a consequence, companies are confronted with the challenge of storing, manipulating, and querying terabytes of graph data for enterprise-critical applications. Although these business applications operate on graph-structured data, they still require direct access to the relational data and typically rely on an RDBMS to keep a single source of truth and access. Existing solutions performing graph operations on business-critical data either use a combination of SQL and application logic or employ a graph data management system. For the first approach, relying solely on SQL results in poor execution performance caused by the functional mismatch between typical graph operations and the relational algebra. To the worse, graph algorithms expose a tremendous variety in structure and functionality caused by their often domain-specific implementations and therefore can be hardly integrated into a database management system other than with custom coding. Since the majority of these enterprise-critical applications exclusively run on relational DBMSs, employing a specialized system for storing and processing graph data is typically not sensible. Besides the maintenance overhead for keeping the systems in sync, combining graph and relational operations is hard to realize as it requires data transfer across system boundaries. A basic ingredient of graph queries and algorithms are traversal operations and are a fundamental component of any database management system that aims at storing, manipulating, and querying graph data. Well-established graph traversal algorithms are standalone implementations relying on optimized data structures. The integration of graph traversals as an operator into a database management system requires a tight integration into the existing database environment and a development of new components, such as a graph topology-aware optimizer and accompanying graph statistics, graph-specific secondary index structures to speedup traversals, and an accompanying graph query language. In this thesis, we introduce and describe GRAPHITE, a hybrid graph-relational data management system. GRAPHITE is a performance-oriented graph data management system as part of an RDBMS allowing to seamlessly combine processing of graph data with relational data in the same system. We propose a columnar storage representation for graph data to leverage the already existing and mature data management and query processing infrastructure of relational database management systems. At the core of GRAPHITE we propose an execution engine solely based on set operations and graph traversals. Our design is driven by the observation that different graph topologies expose different algorithmic requirements to the design of a graph traversal operator. We derive two graph traversal implementations targeting the most common graph topologies and demonstrate how graph-specific statistics can be leveraged to select the optimal physical traversal operator. To accelerate graph traversals, we devise a set of graph-specific, updateable secondary index structures to improve the performance of vertex neighborhood expansion. Finally, we introduce a domain-specific language with an intuitive programming model to extend graph traversals with custom application logic at runtime. We use the LLVM compiler framework to generate efficient code that tightly integrates the user-specified application logic with our highly optimized built-in graph traversal operators. Our experimental evaluation shows that GRAPHITE can outperform native graph management systems by several orders of magnitude while providing all the features of an RDBMS, such as transaction support, backup and recovery, security and user management, effectively providing a promising alternative to specialized graph management systems that lack many of these features and require expensive data replication and maintenance processes

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

From BPMN Models to Labelled Property Graphs

There\u27s a growing interest in leveraging the structured and formal nature of business process modeling languages in order to make them available not only for human analysis but also to machine-readable knowledge representation. Standard serializations of the past were predominantly XML based, with some of them seemingly discontinued, e.g., XPDL after the dissolution of the Workflow Management Coalition. Recent research has been investigating the interplay between knowledge representation and business process modeling, with the focus typically placed on standards such as RDF and OWL. In this paper we introduce a converter that translates the standards-compliant BPMN XML format to Neo4J labelled property graphs (LPG) thus providing an alternative to both traditional XML-based serialization and to more recent experimental RDF solutions, while ensuring conceptual alignment with the standard serialization of BPMN 2.0. A demonstrator was built to highlight the benefits of having such a parser and the completeness of coverage for BPMN models. The proposal facilitates graph-based processing of business process models in a knowledge intensive context, where procedural knowledge available as BPMN diagrams must be exposed to machines and LPG-driven applications