MillenniumDB: A Multi-modal, Multi-model Graph Database

Graph theory, which is a branch of discrete mathematics, has emerged as a powerful tool in various domains, including database management. This abstract investigates the ways in which ideas and methods from graph theory which can be applied to database systems, offering a thorough synopsis of their benefits. Complex interactions within data can be well-modeled by using the basic concepts of graph theory, such as nodes, edges, and relationships. Because of its capacity to represent and query complex relationships, graph databases have become more and more popular in the field of database administration. Graph databases are well-suited for situations such as social networks, recommendation systems, and interconnected data domains because they are excellent at representing and traversing relationships, in contrast to standard relational databases, which are excellent at managing structured data. The abstract delves into the key graph-based data models, such as property graphs, RDF (Resource Description Framework), explaining how they facilitate the representation of diverse relationships. Furthermore, it explores the efficient storage and retrieval mechanisms that leverage graph traversal algorithms to extract valuable insights from interconnected datasets. The document highlights specific use cases where graph theory contributes to database management, including fraud detection, social network analysis, and recommendation systems. Additionally, it discusses the challenges associated with integrating graph databases into existing infrastructures and proposes solutions to address scalability and performance concerns. The abstract also touches upon the advancements in graph database query languages (Cypher) and SPARQL, showcasing their expressive power in querying complex relationships. The inclusion of graph-based indexing and optimization techniques demonstrates how database systems can efficiently handle queries involving large-scale graph data. As graph databases continue to evolve, this abstract concludes by outlining potential future directions in the intersection of graph theory and database management. It emphasizes the importance of ongoing research in developing scalable and efficient solutions for managing interconnected data, ultimately paving the way for more sophisticated and context-aware database systems relationships. Furthermore, it explores the efficient storage and retrieval mechanisms that leverage graph traversal algorithms to extract valuable insights from interconnected datasets.

Abstract. Over the recent years, the usage of semantic web technologies and Resources Description Framework (RDF) data models have been notably increased in many fields. Multiple systems are using RDF data to describe information resources and semantic associations. RDF data plays a very important role in advanced information retrieval, and graphs are efficient ways to visualize and represent real world data by providing solutions to many real-time scenarios that can be simulated and implemented using graph databases, and efficiently query graphs with multiple attributes representing different domains of knowledge. Given that graph databases are schema less with efficient storage for semi-structured data, they can provide fast and deep traversals instead of slow RDBMS SQL based joins allowing Atomicity, Consistency, Isolation and durability (ACID) transactions with rollback support, and by utilizing mathematics of graph they can enormous potential for fast data extraction and storage of information in the form of nodes and relationships. In this paper, we are presenting an architectural design with complete implementation of BIM-GIS integrated RDF graph database. The proposed integration approach is composed of four main phases: ontological BIM and GIS model’s construction, mapping and semantic integration using interoperable data formats, then an import into a graph database with querying and filtering capabilities. The workflows and transformations of IFC and CityGML schemas into object graph databases model are developed and applied to an intelligent urban mobility web application on a game engine platform validate the integration methodology.

The article presents a study of the tasks that determine the features of power-loaded networks, and tasks that are solved using graph models for power-loaded networks. The tasks that determine the features of power-loaded networks are communication check, isolation of network components and components, combinatorial tasks on graphs, calculation of flow distribution, graph simplification, optimal impact on the power grid, and determination of power proportions. This article presents the high-level architecture of Neo4j. Neo4j is an open-source graph database management system implemented in Java and is considered the most comprehensive and promising graph database management system. In comparison with modelling a graph database using a relational database management system, this approach allows applying additional optimization in the case of data with a more complex and loaded structure. The article proposes a model of an energy-loaded network, because it is a system of interaction of a large number of loads or consumers, active sources, communication lines. The declarative query language for graph databases Cypher was used for implementation. A graph database of an electrical network with a description of analytical data processing using a mathematical algorithm and software specifics of the work of queries in a multi-relational database environment has been built. When a query is executed for a user-specified network segment, a connectivity component is allocated; then for the selected elements of the network graph, correspondence is made with the elements of the network itself.

Graph databases are becoming a topic of interest in the research community because of the possibilities that offers in the era of big data. Recently, different types of graph databases have been proposed, however, majority of graph databases are under fifteen years of age and have constant improvements. This article presents a review of graph databases and introduces an architecture which represents the basis for most of the graph databases. Besides, according to data storage type and the data model, a graph database taxonomy is proposed. Such taxonomy allows a categorization from graph databases studied in this research. In the last part we present a survey that describe deeply some popular graph databases. This survey has the aim to provide a guideline to the selection of one of these databases.KeywordsGraph databasesData modelsGraph data management

The article presents an approach to modeling domain ontologies based on graph databases. Ontology is traditionally considered as a means of studying and formalizing the subject area. Based on ontologies, knowledge bases of information systems are formed, which can later be replenished and used to study certain applied aspects of the subject area. At the same time, with the development of NoSQL technologies and graph databases aimed at optimizing work with related data, it becomes possible to design a data warehouse without a strict pre-established domain model. Due to the obvious graph nature of ontologies, graph databases are a promising solution for storing and working with ontologies. However, with all the similarities, the ontology and data models used in graph DBMSs are not so easy to combine. The paper proposes an approach to the integration of ontological and graph models of knowledge representation and considers its application in creating a database for a prototype of a web ontology editor. Visual tools for editing ontologies are described, an algorithm for layer-by-layer rendering of a directed graph is given, a mechanism for generating dynamic forms for editing classes and ontology instances is described, and basic support for ontological design patterns is implemented.

The Green Deal Data Space is born in the big data paradigm where there is a variety of data formats and data models that are exposed as files or web APIs. As a result, we need to default in simple data structure that is transversal enough to be able to represent most of the more specific data models, formats and API payloads. Many data models present a structure that can be represented as tables.TAPIS stands for "Tables from APIS". It is a JavaScript code that uses a common data model that is an array of objects with a list of properties that can contain a simple or a complex value. In TAPIS offers a series of operations that use one or more arrays of objects as inputs and produce a new array of objects as an output. There are operations that create the arrays of objects from files or API queries (a.k.a. data import), others that manipulate the objects (e.g. merge two arrays in a single one) and some operations that generate visual representations of the common data structure including tabular, a map, a graph, etc.TAPIS is limited by its own data model. While many of the data models can be mapped to the common data model, a multidimensional data cube or a data tree cannot be represented in a single table in an efficient way. In the context of the Green Deal Data Space, most of the sensor data, statistical data, geospatial feature based data and administrative data can be considered object based data and can be used in TAPIS. TAPIS is able to connect to Sensor Things API (the sensor protocol selected in AD4GD and CitiObs), S3 buckets (the internal cloud repository used in AD4GD), GeoNetwork (the geospatial metadata catalogue selected in AD4GD and more4nature), and the OGC API features and derivates (the modern web API interfaces standardized by the OGC) but other data inputs will be incorporated, such as Citizen Science data sources and other popular APIs used in the more4nature project. More analytical functionalities are going to be incorporated in the CitiObs project. As part of the AD4GD Green Deal Information Model, there is an operation to associate semantics to each column of a table by linking it to a URI that defines the concept in an external vocabulary (as well as units of measure if appropriate). In order to be compatible with the data space architecture recommended by the International Data Space Association, we are working on supporting the catalogue of the Eclipse Data Connector, and to be able to negotiate a digital contract as a previous step to request access to the relevant data offered in the data space. To do so, we are working on incorporating the data space protocol as part of the TAPIS operations for data import. TAPIS is available as open source at https://github.com/joanma747/TAPIS.AD4GD, CitiObs and more4nature are Horizon Europe projects co-funded by the European Union, Switzerland and the United Kingdom.

Large-scale parallel machines are programmed mainly with the single program, multiple data (SPMD) model of parallelism. While this model has advantages of scalability and simplicity, it does not fit well with divide-and-conquer parallelism or hierarchical machines that mix shared and distributed memory. In this paper, we define the recursive single program, multiple data model (RSPMD) that extends SPMD with a hierarchical team mechanism to support hierarchical algorithms and machines. We implement this model in the Titanium language and describe how to eliminate a class of deadlocks by ensuring alignment of collective operations. We present application case studies evaluating the RSPMD model, showing that it enables divide-and-conquer algorithms such as sorting to be elegantly expressed and that team collective operations increase performance of conjugate gradient by up to a factor of two. The model also facilitates optimizations for hierarchical machines, improving scalability of particle in cell by 8x and performance of sorting and a stencil code by up to 40 % and 14 %, respectively.

In this paper, a Graph-based semantic Data Model (GDM) is proposed with the primary objective of bridging the gap between the human perception of an enterprise and the needs of computing infrastructure to organize information in some particular manner for efficient storage and retrieval. The Graph Data Model (GDM) has been proposed as an alternative data model to combine the advantages of the relational model with the positive features of semantic data models. The proposed GDM offers a structural representation for interacting to the designer, making it always easy to comprehend the complex relations amongst basic data items. GDM allows an entire database to be viewed as a Graph (V, E) in a layered organization. Here, a graph is created in a bottom up fashion where V represents the basic instances of data or a functionally abstracted module, called primary semantic group (PSG) and secondary semantic group (SSG). An edge in the model implies the relationship among the secondary semantic groups. The contents of the lowest layer are the semantically grouped data values in the form of primary semantic groups. The SSGs are nothing but the higher-level abstraction and are created by the method of encapsulation of various PSGs, SSGs and basic data elements. This encapsulation methodology to provide a higher-level abstraction continues generating various secondary semantic groups until the designer thinks that it is sufficient to declare the actual problem domain. GDM, thus, uses standard abstractions available in a semantic data model with a structural representation in terms of a graph. The operations on the data model are formalized in the proposed graph algebra. A Graph Query Language (GQL) is also developed, maintaining similarity with the widely accepted user-friendly SQL. Finally, the paper also presents the methodology to make this GDM compatible with the distributed environment, and a corresponding query processing technique for distributed environment is also suggested for the sake of completeness.

Graph databases such as neo4j are designed to handle and integrate big data from heterogeneous sources. For flexibility and performance they do not ensure data quality through schemata but leave it to the application level. In this paper, we present a model-driven approach for data integration through graph databases with data sources in relational databases. We model query and update operations in neo4j by triple graph grammars and map these to Gremlin code for execution. In this way we provide a model-based approach to data integration that is both visual and formal while providing the data quality assurances of a schema-based solution.

Detecting logical relationships in mechanical, electrical, and plumbing (MEP) systems with BIM using graph matching

This article presents a comprehensive analysis of various graph database models, highlighting their respective advantages and limitations. Graph databases are valuable in ap- plications such as recommendation systems, social networks, and fraud detection because they excel at maintaining complicated data relationships. Neo4j, Janus Graph, and Arango DB are the major graph database suppliers. Scalability, performance, and ease of use are among the benefits offered by these providers. Janus Graph is a suitable candidate for large-scale deployments because to its distributed graph processing capabilities, but Neo4j is noted for its scalability and ease of use. Arango DB is a multi-model database that can handle a variety of data models, including graph databases. The significance of Graph databases over Machine Learning (ML) in many sectors has also been investigated.

Knowledge graph (KG) is a database that stores the information and relationship between entities in the form of graph. This paper presents a knowledge graph construction method for grid substation knowledge graph based on multi-source data. The device connection, alarm information and ledgers are contained in KG, which benefits the substation operation and maintenance. First, entities and relationships are selected according to the actual composition of substation. Then, triples are extracted through domain dictionaries and conditional random field. Entity disambiguation is carried out before establishing the knowledge graph based on corresponding data sources. Finally, knowledge fusion is used to remove the redundancy in the database to complete the construction of the KG. Compared to traditional relational database, graph database can more intuitively show the connection relationship between devices. By adding more links to knowledge graph, it takes less time for monitoring personnel to locate accidents when dealing with alarm information.

The exponentially rising amounts of urban data demand new conceptual and technical methods for their management and storage. The era of the Semantic Web requires a convergence toward commonly shared meanings. The combination of fuzzy cognitive maps with graph databases is a first approach to a solution. This article determines the basic requirements for the storing of fuzzy cognitive maps to test current graph databases for their structural suitability. Six out of 47 graph databases fulfill the requirements and are thus recommended for further research purposes. As a proof of concept, OrientDB is used to present how a semantic convergence can be reached through the combination of fuzzy cognitive maps and graph databases in a cognitive city by tackling fuzziness.

Conventionally, ship design and engineering are segregated activities, carried out with different software packages that thus each have their own place, qualities and tools. And consequently, a different data model. As a report on ongoing work to bridge that gap, this paper first explores existing neutral data models and standards employed or considered in maritime applications and concludes that none of these is directly applicable. It continues with describing the requirements and derived abstract data model of the SEUS project and its design and engineering applications. A graph database is identified as a potentially useful tool for SEUS data modelling, and a hands-on experiment confirms this presumption.

The analysis and utilization of grid big data, big data driven grid analysis and optimization have received extensive attention in recent years, and non-relational databases can handle big data problems efficiently. The graph database is a kind of non-relational database. This paper takes the graph database Neo4j as an example. Based on the domain modeling theory and follows the CIM model, the grid data modeling principle is proposed. Under these principles, some power grid data models and their applicable occasions, and methods for converting these models to each other are proposed. Finally, three graph database models are built using the modeling method proposed in this paper. The query performance of Neo4j and MySQL is compared by three examples. The conclusion is that in the traversal with obvious topological characteristics, such as shortest path traversal, deep traversal and time tree traversal, etc., the graph database model proposed in this paper can greatly improve the retrieval efficiency. It proves the effectiveness of the proposed modeling method and the efficiency of the graph database in specific retrieval.