NoSQL Graph Databases Research Articles

NoSQL Technologies in Big Data Ecosystems: a Comprehensive Review of Architectural Paradigms and Performance Metrics

The exponential growth of data in volume, velocity, and variety has challenged traditional relational database management systems, leading to the emergence and widespread adoption of NoSQL and other innovative database technologies. This article provides a comprehensive review of NoSQL databases and their role in modern Big Data infrastructure stacks. Through a systematic analysis of current literature and industry case studies, we explore the architectural paradigms, performance metrics, and use cases of various NoSQL database types, including document-based, key-value, column-family, and graph databases. Our findings indicate that NoSQL solutions offer significant advantages in scalability, flexibility, and real-time processing capabilities, particularly for unstructured and semi-structured data. However, challenges persist in areas such as data consistency, security, and interoperability with existing systems. We also examine emerging trends, including NewSQL, time-series databases, and the integration of artificial intelligence in database management. This article contributes to the understanding of how organizations can leverage NoSQL technologies to optimize their Big Data infrastructure, highlighting both the opportunities and considerations for implementation. Our conclusions underscore the transformative impact of NoSQL on data management practices and provide directions for future research in this rapidly evolving field.

A New Approach for Product Quality Check Based on Social Networks Opinions Analysis

In this paper, we aim to enhance the relevance of e-commerce web sites by a prior quality checking of proposed products. This checking is done by analyzing social networks and YouTube videos comments.
 To achieve this objective, we have broken down the work into a few steps. The first one consists in scraping text from social networks groups and storing it in a NoSQL graph database. Each scraped word is linked to one or many reactions that are coming from the social network. Therefore, we can utilize the database as a knowledge source that associates each set of terms with a specific type of reaction: positive, negative or neutral. Afterwards, we use a TF-IDF based filtering method to keep only relevant words and eliminate those which are a connected to all reactions. The advantage of this stage is the presence of a knowledge source that can be used for product quality checking.
 In the e-commerce web site, data are coming from multiple e-commerce websites. The latter, offer products without quality checking. Concretely, we aim to allow users to check quality by a simple check button which call an implemented web service using human reactions and comments. After evaluating our approach, we have obtained an accuracy of 0,75. This result means that our method gives a three quarter of chance to have a good product.

Development of a knowledge graph framework to ease and empower translational approaches in plant research: a use-case on grain legumes.

While the continuing decline in genotyping and sequencing costs has largely benefited plant research, some key species for meeting the challenges of agriculture remain mostly understudied. As a result, heterogeneous datasets for different traits are available for a significant number of these species. As gene structures and functions are to some extent conserved through evolution, comparative genomics can be used to transfer available knowledge from one species to another. However, such a translational research approach is complex due to the multiplicity of data sources and the non-harmonized description of the data. Here, we provide two pipelines, referred to as structural and functional pipelines, to create a framework for a NoSQL graph-database (Neo4j) to integrate and query heterogeneous data from multiple species. We call this framework Orthology-driven knowledge base framework for translational research (Ortho_KB). The structural pipeline builds bridges across species based on orthology. The functional pipeline integrates biological information, including QTL, and RNA-sequencing datasets, and uses the backbone from the structural pipeline to connect orthologs in the database. Queries can be written using the Neo4j Cypher language and can, for instance, lead to identify genes controlling a common trait across species. To explore the possibilities offered by such a framework, we populated Ortho_KB to obtain OrthoLegKB, an instance dedicated to legumes. The proposed model was evaluated by studying the conservation of a flowering-promoting gene. Through a series of queries, we have demonstrated that our knowledge graph base provides an intuitive and powerful platform to support research and development programmes.

Domain Ontology Representation Model Based on Graph Databases

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.

Evaluation of a NoSQL Database for Storing Big Geospatial Raster Data

Database systems capable of efficiently storing geospatial data are widespread. However, recent developments in earth observation systems, remote sensing, mobile mapping, and crowd sourcing lead to large amounts of geospatial mass data that can hardly be handled efficiently with the existing solutions. Especially large geospatial raster data require novel concepts for well-organized data storage. A concept for storage of large geospatially and temporally referenced image data using the NoSQL graph database system Neo4j as a research subject of the project “RiverView®” is introduced. New strategies and access structures have been developed to ensure the persistence and performant access to image data in Neo4j. These strategies are compared with the up-and download performance of the widespread Rasdaman array database system.

A Combination of Frequent Pattern Mining and Graph Traversal Approaches for Aspect Elicitation in Customer Reviews

Due to the remarkable increase in e-commerce transactions, people try to have an appropriate choice of purchase through considering other people's reflected experience in product's or service's reviews. Automatic analysis of such corpus requires enhanced developed algorithms based on natural language processing and opinion mining. Moreover, the linguistic differences make extending existing algorithms from one language to another challenging and in some cases impossible. Opinion mining focuses on different subjects of review analysis such as spam detection, aspect elicitation and polarity allocation. In this article, we focus on detection of explicit aspect and propose a methodology to overcome some difficult and problematic aspect compounds in the form of multi- words format in Persian language. Our approach proposes the construction of a directed weighted graph (ADG structure) based on some yielded information from FP-Growth frequent pattern identification algorithm on our corpus of Persian sentence. Traversing some special paths within the ADG graph according to our developed rules could lead us to the extraction of problematic multi-word aspects. We utilize Neo4j NoSQL graph database environment and its Cypher query language in order to create the ADG graph and access the desired paths that reflects our developed rules on the ADG structure which lead us to extract the multi-word aspects. The evaluation of our methodology with the existing approaches on the issue of aspect derivation in Persian language including ELDA, SAM, an MMI-based and an LRT-based algorithms indicates the robustness of our approach.

On Massive JSON Data Model and Schema

JSON (JavaScript Object Notation) is a lightweight semi-structured data format based on the data types of programming language JavaScript. It is a popular data exchange format over the World Wide Web and becomes a dominant standard format for sending API (Application Programming Interface) requests and responses in the past few years. Furthermore, JSON has also attracted attentions of database community research, especially in data intensive applications. JSON is not only can be integrated in traditional database systems, but also widely used in NoSQL database systems and graph database systems. Compared with XML, JSON document is a set of “key-value” pairs, in which the “value” itself can be a JSON document, which allows arbitrary levels of nesting, so it is more flexible to use and more difficult to process accordingly. JSON data model and schema describe the basic data structures and semantics of the underlying JSON data, so it is the fundamental and key aspects for JSON data format. JSON data model and schema are not only foundations for other data management technologies, such as data indexing, data querying, data searching, data mapping, data integrating, and data mining, but also has important theoretical significance and application prospects to provide theoretical basis and technical means for other related research, such as data integration, data conversion and other semi-structured and unstructured data queries. This paper analyzes the key problems of JSON data model and schema, including what data model should adopted by JSON and the specification and schema outline of JSON model.

A SEMANTIC RETRIEVAL SYSTEM IN REMOTE SENSING WEB PLATFORMS

Abstract. This paper proposes a solution to reduce the semantic gap between final users and data/processing providers in a web market place dedicated to remote sensing products. Nowadays, search engine are common tools on the Internet. Users are accustomed to use them and used to get tabular classification of provided answers. These smart agents are set up to answer basic questions using automatic pages redirection or chitchat. In this research, to ensure coherence between user’s requests and platform answers, natural language processing algorithms and knowledge graphs are integrated within a web platform thanks to a NoSQL graph database connected to open thesauri and Geographic Information Systems (GIS). Therefore, the most pertinent services can be proposed based on input sentences including non-technical vocabulary but also geographical components (the user interface includes a text area and an interactive map). While processing chains and remote sensing ontologies were presented in one of our previous studies, this article focuses on natural languages algorithms and knowledge mining.

Practical characterization of large networks using neighborhood information

Characterizing large complex networks such as online social networks through node querying is a challenging task. Network service providers often impose severe constraints on the query rate, hence limiting the sample size to a small fraction of the total network of interest. Various ad hoc subgraph sampling methods have been proposed, but many of them give biased estimates and no theoretical basis on the accuracy. In this work, we focus on developing sampling methods for large networks where querying a node also reveals partial structural information about its neighbors. Our methods are optimized for NoSQL graph databases (if the database can be accessed directly), or utilize Web APIs available on most major large networks for graph sampling. We show that our sampling method has provable convergence guarantees on being an unbiased estimator, and it is more accurate than state-of-the-art methods. We also explore methods to uncover shortest paths between a subset of nodes and detect high degree nodes by sampling only a small fraction of the network of interest. Our results demonstrate that utilizing neighborhood information yields methods that are two orders of magnitude faster than state-of-the-art methods.

Reverse Engineering State and Strategy Design Patterns using Static Code Analysis

This paper presents an approach to detect behavioral design patterns from source code using static analysis techniques. It depends on the concept of Code Property Graph and enriching graph with relationships and properties specific to Design Patterns, to simplify the process of Design Pattern detection. This approach used NoSQL graph database (Neo4j) and uses graph traversal language (Gremlin) for doing graph matching. Our approach, converts the tasks of design pattern detection to a graph matching task by representing Design Patterns in form of graph queries and running it on graph database.

Handling scalable approximate queries over NoSQL graph databases: Cypherf and the Fuzzy4S framework

NoSQL databases are currently often considered for Big Data solutions as they offer efficient solutions for volume and velocity issues and can manage some of complex data (e.g., documents, graphs). However, fuzzy approaches are often not efficient on such frameworks. Thus this article introduces a novel approach to define and run approximate queries over NoSQL graph databases using Scala by proposing the Fuzzy4S framework and the Cypherf fuzzy declarative query language. NoSQL Graph databases are currently gaining more and more interest and are applied in many real world applications. The Fuzzy4S framework is defined with an open DSL (Domain Specific Language) allowing it to define scalable approximate queries at an abstract level. Cypherf is an extension of Cypher which runs over the Neo4J NoSQL graph databases. This work consists of a complete approach embedding the whole chain from end-user declarative query level to implementation issues within the database engine. We provide both the formal definitions for defining approximate graph NoSQL queries and the experimental results which demonstrate the interest and efficiency of our proposition.

Exploiting NoSQL Graph Databases and in Memory Architectures for Extracting Graph Structural Data Summaries

NoSQL graph databases have been introduced in recent years for dealing with large collections of graph-based data. Scientific data and social networks are among the best examples of the dramatic increase of the use of such structures. NoSQL repositories allow the management of large amounts of data in order to store and query them. Such data are not structured with a predefined schema as relational databases could be. They are rather composed by nodes and relationships of a certain type. For instance, a node can represent a Person and a relationship Friendship. Retrieving the structure of the graph database is thus of great help to users, for example when they must know how to query the data or to identify relevant data sources for recommender systems. For this reason, this paper introduces methods to retrieve structural summaries. Such structural summaries are extracted at different levels of information from the NoSQL graph database. The expression of the mining queries is facilitated by the use of two frame-works: Fuzzy4S allowing to define fuzzy operators and operations with Scala; Cypherf allowing the use of fuzzy operators and operations in the declarative queries over NoSQL graph databases. We show that extracting such summaries can be impossible with the NoSQL query engines because of the data volume and the complexity of the task of automatic knowledge extraction. A novel method based on in memory architectures is thus introduced. This paper provides the definitions of the summaries with the methods to automatically extract them from NoSQL graph databases only and with the help of in-memory architectures. The benefit of our proposition is demonstrated by experimental results.

Predictive Performance Comparison Analysis of Relational & NoSQL Graph Databases

From last three decades, the relational databases are being used in many organizations of various natures such as Education, Health, Business and in many other applications. Traditional databases show tremendous performance and are designed to handle structured data with ACID (Atomicity, Consistency, Isolation, Durability) property to manage data integrity. In the current era, organizations are storing more data i.e. videos, images, blogs, etc. besides structured data for decision making. Similarly, social media and scientific applications are generating large amount of semi-structured data of varied nature. Relational databases cannot process properly and manage such large amount of data efficiently. To overcome this problem, another paradigm NoSQL databases is introduced to manage and process massive amount of unstructured data efficiently. NoSQL databases are divided into four categories and each category is used according to the nature and need of the specific problem. In this paper we will compare Oracle relational database and NoSQL graph database using optimized queries and physical database tuning techniques. The comparison is two folded: in the first iteration we compare various kinds of queries such as simpler query, database tuning of Oracle relational database such as sub databases and perform these queries in our desired environments. Secondly, for this comparison we will perform predictive analysis for the results obtained from our experiments.

Rogue behavior detection in NoSQL graph databases

Rogue behaviors refer to behavioral anomalies that can occur in human activities and that can thus be retrieved from human generated data. In this paper, we aim at showing that NoSQL graph databases are a useful tool for this purpose. Indeed these database engines exploit property graphs that can easily represent human and object interactions whatever the volume and complexity of the data. These interactions lead to fraud rings in the graphs in the form of sophisticated chains of indirect links between fraudsters representing successive transactions (money, communications, etc.) from which rogue behaviours are detected. Our work is based on two extensions of such NoSQL graph databases. The first extension allows the handling of time-variant data while the second one is devoted to the management of imprecise queries with a DSL (to define flexible operators and operations with Scala) and the Cypherf declarative flexible query language over NoSQL graph databases. These extensions allow to better address and describe sophisticated frauds. Feasibility have been studied to assess our proposition.

Novel research framework on SN's NoSQL databases for efficient query processing

A new database model called NoSQL plays a vital role in BigData analytics. NoSQL databases are non-relational open source data stores, which have employed massively scaled web site scenarios and upgraded in better performance by retrieving multiple data sets from commodity machines. NoSQL databases can be a hybrid mix of different databases based on specific use case applications. In this paper, we focus on four different NoSQL databases used by social networking sites. Their features are compared and analysed. These features are compared for the sub categories of the four NoSQL databases for fast querying from social networks. In the detailed analysis presented, the features like data storage and fast retrieval phase of query processing are given primary importance. We also present a comparison of the time taken during insert and read operations of social network data of Facebook. The results are compared and most suitable database from NoSQL graph database subcategories for insert and read operations in Facebook is identified.