NoSQL Databases Research Articles

An ontology-based secure design framework for graph-based databases

Graph-based databases are concerned with performance and flexibility. Most of the existing approaches used to design secure NoSQL databases are limited to the final implementation stage, and do not involve the design of security and access control issues at higher abstraction levels. Ensuring security and access control for Graph-based databases is difficult, as each approach differs significantly depending on the technology employed. In this paper, we propose the first technology-ascetic framework with which to design secure Graph-based databases. Our proposal raises the abstraction level by using ontologies to simultaneously model database and security requirements together. This is supported by the TITAN framework, which facilitates the way in which both aspects are dealt with. The great advantages of our approach are, therefore, that it: allows database designers to focus on the simultaneous protection of security and data while ignoring the implementation details; facilitates the secure design and rapid migration of security rules by deriving specific security measures for each underlying technology, and enables database designers to employ ontology reasoning in order to verify whether the security rules are consistent. We show the applicability of our proposal by applying it to a case study based on a hospital data access control.

Soft Querying Features in GeoJSON Documents: The GeoSoft Proposal

Since the advent of textit{JSON} as a popular format for exchanging large amounts of data, a novel category of NoSQL database systems, named textit{JSON}document stores, has emerged for storing textit{JSON} data sets; in fact, these novel databases are able to natively manage collections of textit{JSON} documents. To help analysts and data engineers query and integrate textit{JSON} data sets persistently saved in textit{JSON} document stores, the J-CO Framework has been developed (at the University of Bergamo, Italy): it is built around a novel query language, named J-CO-QL^{+}, that provides sophisticated features, including soft-querying capabilities. However, J-CO-QL^{+} (as the other languages for querying textit{JSON} data sets) is designed to be general purpose; consequently, it can be cumbersome for users to apply it on specific data formats. This is the case of GeoJSON, a specific and popular textit{JSON} data format that is designed to represent geographical information layers. This paper presents the latest evolution of GeoSoft, a novel high-level “domain-specific language” that is specifically designed to express complex queries on the GeoJSON documents, including soft-queries. GeoSoft is inspired to the classical SQL language, so as to reduce the learning curve of potential users. GeoSoft queries are translated into J-CO-QL^{+} scripts, to be actually executed.

KVMod—A Novel Approach to Design Key-Value NoSQL Databases

The growth of structured, semi-structured, and unstructured data produced by the new applications is a result of the development and expansion of social networks, the Internet of Things, web technology, mobile devices, and other technologies. However, as traditional databases became less suitable to manage the rapidly growing quantity of data and variety of data structures, a new class of database management systems named NoSQL was required to satisfy the new requirements. Although NoSQL databases are generally schema-less, significant research has been conducted on their design. A literature review presented in this paper lets us claim the need to create modeling techniques to describe how to structure data in NoSQL databases. Key-value is one of the NoSQL families that has received too little attention, especially in terms of its design methodology. Most studies have focused on the other families, like column-oriented and document-oriented. This paper aims to present a design approach named KVMod (key-value modeling) specific to key-value databases. The purpose is to provide to the scientific community and engineers with a methodology for the design of key-value stores using the maximum automation and therefore the minimum human intervention, which equals the minimum number of errors. A software tool called KVDesign has been implemented to automate the proposed methodology and, thus, the most time-consuming database modeling tasks. The complexity is also discussed to assess the efficiency of our proposed algorithms.

A systematic literature review of authorization and access control requirements and current state of the art for different database models

Purpose Data protection requirements heavily increased due to the rising awareness of data security, legal requirements and technological developments. Today, NoSQL databases are increasingly used in security-critical domains. Current survey works on databases and data security only consider authorization and access control in a very general way and do not regard most of today’s sophisticated requirements. Accordingly, the purpose of this paper is to discuss authorization and access control for relational and NoSQL database models in detail with respect to requirements and current state of the art. Design/methodology/approach This paper follows a systematic literature review approach to study authorization and access control for different database models. Starting with a research on survey works on authorization and access control in databases, the study continues with the identification and definition of advanced authorization and access control requirements, which are generally applicable to any database model. This paper then discusses and compares current database models based on these requirements. Findings As no survey works consider requirements for authorization and access control in different database models so far, the authors define their requirements. Furthermore, the authors discuss the current state of the art for the relational, key-value, column-oriented, document-based and graph database models in comparison to the defined requirements. Originality/value This paper focuses on authorization and access control for various database models, not concrete products. This paper identifies today’s sophisticated – yet general – requirements from the literature and compares them with research results and access control features of current products for the relational and NoSQL database models.

A new RFID Middleware architecture based on a hybrid security technique using data encryption and RBAC for modern real-time tracking applications

Radio Frequency Identification (RFID) is a contactless technology that has developed over the 90s and 20th centuries. It employs electromagnetic or electrostatic coupling in the radio frequency part of the electromagnetic spectrum to uniquely identify traceable objects, and is widely used in various sectors (e.g., medical, Supply Chain Management, transportation, and IoT applications.). Through the supply of real-world monitoring and context information about things, the integration of this technology in such areas delivers various benefits in the future of ubiquitous computing. However, one of the primary challenges will be the capacity to manage data since RFID events have specific characteristics and requires special treatment, such as the large volume of data flow, inaccuracy, temporal and spatial data, are typical examples of RFID event data. The goal of this research is to first highlight the concerns and limitations of existing middleware architectures before introducing and implementing a new Middleware architecture to address the identified issues, specifically real-time processing of massive volumes of data coming from physical RFID infrastructure. This middleware combines role-based access control with an encryption algorithm to increase security, a NoSQL database for storing large amounts of data, complex event processing (CEP) to provide high-volume data stream processing, and improved interoperability via the Data Transformation Module. Finally, our architecture is evaluated and compared to several middleware architectures based on standard ISO/IEC 9126 metrics.

IMPLEMENTATION OF TEXT INDEXING SYSTEM IN WEB-BASED DOCUMENT SEARCH APPLICATION USING MONGODB

The rapid growth of information technology has led to an increase in the amount of data stored in databases every day. Relational databases (SQL) that have been in use for a long time are now being developed with the emergence of NoSQL databases such as MongoDB. MongoDB stores data in BSON format and has a Text Indexes feature that is useful for speeding up text search on string content. This feature is particularly useful in searching for data in the form of texts or strings in large quantities. MongoDB's Text Indexes have a flexible schema that does not require a strict schema structure to index text data, unlike SQL databases that require columns with the appropriate data type to perform indexing. MongoDB's Text Indexes support more languages than SQL because they use an open-source text search engine called Apache Lucene. In this study, the researcher will implement Text Indexing on document data (PDF) that has been converted into text, then inserted into MongoDB before indexing. Afterward, the researcher will compare the performance of search queries between indexed and non-indexed data in MongoDB in terms of speed. The comparison results will be presented in tables and graphs to facilitate understanding. Based on the research conducted, it can be concluded that the use of the text indexing feature in MongoDB can speed up keyword or string search time. In the experiment conducted using 5000 data records, the results showed that the use of text indexing for searching 1 keyword resulted in a search speed improvement of 11705,88%, for searching 2 keywords it was 60833,33%, and for searching 3 keywords it was 44320%.

A survey and conceptual of Internet of Things system for remote healthcare monitoring

Introduction: Healthcare monitoring is a part of medical treatment. This research aims to increase the interoperability of the healthcare monitoring process. So that user does not need to go to the hospital and check their condition with the doctor directly. Methods: This was a qualitative study. The sample was doctors, inpatients, and outpatients. We proposed a concept of Internet of Things platform architecture that could monitor and record patient health. The features we provided are monitoring, immutable data recording using NoSQL database and blockchain by implementing Change Data Capture, and data analytics. Result: In this research, we proposed the Matter protocol for data communication. Matter protocol opens the possibility of increasing Internet of Things interoperability. On the other hand, implementation of blockchain could increase the security in the data at rest step, we also increase traceability by implementing Change Data Capture in the NoSQL database and store all data to the blockchain. Conclusion: Our proposed conceptual system could be used for building healthcare monitoring using an Internet of Things system that needs high interoperability and low energy consumption. The outpatient activity could be monitored in their own house or anywhere, and the patient does not need to go to the hospital to check their health.

Phase space load balancing priority scheduling algorithm for cloud computing clusters

Due to the development of new technologies such as the Internet and cloud computing, high requirements have been placed on the storage and management of big data. At the same time, new applications in the cloud computing environment also pose new requirements for cloud storage systems, such as strong scalability and high concurrency. Currently, the existing nosql database system is based on cloud computing virtual resources, supporting dynamic addition and deletion of virtual nodes. Based on the study of phase space reconstruction, the necessity of considering traffic flow as a chaotic time series is analyzed. In addition, offline data migration methods based on load balancing are also studied. Firstly, a data migration model is proposed through analysis, and the factors that affect migration performance are analyzed. Based on this, optimization objectives for migration are proposed. Then, the system design of data migration is presented, and optimization research is conducted from two aspects around the migration optimization objectives: optimizing from the data source layer, and proposing the LBS method to convert data sources into distributed data sources, ensuring the balanced distribution of data and meeting the scalability requirements of the system. This paper applies cloud computing technology and phase space reconstruction to load balancing scheduling algorithms to promote their development.

Asgard: Are NoSQL databases suitable for ephemeral data in serverless workloads?

Serverless computing platforms are becoming increasingly popular for data analytics applications due to their low management overhead and granular billing strategies. Such analytics frameworks use a Directed Acyclic Graph (DAG) structure, in which serverless functions, which are fine-grained tasks, are represented as nodes and data-dependencies between the functions are represented as edges. Passing intermediate (ephemeral) data from one function to another has been receiving attention of late, with works proposing various storage systems and methods of optimization for them. The state-of-practice method is to pass the ephemeral data through remote storage, either disk-based (e.g., Amazon S3), which is slow, or memory-based (e.g., ElastiCache Redis), which is expensive. Despite the potential of some prominent NoSQL databases, like Apache Cassandra and ScyllaDB, which utilize both memory and disk, prevailing opinions suggest they are ill-suited for ephemeral data, being tailored more for long-term storage. In our study, titled Asgard, we rigorously examine this assumption. Using Amazon Web Services (AWS) as a testbed with two popular serverless applications, we explore scenarios like fanout and varying workloads, gauging the performance benefits of configuring NoSQL databases in a DAG-aware way. Surprisingly, we found that, per end-to-end latency normalized by $ cost, Apache Cassandra's default setup surpassed Redis by up to 326% and S3 by up to 189%. When optimized with Asgard, Cassandra outdid its own default configuration by up to 47%. This underscores specific instances where NoSQL databases can outshine the current state-of-practice.

Key-Value Pairs and NoSQL Databases: A Novel Concept to Manage Biologging Data in Data Repositories

Key-Value Pairs and NoSQL Databases: A Novel Concept to Manage Biologging Data in Data Repositories

Retracted: Application of Heterogeneous Network Oriented to NoSQL Database in Optimal Postevaluation Indexes of Construction Projects

Retracted: Application of Heterogeneous Network Oriented to NoSQL Database in Optimal Postevaluation Indexes of Construction Projects

Migrating data from document-oriented database to graph-oriented database

In data migration between different types of NoSQL databases, data may not be directly transferred to the targeted database compared to migration of data between the same types of database. This is due to the heterogeneity of the storage paradigm of the NoSQL databases. For example, when migrating data from a document-oriented database such as MongoDB, which stores data in JSON (Java Object Notation) format to Neo4j, a graph-oriented database stores data in nodes, the differences among these databases’ storage paradigms require different representations of the data model in the targeted graph-oriented database. This paper proposed a sequential approach to migrate data from MongoDB to Neo4j. The approach migrates MongoDB data to Neo4j and verifies the migrated data using a comparative method. The paper discusses the migration algorithm and how complex fields in MongoDB, such as nested documents, are presented in Neo4j.

Piezoelectric system with software management

This article is a product of the project "Piezoelectric system with software management", developed by Jayr Antonio Fandiño Gómez in conjunction with the University of Cundinamarca Ubaté sectional. Objective: To develop a source of clean energy with the help of piezoelectric materials, to take advantage of the passage of people within the University of Cundinamarca, Ubaté sectional, generating savings of "energy" resources. Method: Different phases have been carried out using different sensors in order to create a better product which is capable of generating clean energy that can be used for the lighting supply of this public space. Results: A piezoelectric tablet of large dimensions and optimized has been created, which was implemented for testing within the University of Cundinamarca, Ubaté sectional generating results that led to the creation of a NoSQL database. Conclusion: By implementing a new renewable energy system in an area where there are environmental concerns, awareness of the different problems generated by the advancement of technology will be generated. Limitation of the study: The acquisition of the sensors can be complicated due to the fact that this product is mostly created abroad.

Modeling, manipulating and evolving hybrid polystores with HyDRa

Data-intensive system evolution is a complex and error-prone process, as most evolution scenarios impact several interdependent artefacts such as the application code, the data structures or data instances. This process is becoming even more challenging with the emergence of heterogeneous database architectures, commonly called hybrid polystores, that rely on a combination of several, possibly overlapping relational and NoSQL databases. This paper presents HyDRa, a framework aiming to facilitate the design, the manipulation and evolution of polystores thanks to (1) a novel modeling language representing conceptually and physically the databases and (2) an automatically generated conceptual data access API (Application Programming Interface). HyDRa automatically enforces cross-database data integrity constraints, does not require developers to master multiple data models and query languages and protect application code from schema and data reconfiguration changes.

Anomaly detection for software defined datacenter networks using X-Pack

The global data center market is growing as more and more enterprises are increasingly adopting cloud computing services and applications. Data centers are evolving towards highly virtualized architectures where transformation to software defined network (SDN) based solutions provides benefits in terms of network programmability, automation, and flow visibility. With the benefits, the need for securing network becomes essential as many critical applications are hosted on to such networking platforms. Anomaly detection is a continuous process of monitoring the traffic pattern and alerting the user about the anomalies if detected. For such real time analysis NoSQL and relational databases are less efficient. This paper proposes a framework for anomaly detection and alerting system using Elasticsearch database for SDN. Traffic patterns generated from SDN devices are continuously monitored and predefined actions are taken immediately if an anomaly is detected. The proof of concept is implemented in NOKIAs Nuage Networks Laboratory and the results showed a real time anomaly detection and took relevant actions within minimum time.

Enhancing Data Management with MongoDB and its Rest API

Abstract: This article examines the integration of MongoDB, a powerful NoSQL database, with its REST API, enabling developers to interact with MongoDB using HTTP-based requests. The MongoDB REST API provides a straightforward and flexible approach to managing data, allowing for easy integration with various platforms and frameworks. This article explores the key features, architecture, implementation considerations, performance optimizations, and real-world use cases of the MongoDB REST API. By understanding the capabilities and best practices associated with this integration, developers can leverage MongoDB's robust features and the versatility of a RESTful interface to enhance their data management workflows.

Are NoSQL Databases Affected by Schema?

During data modelling, NoSQL databases are usually considered schema-less databases. They do not support rigid schema as traditional databases. Instead, offer flexibility and support dynamic schema, which means schema design can be changed at run time to support analytical tasks. NoSQL databases allow storing similar information with multiple schemas, which facilitates data evolution by eliminating restrictions on the schema. It raises the question of whether the schema matters in NoSQL databases, given that explicit schema declaration is not required before data storage. Furthermore, if the schema does matter, what is its potential impact on application performance? This paper examines these questions by taking three types of NoSQL databases: Document store, Column store, and Key-Value store. All possible schema structure alternatives are designed using a sample Entity Relationship (ER) model. Then, a set of common queries are performed on various schema alternatives to analyse the impact of query performance in terms of query response time, speedup factor, read and write Latency, and database size. The results suggest that the scheme alternatives affect the database performance in different NoSQL models.

A Comparative Study of Relasional and NoSQL database for Big Data Analytics

The Database technology suite is rapidly evolving and consistently answering solutions user needs, one of which is the ability of the database to accommodate one piece of information quite a lot, the ability of the Database to accommodate data must be balanced together speed in loading the information needed by the user. In these fast-paced times today’s sophisticated information technology forces us to be able to serve data requests as well as process data quickly and efficiently, but if a system has accommodated too much a lot of data will certainly experience a time constraint in processing the data. The research method used is the type of library research (Library Research). The data obtained is sourced from the literature. NoSQL database discussion literature searched using keywords: SQL, NoSQL, databases from various publication databases. The results of the study are comparisons between relational databases and NoSQL involving factors
 Such as data models, scalability, data flexibility, and query and analytics capabilities. Databases Relational is more suitable for structured data and strict consistency, whereas database NoSQL is better suited for unstructured data and prioritizes scalability and flexibility.

Comparison of Response Time Database RDBMS with NoSQL on Electronic Medical Records (EMR)

The electronic medical record is a system for storing and managing patient medical data in electronic form. EMR replaces traditional medical record systems that still use paper, allowing doctors and other medical personnel to access patient medical records more quickly and easily from anywhere, as long as they are connected to the same network. In the EMR, patient medical data is stored in electronic format, such as text, images, and test results. EMR allows doctors to create patient medical records, make drug prescriptions, place laboratory orders and radiology orders, and access other patient medical data in one centralized system. EMR can also enable doctors to perform data analysis to gain insight into a patient's health condition and improve the treatment provided. In this study, the ERM database is used, with one database using PostgreSQL and the other using NoSQL. The purpose of this research is to test the speed performance of PostgreSQL and NoSQL with insert, select, and delete commands. Testing Method Using 1,000 records, 5,000 records, and 10,000 records, with each record being tested three times, and then taking the average, The results of this study are that the NoSQL database insert command has a speed of 3,900 times faster than the PostgreSQL database with 1,000 data records, whereas with 5,000 data records the NoSQL database is 471 times faster, while with 10,000 data records the NoSQL database is 355 times faster, and the select command for 1 NoSQL database table is 8 times faster than the PostgreSQL database, while for the above 1 PostgreSQL database table is faster.

Real-time processing and visualization for smart infrastructure data

As instrumented infrastructure becomes increasingly prevalent, managing the massive scale of real-time data collected is fundamental to providing relevant and timely information to decision-makers. To address these needs, a novel open-source framework is developed to process and intuitively present high-volume data in near real-time. This design is centered around data accessibility, supporting decision-making, and adaptability/reuse. In this work, the framework is tailored to vibration-based structural health monitoring, which screens structural condition in near real-time. To promote timely intervention, distributed computing technologies are employed to accelerate the processing, storage, and visualization of data. Data are processed in parallel and then inserted into a NoSQL database. A REST-based web application allows interaction with this stored data via a customizable visualization interface. This supports frequency domain monitoring for a 5-story building instrumented with 224 accelerometers. A simulated scenario exemplifies how the interface can assist decisions about occupant safety and structural maintenance.