Traditional Database Management Systems Research Articles

Integrating NoSQL, Hilbert Curve, and R*-Tree to Efficiently Manage Mobile LiDAR Point Cloud Data

The widespread use of Light Detection and Ranging (LiDAR) technology has led to a surge in three-dimensional point cloud data; although, it also poses challenges in terms of data storage and indexing. Efficient storage and management of LiDAR data are prerequisites for data processing and analysis for various LiDAR-based scientific applications. Traditional relational database management systems and centralized file storage struggle to meet the storage, scaling, and specific query requirements of massive point cloud data. However, NoSQL databases, known for their scalability, speed, and cost-effectiveness, provide a viable solution. In this study, a 3D point cloud indexing strategy for mobile LiDAR point cloud data that integrates Hilbert curves, R*-trees, and B+-trees was proposed to support MongoDB-based point cloud storage and querying from the following aspects: (1) partitioning the point cloud using an adaptive space partitioning strategy to improve the I/O efficiency and ensure data locality; (2) encoding partitions using Hilbert curves to construct global indices; (3) constructing local indexes (R*-trees) for each point cloud partition so that MongoDB can natively support indexing of point cloud data; and (4) a MongoDB-oriented storage structure design based on a hierarchical indexing structure. We evaluated the efficacy of chunked point cloud data storage with MongoDB for spatial querying and found that the proposed storage strategy provides higher data encoding, index construction and retrieval speeds, and more scalable storage structures to support efficient point cloud spatial query processing compared to many mainstream point cloud indexing strategies and database systems.

Integrating Novel Machine Learning for Big Data Analytics and IoT Technology in Intelligent Database Management Systems

Database Management Systems (DBMS) advancement has been crucial to Information Technology (IT). Traditional DBMS needed help managing large and varied datasets under strict time constraints due to the emergence of Big Data and the widespread use of Internet of Things (IoT) devices. The growing intricacy of data and the need for instantaneous processing presented substantial obstacles. This research suggests a Machine Learning-based Intelligent Database Management Systems (ML-IDMS) technique. This invention combines the skills of Machine Learning with DBMS, improving flexibility and decision-making capacities. The ML-IDMS is specifically developed to tackle current obstacles by providing capabilities such as instantaneous data retrieval, intelligent heat measurement, and effective neural network initialization. The simulation results showcase the effectiveness of ML-IDMS, as shown by impressive metrics such as query execution time (19.27 sec), storage efficiency (83.78%), data accuracy (90%), redundancy reduction (66.42%), network throughput (7.93 Gbps), and end-to-end delay (14.4 ms). The results highlight the efficacy of ML-IDMS in managing various data circumstances. ML-IDMS addresses current obstacles and establishes a standard for future intelligent data management and analytics progress.

Neo4jsbml: import systems biology markup language data into the graph database Neo4j.

Systems Biology Markup Language (SBML) has emerged as a standard for representing biological models, facilitating model sharing and interoperability. It stores many types of data and complex relationships, complicating data management and analysis. Traditional database management systems struggle to effectively capture these complex networks of interactions within biological systems. Graph-oriented databases perform well in managing interactions between different entities. We present neo4jsbml, a new solution that bridges the gap between the Systems Biology Markup Language data and the Neo4j database, for storing, querying and analyzing data. The Systems Biology Markup Language organizes biological entities in a hierarchical structure, reflecting their interdependencies. The inherent graphical structure represents these hierarchical relationships, offering a natural and efficient means of navigating and exploring the model's components. Neo4j is an excellent solution for handling this type of data. By representing entities as nodes and their relationships as edges, Cypher, Neo4j's query language, efficiently traverses this type of graph representing complex biological networks. We have developed neo4jsbml, a Python library for importing Systems Biology Markup Language data into a Neo4j database using a user-defined schema. By leveraging Neo4j's graphical database technology, exploration of complex biological networks becomes intuitive and information retrieval efficient. Neo4jsbml is a tool designed to import Systems Biology Markup Language data into a Neo4j database. Only the desired data is loaded into the Neo4j database. neo4jsbml is user-friendly and can become a useful new companion for visualizing and analyzing metabolic models through the Neo4j graphical database. neo4jsbml is open source software and available at https://github.com/brsynth/neo4jsbml.

Scalability Performance Analysis of Blockchain Using Hierarchical Model in Healthcare.

Blockchain technology has become crucial in improving the privacy and security of enterprise applications in the cyber world. However, scalability has become a significant concern for researchers in large organizations, especially those with complex hierarchies and access privileges. As a result, the existing models and consensus algorithms suffer from various issues. Medical centers and healthcare providers are particularly affected by this problem due to the vast amount of data, making it a critical weakness of traditional database management systems. To address this issue, the authors propose a hierarchical model within the Hyperledger Fabric enterprise application, focusing on the healthcare sector as a use case. This model includes multiple organizations at different levels of the hierarchy, such as hospitals, hospital governance, and insurance companies. The initial implementation of this model includes two levels of hierarchy, demonstrating networks of hospitals joining an insurance company. The primary objective of the experiment is to test and improve the network's performance using this model. The model's performance is evaluated by manipulating and scaling environmental factors such as the number of organizations, transaction numbers, channels, block intervals, and block sizes. The benchmarking tool used for this assessment is Hyperledger Caliper, which measures indicators such as success and failure rates, throughput, and latency. Currently, the research focuses only on testing the model's scalability using patient data.

Evaluation of Responsive With Big Data Technologies

The volume of produced digital data & everyday usage is increasing as a result of rising man-machine interaction, process automation, and falling hardware and software costs.
 The vast amount of digital information produced per second worldwide in unstructured, structured, and semi-structured formats is referred to as big data in this context.
 Big data analytics is a new topic that has inspired researchers all over the world to create, develop, and implement a variety of mechanisms, technologies, architectures, and areas for evaluating the enormous amount of data created every day. Traditional database management systems struggle to make sense of the massive amounts of data that make up "big data." The paper describes a few analyses, including word count, sentiment analysis, and responsive analysis.
 Responsiveness is crucial for improving the system, identifying vulnerabilities, and ensuring that tasks are distributed fairly. Responsive is important data that can be used for making wise decisions.
 Responsive is significant for both strengths and weaknesses, not just when it identifies flaws. If the responsive analysis is performed incorrectly, the analysis's conclusion will likewise be incorrect. As a result, the system as a whole will be inaccurate because the pattern that was discovered will also be incorrect. We will utilize the Map-Reduce framework to create this suggested system for responsive analysis, and Hadoop will be used for storage.

Big Data Management and Analytics as a Cloud Service

Traditional database management systems are inadequate for handling uncertain data due to design limitations. Various methods address database uncertainty, often oversimplifying and restricting representable uncertainties. Rapid data growth from technologies like IoT, multimedia, and social media has led to massive diverse data, known as big data, categorized into structured, semi-structured, and unstructured types. Coping with big data's challenges is encapsulated in the "Vs Model" with volume, velocity, and variety dimensions. It aims to gather variable information for causal understanding and informed decisions. Big data offers competitive advantages: smart decisions (69%), operational control (54%), customer insights (52%), and cost cuts (47%). This research studies big data management and cloud computing, where data and apps are accessed via the internet, saving resources globally. Cloud's simplicity and power facilitate tasks and storage. Risks include bankruptcy or breaches, yet cloud remains valuable for preserving big data. Vital factors in big data's value creation involve converting IT costs to assets and connecting big data outcomes to performance. This abstract summarizes exploration of traditional limitations, big data's essence, cloud's potential, and value creation nuances.

TIME OPTIMIZATION OF PROCESS OF DATA CONSISTENCY IN NOSQL

It is emphasized that the performance of a distributed information system depends on many factors, including the size and structure of the database, the hardware used, the number of replicas and their geographical location. A conclusion was made about the perspective of the concept of non-relational NoSQL databases and it was concluded that it was proposed for efficient storage and provision of quick access to large volumes of information, the so-called Big Data, which cannot be achieved using traditional relational database management systems. The essence of the concept of “NoSQL database” is revealed and the currently existing difficulties in using large systems are named. In NoSQL databases, multiple replication (copying) of a record is used to ensure high fault tolerance. But there is a significant drawback for them, because these systems do not support the mode of conducting transactions and blocking, so there is a problem of data reconciliation. It is emphasized that NoSQL databases mainly use two methods of placing and updating the time of the data consistency process in NoSQL: according to the principle of “master-slave” and “ring”. A model of strong data reconciliation in NoSQL is described and an example of time optimization for the healthcare system is considered. It has been established that even with large values of the intensity of receiving requests for reading λ, the waiting time does not exceed 3 ms. However, when performing analytical queries, this time can increase, so with large N and high intensity of incoming read requests, this time can reach 7 ms.

RDBMS to distributed in-memory data grid: A paradigm shift for improved performance

As per available Cisco VNI report [1], global IP traffic will grow more than three times and global network device connections will be more than 28.5 billion. Availability of high-speed network and connectivity will lead to more online business opportunities to enterprises and in turn will generate large data. Sometimes these large datasets need to be processed in near real time to do some critical analysis. Traditional databases are not designed to do real time data analysis and have challenges in terms of performance and throughput. This paper describes various widely used data storage tools and frameworks currently available and how to achieve the best performance while processing large datasets using in memory data grid. It briefly covers the evolution of in-memory data grid from traditional relational database management systems. It also provides a high-level feature comparison across available in-memory database solutions and how they provide the best of solution for processing real time data beyond Hadoop referencing use cases like fraud detection and genetic data processing. It also emphasizes on leveraging power of GPU alongside CPU to achieve high performance and throughput while processing large data with multiple iterations.

Intelligent Governance of Network Popular Opinion Based on DM Technology in the New Media Age

The public’s desire to protect their own interests has increased in recent years. As information technology advances, the number of Internet users grows, the penetration rate improves, and the number of self-media users of network new media grows. Using historical data to predict future behavior and learn from massive amounts of data, humanity has entered the new media age, thanks to the rapid advancement of mobile Internet technology. It solves the problem that traditional database management systems cannot find hidden relationships and rules in data and cannot predict the future based on the data they have. This paper investigates the use of DM technology to intelligently govern network public opinion. Finance, telecommunications, network-related industries, retailers, manufacturing, medical care, pharmaceutical industry, and scientific fields have all made extensive use of it. Data mining (abbreviated as DM) is a type of data analysis that extracts credible, novel, effective, and understandable patterns from large amounts of data. It is closely related to database knowledge discovery.

Performance evaluation of various deployment scenarios of the 3-replicated Cassandra NoSQL cluster on AWS

A concept of distributed replicated NoSQL data storages Cassandra-like, HBase, MongoDB has been proposed to effectively manage Big Data set whose volume, velocity and variability are difficult to deal with by using the traditional Relational Database Management Systems. Tradeoffs between consistency, availability, partition tolerance and latency is intrinsic to such systems. Although relations between these properties have been previously identified by the well-known CAP and PACELC theorems in qualitative terms, it is still necessary to quantify how different consistency settings, deployment patterns and other properties affect system performance.This experience report analysis performance of the Cassandra NoSQL database cluster and studies the tradeoff between data consistency guaranties and performance in distributed data storages. The primary focus is on investigating the quantitative interplay between Cassandra response time, throughput and its consistency settings considering different single- and multi-region deployment scenarios. The study uses the YCSB benchmarking framework and reports the results of the read and write performance tests of the three-replicated Cassandra cluster deployed in the Amazon AWS. In this paper, we also put forward a notation which can be used to formally describe distributed deployment of Cassandra cluster and its nodes relative to each other and to a client application. We present quantitative results showing how different consistency settings and deployment patterns affect Cassandra performance under different workloads. In particular, our experiments show that strong consistency costs up to 22 % of performance in case of the centralized Cassandra cluster deployment and can cause a 600 % increase in the read/write requests if Cassandra replicas and its clients are globally distributed across different AWS Regions.

IoTranx: Transactions for Safer Smart Spaces

Smart spaces such as smart homes deliver digital services to optimize space use and enhance user experience. They are composed of an Internet of Things (IoT), people, and physical content. They differ from traditional computer systems in that their cyber-physical nature ties intimately with the users and the built environment. The impact of ill-programmed applications in such spaces goes beyond loss of data or a computer crash, risking potentially physical harm to the space and its users. Ensuring smart space safety is therefore critically important to successfully deliver intimate and convenient services surrounding our daily lives. By modeling smart space as a highly dynamic database, we present IoT Transactions, an analogy to database transactions, as an abstraction for programming and executing the services as the handling of the devices in smart space. Unlike traditional database management systems that take a “clear room approach,” smart spaces take a “dirty room approach” where imperfection and unattainability of full control and guarantees are the new normal. We identify Atomicity, Isolation, Integrity and Durability (AI 2 D) as the set of properties necessary to define the safe runtime behavior for IoT transactions for maintaining “permissible device settings” of execution and to avoid or detect and resolve “impermissible settings.” Furthermore, we introduce a lock protocol, utilizing variations of lock concepts, that enforces AI 2 D safety properties during transaction processing. We show a brief proof of the protocol correctness and a detailed analytical model to evaluate its performance.

EPISTEMOLOGY OF INNOVATION AND DEVELOPMENT OF BUSINESS ENTITIES

The conceptual apparatus of the categories «innovation» and «innovative development» is investigated in the article. Innovation is defined as a sphere of free ideas, covering the issues of organizational and methodological provision for continuous improvement of the enterprise activity, reproduction of rules and regulations governing the development and dissemination of innovations for the development of economic systems. It is substantiated that the essence of innovation is revealed through the processes of informatization and intellectualization with provision of the development of economic, social and information systems. Factors for ensuring the implementation of radical innovations are identified, namely: values (standards, priorities), procedures (formal, informal) and resources (tangible, intangible). The expediency of optimizing the model of enterprise organization on the basis of the development of a personalized parameter – the organizational code is determined. It is substantiated that the main idea of constructing the model of innovation management is the trinity of factors of balanced development, namely: understanding of the real situation, formation of expectations and development of necessary measures. The importance of imitation in the formation of the strategy of innovative development is determined and the stages of transformation of the organizational business model are described. The strategy of innovative changes focused on sustainable business development is proposed, which contributes to increase its efficiency and effectiveness. Emphasis is placed on the use of big data technology as an alternative to traditional database management systems, which contributes the efficient use of big amounts of data, stimulates the development of various management solutions and increases the ability to consider and evaluate alternatives to enterprise development. Innovation is identified as a central factor in the digital transformation and a priority of digital business development that is a new principle of efficiency and contributes to expand the target audience with increasing the scale of value proposition.

Fuzzy based decision making approach for big data research on health information management system

In the present area of various health datasets management are considered as more intangible and needs protection because those datasets has several scientific facts and ideas which improves quality of human’s way of living. Though protection is the significant parameter in health datasets system, managing those health data in an efficient way reflects high end area of research. Though the traditional database management system have its own personalized feature, resulted in the different survival state, Traditional approaches fails to manage health datasets in database management system in an effective and efficient way. In this work improved big data management heuristic algorithm has been introduced for the feasibility of data management strategy using fuzzy based decision making approach. Then the evaluated alternatives are arranged in a hierarchical structure with several levels, and the weighting function of each level criterion is quantified on experimental validation shown promising results in terms of efficiency and accuracy in data management.

Impact of Data Compression on the Performance of Column-oriented Data Stores

Compression of data in traditional relational database management systems significantly improves the system performance by decreasing the size of the data that results in less data transfer time within the communication environment and higher efficiency in I/O operations. The column-oriented database management systems should perform even better since each attribute is stored in a separate column, so that its sequential values are stored and accessed sequentially on the disk. That further increases the compression efficiency as the entire column is compressed/decompressed at once. The aim of this research is to determine if data compression could improve the performance of HBase, running on a small-sized Hadoop cluster, consisted of one name node and nine data nodes. Test scenario includes performing Insert and Select queries on multiple records with and without data compression. Four data compression algorithms are tested since they are natively supported by HBase - SNAPPY, LZO, LZ4 and GZ. Results show that data compression in HBase highly improves system performance in terms of storage saving. It shrinks data 5 to 10 times (depending on the algorithm) without any noticeable additional CPU load. That allows smaller but significantly faster SSD disks to be used as cluster’s primary data storage. Furthermore, the substantial decrease in the network traffic is an additional benefit with major impact on big data processing.

Conventional database management systems

Before the advent of Database Management Systems, computer programmers or developers spent a lot of time writing codes to create and manage databases also known as data files before venturing into the actual programming work, thereby making it possible for only trained computer professionals to operate and manipulate databases or data files. It left the programmers who are targeting high performance application to deal with first data integrity and security challenges, inefficient process to access and manipulate stored data, data recovery challenges in the case of crashes and many other challenges. In this paper, we present a review of the traditional database management system (DBMS).First; we gave a detailed explanation of the meaning, types and uses of a database management system. We highlighted the features of Conventional Database Management Systems and the challenges it faces today especially in the handling of high-profile data for many enterprise systems, hardware requirements, human resources, network challenges and costs are also discussed in this research. Finally, we propose a solution to the challenges facing the conventional database system. From our findings, we discovered that the Big Data technology is a potential solution to the limitations of the conventional database management systems. This research will be beneficial to scholars and researchers who are interested in Database management systems.

An Experimental Analysis of the Use of Different Storage Technologies on a Relational DBMS

Traditional Database Management Systems (DBMSs) are built with the premise that magnetic disks such as hard disks drives (HDDs) store the data. Recently, several alternatives to HDDs have emerged, such as the solid-state drives (SSDs) based on non-volatile memory (NVM) technology such as 3D XPoint and the new generations of dynamic random access memories (DRAMs). Different characteristics of these storage technologies may impact the performance of DBMSs. In this work, we analyze the performance of a DBMS using three storage technologies as data locations:HDD, SSD NVM, and DRAM, as well as a hybrid way combining all three. To do this, we use two workloads, analytical and transactional, and we observe throughput as well as latency. After, we discuss the reasons for the results obtained for each type of storage. We also show that the query processing can benefit from the different characteristics of each storage technology to perform faster queries and, finally, we analyze the benefits of using a hybrid storage system.

EXPLORING TIMEOUT AS A PERFORMANCE AND AVAILABILITY FACTOR OF DISTRIBUTED REPLICATED DATABASE SYSTEMS

A concept of distributed replicated data storages like Cassandra, HBase, MongoDB has been proposed to effectively manage the Big Data sets whose volume, velocity, and variability are difficult to deal with by using the traditional Relational Database Management Systems. Trade-offs between consistency, availability, partition tolerance, and latency are intrinsic to such systems. Although relations between these properties have been previously identified by the well-known CAP theorem in qualitative terms, it is still necessary to quantify how different consistency and timeout settings affect system latency. The paper reports results of Cassandra's performance evaluation using the YCSB benchmark and experimentally demonstrates how to read latency depends on the consistency settings and the current database workload. These results clearly show that stronger data consistency increases system latency, which is in line with the qualitative implication of the CAP theorem. Moreover, Cassandra latency and its variation considerably depend on the system workload. The distributed nature of such a system does not always guarantee that the client receives a response from the database within a finite time. If this happens, it causes so-called timing failures when the response is received too late or is not received at all. In the paper, we also consider the role of the application timeout which is the fundamental part of all distributed fault tolerance mechanisms working over the Internet and used as the main error detection mechanism here. The role of the application timeout as the main determinant in the interplay between system availability and responsiveness is also examined in the paper. It is quantitatively shown how different timeout settings could affect system availability and the average servicing and waiting time. Although many modern distributed systems including Cassandra use static timeouts it was shown that the most promising approach is to set timeouts dynamically at run time to balance performance, availability and improve the efficiency of the fault-tolerance mechanisms.

A Generic and Extensible Core and Prototype of Consistent, Distributed, and Resilient LIS

The majority of the existing land information systems (LIS) are centralized, transaction processing systems based on object-relational database management systems for data storage, management, and retrieval. These traditional database management systems are dominantly based on a share-everything or share disk architecture and face challenges in meeting the performance and scalability requirements of distributed, data-intensive systems, including LIS. They support vertical, rather than horizontal scalability, which is of particular importance in distributed systems. In some cases, due to legal, administrative, or infrastructure constraints, LIS need to be distributed rather than centralized systems. Distributed computing systems and share-nothing architecture have become very popular, including new data processing platforms and frameworks with horizontal scalability and fault tolerance capabilities. In this paper, we present cdrLIS—a generic and extensible core of LIS based on relevant international standards and the NewSQL database management system (DBMS) that enables the implementation of consistent, distributed, highly-available, and resilient LIS. A generic core is implemented in the Go programming language and can be easily extended and adopted towards the implementation of a specific country profile. cdrLIS can be deployed either on a computer cluster or on cloud computing platforms and thus support the design and building of a new generation of distributed and resilient data-intensive applications and information systems in the land administration domain.

No SQL Database Management Systems for Big Data

It is well known that, at the present, a huge amount of information, often referred as Big Data, is processed by each domain of modern society. Big data are well defined by the seven dimensions: Volume, Velocity, Variety, Variability, Veracity, Visualization and Value. The traditional database management systems cannot handle the requirements of high availability, scalability and reliability emerged with Big Data. The good news is that we are now in the age of NoSQL databases. NoSQL do not have a fixed structure, they have a flexible structure and are suited for storing unstructured data produced in a large scale in various field. This work outlines the four main types of NoSQL databases and presents some of their representative solutions.

Simplified schema queries for supporting BIM-based rule-checking applications

Following decades of research, the potential of automated rule-checking applications for building models to boost the productivity in the AECO industry has yet to fully materialize. Most efforts have been dedicated to dealing with the complexities associated with formalizing rules related to building designs for computability. To make automated rule checking applications successful, data availability and accessibility are critical components that must be addressed. Unfortunately, to-date they have not received the same level of attention. To lay a solid foundation for a robust environment of automated BIM-based rule-checking systems, this paper proposes a novel approach of transforming building data into a simplified schema (BIMRLSS) as a means of addressing this critical issue. The paper also describes an implementation of the proposed approach on a traditional database management system, which offers the primary advantage of facilitating high-performance queries and supporting a wide range of query types in real-time. More importantly, this approach not only allows alphanumeric data queries, but also enables queries on the geometry by using various spatial operators. Allowing complex spatial-based queries provides a means to significantly simplify the traditional programmatic approach to BIM-based rule-checking. This data-focused approach allows us to lower a significant barrier that currently prevents BIM-based rule-checking systems from reaching their full potential, making the checking process easier and more robust. To highlight the implications and practical benefits of the proposed method, this paper also demonstrates several application scenarios by utilizing a wide range of queries.