Distributed Database Management System Research Articles

Performance Analysis of Distributed Database Management System for IoT in the Context of Smart Agriculture in Indonesia

The integration of Distributed Database Management Systems (DDBMS) for Internet of Things (IoT) applications in smart agriculture presents a transformative paradigm in the dynamic landscape of Indonesian agriculture. This qualitative research investigates the challenges and opportunities associated with this integration, drawing insights from interviews with diverse stakeholders, including farmers, technology developers, and policymakers. The technical challenges encompass data synchronization, network latency, and security considerations, emphasizing the need for standardized protocols and robust security measures. Infrastructure challenges, such as connectivity issues and power supply dependency, underscore the importance of rural infrastructure development. User acceptance challenges highlight resistance to change and the perceived complexity of DDBMS, emphasizing the significance of targeted awareness and training programs. Despite these challenges, stakeholders recognize the advantages of scalability, enhanced data availability, and improved decision-making capabilities offered by DDBMS. The study proposes recommendations, including technological solutions, policy interventions, and capacity-building initiatives, to address identified challenges and promote the effective utilization of DDBMS in Indonesian smart agriculture. This research contributes context-specific insights to the global discourse on IoT and DDBMS in agriculture.

A Secure and Optimization Based Clustering for Vertical and Horizontal Fragmentation in Distributed Database Management System

Distributed database systems are increasingly important due to the massive data output, and their effectiveness is largely based on their design. Two key processes, fragmentation and allocation, are used to improve the efficiency and efficacy of these systems. Effective data fragmentation requires both horizontal and vertical categorization of tuples. Advanced optimization techniques are used for both fragmentations, such as the Enhanced Arithmetic Optimization (EAO) algorithm with Opposition-based Learning (OBL) and Levy Flight Distributer (LFD) for vertical fragmentation and the hybrid Aquila Optimizer (AO) with Artificial Rabbit Optimization (ARO) algorithm for horizontal fragmentation. The fragmented data is securely transmitted using the Fully Homomorphic Encryption (FHE) algorithm. The implementation is executed using the Python language, and the performance of the proposed algorithms is evaluated using different performance parameters. The execution time analysis shows that the proposed EAOA algorithm consumes 5.5 seconds for vertical fragmentation, while the hybrid AARO algorithm takes 5.9 seconds for horizontal fragmentation. The vertical fragmentation is found to be better than the horizontal one in DDBMS.

Security Issues in Distributed Database Management Systems: Challenges and Opportunities

Database management systems (DBMS) are essential systems of software technologies for multi-level and multi-angled operations with the utmost security. Currently, its form of distributed database, i.e. DDB, is very popular in communication networks. The current write-up is aimed at investigating important security aspects and issues, exploring some major challenges pertinent to the security of DDB, finding solutions to the same, and eventually talking about some future opportunities through promoting research towards curbing these security-related points. To accomplish these tasks, the author has reviewed some of the latest and most pertinent research articles of the last ten years. It comprises the application of DDBMS, major pertinent issues within its range of investigation, and a conclusion.

Cassandra is a Better Option for Handling Big Data in a No-SQL Database

With no single point of failure and the flexibility to handle enormous volumes of data across numerous commodity servers, Apache Cassandra is an opensource distributed database management system. With asynchronous master less replication, Cassandra provides strong support for clusters spanning several datacenters and enables low latency operations for all clients. Like skilled carpenters, data engineers are aware that certain tasks call for various tools. Choosing the appropriate equipment and being knowledgeable about their use can be the most crucial aspects of any job. A distributed information system for managing massive amounts of structured data across multiple goods servers, Apache Cassandra, a top-level Apache project developed at Facebook and based on Google's Big Table and Amazon's Generator, offers extremely offered service and has no single point of failure. In comparison to other NoSQL databases and relational databases, Cassandra offers features including continuous availability, linear scale performance, operational simplicity, and straightforward knowledge distribution over various knowledge centers and cloud availability zones. Cassandra's capacity to scale, perform, and supply continuous time period is due to its design. Cassandra uses an attractive, simple-to-setup, and simple-to-maintain lordless "ring" design rather than a traditional master-slave or a manual and labor-intensive shared system. With continuous availability, linear scale performance, operational simplicity, and simple data distribution over numerous data centers and cloud availability zones, Apache Cassandra is a massively scalable open-source non-relational database. Cassandra was initially created at Facebook; it was open sourced in 2008; and in 2010, it was elevated to the status of a top-level Apache project.

Performance Evaluation of Distributed Database Strategies Using Docker as a Service for Industrial IoT Data: Application to Industry 4.0

Databases are an integral part of almost every application nowadays. For example, applications using Internet of Things (IoT) sensory data, such as in Industry 4.0, are a classic example of an organized storage system. Due to its enormous size, it may be stored in the cloud. This paper presents the authors’ proposition for cloudcentric sensory measurements and measurements acquisition. Then, it focuses on evaluating industrial cloud storage engines for sensory functions, experimenting with three open-source types of distributed Database Management Systems (DBMS); MongoDB and PostgreSQL, with two forms of PostgreSQL schemes (Javascript Object Notation (JSON)-based and relational), against their respective horizontal scaling strategies. Several experimental cases have been performed to measure database queries’ response time, achieved throughput, and corresponding failures. Three distinct scenarios have been thoroughly tested, the most common but widely used: (i) data insertions, (ii) select/find queries, and (iii) queries related to aggregate correlation functions. The experimental results concluded that PostgreSQL with JSON achieves a 5–57% better response than MongoDB for the insert queries (cases of native, two, and four shards implementations), while, on the contrary, MongoDB achieved 56–91% higher throughput than PostgreSQL for the same set up. Furthermore, for the data insertion experimental cases of six and eight shards, MongoDB performed 13–20% more than Postgres in response time, achieving × 2 times higher throughput. Relational PostgreSQL was × 2 times faster than MongoDB in its standalone implementation for selection queries. At the same time, MongoDB achieved 19–31% faster responses and 44–63% higher throughput than PostgreSQL in the four tested sharding subcases (two, four, six, eight shards), accordingly. Finally, the relational PostgreSQL outperformed MongoDB and PostgreSQL JSON significantly in all correlation function experiments, with performance improvements from MongoDB, closing the gap with PostgreSQL towards minimizing response time to 26% and 3% for six and eight shards, respectively, and achieving significant gains towards average achieved throughput.

Query Processing in Blockchain Systems: Current State and Future Challenges

When, in 2008, Satoshi Nakamoto envisioned the first distributed database management system that relied on cryptographically secured chain of blocks to store data in an immutable and tamper-resistant manner, his primary use case was the introduction of a digital currency. Owing to this use case, the blockchain system was geared towards efficient storage of data, whereas the processing of complex queries, such as provenance analyses of data history, is out of focus. The increasing use of Internet of Things technologies and the resulting digitization in many domains, however, have led to a plethora of novel use cases for a secure digital ledger. For instance, in the healthcare sector, blockchain systems are used for the secure storage and sharing of electronic health records, while the food industry applies such systems to enable a reliable food-chain traceability, e.g., to prove compliance with cold chains. In these application domains, however, querying the current state is not sufficient—comprehensive history queries are required instead. Due to these altered usage modes involving more complex query types, it is questionable whether today’s blockchain systems are prepared for this type of usage and whether such queries can be processed efficiently by them. In our paper, we therefore investigate novel use cases for blockchain systems and elicit their requirements towards a data store in terms of query capabilities. We reflect the state of the art in terms of query support in blockchain systems and assess whether it is capable of meeting the requirements of such more sophisticated use cases. As a result, we identify future research challenges with regard to query processing in blockchain systems.

Design and Implementation of EFSC Algorithm for Hybrid Representation of Distributed Databases

In distributed database management systems, fragmenting base connections increases concurrency and hence system throughput for query processing. User queries use hybrid fragmentation methods focused on vector bindings, and deductive database implementations lack query-access-rule dependence. As a result, for hierarchical deductive information implementations, a hybrid fragmentation solution is used. The method considers the horizontal partition of base relations based on the bindings placed on user requests, then produces vertical fragments of the horizontally partitioned relations, and finally clusters rules based on attribute affinity and query and rule access frequency. The suggested fragmentation approach makes distributed deductive database structures easier to develop.

A Newly Proposed Cloud Computing Based Strengthen Distributed Database System Model and Its Applications in Database Modelling

Abstract: In the modern era of today’s exceptional Information age, the day to day transactions of huge sensitive data sets, which is in the form of PBs (Peta-Bytes) 250 bytes and YBs (Yotta – Bytes) 280 bytes are drastically increases with enormous speed on CLOUD data storage environment. CLOUDs data storage environment are one of the most superior and reliable platform for storing a large sets of data both at enterprise level or local level. Because CLOUD provides online data fetching capability to restore or fetching data at any geographical locations through login their correspondent credentials. But to enhancement or spread of these large data sets are becomes also very complex with respect to maintenance of these data with take concern of consistency and data security, because to maintain these large data sets with full of consistency and integrity are really a very typical and rational tasks, so here In this paper we proposed a distributed database management systems for CLOUD interface also preserves or to take concern data security features with full restoration of CIA (Confidentiality, Integrity, Availability or Authenticity) trade of Information Security. Here we also improvised the mechanisms of traditional distributed database management systems because the tendency to preserves information and recover ability after any misconceptions happens that we restore data which belongs to similar person may have to be stored at different locations, but this newly proposed distributed database systems architecture contains all information or record which belong to similar person are stored in one database rather restore it different databases but the location of these data have to be changes mean while that the content or data which resides in one databases have to be moved to some other database and also preserves the security features, and this model also have capability to run older traditional methodology based distributed database management systems using this model. So the detailed description about these models and communication infrastructure among different CLOUDs are append in the upcoming sections of this paper. Keywords: Cloud based Distributed Database system model, Distributed system, Distributed Database model of CLOUD, Cloud Distributed Database, CLOUD based database systems

Information Security of New Media Art Platform of Distributed System Based on Blockchain Technology

New media art is different from ready-made art, installation art, body art, and land art. New media art is a new art subject category with “optical” media and electronic media as the basic language. And, because of the continuous development of computer networks, distributed database management systems are becoming more and more popular. This article aims to study the information security control problem in the distributed new media system. Through comprehensive access control, reliable support, and many-to-many random mutual encryption, it solves the security requirements of supporting mobile computing in a distributed network environment, communication, and hierarchical grouping. Group key management and other issues have studied some key security technologies for building secure distributed information systems. This article proposes covering the behavior-based access control model ABAC (operation-based access control model), the architecture of the new Trusted Platform Module, the architecture of multithreaded crypto chips, and their service methods, as well as distributed information systems and key management solutions. Approximately obeying the chi-square distribution with 2 degrees of freedom, when the significance level is taken as 5%, the chi-square value for the skin is 5.99. Different initial values are selected for the chaotic sequence, 200 groups of chaotic sequences with a length of 2000 are selected for sequence inspection, and the pass rate is 97.5%. It can be seen that the autocorrelation and cross-correlation characteristics of the improved spatiotemporal chaotic sequence are still relatively ideal, so the usability of the platform is relatively high.

Distributed Database Management With Integration of Blockchain and Long Short-Term Memory

Supply chain management is the broad range of activities required to plan, control, and execute the flow of a product. As a less corruptible and more automated alternative to traditional databases, blockchains are well suited to the complicated record-keeping. However distributed database management system is a centralized software system; the blockchain technology can overcome the problem of synchronization between multiple databases; it also ensures that integrity problems are solved. In the proposed model, Ethereum blockchain is used to solve a few major supply chain problems to manage a distributed database. The model has incorporated techniques to predict the rise and fall of the demand for the medicine in the market by using machine learning algorithms such as linear regression and LSTM; also, the trend predicted by both the models has been compared. The result shows that while using linear regression the predicted trend is not very accurate and cannot trace the actual trend closely whereas BLSTM has performed well in predicting the trends of time series data.

State of Art for Distributed Databases: Faster Data Access, processing, Growth Facilitation and Improved Communications

The technological development has been experiencing rapid growth in the recent years. Individuals need access to required data and information more readily than ever before. To consider this need, the resource development and management are prioritized by the digital world entrepreneurs. In order to provide quick access to the individuals and provide necessary support are fundamentally important for the users. In the present aspects of the digital world, the concept of distributed database, grid system, and cloud systems have completely replaced the need for independent databases. Because of the increasing need and requirements of the computer power and capacity, digital world has been adopting different strategic concerns in order to promote and interconnect dispersedly reserved databases. The concept of distributed database provides the solution for the growing need for addressing the vital aspects of the data management and provision of the access to the required data. This article analyses the concept of database management system, considering relevant review of literature, systematic analysis, investigating the rules for distributed database management system DDBMS, finding appropriate architecture for the DDBMS solutions and providing justified recommendations based on the users’ need and perception.

A Scalable Smartwatch-Based Medication Intake Detection System Using Distributed Machine Learning.

Poor Medication adherence causes significant economic impact resulting in hospital readmission, hospital visits and other healthcare costs. The authors developed a smartwatch application and a cloud based data pipeline for developing a user-friendly medication intake monitoring system that can contribute to improving medication adherence. The developed Android smartwatch application collects activity sensor data using accelerometer and gyroscope. The cloud-based data pipeline includes distributed data storage, distributed database management system and distributed computing frameworks in order to build a machine learning model which identifies activity types using sensor data. With the proposed sensor data extraction, preprocessing and machine learning algorithms, this study successfully achieved a high F1 score of 0.977 with 13.313 seconds of training time and 0.139 seconds for testing.

Multi-objective Multi-Join Query Optimization using Modified Grey Wolf Optimization

Nowadays information retrieved by a query is based upon extracting data across the world, which are located in different data sites. In distributed database management systems (DDBMS), due to partitioning or replication of data among several sites the relations required for an answer of a query may be stored at several data sites (DS). Many experimental results have showed that combination of optimal join order (OJO) and optimal selection of relations in query plan (QP) gives out better results compare to the several existing query optimising methodologies like teacher-learner based optimisation (TLBO), genetic algorithm (GA), etc. In this paper an approach has been proposed to compute a best optimal QP that could answer the user query with minimal cost values and minimum time using modified grey wolf optimisation algorithm (MGWO) which is multi-objective constrained. Proposed approach also aims for producing OJO in order to reduce the dimensionality complexity of the QP.

Building a Retail Sysem Based on Distributed Databases Model

In recent years, distributed databases have become an important field of processing information, overcoming some limitations of centralized database such as overloading, bottlenecking while accessing, availability/ reliability of low fault tolerance. Our article proposes to build a distributed system (functions and databases) for POS (point of sale) retailers, data will be distributed across different locations but can still be linked together when required. At each location retailers can sell directly on the system (online or offline) so they can administer local databases and execute their local applications (business). Here we deploy the system on the distributed database management system based on Client-Server model. Therefore, aside from local management of data at clients (POS), there is also a server (manager) that stores data, manages and controls the entire system.

A lightweight and efficient temporal database management system in TDSQL

Driven by the recent adoption of temporal expressions into SQL:2011, extensions of temporal support in conventional database management systems (a.b.a. DBMSs) have re-emerged as a research hotspot. In this paper, we present a lightweight yet efficient built-in temporal implementation in Tencent's distributed database management system, namely TDSQL. The novelty of TDSQL's temporal implementation includes: (1) a new temporal data model with the extension of SQL:2011, (2) a built-in temporal implementation with various optimizations, which are also applicable to other DBMSs, and (3) a low-storage-consumption in which only data changes are maintained. For the repeatability purpose, we elaborate the integration of our proposed techniques into MySQL. We conduct extensive experiments on both real-life dataset and synthetic TPC benchmarks by comparing TD-SQL with other temporal databases. The results show that TDSQL is lightweight and efficient.

Multiserver Communication in Distributed Database Management System

Multiserver Communication in Distributed Database Management System

MIND: An approach to optimize communication time via middleware tuning

Minimizing the communication time due to the transfer over a network of the intermediary results produced during the execution of a distributed query is a fundamental problem in distributed database management systems. We take a new look at this problem by investigating the relationship between the communication time and a remote data access middleware. We focus on two middleware parameters that are manually tuned by database administrators or programmers: the fetch size (i.e., the number of tuples that are communicated at once) and the message size (i.e., the size of the buffer at the middleware level). We present an experimental study which shows that these parameters have a crucial impact on the communication time. Then, we propose the MIND framework, which tunes the aforementioned middleware parameters, while adapting to different queries (that may vary in terms of selectivity) and networks (that may vary in terms of bandwidth). The main technical contributions of MIND are (i) a communication time estimation function that takes into account the middleware parameters, the size of the query result and the network environment, and (ii) an iterative optimization algorithm to find the fetch size and the message size that allow a good trade-off between low resource consumption and low communication time. We conclude with an experimental study that emphasizes the effectiveness of the MIND framework.

QoS Aware Replica Control Strategies for Distributed Real Time Database Management System

In distributed real time database management system (DRT-DBMS), QoS aware replication control strategy is necessary to ensure availability and to improve the system performance. Since user request and need of data is dynamic in nature, it requires an approach which addresses the problem of data distribution and also resolves them in DRT-DBMS. A large quantity of data file is produced so we need a dynamic replication approach through which we can reduce the data access time. In order to replicate it, at the nearest site, replica placement and replacement algorithms are required to be evaluated. This paper presents a heuristic approach Greedy-Cover Firefly algorithm in which replica is placed dynamically based on QoS requirement and replaced with the optimal adaptive replica replacement algorithm for each data object. The replication cost required for copying data object to each set of a node is calculated to select the optimal node. Here, the replication decision is taken on the basis of access history of data on multiple sites. Due to the limited storage capacity of each node, the value of each file is calculated, and the file with the smallest value is removed to replace other files. A detailed simulation shows that the proposed algorithms can greatly improve the system performance to reduce the system resources consumed with respect to time and QoS requirement.

Overview of Modern Nosql Database Management Systems. Case Study: Apache Cassandra

Abstract The wide spread of IoT devices makes possible the collection of enormous amounts of sensor data. Traditional SQL (structured query language) database management systems are not the most appropriate for storing this type of data. For this task, distributed database management systems are the most adequate. Apache Cassandra is an open source, distributed database server software that stores large amounts of data on low-coast servers, providing high availability. The Cassandra uses the gossip protocol to exchange information between the distributed servers. The query language used is the CQL (Cassandra Query Language). In this paper we present an alternative solution to traditional SQL-based database management systems - the so called NoSQL type database management systems, summarize the main types of these systems and provide a detailed description of the Apache Cassandra open source distributed database server installation, configuration and operation.

ATH: Auto-Tuning HBase’s Configuration via Ensemble Learning

HBase is a distributed database management system and is becoming increasingly popular for applications that need fast random access to a large amount of data. However, it has a number of performance-critical configuration parameters, which may interact with each other in a complex way, making manually tuning them for optimal performance extremely difficult. In this paper, we propose a novel approach to auto-tune the configuration parameters for a given HBase application, called Auto-Tuning HBase (ATH). The key is an accurate performance model with low cost, which takes configuration parameters as inputs. To this end, we systematically explore different modeling techniques and decide to employ an ensemble learning algorithm to build the performance model. Subsequently, we leverage genetic algorithm to search the optimal configuration parameters for the application by using the performance model. As such, ATH can quickly as well as automatically identify a set of configuration parameter values to make the performance of the application optimal. We validate ATH in a cluster with ten nodes by using five typical applications from Yahoo! Cloud Serving Benchmark. The experimental results show that ATH can improve throughput by 41% on average and up to 97% compared with the default configurations. At the same time, the latency of HBase operations is reduced by 11.3% on average and up to 57%.