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Medical Object Detection (MOD) is a clinically relevant image processing method that locates structures of interest in radiological image data at object-level using bounding boxes. High-performing MOD models necessitate large datasets accurately reflecting the feature distribution of the corresponding problem domain. However, strict privacy regulations protecting patient data often hinder data consolidation, negatively affecting the performance and generalization of MOD models. Federated Learning (FL) offers a solution by enabling model training while the data remain at its original source institution. While existing FL solutions for medical image classification and segmentation demonstrate promising performance, FL for MOD remains largely unexplored. Motivated by this lack of technical solutions, we present an open-source, self-configuring and task-agnostic federated MOD framework. It integrates the FL framework Flower with nnDetection, a state-of-the-art MOD framework and provides several FL aggregation strategies. Furthermore, we evaluate model performance by creating simulated Independent Identically Distributed (IID) and non-IID scenarios, utilizing the publicly available datasets. Additionally, a detailed analysis of the distributions and characteristics of these datasets offers insights into how they can impact performance. Our framework's implementation demonstrates the feasibility of federated self-configuring MOD in non-IID scenarios and facilitates the development of MOD models trained on large distributed databases. [ABSTRACT FROM AUTHOR]

We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory--a mathematical framework that accounts for truth, falsity, and indeterminacy--with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. [ABSTRACT FROM AUTHOR]

As an essential carrier of cross-border trade, the maritime supply chain has always been plagued by long service cycles, complex structures, and heterogeneous information from multiple sources. In this context, as a distributed database system, the emerging blockchain technology has the characteristics of decentralization, tamper-proofness, and traceability, which can be correctly applied in the maritime industry to promote its transformation and upgrading. However, blockchain application in the maritime supply chain remains notably absent, leaving this critical topic almost unexplored. Thus, we first conduct a comprehensive literature review and an industrial investigation to clarify the current status, problems, and challenges of the blockchain-based maritime supply chain system (BMSCS). We construct a new operation management mode of the maritime supply chain and propose an integrated BMSCS suitable for global economic development. Finally, based on comparison of the current research results, reasonable suggestions for the future operation and development of the BMSCS are put forward to enhance the coordination among members, accelerate the application of blockchain in the maritime industry, and gradually realise the intelligent operation of the maritime supply chain. [ABSTRACT FROM AUTHOR]

Abstract The modern world has its foundation built on data. Data is generated all the time, and many aspects of the society and economy are built upon it. In today's world, data is employed in better decision‐making, innovation, and improving the efficiency of systems. It becomes essential to protect it from being tampered with or misused. Encryptions based on cryptographic systems are used in mainstream data protection these days. There have been newer iterations of mechanisms based on new techniques using blockchain and the zero trust model and even systems that employ fog computing along with content delivery networks. The advent of computation through quantum technologies also helped in developing data security with post‐quantum mechanisms. The critical conditions for the working of these mechanisms hinder their adoption primarily. This also leads current systems to lack one or more of these technologies. Thus, the importance of a unified system that can employ quantum technologies effectively becomes prominent and requires a definitive investigation. In this article, a Quantum‐IoT‐based data protection scheme that can be of value to Industry 4.0 is analyzed. Light is also shed on innovations in quantum computing that can help solve not just current problems but also the future.

Time-consuming ocean transportation for international trades requires enterprises to advance payments for goods, which can be fatal for small and medium enterprises with insufficient self-owned capital. Logistics finance is an effective solution for tackling this problem. However, traditional logistics finance has long been plagued by information asymmetry, tremendous paperwork, and long financing cycles. As a distributed database with characteristics of transparency, traceability, and immutability, blockchain technology shows good potential in addressing pain points on traditional logistics financing platforms. This paper focuses on the operational mechanism and value analysis of the blockchain-based port logistics finance platform. The conceptual architecture of the blockchain-based port logistics finance platform is constructed, and a value analysis model based on the Newsboy Model is established for quantitatively analysing the platform's value. The results revealed that the introduction of the blockchain into port logistics financing can significantly improve small to medium-sized enterprises' profitability by providing reliable financing channels. The results also indicate that participants' profit on the blockchain-based platform may be influenced by the speed of market expansion, initial capital size, and financing cost. This paper also discusses theoretical and managerial implications by providing insights into the participants' decision-making on the platform under different constraints. [ABSTRACT FROM AUTHOR]

Data collection is the primary link in the simulation execution process. The integrity and efficiency of data acquisition have a significant impact on the final effect and efficiency of the entire training simulation activity. However, in the existing distributed simulation system based on high level architecture (HLA), centralized data collection is difficult to handle massive data in a single step, which will affect the normal advancement of simulation while distributed data collection will cause a large number of redundant data, and the development of the collection module does not have universality. In response to the above problems, based on weak distributed data acquisition structure, maniple collection members are used to realize parallel data collection, and the distribution strategy of collection tasks in formulated among multiple members by the non-dominated sorting genetic algorithm-II (NSGA-II) to achieve a balanced distribution of data collection loads. The experimental results on simulation result and real system show that the proposed method can significantly improve the efficiency of data collection while reducing the central processing unit (CPU) and memory consumption during the execution of data collection members. [ABSTRACT FROM AUTHOR]

Traditional database systems like relational databases can store data which are structured with predefined schema, but in the case of bigdata, the data comes in different formats or are collected from diverse sources. The distributed databases like not only spark querying language (NoSQL) repositories are often used in relation to bigdata analytics, but a continual updating is required in business because of the streaming data that comes from stock trading, online activities of website visitors, and from the mobile applications in real time. It will not have to delay, for some report to show up, to assess and analyse the current situation, to move forward with the next business choice. Apache Spark’s structured streaming offer capabilities for handling streaming data in a batch processing mode with faster responses compared to MongoDB which is a document-based NoSQL database. This study completes similar queries to evaluate Spark SQL and NoSQL database performance, focusing on the upsides of Spark SQL over NoSQL databases in streaming data exploration. The queries are completed with streaming data stored in a batch mode. [ABSTRACT FROM AUTHOR]

The Journal of Information Systems (JIS) is the academic journal of the Accounting Information Systems Section of the American Accounting Association, publishing high-quality research that advances AIS knowledge. JIS welcomes research articles in various areas related to AIS or IS/IT-related accounting and auditing, with a focus on engaging a broad audience while maintaining research quality. The journal also values innovative research and straightforward language to communicate complex issues effectively. JIS follows a detailed submission process and review guidelines to ensure the quality and integrity of published articles. [Extracted from the article]

Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization. [ABSTRACT FROM AUTHOR]

The phenomena with within-strata characteristics that are more similar than between-strata characteristics are ubiquitous (e.g., land-use types and image classifications). It can be summarized as spatial stratified heterogeneity (SSH), which is measured and attributed using the geographical detector (Geodetector) q-statistic. SSH is typically calibrated by stratification and hundreds of algorithms have been developed. Little is discussed about the conditions of the methods. In this work, a novel stratification method based on head/tail breaks is introduced for the purpose of better capturing the SSH of geographical variables with a heavy-tailed distribution. Compared to conventional sample-based stratifications, the presented approach is a population-based optimized stratification that indicates an underlying scaling property in geographical spaces. It requires no prior knowledge or auxiliary variables and supports a naturally determined number of strata instead of being subjectively preset. In addition, our approach reveals the inherent hierarchical structure of geographical variables, characterizes its dominant components across all scales, and provides the potential to make the stratification meaningful and interpretable. The advantages were illustrated by several case studies in natural and social sciences. The proposed approach is versatile and flexible so that it can be applied for the stratification of both geographical and nongeographical variables and is conducive to advancing SSH-related studies as well. This study provides a new way of thinking for advocating spatial heterogeneity or scaling law and advances our understanding of geographical phenomena. [ABSTRACT FROM AUTHOR]

A Data Warehouse (DW) is a centralized database that stores large volumes of historical data for analysis and reporting. In a world where enterprise data grows exponentially, new architectures are being investigated to overcome the deficiencies of traditional Database Management Systems (DBMSs), driving a shift towards more modern, cloud-based solutions that provide resources such as distributed processing, columnar storage, and horizontal scalability without the overhead of physical hardware management, i.e., a Database as a Service (DBaaS). Choosing the appropriate class of DBMS is a critical decision for organizations, and there are important differences that impact data volume and query performance (e.g., architecture, data models, and storage) to support analytics in a distributed cloud environment efficiently. In this sense, we carry out an experimental evaluation to analyze the performance of several DBaaS and the impact of data modeling, specifically the usage of a partially normalized Star Schema and a fully denormalized Flat Table Schema, to further comprehend their behavior in different configurations and designs in terms of data schema, storage form, memory availability, and cluster size. The analysis is done in two volumes of data generated by a well-established benchmark, comparing the performance of the DW in terms of average execution time, memory usage, data volume, and loading time. Our results provide guidelines for efficient DW design, showing, for example, that the denormalization of the schema does not guarantee improved performance, as solutions performed differently depending on its architecture. We also show that a Hybrid Processing (HTAP) NewSQL solution can outperform solutions that support only Online Analytical Processing (OLAP) in terms of overall execution time, but that the performance of each query is deeply influenced by its selectivity and by the number of join functions. [ABSTRACT FROM AUTHOR]

Effective data fragmentation is essential in enhancing the performance of distributed database management systems (DDBMS) by strategically dividing extensive databases into smaller fragments distributed across multiple nodes. This study emphasizes horizontal fragmentation and introduces an advanced machine learning algorithm, Red Fox Optimization-based Support Vector Machine (RFO-SVM), designed for optimizing the data fragmentation process. The input database undergoes meticulous pre-processing to address missing data concerns, followed by analysis through RFO-SVM. This algorithm efficiently classifies features and target labels based on class labels. The RFO algorithm optimizes critical SVM parameters, including the kernel, kernel parameter, and boundary parameter, leveraging the accuracy metric. The resulting classified data serves as fragments for the fragmentation process. To ensure precision in fragmentation, a Genetic Algorithm (GA) allocates these fragments to diverse nodes within the DDBMS, optimizing the total allocation cost as the fitness function. The proposed model, implemented in Python, significantly contributes to the efficient fragmentation and allocation of databases in distributed systems, thereby enhancing overall performance and scalability. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing has attracted great interest in various scientific and technical fields recently as one of the widely adopted networking technologies. Despite their many benefits and applications, it still faces many security and trust challenges, including managing and controlling services, privacy, data integrity in distributed databases, data backup, and synchronization. Moreover, due to its centralized architecture, and lack of transparency and traceability, the results of the trust assessment cannot be fully recognized by all users. However, creating a trust‐based transaction environment has become its key factor. Blockchain, with its nature of decentralization and security, can be leveraged to address these challenges and build a distributed and decentralized trust architecture, due to the underlying characteristics such as transparency, traceability, decentralization, security, immutability, and automation. This article makes a comprehensive study of how blockchain is applied to deliver security services in the cloud computing model, focusing on up‐to‐date approaches, opportunities, and future directions. This survey also discusses the benefits of the technical fusion of blockchain and cloud. It provides a classification of proposed systems based on privacy, data sharing, authentication, and access control, as well as auditing and data integrity. Finally, the main conclusions of this study will be the challenges and future directions to stimulate further research in this promising field. [ABSTRACT FROM AUTHOR]

Nowadays, with the simplification of cloud storage complexity and expanding its capacity, the cloud has become more widely accessible. Data owners increasingly shift away from traditional systems to leverage cloud resources, enhancing capacity and geographic diversity. Despite these advancements, the need to safeguard confidential data prompts a transformation to encryption before outsourcing, establishing additional standards for data privacy. This shift renders conventional data utilization through standard keyword searching obsolete. Therefore, there is a crucial need for an encrypted cloud information search service. To address the challenge of privacy-preserving multi-keyword searches, this paper proposes an efficient solution that enhances the existing framework. The proposed system uses an advanced encryption standard (AES) to improve data security and management in a cloud environment. Each private server maintains a registered information file and deletes merged data from the free cloud after observation. Customers can manage order records in the cloud data warehouse by transparently logging observations. Access to server information requires client inspection requests and verification. Private servers validate capacity and issue tokens to the Key Distribution Center (KDC) for key distribution. Finally, the term frequency-inverse dense frequency (TF-IDF) equation is used for data positioning. This approach ensures secure, authenticated, and efficient data handling. This solution focuses on two key objectives: firstly, incorporating synonym-based search to facilitate multi-keyword synonym queries, and secondly, implementing a ranked search to yield more detailed search results. While existing search methods support only fuzzy keywords or precise queries over encrypted cloud data. The authors assess its performance by employing real-life datasets, and comprehensive experiments affirm the validity and practicality of the proposed scheme. [ABSTRACT FROM AUTHOR]

Technological development in all areas of life has played an effective role in the development of electronic systems. They have become one of the necessary modern building systems used by the government in order to facilitate the work of employees and reduce the effort and time of the employee and citizen worker. This research suggested searching for an electronic platform for private property matters between departments so that all specializations would be transferred from the basic system to the electronic system. This system is considered a platform and does not require submitting ownership transfer requests easily and quickly via the Internet. The system provides a great opportunity to progress their transactions on a regular and continuous basis, providing accurate information about the progress of their transactions and the specific time for completing them.

Blockchain, essentially a distributed and immutable database, has emerged as a potential solution to governance problems of accountability, transparency, and trust in the construction industry. In terms of construction quality management (CQM) applications, previous works mainly took a technical perspective, and little is known about the potential of blockchain-based governance for quality opportunism. Moreover, we have seen very few successful blockchain implementations in construction practices. Stakeholders meet the trade-off between potential benefits and risks in blockchain adoption decisions. Previous works mainly focused on barrier analysis, neglecting the dynamic interactions of stakeholders' strategic behaviors on adopting blockchains. Against this backdrop, this research aims to systematically analyze blockchain impacts on CQM from a governance perspective and then investigate the owner and the main contractors' strategic behaviors (e.g., adopting blockchains in CQM or not). The agency theory was used as the theoretical lens to examine how blockchain curbs contractors' quality opportunistic behaviors as a governance mechanism. An evolutionary game theory model was developed to simulate adoption decision interactions between the owner and the main contractor. Finally, policy suggestions were highlighted for policymakers. This research could be one of the first studies examining blockchain potential to CQM from an opportunism governance perspective. Such discussions could inspire more discussions on blockchain-based governance and broaden researchers' and practitioners' understandings of blockchain impacts. The proposed policy suggestions from the equilibrium analysis could facilitate blockchain diffusion in the construction industry. [ABSTRACT FROM AUTHOR]

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A blockchain is a distributed database that contains the records of transactions that are shared among all members of a community. Most members must confirm each and every transaction in order for a fraudulent transaction to fail to occur. As a rule, once a record is created and accepted by the blockchain, it cannot be altered or deleted by anyone. This study focuses on improving the investigation task in the database forensics field by utilizing blockchain technology. To this end, a novel conceptual framework is proposed for the forensic analysis of data from database systems engaging blockchain technology. This is the first time that blockchain technology is followed in database forensics for the purpose of tracing digital evidence. The design science research method was adopted to accomplish the objectives of the present study. The findings displayed that with the developed forensics framework, the data regarding database incidents could be gathered and analyzed in a more efficient manner. [ABSTRACT FROM AUTHOR]

This paper focuses on the scheduling problem of workflow tasks that exhibit interdependencies. Unlike independent batch tasks, workflows typically consist of multiple subtasks with intrinsic correlations and dependencies. It necessitates the distribution of various computational tasks to appropriate computing node resources in accordance with task dependencies to ensure the smooth completion of the entire workflow. Workflow scheduling must consider an array of factors, including task dependencies, availability of computational resources, and the schedulability of tasks. Therefore, this paper delves into the distributed graph database workflow task scheduling problem and proposes a workflow scheduling methodology based on deep reinforcement learning (DRL). The method optimizes the maximum completion time (makespan) and response time of workflow tasks, aiming to enhance the responsiveness of workflow tasks while ensuring the minimization of the makespan. The experimental results indicate that the Q-learning Deep Reinforcement Learning (Q-DRL) algorithm markedly diminishes the makespan and refines the average response time within distributed graph database environments. In quantifying makespan, Q-DRL achieves mean reductions of 12.4% and 11.9% over established First-fit and Random scheduling strategies, respectively. Additionally, Q-DRL surpasses the performance of both DRL-Cloud and Improved Deep Q-learning Network (IDQN) algorithms, with improvements standing at 4.4% and 2.6%, respectively. With reference to average response time, the Q-DRL approach exhibits a significantly enhanced performance in the scheduling of work flow tasks, decreasing the average by 2.27% and 4.71%when compared to IDQN and DRL-Cloud, respectively. The Q-DRL algorithm also demonstrates a notable increase in the efficiency of system resource utilization, reducing the average idle rate by 5.02% and 9.30% in comparison to IDQN and DRL-Cloud, respectively. These findings support the assertion that Q-DRL not only upholds a lower average idle rate but also effectively curtails the average response time, thereby substantially improving processing efficiency and optimizing resource utilization within distributed graph database systems. [ABSTRACT FROM AUTHOR]

The Journal of Information Systems (JIS) is an academic journal that publishes high-quality research on Accounting Information Systems (AIS). The journal welcomes research articles and commentaries that contribute to AIS or IS/IT-related accounting or auditing research. JIS aims to engage a broad audience, including researchers from various disciplines and professionals in accounting-related services and technology firms. The journal covers a wide range of topics, including artificial intelligence, cybersecurity, data analytics, and more. JIS follows a rigorous review process and encourages concise and nonmathematical summaries of articles to facilitate understanding for both practitioners and academics. [Extracted from the article]

In distributed and cloud database system, large amount of resources are used by complex queries that increase in payment overheads of cloud users. These resource needs can be minimised by evaluating common join operations and caching their results so that they could be applied to the execution of additional queries. In this research, the Query Optimization Technique Using Materialization (QOTUM) system is designed and developed that uses the mechanism of materialized views during query execution at distributed database in cloud environment. Extensive experiments are conducted with the help of standard benchmarks datasets and query workloads in cloud environment. The performance of QOTUM system is studied and evaluated against conventional SQL system based on various performance parameters. [ABSTRACT FROM AUTHOR]

Background: A blockchain can be described as a distributed ledger database where, under a consensus mechanism, data are permanently stored in records, called blocks, linked together with cryptography. Each block contains a cryptographic hash function of the previous block, a timestamp, and transaction data, which are permanently stored in thousands of nodes and never altered. This provides a potential real-world application for generating a permanent, decentralized record of scientific data, taking advantage of blockchain features such as timestamping and immutability. Implementation: Here, we propose INNBC DApp, a Web3 decentralized application providing a simple front-end user interface connected with a smart contract for recording scientific data on a modern, proof-of-stake (POS) blockchain such as BNB Smart Chain. Unlike previously proposed blockchain tools that only store a hash of the data on-chain, here the data are stored fully on-chain within the transaction itself as "transaction input data", with a true decentralized storage solution. In addition to plain text, the DApp can record various types of files, such as documents, images, audio, and video, by using Base64 encoding. In this study, we describe how to use the DApp and perform real-world transactions storing different kinds of data from previously published research articles, describing the advantages and limitations of using such a technology, analyzing the cost in terms of transaction fees, and discussing possible use cases. Results: We have been able to store several different types of data on the BNB Smart Chain: raw text, documents, images, audio, and video. Notably, we stored several complete research articles at a reasonable cost. We found a limit of 95KB for each single file upload. Considering that Base64 encoding increases file size by approximately 33%, this provides us with a theoretical limit of 126KB. We successfully overcome this limitation by splitting larger files into smaller chunks and uploading them as multi-volume archives. Additionally, we propose AES encryption to protect sensitive data. Accordingly, we show that it is possible to include enough data to be useful for storing and sharing scientific documents and images on the blockchain at a reasonable cost for the users. Conclusion: INNBC DApp represents a real use case for blockchain technology in decentralizing biomedical data storage and sharing, providing us with features such as immutability, timestamp, and identity that can be used to ensure permanent availability of the data and to provide proof-of-existence as well as to protect authorship, a freely available decentralized science (DeSci) tool aiming to help bring mass adoption of blockchain technology among the scientific community. [ABSTRACT FROM AUTHOR]

Blockchain, as a distributed trust database, has been widely applied in the field of trustworthy sharing of Internet of Things (IoT) sensor data. A single hash mechanism has achieved, to some extent, the trustworthy on-chain storage of blockchain sensor data, that is, the consistency of data on and off the chain. However, it still faces potential security risks such as collision attacks, short password attacks, and rainbow table attacks. To address this issue, this paper proposes a resiliently secure blockchain sensor data trustworthy sharing model based on a mimic hash mechanism. Specifically, in response to the security risks that may arise from the single hash mechanism, this study innovatively introduces a mimic hash mechanism and proposes two methods for constructing mimic hashes based on Verifiable Random Function (VRF) and Cyber Mimic Defense (CMD) in dedicated Wireless Sensor Networks (WSNs) and open public networks, respectively. Theoretical analysis and experimental results demonstrate that this model effectively solves the problem of trustworthy on-chain storage of sensor data in edge computing environments, enhancing the trustworthiness and security of the data on the chain. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) has been attracting increasing attention in the architecture, engineering, and construction (AEC) industry in recent years. The wide spread of BIM marks the transition from 2D and paper-based archiving to 3D and digital archiving of project information. However, the commonly used centralized managing approaches for BIM data have high risks of data loss and data tampering. With cryptographic algorithms and distributed databases, blockchain has the potential to address the limitations of data loss and trust in conventional BIM management methods. Therefore, this paper proposes a blockchain-based framework for digital archiving of BIM data. In this paper, the axiomatic design approach is utilized to design the archiving framework in order to systematically map the design parameters with the functional requirements and minimize the information contents. The proposed framework contains five modules, including the BIM module, building plan approval (BPA) module, building data simplification (BDS) module, distributed data storage (DDS) module, and digital document verification (DDV) module. Hyperledger Fabric is utilized to develop the blockchain system based on the proposed framework. In the end, a simple BIM model with 240 components and around 11,000 lines of content in the Industry Foundation Classes (IFC) file is taken as an illustrative example to validate the proposed blockchain approach. By storing the BIM model on to the developed blockchain, it is found that each transaction of a blockchain can store over 5000 lines of IFC contents with 0.09 s of uploading latency. The results show that the proposed blockchain-based approach can effectively and securely archive BIM data. [ABSTRACT FROM AUTHOR]

In distributed and cloud database system, large amount of resources are used by complex queries that increase in payment overheads of cloud users. These resource needs can be minimised by evaluating common join operations and caching their results so that they could be applied to the execution of additional queries. In this research, the Query Optimization Technique Using Materialization (QOTUM) system is designed and developed that uses the mechanism of materialized views during query execution at distributed database in cloud environment. Extensive experiments are conducted with the help of standard benchmarks datasets and query workloads in cloud environment. The performance of QOTUM system is studied and evaluated against conventional SQL system based on various performance parameters.

One of the best forms of entertainment today is online multimedia with movies and various other entertainment sources at the tip of our fingers. However, finding a movie from the numerous options available in a movie database to suit a user's taste is a non-trivial task. Most recommendation systems do not consider the user's limitations, such as whether the user has the means to watch the movie suggested by the system. This paper proposes a system to provide recommendations that are platform specific. Two sources of movies have been considered, viz., Netflix and Amazon Prime, and other data sets can be included as well. In addition to providing recommendations, the proposed system helps filter movies based on user-specified criteria. It applies big data analytics for faster execution of both movie recommendation and data filtering and can work with large and distributed movie databases. [ABSTRACT FROM AUTHOR]

The act of gathering, looking over, and analyzing a lot of data to find patterns, insights, and market trends that can help businesses make more effective choices is known as big data analysis (BDA). Quick and effective access to this data allows businesses to be flexible in developing strategies to hold onto their competitive edge. To analyze massive amounts of data quickly through parallel processing, the structure of the Hadoop software employs the MapReduce methodology. Computational solid resources are necessary for BDA, although they are not always available. Developing new clustering techniques that could handle this kind of data processing became crucial. Therefore, in this research, we presented a novel, effective fuzzy-based Improved Multiview Fuzzy C-Means Algorithm (IMV-FCA) to boost the clustering strategy. To summarize, fuzzy-based IMV-FCA clustering presents the ensemble of the MobileNet V2 model, and three-layered stacked Bidirectional LSTM (MVSBiLSTM) to increase computing speed and effectiveness. It also presents a function that calculates the separation among the cluster center and the particular instance, to assist with better clustering. By simulating shared memory space and parallelizing on the framework known as MapReduce on the Hadoop cloud computing platform, the distributed database is utilized to improve the method’s effectiveness while reducing its time complexities. The experimental investigation was conducted on existing approaches, and the proposed approach was analyzed using three standard datasets. While differentiating from existing approaches, the presented approach yields greater performances in terms of various metrics. [ABSTRACT FROM AUTHOR]

With the progress of society, the supply of electricity has become an inevitable choice for the development of all walks of life. The stability, safety, and economic dispatch of the electric energy system directly impact the entire country's economic development, from national defense, military, and even people's daily life, all closely associated with the smooth running of the network. In the aspect of power grid planning and operation, the economic security dispatching problem of power system is a very typical optimization problem. The problem is how to maximize the operating cost of the system under the premise of satisfying the system's load limit and safety and stability. Due to the rapid development of power technology, the research on power system security and economic planning is essential. As an important means to promote China's energy structure adjustment and promote the supply side structural reform, the regional spot electricity market is playing an increasingly important role in building a resource optimization allocation mechanism, improving China's energy resource allocation efficiency, and promoting social and economic development. However, the development of China's regional spot power market is still in its infancy, and the transaction price of the regional spot power market is quite different from that of other countries. It was given these problems, this article discusses the use of particle swarm algorithms for secure and economic scheduling of power systems in the context of securely reading blockchains and distributed data. The research results showed that: The introduction of environmental pollution penalty programs improved the priority dispatch of wind and photovoltaic power when considering environmental penalty programs and backup penalties for wind and photovoltaic power. The setting of the coefficient of the environmental penalty term depended on the designer's emphasis on the priority scheduling of wind power and photovoltaic (PV) power generation (PG), and the standby capacity penalty clause achieved reasonable dispatch and utilization of wind power and PV PG, which reduced the dramatic fluctuations and intermittent volume of the system, and facilitated safe and consistent operation of the network. The research of this paper shows the positive relationship between the blockchain and distributed data security reading algorithm and the clearing method of regional spot power market, and points out a new method for its development. [ABSTRACT FROM AUTHOR]

Students' absence recording is a fundamental daily process that occurs within colleges, often relying on paper forms, which is inefficient when the college administration wants to gather absences from multiple remote departments (sites) and calculate the college's students' absence rates. In response to these challenges, a heterogeneous distributed database system was designed to declare and automate student absences in the colleges using smartphones or laptops and send absences in real-time from inside lecture halls or laboratories in departments to the college administration to generate absence reports. The system employed a client-server model to deliver services to the college and its various departments through the local area network (LAN) and the Internet. MSSQL database tables were designed and fragmented across college sites using horizontal fragmentation technology and then encrypted with the AES265 algorithm. The proposed system used private SQLite or MSSQL databases for the lecture's Android or Windows devices. On the other hand, college administration staff used a secure website built with ASP.NET to access the MySQL database server, which contains the absence table populated from all sites. The system was protected against false absence declarations by utilizing the local server's timing factor, allowing the lecturer only to declare absences on specific days and times. The experimental results showed high reliability and accuracy of the system and high satisfaction among the users about achieving the desired goals. The findings demonstrated a strong preference for the system among the lecturers over other systems that relied on student, biometric, and AI-based devices. At the same time, the college administration staff preferred automatic absence collection using the distributed design of the system over manual collection. [ABSTRACT FROM AUTHOR]

This article presents a secure framework for remote healthcare monitoring in the context of home isolation, thereby addressing the concerns related to untrustworthy client connections to a hospital information system (HIS) within a secure network. Our proposed solution leverages a public blockchain network as a secure distributed database to buffer and transmit patient vital signs. The framework integrates an algorithm for the secure gathering and transmission of vital signs to the Ethereum network. Additionally, we introduce a publish/subscribe paradigm, thus enhancing security using the TLS channel to connect to the blockchain network. An analysis of the maintenance cost of the distributed database underscores the cost-effectiveness of our approach. In conclusion, our framework provides a highly secure and economical solution for remote healthcare monitoring in home isolation scenarios. [ABSTRACT FROM AUTHOR]

Query optimization is one of the key factors affecting the performance of database systems that aim to enact the query execution plan with minimum cost. Particularly in distributed database systems, due to the multiple copies of the data that are stored in different data nodes, resulting in the dramatic increase in the feasible query execution plans for a query statement. Because of the increasing volume of stored data, the cluster size of distributed databases also increases, resulting in poor performance of current query optimization algorithms. In this case, a dynamic perturbation-based artificial bee colony algorithm is proposed to solve the query optimization problem in distributed database systems. The improved artificial bee colony algorithm improves the global search capability by combining the selection, crossover, and mutation operators of the genetic algorithm to overcome the problem of falling into the local optimal solution easily. At the same time, the dynamic perturbation factor is introduced so that the algorithm parameters can be dynamically varied along with the process of iteration as well as the convergence degree of the whole population to improve the convergence efficiency of the algorithm. Finally, comparative experiments conducted to assess the average execution cost of Top-k query plans generated by the algorithms and the convergence speed of algorithms under the conditions of query statements in six different dimension sets. The results demonstrate that the Top-k query plans generated by the proposed method have a lower execution cost and a faster convergence speed, which can effectively improve the query efficiency. However, this method requires more execution time. [ABSTRACT FROM AUTHOR]

Dynamic allocation and reallocation of data fragments in distributed databases, particularly in the context of NoSQL databases, are essential for optimizing performance and resource utilization. This paper explores the application of dynamic fragment allocation and reallocation algorithms in a distributed database environment through a case study focused on university data management. By leveraging a well-defined database schema custom-made for university information management, the study highlights the integration of dynamic allocation strategies to efficiently manage faculties, departments, students, teachers, courses, and timetables. Through this case study, the paper demonstrates the significance of dynamic allocation and reallocation algorithms in enhancing data processing efficiency and optimizing resource utilization within distributed database systems. [ABSTRACT FROM AUTHOR]

Big data analytics (BDA) is a systematic way to analyze and detect various patterns, relationships, and trends in vast amounts of data. Big data analysis and processing require significant effort, techniques, and equipment. The Hadoop framework software uses the MapReduce approach to do large-scale data analysis using parallel processing in order to generate results as soon as possible. Due to the traditional algorithm's longer execution time and difficulty in processing big amounts of data, this is one of the main issues. Clusters are highly correlated inside each other but are not highly correlated with one another. The technique of effectively allocating limited resources is known as an optimization algorithm for clustering. For processing large amounts of data with several dimensions, the conventional optimization approach is insufficient. By using a fuzzy method, this can be prevented. In this paper, we proposed Fuzzy based energy efficient clustering approach to enhance the clustering mechanism. In summary, Fuzzy based energy efficient clustering introduces a function that measures the distance between the cluster center and the instance, which aids in improved clustering, and we then present the MobileNet V2 model to improve efficiency and speed up computation. To enhance the method's performance and reduce its time complexity, the distributed database simulates the shared memory space and parallelizes on the MapReduce framework on the Hadoop cloud computing platform. The proposed approach is evaluated using performance metrics such as Accuracy, Precision, Adjusted Rand Index (ARI), Recall, F1-Score, and Normalized Mutual Information (NMI). The experimental findings indicate that the proposed approach outperforms the existing techniques in terms of clustering accuracy. [ABSTRACT FROM AUTHOR]

Three-dimensional resistivity/chargeability tomography based on distributed data acquisition technology is likely to provide abundant information for mineral exploration. To realize true 3D tomography, establishing transmitter sources with different injection directions and collecting vector signals at receiver points is necessary. We implemented 3D resistivity/ chargeability tomography to search for new ore bodies in the deep and peripheral areas of Huaniushan, China. A distributed data acquisition system was used to form a vector receiver array in the survey area. First, by using the expanding gradient array composed of 11 pairs of transmitter electrodes, we quickly obtained the 3D distributions of the resistivity and chargeability of the whole area. Based on the electrical structure and geological setting, a NE-striking potential area for mineral exploration was determined. Next, a pole–dipole array was employed to depict the locations and shapes of the potential ore bodies in detail. The results showed that the inversion data for the two arrays corresponded well with the known geological setting and that the ore veins controlled by boreholes were located in the low-resistivity and high-chargeability zone. These results provided data for future mineral evaluation. Further research showed that true 3D tomography has obvious advantages over quasi-3D tomography. The expanding gradient array, characterized by a good signal strength and field efficiency, was suitable for the target determination in the early exploration stage. The pole–dipole array with high spatial resolution can be used for detailed investigations. Choosing a reasonable data acquisition scheme is helpful to improve the spatial resolution and economic efficiency. [ABSTRACT FROM AUTHOR]

The main advantage of Vehicular Adhoc Networks being an inherent part of Intelligent Transportation Systems is to ensure the safety and comfort of commuters on the roads. In VANETs, due to the wide open nature of wireless communications, data transmission has been subjected to variety of security and privacy threats which causes disruptions in the network. Hence data has to be authenticated prior to communication. To address this problem enhanced elliptic curve cryptography based certificate-less signature aggregation scheme has been proposed to ensure full privacy preservation and to provide efficient security to the VANETs. Through formal and informal security analysis the proposed scheme has been tested and proven to be secure under suppositions made using Diffie-Hellman assumptions by using Random Oracle Model. The proposed scheme is implemented using MIRACL C + + library and by using Veins Integrated Simulation Framework. The proposed scheme has been analyzed and compared with the existing standards which prove that the proposed scheme is efficient in terms of computation cost and communication costs. The major novelty of the proposed scheme is the use of aggregation and point addition which does not cause delay in verification and also reduces overhead in the road-side units by employing elliptic curve cryptography based distributed data authentication scheme. Hence, the traditional problems of certificate management and key escrow problem is solved by the proposed scheme in an efficient manner. This makes the proposed scheme to be much suitable for high volatile and resource-constrained vanet environments. [ABSTRACT FROM AUTHOR]

The construction of an efficient and practical database management system based on large-scale three-dimensional spatiotemporal databases presents a novel challenge in the field of natural resource information technology. This research, with the objective of supporting the contemporary era of unified natural resource management and building upon the foundation of a national-level three-dimensional spatiotemporal natural resource database, proposes a comprehensive framework for a distributed architecture database management system. Subsequently, the research outlines the system's components, encompassing a service publishing system, online application system, professional management system, and operational supervision system, and designs core functionalities for the integrated management of natural resource elements within a three-dimensional spatiotemporal context. Building upon this framework, the research utilizes cloud-based database software and hardware environments, along with distributed technology pathways, to establish a national-level three-dimensional spatiotemporal database management system for natural resources. Furthermore, it conducts typical applications in the field of natural resources. The results demonstrate that the database management system effectively achieves the integration, representation, and analytical application of large-scale natural resource survey and monitoring data within a three-dimensional spatiotemporal context. It plays a crucial supportive role in the development of a national-level three-dimensional spatiotemporal natural resource database and holds valuable insights for the construction of provincial-level database management systems and other related systems in various domains. [ABSTRACT FROM AUTHOR]

Based on the fast detection of mycotoxins and heavy metals in grain by each storage enterprise of Beijing reserve grain, the real-time migration of monitoring data is realized by adopting the B/S architecture, terminal WEB browser, and using the wireless transmission function of the rapid detection equipment to collect and transmit the data through the encryption algorithm; by using P2MP point-to-multipoint network communication, the data collected by multiple devices online/offline are uploaded through the file uploading system and the relational database extraction tool to achieve synchronous integration of data from multiple sources; by using relational database MySQL, column storage database HBase and distributed file system HDFS, distributed full-text search ElasticSearch and distributed memory database Redis to achieve cloud storage; by using Storm for real-time computation, Streaming for streaming operation, Spark for memory computing, and MapReduce for batch computing to achieve rapid processing of monitoring data; by using artificial intelligence technology and MLlib/Mahout for data mining and modelling to form a spatial and temporal sequence model of Beijing's grain producing areas and credit evaluation model of grain purchasing and marketing enterprises, so as to achieve the dynamic early warning of grain quality and safety and visual expression of the data, and provide early warning judgments to facilitate real-time control by government departments and real-time response to the situation. It promotes real-time control, real-time response and traceability management of grain quality and safety by management departments, enhances healthy competition and the establishment of an integrity system in grain circulation and storage, and provides a scientific basis for government decision-making. [ABSTRACT FROM AUTHOR]

Moodle is a widely used Learning Management System in various educational institutions worldwide. However, frequent reports on internet forums indicate performance degradation when massive simultaneous users access Moodle. One of the most resource-intensive components supporting Moodle is the database, as all user-accessed data is stored in it. This study aims to optimize Moodle’s performance through distributed databases. Distributing the database into multiple database servers allows the database load to be distributed across all the database servers, resulting in an overall improvement in Moodle performance. This study compares the performance of Moodle installed on a single server with that installed on multiple database servers. Various testing parameters are employed to get valid results. Namely, course read, course write, and database performance, utilizing the server performance plugin available in Moodle. This research reveals a performance improvement of 384% in course read, 193% in course write, and 260% in the Moodle database in the multi-server scenario compared to the single-server scenario. This result validates that the database is the most crucial part of Moodle. [ABSTRACT FROM AUTHOR]

Nowadays, many people spend their time on the Internet, and the number of people subscribed to mobile phones is 69.4% of the 5.61 billion population in the world. To handle this situation, we need to implement a high-performance Distributed Cluster System (DCS) in the correct architecture as well. We separated the cluster for each purpose and gave it a unique VLAN. This study uses a mix of methodologies between case study and system development with evaluation after implementation. We observe all aspects of built-in technologies. In this research, monolith spikes us for performance issues, and also, the infrastructure is messy implemented. Event Based System (EBS) helps DCS to absorb high processing tasks in peak situations. EBS can easily lose a couple as needed. Labeling the incoming data assists us in managing inconsistent distributed data in the environment. Our research was evaluated for two weeks. The result is very pleasant, and the requirements in this research were satisfied. [ABSTRACT FROM AUTHOR]

The Journal of Information Systems (JIS) is an academic journal that publishes high-quality research on Accounting Information Systems (AIS). The journal welcomes research articles and commentaries that contribute to AIS or IS/IT-related accounting or auditing research. JIS aims to engage a broad audience, including researchers from various disciplines and professionals in accounting-related services and technology firms. The journal covers a wide range of topics, including artificial intelligence, cybersecurity, data analytics, and more. JIS follows a rigorous review process and encourages concise and clear communication of research findings. [Extracted from the article]

The applications being developed within the U.S. Exascale Computing Project (ECP) to run on imminent Exascale computers will generate scientific results with unprecedented fidelity and record turn-around time. Many of these codes are based on particle-mesh methods and use advanced algorithms, especially dynamic load-balancing and mesh-refinement, to achieve high performance on Exascale machines. Yet, as such algorithms improve parallel application efficiency, they raise new challenges for I/O logic due to their irregular and dynamic data distributions. Thus, while the enormous data rates of Exascale simulations already challenge existing file system write strategies, the need for efficient read and processing of generated data introduces additional constraints on the data layout strategies that can be used when writing data to secondary storage. We review these I/O challenges and introduce two online data layout reorganization approaches for achieving good tradeoffs between read and write performance. We demonstrate the benefits of using these two approaches for the ECP particle-in-cell simulation WarpX, which serves as a motif for a large class of important Exascale applications. We show that by understanding application I/O patterns and carefully designing data layouts we can increase read performance by more than 80 percent.

A Data Warehouse (DW) is a centralized database that stores large volumes of historical data for analysis and reporting. In a world where enterprise data grows exponentially, new architectures are being investigated to overcome the deficiencies of traditional Database Management Systems (DBMSs), driving a shift towards more modern, cloud-based solutions that provide resources such as distributed processing, columnar storage, and horizontal scalability without the overhead of physical hardware management, i.e., a Database as a Service (DBaaS). Choosing the appropriate class of DBMS is a critical decision for organizations, and there are important differences that impact data volume and query performance (e.g., architecture, data models, and storage) to support analytics in a distributed cloud environment efficiently. In this sense, we carry out an experimental evaluation to analyze the performance of several DBaaS and the impact of data modeling, specifically the usage of a partially normalized Star Schema and a fully denormalized Flat Table Schema, to further comprehend their behavior in different configurations and designs in terms of data schema, storage form, memory availability, and cluster size. The analysis is done in two volumes of data generated by a well-established benchmark, comparing the performance of the DW in terms of average execution time, memory usage, data volume, and loading time. Our results provide guidelines for efficient DW design, showing, for example, that the denormalization of the schema does not guarantee improved performance, as solutions performed differently depending on its architecture. We also show that a Hybrid Processing (HTAP) NewSQL solution can outperform solutions that support only Online Analytical Processing (OLAP) in terms of overall execution time, but that the performance of each query is deeply influenced by its selectivity and by the number of join functions.