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Today, the confidentiality and security of patient medical records is of great importance. This study proposes a scheme that aims to store, access, and share medical data without risking security vulnerabilities and attacks. In the proposed scheme, medical data are divided into sensitive and non-sensitive patient data. Three consensus mechanisms (Proof of Work (PoW), Proof of Stake (PoS), and Proof of Authority (PoA)) are implemented and compared to each other in terms of performance. The performance analysis of the proposed scheme shows that PoW provides approximately 21% and 9% better results than PoA and PoS for non-sensitive data in terms of block size, respectively. It also provides approximately 23% and 32% better results than PoA and PoS for sensitive data in terms of memory usage, respectively. The security analysis demonstrates that it has many security features and is strong against man-in-the-middle, impersonation, and modification attacks. [ABSTRACT FROM AUTHOR]

For the time being, many government or private organizations for recruitment of staff or educational institutions moving towards online based tests. The online examination system is a software application used for conducting examination using computer systems. It helps to the recruitment agency or any govt. or private organizations for conducting any job recruitment examinations transparently. Due to this system results are processed without delay and efficiently evaluated to assess the candidates abilities. But the biggest challenge for online examination system is data integrity, security and privacy. The current system is resolving the privacy issue by providing authentication credentials such as user name, password to the candidates. So that only authorized users with proper credentials can login to the system and attempt the exam. But the data confidentiality and integrity are biggest challenges for the system. As the data stored in system database is in plain text format, hence it may be modified or misused by the internal staff of the organization. This paper presents the frame work for secure storage and management of candidates data using encryption scheme, distributed databases in cloud database system. The proposed framework enhances the data confidentiality, integrity and avoids any cheating by internal staff or third party institutions. This paper conducts experimental work on proposed framework and analyses the results of the system. [ABSTRACT FROM AUTHOR]

With the continuous development of network technology, the scale of data has shown explosive growth, which leads gradually to replacing traditional single machine databases with distributed databases. Distributed databases solve large-scale data storage problems through collaborative work among nodes, but due to increased communication costs between nodes, its query efficiency is not as good as a standalone database. In a distributed architecture, the data of storage nodes is only used as redundancy for multiple backups, providing data recovery in case of system failure, and it is not utilized to improve query efficiency. In response to the above issues, this article proposes an operator parallel optimization strategy for distributed databases. By splitting key physical operators, the split sub requests are evenly distributed to multiple nodes in the storage layer, which are processed in parallel by multiple nodes, thereby reducing query response time. The above strategy has been applied on distributed database CBase, and experiments have shown that the parallel optimization strategy proposed in this paper can significantly shorten SQL request query time and improve system resource utilization.

With the continuous development of information technology, distributed database has become a research hotspot. Due to the limit support for SQL and defects in transaction processing and consistency of distributed databases based on NoSQL architecture, NewSQL databases based on LSM-Tree become gradually the mainstream of applications, such as TiDB and OceanBase. The distributed LSM-Tree storage architecture divides the data into baseline data and incremental data. Through the compaction operation, the incremental data of different partitions and the baseline data are continuously merged and stored on the disk, thereby reducing memory pressure. However, compaction will occupy a large amount of system resources and seriously affect system availability. This paper proposes an asynchronous compaction mechanism based on LSM-Tree architecture. By subdividing the compaction process, the data merging is asynchronous, which effectively shortens the time for a single compaction operation. Experiments show that the asynchronous compaction mechanism proposed in this paper can significantly shorten the data merging time and improve the robustness and usability of the system in high-frequency writing scenarios.

Network-based intrusion detection systems (NIDS) are perceptively distributed devices within computer networks. They aim to examine traffic passing through the network on which they are installed passively. The database is the most vital part of network intrusion detection systems, as all the data converted information from the NIDS needs to be saved in a patterned structured manner. Understanding the usability of several available types of databases like central databases, Distributed databases, operational databases, etc., it is on the developer's end to choose the most comprehensive one. Data transformation and performance speed are essential features that a stable database can handle. In this paper, we have analyzed the performance of multiple databases to find out the proficient way that favors NIDS optimization. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile payment, the traditional centralized database used by the financial core business is confronted with challenges in terms of performance and availability. For this reason, some researchers propose a distributed database solution based on the architecture of separation of computing and storage, and build a physically decentralized and logically centralized distributed database management system through network connection. Global index is an important method for distributed database to improve query efficiency, but current global indexing is mainly achieved through the use of synchronization mechanisms in traditional networks.Under the typical transaction and batch import data scenarios of financial data management, this mechanism faces the problems that need to be solved urgently, such as small number of indices in a single table, reduced throughput, and jittery transaction response time. Therefore, an asynchronous global index architecture of distributed database is proposed. This mechanism is applied to adding MQ message queue and distributed cache, and uses RDMA network to realize an asynchronous global index to meet the demands of the real financial scenarios. Through sufficient comparative experiments with Oracle and CockroachDB, experimental results show that the performance is improved by more than 60% compared with the existing methods, and the demand for system resources is reduced by more than 20% in the batch import and transaction of financial core business data.

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]

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]

In order to reduce the resource occupancy and retrieval efficiency of geological drilling databases, this study proposes a distributed horizontal expansion method for query optimization of geological drilling databases by constructing a comprehensive geological data subtree, analyzing the characteristics of distributed databases and elements in geological databases, and quickly retrieving data resources based on element attributes. In addition, this study has designed a method to horizontally extend the database designed for drilling holes using a multi-constraint model in order to achieve extension optimization of the distributed geological drilling database. Experiments are conducted to verify the performance and applicability of the proposed method. The experiment shows that when the geological data capacity is 80 GB, the capacity level of the geological database can be extended to 41 × 105TB using the method proposed in this study. The retrieval efficiency is higher than 89% and the resource occupancy rate is lower than 12% after the horizontal expansion of the database. By using this research method, the horizontal expansion of the geological drilling database is more effective, and can effectively reduce the resource occupancy rate and retrieval efficiency of the geological drilling databases. This has value significance for geological drilling with efficiency improvement and development.

Mobile Cloud Computing is one of the most prominent future mobile technology infrastructures, because it accumulates the benefits of mobile computing and cloud computing, and provides optimized services for users. In a distributed database system in the cloud, the connections necessary for a query layout may be stored in multiple sites, increasing the number of equivalent designs possible in the search for an optimal query implementation plan. However, a thorough search of all possible designs in such a large search space is not computationally rational. In this study, our goal is to identify a cost model consisting of a multi-objective function with variable (and possibly conflicting) QoS parameters to solve the query optimization problem in inhomogeneous cloud databases (in terms of pricing models) and mobile. Then, we propose a new strategy for the optimization of queries in these environments using the Learning Based Optimization (TLBO) algorithm. Finally, the results are evaluated in the CloudSim environment and compared with genetic optimization and ant colony optimization (ACO). [ABSTRACT FROM AUTHOR]

The prevailing education management systems in colleges and universities are antiquated, adversely impacting not only the administrative efficiency but also the formulation of faculty teaching plans and class schedules, as well as the management of student electives and credit accrual. This paper presents an innovation and optimization of the existing system, wherein education management processes are categorized into seven distinct modules. It delineates the flow and integration of heterogeneous data sources within the system, leveraging heterogeneous database technology as the foundational framework. Subsequently, we redesign heterogeneous computation scheduling and adaptation, enhancing distributed database allocation and query algorithms. The culmination of this research is the development of a novel pathway for the education management information system in higher education institutions. Upon empirical testing of the system proposed in this paper, we explore its practical implications through IPA analysis. User satisfaction is notably high in areas such as A4 invigilation information, A13 subjective grading of routine assessments, A14 querying and feedback on grades, and A15 remediation of failed courses, with performance ratings of 4.77008, 4.73981, 4.8961, and 4.86558 respectively. These indices predominantly reside in the “continue to maintain” and “oversupply” zones, indicating the substantial efficacy of the innovative college management information system introduced herein. This study significantly contributes to the standardization and intellectualization of college administrative processes, proposing new paradigms and methodologies for the integration of educational management and information technology within tertiary institutions.

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Performance optimization of distributed transactions is one of the hottest topics in academic and industry community. Concurrency control technology based on two-phase locking can guarantee the correctness of concurrent transaction scheduling, thus it is widely implemented in mainstream commercial and open source distributed data-bases. However, existing works show that the performance bottleneck of distributed transaction processing tech-nology, based on traditional TCP/IP protocol and Share-Nothing architecture, comes from low CPU utilization of transaction scheduler and high network latency between transaction scheduler and storage nodes. To address the above two problems, an optimization technology of two-phase locking (2PL) concurrency control based on new hardware RDMA (remote direct memory access) is proposed to improve the performance of distributed transactions by using RDMA??s characteristics, e.g., high bandwidth, low latency and kernel bypass (eliminating the CPU overhead caused by TCP/IP protocol stack). The main technical contributions of this paper include the rewriting and opti-mization of network communication operators based on RDMA, and the atomicity guarantee when using RDMA one-sidedly to acquire and release read-write locks. Experimental results based on YCSB benchmark show that: one-sided mutex lock algorithm and one-sided mutex/shared lock algorithm have relative advantages under low and high contention workload respectively; 2PL concurrency control protocols with RDMA achieve up to 5.3x and 10.6x performance gain for NO WAIT and WAIT DIE respectively under high contention workload.

In order to promote the intelligent development of online education and optimize the status quo of online education, an online education method based on distributed multi-layer data processing technology is proposed. The method integrates the database information and builds the BTREE index by calculating the distributed database resource information, the initial information sequence, the main information set and the trade-off coefficient of information attributes. The bidirectional index method is used to extract the core data segment of integrated data and establish the time series table. The schedule is used to constrain the data twice, and the coincidence degree of the initial data transmission path is compared with the actual path parameters after evaluation, so as to realize the automatic matching of the data transmission path. The experimental results show that compared with the traditional transmission path matching method, the designed automatic matching method improves the matching accuracy of the complete data transmission path by 24% and the broken data transmission path by 19%, which has practical advantages. It is proved that this method can promote the precise personality teaching of online education, so as to achieve the effect of optimizing online education. It can also provide reference for the intelligent development of other online education. [ABSTRACT FROM AUTHOR]

The cost and the speedness of data access on a query depend on the numbers of attributes. In fact the query function will search for data from various attributes in the database table, whereas not all the attributes are needed on the accessed tables. To avoid the not necessary attributes, Vertical Fragmentation methods can be used. The purpose of this research aims to compare the vertical fragmentation of database tables using Bond Energy algorithm and Graph-Based Vertical Partitioning algorithm to create a distributed database. The results of both algorithms will determine which attributes will be avoided. Then, the results of the two algorithms are compared and evaluated using Partition Evaluator to show the amount of the access data costs that are used, so to find which is the better algorithm to be applied. Based on some experimental results, GBVP algorithm showed a better performance with higher Partition Evaluator value respectively at 4,98; 10,41; 17,95; 4,64; and 14,07 as well as the lower value of execution time respectively at 0,003; 0,002; 0,002; 0,002; 0,003 and 0,003. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

Following the COVID epidemic, the healthcare sector faced numerous issues as telehealth became more prevalent and the necessity for a safe and efficient healthcare record system became critical. Many issues plague the healthcare industry today, including security, trust, data availability, and drug traceability. Blockchain technology is a relatively new technology that has demonstrated its effectiveness in a variety of industries, including finance, banking, bitcoin, and healthcare. This research investigates the impact of blockchain on the trustworthiness of healthcare. The authors adopt a descriptive-analytical methodology. The trustworthiness of the healthcare sector is considered a dependent variable. However, blockchain technology is considered an independent variable. Three dimensions of dependent variables were explored: integrity, confidence, and reliability. For studying this impact, a questionnaire of 25 items was created and distributed to the stakeholders in the healthcare sector in the north of Lebanon. The collected answers were analyzed using the SPSS application and statistical tools. The results verify that the use of blockchain technology has a great impact on healthcare trustworthiness with its three dimensions (integrity, confidence, and reliability). [ABSTRACT FROM AUTHOR]

Technologies like Internet of Things (IoT), cloud, artificial intelligence, blockchain etc. have become a perceptible part of our lives resulting in the generation of enormous amounts of data. Consequently, the systems used for storage and processing of this data are required to be scalable for handling the huge volumes of data. A shared, multitenant system such as a cloud-based storage-as-a-service provides scalability of storage as well as economics of sharing. However, there is a risk of data tampering when multiple tenants work in a shared environment. The benefits of a multitenant solution can be leveraged only if tenants' data is isolated from each other. Further, prevention of data tampering from malicious tenant nodes is also required. Therefore, the paper proposes the use of a private blockchain for an implementation of a multi-tenant-based storage system. The objective is to develop a scalable system where tenants' data is not at a risk of tampering. The efficacy of the proposed system has been demonstrated with synthetic data of multiple tenants using a Software as a Service (SaaS) healthcare application. [ABSTRACT FROM AUTHOR]

A secure concurrency control algorithms must be devoid of covert channels originating from data conflicts between transactions, in addition to ensuring database integrity. Current secure concurrency control algorithms mistreat transactions with higher access classes. Because it better solves the priority inversion problem. A widely used concurrency control algorithm is the two-phase locking with high priority (2 PL-HP). The inclusion of cyclic restart, deadlock, unnecessary abort, pseudo-priority inversion, and starvation, on the other hand, may reduce its performance. In this paper, a new enhanced of secure two-phase locking protocol with multilevel secure distributed database systems is presented, the idea of enhanced secure Two-Phase locking protocol by adding the security level to assign the priority to the transaction. The enhanced add a secure manager to decide the level of the security and give the instruction to process the transaction in real lock with data x/virtual lock with version data x0 or decided to Abort the transaction based on the security level. The suggested study intends to reduce transaction miss rate by addressing the following issues: deadlock by splitting the execution stage into locking and processing phases, it reduces unnecessary cycle restart, transaction aborts by checking the security priority level (high, medium, low), and generating virtual lock and versions data. Finally, simulation performance confirms that improvement in transactions miss's percent and reduction in transactions' rollbacks compared to other secure concurrency control.

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cloud technology is the key technology to improve the flexibility and scalability of distributed system. The introduction of cloud technology into avionics system can reduce the product R&D cycle and cost, but first, it is necessary to design a cloud model that meets the needs of airborne environment. Based on the analysis of the service model structure of commercial cloud, combined with the relevant standards in the industry, a new avionics cloud model was proposed. A hierarchical cloud model architecture was established, including physical resource layer. Local resource management layer, resource virtual layer, platform management layer and application function layer. The virtualization technology was used to establish resource virtual model and isolate software app from hardware resources and software environment. The distributed data dynamic publishing and subscription was used to improve the flexibility of application deployment. The distributed resource management was used to realize resource pool management, health monitoring and fault management. The distributed storage model was used to provide distributed data and file sharing capabilities. Following industry standards and providing service-oriented avionics cloud computing model for application software has certain guiding significance for engineering practice. [ABSTRACT FROM AUTHOR]

Subquery is widely used in database. It can be divided into related subquery and non-related subquery according to whether it is dependent on the table of the parent query. For related subqueries, it is necessary to take a tuple from the parent query before executing the subquery, that is, the content of the subquery needs to be repeatedly operated. Disk access costs of this strategy is very big, in the distributed database, because of data communication overhead, in the parent query yuan set is too low efficiency, therefore, for the class sub queries, on the basis of the optimization of the existing query strategy, combining with the characteristics of distributed database, put forward by the subquery on to join queries, eliminate redundant clauses in the subquery, eliminate accumulation function method based on distributed database query optimization strategy, and the effectiveness of the present optimization strategy is verified by experiment.

The article offers information on artificial-intelligence assisted dynamic modeling for data management in distributed system. Topics discussed include challenges in managing data and its protection which increases need for efficient information security management for systems, it discusses artificial intelligence-assisted dynamic modeling (AI-DM) used for data management in a distributive system; and that the suggested model provides users with links from anywhere to the distributed database.

Open-source technologies enable communication channels between web platforms and innovative architectures to provide reliable data distribution, in which healthcare applications can particularly benefit from them. This work presents a communication channel design to improve the user experience about telemedicine apps, especially when patients are in remote locations while assuring their information using an innovative approach. The general purpose is to avoid users having to physically go to medical facilities by the correct data management related to their appointments and medical history. By preventing the attendance to healthcare facilities, patients do not expose themselves unnecessarily to viruses and bacteria. Therefore, this research includes a data communications model based on the FIWARE platform and cloud technologies for reliable user medical information distribution. The prototype is developed based on open-source technologies and registered the evaluation of different performance metrics that included cases scenarios in which administrators of healthcare centers configured options according to the availability of assets and informatics resources. The results show the effectiveness of the communication model under realistic conditions for encouraging the acceptance of telemedicine alternatives, especially when patients and medical staff present limitations regarding mobility. [ABSTRACT FROM AUTHOR]

Data processing can be roughly divided into two categories, online transaction processing OLTP(on-line transaction processing) and online analytical processing OLAP(on-line analytical processing). OLTP is the main application of traditional relational databases, and it is some basic daily transaction processing, such as bank pipeline transactions and so on. OLAP is the main application of the data warehouse system, it supports some more complex data analysis operations, focuses on decision support, and provides popular and intuitive analysis results. As the amount of data processed by enterprises continues to increase, distributed databases have gradually replaced stand-alone databases and become the mainstream of applications. However, the current business supported by distributed databases is mainly based on OLTP applications, lacking OLAP implementation. This paper proposes an implementation method of HTAP for distributed database CBase, which provides an implementation method of OLAP analysis for CBase, and can easily deal with data analysis of large amounts of data.

Copyright of Radio Electronics, Computer Science, Control is the property of Zaporizhzhia National Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Blockchain is today one of the main technologies of the digital economy, which in a general sense is a distributed database, such as an accounting book that stores information about all transactions performed by its participants. The article presents data on the status and expected results of blockchain development in the world and in Ukraine as well, and illustrates that the implementation of the blockchain technology is now occurring in various areas, one of the most promising of which is accounting and auditing. It is emphasized that domestic accounting and auditing are based on outdated methods and techniques, and the blockchain technology, in the context of its key properties, can significantly transform their capabilities, improve the efficiency and innovativeness of such accounting and auditing. Particular attention is focused on the triple entry concept as a basic component of the blockchain technology in the accounting context. Among the most promising areas of blockchain implementation are accounting of accounts receivable and accounts payable and their repayment, assessment and payment of taxes, operational accounting and reporting, as well as work with documents and their storage. The advantages and disadvantages of the blockchain technology use in accounting and auditing practice are considered. It is established that on the level with the undeniable advantages of blockchain (saving resources, ensuring the reliability and transparency of data, the ability to carry out rapid and automated real-time audit, simplifying transactions, minimizing accounting errors, etc.), critical evaluation of all the disadvantages associated with reliability, speed, security and scalability of this technology has to be done. There should not be ignored the fact that in order to implement the blockchain, it is necessary to make changes in the organizational culture of an enterprise, change the way of employees’ thinking, modify business processes, which require additional time and financial costs.

A loosely coupled distributed system enables interoperability in healthcare structures and can be used to create flexible architecture. The key limitation in adopting electronic health (e-Health) services in developing countries is the health data interoperability problem. Most developing countries are being confronted with the utilization of e-Health systems for their health care services. This study aims to develop a prospective e-Health system for developing countries to improve the effectiveness of health care services. This paper presents an enhanced e-Health framework using distributed memory and heterogeneous data handling mechanisms with loose coupling. The distributed e-Health has been designed using e-Health standards that are compatible with health level 7 (HL7) and can facilitate interoperability among health information systems. The Message Passing Interface (MPI) has been implemented to store regular backups of heterogeneous health data. The HL7 standards can also handle security threats. Data interchange from one e-Health system server to another has been conducted based on the experimental results. The distributed database has been made accessible from a remote server by using loosely coupled technology. It also provided the e-Health data recovery features and handled an extensive database through a distributed environment. According to the study, the developed system can deliver a low-cost service to the intended developing-country community effectively.

A secure concurrency control algorithms must be devoid of covert channels originating from data conflicts between transactions, in addition to ensuring database integrity. Current secure concurrency control algorithms mistreat transactions with higher access classes. Because it better solves the priority inversion problem. A widely used concurrency control algorithm is the two-phase locking with high priority (2 PL-HP). The inclusion of cyclic restart, deadlock, unnecessary abort, pseudo-priority inversion, and starvation, on the other hand, may reduce its performance. In this paper, a new enhanced of secure two-phase locking protocol with multilevel secure distributed database systems is presented, the idea of enhanced secure Two-Phase locking protocol by adding the security level to assign the priority to the transaction. The enhanced add a secure manager to decide the level of the security and give the instruction to process the transaction in real lock with data x/virtual lock with version data x0 or decided to Abort the transaction based on the security level. The suggested study intends to reduce transaction miss rate by addressing the following issues: deadlock by splitting the execution stage into locking and processing phases, it reduces unnecessary cycle restart, transaction aborts by checking the security priority level (high, medium, low), and generating virtual lock and versions data. Finally, simulation performance confirms that improvement in transactions miss's percent and reduction in transactions' rollbacks compared to other secure concurrency control. [ABSTRACT FROM AUTHOR]

We propose a simple model for distributed query processing based on the concept of a distributed array. Such an array has fields of some data type whose values can be stored on different machines. It offers operations to manipulate all fields in parallel within the distributed algebra. The arrays considered are one-dimensional and just serve to model a partitioned and distributed data set. Distributed arrays rest on a given set of data types and operations called the basic algebra implemented by some piece of software called the basic engine. It provides a complete environment for query processing on a single machine. We assume this environment is extensible by types and operations. Operations on distributed arrays are implemented by one basic engine called the master which controls a set of basic engines called the workers. It maps operations on distributed arrays to the respective operations on their fields executed by workers. The distributed algebra is completely generic: any type or operation added in the extensible basic engine will be immediately available for distributed query processing. To demonstrate the use of the distributed algebra as a language for distributed query processing, we describe a fairly complex algorithm for distributed density-based similarity clustering. The algorithm is a novel contribution by itself. Its complete implementation is shown in terms of the distributed algebra and the basic algebra. As a basic engine the Secondo system is used, a rich environment for extensible query processing, providing useful tools such as main memory M-trees, graphs, or a DBScan implementation. [ABSTRACT FROM AUTHOR]

Enormous amounts of unstructured data such as images, videos, emails, sensors' data and documents of multiple types are being generated daily by varied applications. Apart from the challenges related to collection or processing of this data, its efficient storage is also a significant challenge since this data do not conform to any predefined storage model. Therefore, any enterprise dealing with huge unstructured data requires a scalable storage system that can provide data durability and availability at a low cost. The paper proposes a tenant-centric approach to develop an object-based software defined storage system for SaaS multi-tenant applications. We present TOSDS (Tenant-centric Object-based Software Defined Storage), a system that can efficiently meet the storage requirements of users or tenants with diverse needs who are using a multitenant SaaS application. The experimental verification of TOSDS illustrates its effectiveness in storage utilization as well as tenant isolation. [ABSTRACT FROM AUTHOR]

Blockchain is one of the technologies that does not need central authority. With its distributed structure, it is one of the most popular technologies in many fields, including the financial world, in recent years. Academic research on blockchain technology, which begins to hear a new field of application almost every day, is limited. In our country, there are not enough studies both in practice and academically. In the light of these data, an evaluation was made between the blockchain technology and the central database and distributed database systems in this article. As a result of the researches made in order to obtain the ranking, the prominent criteria in the system structures were determined. The weights of the determined criteria were obtained by the Analytical Hierarchy Process (AHP) method, which is one of the multi-criteria decision making (MCDM) methods. By solving the PROMETHEE method with the determined weights, blockchain technology, distributed database, central database sorting results were reached. [ABSTRACT FROM AUTHOR]

Read separating from write is a strategy that NewSQL adopts to incorporate the advantages of traditional relation database and NoSQL database. Under this architecture, baseline data is split into multiple partitions stored at distributed physical nodes, while delta data is stored at single transaction node. For reducing the pressure of transaction node and improving the query performance, delta data needs to be synchronized into storage nodes. The current strategies trigger the procedure of data synchronization per partition, meaning that unchanged partitions will also participate in data synchronization, which consumes extra network cost, local IO and space resources. For improving the efficiency of data synchronization meanwhile mitigating space utilization, the fine-grained data synchronization strategy is proposed, whose main idea includes that fine-grained logical partitions upon original coarse-grained partitions is established, providing more correct synchronized unit; the delta data sensing strategy is introduced, which records the mapping between changed partitions and its delta data; instead of partition driven, the data synchronization through the delta-broadcasting mechanism is driven, constraining that only changed partitions can participate in data synchronization. The fine-grained data synchronization strategy on Oceanbase is implemented, which is a distributed database with read separating from write, and the results show that our strategy is better than other strategies in efficiency of data synchronizing and space utilization.

Blockchain is today one of the main technologies of the digital economy, which in a general sense is a distributed database, such as an accounting book that stores information about all transactions performed by its participants. The article presents data on the status and expected results of blockchain development in the world and in Ukraine as well, and illustrates that the implementation of the blockchain technology is now occurring in various areas, one of the most promising of which is accounting and auditing. It is emphasized that domestic accounting and auditing are based on outdated methods and techniques, and the blockchain technology, in the context of its key properties, can significantly transform their capabilities, improve the efficiency and innovativeness of such accounting and auditing. Particular attention is focused on the triple entry concept as a basic component of the blockchain technology in the accounting context. Among the most promising areas of blockchain implementation are accounting of accounts receivable and accounts payable and their repayment, assessment and payment of taxes, operational accounting and reporting, as well as work with documents and their storage. The advantages and disadvantages of the blockchain technology use in accounting and auditing practice are considered. It is established that on the level with the undeniable advantages of blockchain (saving resources, ensuring the reliability and transparency of data, the ability to carry out rapid and automated real-time audit, simplifying transactions, minimizing accounting errors, etc.), critical evaluation of all the disadvantages associated with reliability, speed, security and scalability of this technology has to be done. There should not be ignored the fact that in order to implement the blockchain, it is necessary to make changes in the organizational culture of an enterprise, change the way of employees’ thinking, modify business processes, which require additional time and financial costs.

A method has been developed for finding the shortest Hamiltonian path in an arbitrary graph based on the rank approach, which provides high efficiency and a significant reduction in the error in solving the problem of organizing the process of managing multiple transactions and queries when they are implemented in network databases. In many cases, existing solutions do not provide the necessary results in terms of access time and accuracy of the found solution. Using the developed method allows minimizing the idle time of computing devices, reducing the volume and time of data transfer from one device to another, increases overall scalability, minimizes data access time, etc. An important advantage of the proposed method is to reduce the number of elementary operations and the number of vectors being processed the queue of the operations of the request, which leads to a significant reduction in time to implement the procedures for the formation of echer di operations in the requests. Methods of graph theory were used in this paper. The effectiveness of the task solution was evaluated using a systems approach, system analysis and the theory of operations research. Processing of experimental data obtained during the work was carried out in accordance with the provisions of mathematical statistics.

Today, databases are an integral part of most modern applications designed to store large amounts of data and to request from many users. To solve business problems in such conditions, databases are scaled, often horizontally on several physical servers using replication technology. At the same time, many business operations require the implementation of transactional compliance with ACID properties. For relational databases that traditionally support ACID transactions, horizontal scaling is not always effective due to the limitations of the relational model itself. Therefore, there is an applied problem of efficient implementation of ACID transactions for horizontally distributed databases. The subject matter of the study is the methods of implementing ACID transactions in distributed databases, created by replication technology. The goal of the work is to increase the efficiency of ACID transaction implementation for horizontally distributed databases. The work is devoted to solving the following tasks: analysis and selection of the most relevant methods of implementation of distributed ACID transactions; planning and experimental research of methods for implementing ACID transactions by using of NoSQL DBMS MongoDB and NewSQL DBMS VoltDB as an example; measurements of metrics of productivity of use of these methods and formation of the recommendation concerning their effective use. The following methods are used: system analysis; relational databases design; methods for evaluating database performance. The following results were obtained: experimental measurements of the execution time of typical distributed transactions for the subject area of e-commerce, as well as measurements of the number of resources required for their execution; revealed trends in the performance of such transactions, formed recommendations for the methods studied. The obtained results allowed to make functions of dependence of the considered metrics on loading parameters. Conclusions: the strengths and weaknesses of the implementation of distributed ACID transactions using MongoDB and VoltDB were identified. Practical recommendations for the effective use of these systems for different types of applications, taking into account the resources consumed and the types of requests.

Complex scientific experiments from various domains are typically modeled as workflows and executed on large-scale machines using a Parallel Workflow Management System (WMS). Since such executions usually last for hours or days, some WMSs provide user steering support, i.e., they allow users to run data analyses and, depending on the results, adapt the workflows at runtime. A challenge in the parallel execution control design is to manage workflow data for efficient executions while enabling user steering support. Data access for high scalability is typically transaction-oriented, while for data analysis, it is online analytical-oriented so that managing such hybrid workloads makes the challenge even harder. In this work, we present SchalaDB, an architecture with a set of design principles and techniques based on distributed in-memory data management for efficient workflow execution control and user steering. We propose a distributed data design for scalable workflow task scheduling and high availability driven by a parallel and distributed in-memory DBMS. To evaluate our proposal, we develop d-Chiron, a WMS designed according to SchalaDB’s principles. We carry out an extensive experimental evaluation on an HPC cluster with up to 960 computing cores. Among other analyses, we show that even when running data analyses for user steering, SchalaDB’s overhead is negligible for workloads composed of hundreds of concurrent tasks on shared data. Our results encourage workflow engine developers to follow a parallel and distributed data-oriented approach not only for scheduling and monitoring but also for user steering.

In this article we propose the practical implementation of a distributed database using the SQL query language. Our example consists in presenting a didactic application that can be used in the management of books in a student library. [ABSTRACT FROM AUTHOR]

Motivated by a recent conjecture concerning the expressiveness of declarative networking, we propose a formal computation model for "eventually consistent" distributed querying, based on relational transducers. A tight link has been conjectured between coordination-freeness of computations, and monotonicity of the queries expressed by such computations. Indeed, we propose a formal definition of coordination-freeness and confirm that the class of monotone queries is captured by coordination-free transducer networks. Coordination-freeness is a semantic property, but the syntactic class of "oblivious" transducers we define also captures the same class of monotone queries. Transducer networks that are not coordination-free are much more powerful. [ABSTRACT FROM AUTHOR]

Processing and storage of a large amount of information is one of the difficult and interesting tasks at the moment. The performance of the system as a whole depends on how well the performance and reliability of the database are implemented. One of the most difficult aspects of this issue is the handling of a database query and its efficient execution. In this paper we consider modern methods and models of query processing in databases. We offer an algorithm to service the request of users, which involves the use of parallel technologies in the exchange of information with the nodes of a distributed database and a dictionary, and also allows to increase the query execution time, which in turn will increase the speed of the system as a whole. We bring the current at the moment the technology of storing large amounts of data: distributed and parallel databases, MapReduce.

Summary: Thus far, medical data have been used to serve the need of people's healthcare. In some countries, in recent years, a lot of hospitals have altered the conventional paper medical records into electronic health records. The data in these records grow continuously in real time, which generates a large number of medical data available for physicians, researchers, and patients in need. Systems of electronic health records share a common feature that they are all constituted from open sources for Big Data with distributed structure in order to collect, store, exploit, and use medical data to track down, prevent, treat human's diseases, and even forecast dangerous epidemics. [ABSTRACT FROM AUTHOR]

In an ideal world, a society is governed by laws and any member of the society that breaks these laws faces measurable consequences. In such a case, a citizen is expected to report any violation of law to the concerned authorities. Illegal activities are an issue for every nation, and the majority of crimes committed go unreported due to fear and inconvenience of travel. This causes a need for an alternative investigative system, with non-interactive capabilities. In this work, we focus on developing a system for filing complaints online, along with a real time map for viewing hotspots and understanding crime dynamics in a neighborhood. The application comprises four functional modules, user verification, file a complaint with details and location, distributed database for storing the details of the complaint, and a live map of reported events in a web dashboard. This system paves the way for civilians to report crime incidents with the location pinpointed on a real time map along with detailed information regarding the same. Keeping in mind the restricted outdoor travel and exposure in situations of global pandemics such as the COVID-19, the proposed system could be a great alternative for offline crime reporting since crimes like domestic violence have been surfacing. [ABSTRACT FROM AUTHOR]

Collecting statistics is a time- and resource-consuming operation in database systems. It is even more challenging to efficiently collect statistics without affecting system performance, meanwhile keeping correctness in distributed database. Traditional strategies usually consider one dimension during collecting statistics, which is lack of adaptiveness. In this paper, we propose an adaptive strategy for statistics collecting(ASC), which well balances collecting efficiency, correctness of statistics and effect to system performance. We formally define the procedure of collecting statistics and abstract the relationships among collecting efficiency, correctness of statistics and effect to system performance, and introduce an elastic structure(ESI) storing necessary information generated during proceeding our strategy. ASC can pick appropriate time to trigger collecting action and filter unnecessary tasks, meanwhile reasonably allocating collecting tasks to appropriate executing locations with right executing models through the information stored at ESI. We implement and evaluate our strategy in a distributed database. Experiments show that our solutions generally improve the efficiency and correctness of collecting statistics, moreover, reduce the negative effect to system performance comparing with other strategies. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy and efficiency of the clustering mining algorithm, this paper focuses on the clustering mining algorithm for large data. Firstly, the traditional clustering mining algorithm is improved to improve the accuracy, and then the improved clustering algorithm is parallelized to improve the efficiency. In order to improve the accuracy of clustering, an incremental K-means clustering algorithm based on density is proposed on the basis of K-means algorithm. Firstly, the density of data points is calculated, and each basic cluster is composed of the center points whose density is not less than the given threshold and the points within the density range. Then, the basic cluster is merged according to the distance between the two cluster centers. Finally, the points that are not divided into any cluster are divided into the clusters nearest to them. In order to improve the efficiency of the algorithm and reduce the time complexity of the algorithm, the distributed database was used to simulate the shared memory space and parallelize the algorithm on the Hadoop platform of cloud computing. The simulation results show that the clustering accuracy of the proposed algorithm is higher than that of the other two algorithms by more than 10%. [ABSTRACT FROM AUTHOR]

With the use of distributed systems, different users can instantly access data from different locations and perform some operations on the data. However, the unauthorized access of multiple users to the system from different points at the same time can lead to dangerous results in terms of data security and confidentiality of the data. This study is based on intrusion detection and prevention systems built on distributed databases and classifies the methods used to analyze and evaluate successes comparatively. It is observed that the artificial immunity algorithm we have described in artificial intelligence techniques, which is one of the methods classified as three categories, gives more successful results compared to the other techniques mentioned in the data mining and statistical methods. [ABSTRACT FROM AUTHOR]