Search

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]

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.

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.

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]

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.)

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 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]