AI-Assisted Dynamic Modeling for Data Management in a Distributed System

Author(s):  
Yihao Tian

Data management is an administrative mechanism that involves the acquisitions, validations, storage, protection, and processing of data needed by its users to ensure that data are accessible, reliable, and timely. It is a challenging task to manage protections for information properties. With the emphasis on distributed systems and Internet-accessible systems, the need for efficient information security management is increasingly important. In the paper, artificial intelligence-assisted dynamic modeling (AI-DM) is used for data management in a distributed system. Distributed processing is an effective way to enhance the efficiency of database systems. Therefore, each distributed database structure’s functionality depends significantly on its proper architecture in implementing fragmentation, allocation, and replication processes. The proposed model is a dynamically distributed internet database architecture. This suggested model enables complex decision-making on fragmentation, distribution, and duplication. It provides users with links from anywhere to the distributed database. AI-DM has an improved allocation and replication strategy where no query performance information is accessible at the initial stage of the distributed database design. AI-DM findings show that the proposed database model leads to the reliability and efficiency of the enhanced system. The final results are obtained by analyzing the dynamic modeling ratio is 87.6%, increasing decision support ratio is 88.7%, the logistic regression ratio is 84.5%, the data reliability ratio is 82.2%, and the system ratio is 93.8%.

Author(s):  
Adel A. Sewisy ◽  
Ali A. Amer ◽  
Hassan I. Abdalla

In this paper, heuristic query-driven clustering-based vertical fragmentation technique is efficiently developed. The intrinsic idea is to meet the idealistic case of DDBS design which comes to happen as each query attune “closely match” its relevant fragment. The proposed technique is mainly sought to breed clusters of queries in the first place. Consequently, these clusters would be tacitly used to generate intended disjoint fragments. Moreover, the allocation process has been considered so that replicated and non-replicated scenarios of data are applied. This technique basically meant to be efficaciously applicable at the initial stage of DDBS design without the need for data statistics or empirical results, in either dynamic or static DDBS environment. Many existing design-related techniques are being incorporated to make a promising work, particularly as communication costs being the foremost design objective. Throughout this work, the experimental results and internal evaluation are extensively illustrated to demonstrate the effectiveness and validity of proposed technique.


2003 ◽  
Vol 12 (03) ◽  
pp. 297-313 ◽  
Author(s):  
James Tracy ◽  
Liwu Chang ◽  
Ira S. Moskowitz

We propose an inference prevention agent as a tool that enables each of the databases in a distributed system to keep track of probabilistic dependencies with other databases and then use that information to help preserve the confidentiality of sensitive data. This is accomplished with minimal sacrifice of the performance and survivability gains that are associated with distributed database systems.


2003 ◽  
Vol 02 (02) ◽  
pp. 265-285 ◽  
Author(s):  
AZAD AZADMANESH ◽  
AXEL W. KRINGS ◽  
BAHADOR GHAHRAMANI

In a distributed system, it is often necessary for nodes to agree on a particular event or to coordinate their activities. Applications of distributed agreement are many, such as Commit Protocols in distributed database systems, selection of a monitor node in a distributed system, detecting an intruder, or agreeing on the malicious behavior of a node. Among many forms of Distributed Agreement, one form is called Approximate Agreement (AA), in which the nodes, by exchanging their local values with other nodes, need to agree on values which are approximately equal to each other. Research on AA for fully connected networks is relatively mature. In contrast, the study of AA in partially connected networks has been very limited. More specifically, no general solution to the AA problem exists for such networks. This research solves the AA problem for a specific, scalable, partially connected network with limited relays. The research considers the worst failure mode of nodes, called Byzantine, and hybrid failure modes. The results show low communication cost in comparison to fully connected networks. The network is designed to take advantage of the results available for fully connected networks. Thus, the analysis for obtaining the expressions for Convergence Rate and Fault Tolerance becomes relatively easy.


2012 ◽  
Vol 6 (1) ◽  
pp. 60
Author(s):  
Adi Wibowo ◽  
Muh. Abdur Rohman ◽  
Beta Noranita ◽  
Djalal Er Riyanto

Data kependudukan merupakan suatu hal yang harus dikelola oleh pemerintah, baik daerah maupun pusat. Mekanisme pendataan yang disimpan pada masing-masing daerah dan tidak adanya komunikasi yang menyinkronkan data menyebabkan pencatatan ganda NIK. Sistem basis data terdistribusi merupakan kumpulan basis data yang tersebar di dua komputer atau lebih yang terhubung dalam jaringan komputer. Sistem basis data terdistribusi memberikan keuntungan ketersediaan data serta otonomi dalam pengelolaan data pada masing-masing lokal. Metode basis data terdistribusi yang digunakan adalah metode fragmentasi horizontal. Rancangan basis data terdistribusi data kependudukan dapat digunakan untuk mencegah pencatatan ganda NIK. Prototype Sistem Informasi Administrasi Kependudukan Berbasis Data Terdistribusi (SIAK BDT) yang dibuat digunakan untuk melakukan manajemen data kependudukan seperti menambah, mengubah, dan menampilkan data kependudukan dari berbagai lokasi yang berbeda, serta ketersediaan data. Pengujian terhadap prototype SIAK BDT dilakukan dengan metode simulasi. Dari hasil pengujiannya, prototype SIAK BDT mampu melakukan manajemen data penduduk dari berbagai lokasi yang berbeda dan ketersediaan data. Population data is a matter that must be managed should the government, either local or central. Data collection mechanism that is stored in each region and the lack of communication led to record multiple data sync NIK. Distributed database system is a collection of databases that are spread across two or more computers connected in a network computer. Distributed database systems provide the advantage of data availability as well as autonomy in the management of data on each local. Distributed database methods used are horizontal fragmentation method. The design of distributed database population data can be used to prevent double registration of NIK. Prototype of Sistem Informasi Administrasi Kependudukan Berbasis Data Terdistribusi (SIAK BDT) made is used to perform demographic data management such as adding, changing, and displaying population data from a variety of different locations, and availability of data. Prototype testing of SIAK BDT performed by the method of simulation. From the test results, prototype SIAK BDT able to perform data management people from many different locations and availability of data.


2014 ◽  
Vol 13 (9) ◽  
pp. 4859-4867
Author(s):  
Khaled Saleh Maabreh

Distributed database management systems manage a huge amount of data as well as large and increasingly growing number of users through different types of queries. Therefore, efficient methods for accessing these data volumes will be required to provide a high and an acceptable level of system performance.  Data in these systems are varying in terms of types from texts to images, audios and videos that must be available through an optimized level of replication. Distributed database systems have many parameters like data distribution degree, operation mode and the number of sites and replication. These parameters have played a major role in any performance evaluation study. This paper investigates the main parameters that may affect the system performance, which may help with configuring the distributed database system for enhancing the overall system performance.


2014 ◽  
Vol 7 (12) ◽  
pp. 1219-1230 ◽  
Author(s):  
Jörn Kuhlenkamp ◽  
Markus Klems ◽  
Oliver Röss

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