Information Transferral within a Distributed Data Base Via a Generalised Mapping Language

In the Data Technology (DT) era, data have become the most important means of production. With the rapid development of cloud computing, the database technology has become the most basic data technology facility. Especially, the Distributed Databases have been widely used to store and process large-scale data, which are applied in different scenarios than relational databases. In addition, blockchain technology has led to changes in decentralized architecture. The BigchainDB database is based on a distributed database with the features of blockchain. It has not only the advantages of distributed database, such as high throughput, low latency and easy expansion, but also has the characteristics of blockchain, such as decentralized control, tamper-proof and traceability. This paper mainly analyzes the comparison between the BigchainDB and other distributed databases in terms of execution performance. In practice, this paper focuses on the characteristics of BigchainDB, then compares the performance of creating and querying transactions between the BigchainDB and two important distributed databases: HadoopDB and Hive. The experiments in this paper show three things. Firstly, the BigchainDB is obviously better than HadoopDB and Hive in transaction creation. Secondly, the transaction query performance of the BigchainDB is one order higher than them. Thirdly, the BigchainDB is three orders of magnitude higher than them in fuzzy query performance. These results demonstrate that BigchainDB is significantly better than HadoopDB and Hive in both the performance of read and write.

In the era of big data and machine learning, data privacy has become an increasingly critical concern. Traditional centralized data processing methods pose significant risks related to data leakage, unauthorized access, and regulatory non-compliance. Federated Learning (FL) and Distributed Databases (DD) have emerged as promising solutions to address these challenges. Federated Learning enables collaborative model training across decentralized devices or servers while keeping data localized, thus preserving privacy. Similarly, Distributed Databases facilitate secure data storage and access across multiple nodes, reducing the risks associated with single points of failure. This paper explores the synergy between Federated Learning and Distributed Databases in enhancing data privacy, providing a comprehensive review of the current literature, methodologies, and implementations. We examine key protocols, frameworks, and security mechanisms that support privacy-preserving machine learning. Through detailed analysis and case studies, we present the effectiveness of integrated FL-DD systems in real- world scenarios such as healthcare, finance, and IoT. We conclude with a discussion on challenges, future research directions, and the implications of combining FL and DD for building secure, scalable, and privacy-aware data systems. Keywords-Federated Learning, Distributed Databases, Data Privacy, Machine Learning, Secure Computation, Data Decentralization, Privacy- Preserving Models, IoT Security, Blockchain, Data Governance

Data allocation issue is one of the significant and essential discussions in distributed data base systems. In these systems, operators are located in different places and have to get access to a joint data base. These data have been distributed within the network. Many models have been designed in order to resolve this problem. Also, some algorithms are devised for the purpose of decreasing data transferring expense and decreasing the time of responding. The most important expense belongs to data transferring. This cost equals to a query's access to the required data from different sites to the place in which the query has been produced. In this article, we deal with modularization algorithms which make data transferring expense reduce during a query execution. These algorithms accomplish this by optional modularization of the sites in which the data are located.

We consider the effect on system performance of the distribution of a data base in the form of multiple copies at distinct sites. The purpose of our analysis is to determine the gain in READ throughput that can be obtained in the presence of consistency preserving algorithms that have to be implemented when UPDATE operations are carried out on each copy. We show that READ throughput diminishes if the number of copies exceeds an optimal value. The theoretical model we develop is applied to a system in which consistency is preserved through the use of Ellis' ring algorithm.

A homogeneous computer network Analysis and simulation

Distributed data bases

Distributed data bases — An advanced course

Stable Equilibria in Multilocus Genetic Systems: Statistical Investigation

Objectives: To develop a memory efficient, incremental and interactive distributed FPM having less communication and synchronization overhead with good load balancing capability, to analyze the dynamic transactional data in a distributed database. Methods/Analysis: This technique adopts prefix based equivalence class partitioning scheme to generate frequent item sets without generating local frequent sets with low memory consumption. This approach uses a range of support values to update the frequent patterns with less time complexity. This paper proposes distributed FPM techniques with both count distributed and compressed data distributed parallel approaches. The performance of the algorithms are tested and compared with popular distributed FPM algorithms using standard datasets. Findings: To deal with the massive dynamic data stored in distributed databases, this approach develops three distributed frequent set generation algorithms, which update frequent patterns by reusing the previously stored pattern information with no complex calculations or data structures. The proposed approaches also provide the user with the facility to interactively adjust the minimum support value as per their own conveniences by keeping the nearly frequent itemsets with the help of two minimum support thresholds (low, high). Measures have been taken to reduce the additional itemset storage and computations as well as to achieve good load balancing with low communication and synchronization overhead. Since the proposed algorithms adopt prefix based equivalent class partitioning technique at each n-itemset level and undergo four levels of itemset filtering to remove infrequent items from each class before calculating the individual item count, the inter node communication required is less in this approach. To eliminate the drawbacks of both count and data distribution approaches one of the algorithms proposed adopts a hybrid approach which distributes the compressed data only once, hence communication overhead is less compared with other DD algorithms. Conclusion/Application: The proposed distributed techniques reduce memory utilization and itemset comparisons compared to the existing approaches. The performances are tested and evaluated for market analysis and online credit card fraud detection applications.

The GSM Recommendations give the standards for a Pan-European mobile radio telecommunication system. The Location Registers form a Distributed Data Base whose interworking with other entities of the GSM System provides an integrated directory function. The properties and functionality of the GSM Location Registers and the implementation of the Distributed Data Base in the system “ECR 900” are described.

Three important areas of electric utility distribution system research are identified. These areas deal with planning methods, system surveillance and control, and data file technology. The basic objectives of this project in each of the three areas were to: assess present practices and available tools commonly used in each of the three areas; identify the needs for new techniques, tools, equipment, and other items to improve present practices by making them more efficient, more exact, or more reliable; and recommend research and development required to bring the new techniques, tools, equipment, and other items into existence and to implement their use in distribution system planning methods, system surveillance and control, and data file operation at an early date. A critical review is presented of current practices in the areas of distribution system planning, load forecasting, service reliability, construction and resource management, distribution automation, load management and distribution data base. The recommended research topics in six broad areas expansion planning, reliability evaluation and analysis, economic utilization, information management, distribution automation, and load management are summarized.

PM-AIM must provide to customers in a timely fashion information about Army acquisitions. This paper discusses ways that PM-AIM can reduce the volume of data that must be transmitted between sites. Although this paper primarily discusses techniques of data compression, it also briefly discusses other options for meeting the PM-AIM requirements. The options available to PM-AIM, in addition to hardware and software data compression, include less-frequent updates, distribution of partial updates, distributed data base design, and intelligent network design. Any option that enhances the performance of the PM-AIM network is worthy of consideration. The recommendations of this paper apply to the PM-AIM project in three phases: the current phase, the target phase, and the objective phase. Each recommendation will be identified as (1) appropriate for the current phase, (2) considered for implementation during the target phase, or (3) a feature that should be part of the objective phase of PM-AIM's design. The current phase includes only those measures that can be taken with the installed leased lines. The target phase includes those measures that can be taken in transferring the traffic from the leased lines to the DSNET environment with minimal changes in the current design. The objective phase includes all the things that should be done as a matter of course. The objective phase for PM-AIM appears to be a distributed data base with data for each site stored locally and all sites having access to all data.

To keep pace with rapid changes in corporate business requirements, organizations have begun to integrate distributed data base technology into their information architectures. Distributed data base management systems (DDBMSs) in particular promote the departmental sharing of data as well as easy access to data in a distributed environment, both key aspects of any data management strategy. This article describes the elements of distributed data base technology to help IS managers control the integration of distributed data bases in their organizations.

The domain name system (DNS) is a cornerstone for the Internet and the most globally deployed Internet service that provides user-friendly naming for Internet hosts. DNS is a distributed data base that allows convenient storing and retrieving of resource records. Recently an interesting feature was added to BIND implementation of the DNS system to support secure dynamic updates. In this paper, we use the DNS and its ability to support dynamic updates to propose a mobility solution that locates and tracks mobile hosts while they change there points of attachment (IP addresses) in the network. The main objectives of this paper is to study and re-evaluate the DNS system with respect to dynamic updating protocol and provide a performance study to determine the ability of the DNS to support mobility and real time services. The experimental results have shown that updating speeds (i.e. propagation of updates along the system) are fast enough for the DNS to provide real time services, and we can conclude that the conventional DNS system with the dynamic updating feature can be used in a macro mobility handling scheme.

Data access in distributed data base management systems