A Novel Solution for Distributed Database Problems

Sawiris, Bishoy Sameeh Matta; Abdel-Fattah, Manal A.

doi:10.14569/ijacsa.2020.0111059

Cited by 2 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, the researchers illustrate the performance of the proposed BOBS-CRABID model in terms of throughput, response time, energy consumption, attack detection rate, accuracy, computation overhead and time. The performance of BOBS-CRABID is compared with two previous works, DDP [37], and Hybrid BID [33]. The experimental results show that the proposed BOBS-CRABID model achieves better performance when compared with the existing methods.…”

Section: Comparative Studymentioning

confidence: 98%

Centralization of Big Data Using Distributed Computing Approach in IoT

Sawiris¹,

El-Gaber²,

Abdel-Fattah³

2021

IJIES

View full text Add to dashboard Cite

Centralized big data is an emerging field that manages information in one common repository. Currently, Internet of Things (IoT) devices are increasing massively for sensing and transmitting data to the big data environment. This information is sensitive and easily traced by attackers due to the centralized data management. To address the security and privacy issues and avoid the system in a single point of failure, in this paper the researchers proposed a new model, namely, BOBS CRABID i.e. Blockchain Based Secure Centralized Big Data model using distributed computing approach. In big data, Hadoop MapReduce is one of the distributed computing approaches which handles all information in a distributed manner. The BOBS CRABID model is designed by three ideas as Multi-Factor authentication, IoT Devices Data Collection and Processing, and Optics based MapReduce for Data Clustering. In multi-factor authentication, a lightweight camellia key generation algorithm (LCKGA) is used for their authenticating all devices based on ID, IP address, MAC and PUF. These credentials are saved in Blockchain Security Entity for authentication verification. All transactions are hashed and stored in the blockchain using Keccak hash algorithm which performs better than the traditional hashing (SHA) 2. Then the researchers conducted IoT devices data collection and pre-processing using Cross Correlation and Min-Max Normalization for redundant data pruning and normalization, respectively. Finally, clustering is performed in a distributed way for optimizing the storage and scalability performance. For that MapReduce is used in which OPTICS algorithm is applied for clustering based on the data size, type of context and nature of data (sensitive or non-sensitive). In this way, data is stored in the big data environment. Experiments were conducted for the proposed BOBS CRABID model and compared with the well-known methods using Hadoop 2.7.2. The proposed BOBS-CRABID model achieves better performance in terms of throughput (increase 1500mbps), response time (reduce 150ms), energy consumption (reduce 35%), attack detection rate (increase 30%), accuracy (increase 20%), computation overhead (300 kb), and time reduce 4s).

show abstract

Section: Comparative Studymentioning

confidence: 98%

Centralization of Big Data Using Distributed Computing Approach in IoT

Sawiris¹,

El-Gaber²,

Abdel-Fattah³

2021

IJIES

View full text Add to dashboard Cite

show abstract

“…Thus, to increase and preserve the data availability and reliability, it is better to store the data in multiple servers rather than in one server [1]- [3]. This method is called distributed database system [1], [4], [5]. Hence, in the event of disaster, data availability is ensured because distributed systems have higher reliability and incremental growth [6].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, in the event of disaster, data availability is ensured because distributed systems have higher reliability and incremental growth [6]. Distributed databases systems (DDBS) are a set of logically networked computer databases, managed by different sites, locations and accessible to the user locally or via the Internet for different tasks as a single database by running transactions in parallel [4], [7]. The design of DDBS is a very demanding task since an optimum level of performance must be continuously satisfied [8], [9].…”

Section: Introductionmentioning

confidence: 99%

An automate failure recovery for synchronous distributed database system

Noor

Fauzi

et al. 2022

IJEECS

View full text Add to dashboard Cite

Periodically, researchers have been sharing their constant attempts to improve the existing methods for data replication in distributed database system. The main goal is to work for an efficient distributed environment. An efficient environment may handle huge amount of data and preserve data availability. The occasionally failures in distributed systems will affect the end results, such as data loss, income loss etc. Thus, to prevent the data loss and guarantee the continuity of the business, many organizations have applied disaster recovery solutions in their system. One of the widely used is database replication, because it guarantees data safety and availability. However, disaster still can occur in database replication. Hence, an automatic failure recovery technique called distributed database replication with fault tolerance (DDR-FT) has been proposed in this research. DDR-FT uses heartbeat message for node monitoring. Subsequently, a foundation of binary vote assignment for fragmented database (BVAFD) replication technique has been used. In DDR-FT, the data nodes are continuously monitored while auto reconfiguring for automatic failure recovery. From the conducted experiments, it is proved that DDR-FT can preserve system availability. It shows that DDR-FT technique provides a convenient approach to system availability for distributed database replication in real time environment.

show abstract

A Novel Solution for Distributed Database Problems

Cited by 2 publications

References 0 publications

Centralization of Big Data Using Distributed Computing Approach in IoT

Centralization of Big Data Using Distributed Computing Approach in IoT

An automate failure recovery for synchronous distributed database system

Contact Info

Product

Resources

About