Browsing by Author "Mustafa, Rashed"

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A BETTER WAY FOR FINDING THE OPTIMAL NUMBER OF NODES IN A DISTRIBUTED DATABASE MANAGEMENT SYSTEM
(Daffodil International University, 2009-07-01) Mustafa, Rashed; Hossain, Md. Javed; Chowdhury, Thomas
Distributed Database Management System (DDBMS) is one of the prime concerns in distributed computing. The driving force of development of DDBMS is the demand of the applications that need to query very large databases (order of terabytes). Traditional Client- Server database systems are too slower to handle such applications. This paper presents a better way to find the optimal number of nodes in a distributed database management systems.
Mediator Based Architecture To Address Data Heterogeneity
(Daffodil International University, 2013-07) Mustafa, Rashed; Rahman, Hasan Hafizur
Mediator Based Architecture is an emerging technology used to address data heterogeneity issues. As the number of data sources increases, the data integration process becomes an administrative and performance bottleneck because of data heterogeneity. Mediator-based approach allows the integration of data from heterogeneous data sources, which are usually not centralized. A mediator system is set between a number of data sources and applications. Now a day, the searching and combination of information from distributed, autonomous and heterogeneous software systems can be considered as a challenge for the computer users. The mainstream of this work is to address the issues of data heterogeneity where data are located in various distributed system in different format. Hence, multiple data accessing point is required to retrieve information from various distributed system. The mediator service allows single point of access, enabling the retrieving of appropriate information. To achieve this goal, the service dynamically integrates and customizes data from various data providers. This paper concentrates the investigation of data sources, interfaces and capabilities for mediators, and the design and implementation of a model- “Integration Mediation Kit (IMK)”. The kit can think the entire source query in a scalable way.
MEDIATOR BASED ARCHITECTURE TO ADDRESS DATA HETEROGENEITY
(Daffodil International University, 2012-07) Mustafa, Rashed; Rahman, , Hasan Hafizur; Hossain, Mohammed Shahadat
Mediator Based Architecture is an emerging technology used to address data heterogeneity issues. As the number of data sources increases, the data integration process becomes an administrative and performance bottleneck because of data heterogeneity. Mediator-based approach allows the integration of data from heterogeneous data sources, which are usually not centralized. A mediator system is set between a number of data sources and applications. Now-a-days, the searching and combination of information from distributed, autonomous and heterogeneous software systems can be considered as a challenge for the computer users. The mainstream of this work is to address the issues of data heterogeneity where data are located in various distributed system in diﬂerent format. Hence, multiple data accessing point is required to retrieve information from various distributed system. The mediator service allows single point of access, enabling the retrieving of appropriate information. To achieve this goal, the service dynamically integrates and customizes data from various data providers. This paper concentrates the investigation of data sources, interfaces and capabilities for mediators, and the design and implementation of a model- “Integration Mediation Kit”. The kit can think the entire source query in a scalable way.
Sentiment Analysis of E-commerce Consumer Based on Product Delivery Time Using Machine Learning
(Daffodil International University, 2022-02-22) Hossain, Md. Jahed; Joy, Dabasish Das; Das, Sowmitra; Mustafa, Rashed
In this modern era, e-commerce sites, online selling, and purchasing are at the top of the list. Product quality and delivery time usually divert people’s sentiments about e-commerce. We conducted a sentiment analysis of consumer comments on Daraz and Evaly’s Facebook pages, and data were gathered from these two pages comments of Facebook. We evaluated the mood of client comments in which they expressed their opinions and experience regarding e-commerce pages services. With diverse models such as logistics regression, decision tree, random forest, multinomial naive Bayes, K-neighbors, and linear support vector machine in n-grams, we employ unigram, bigram, and trigram features. With 90.65 and 89.93% accuracy in unigram and trigram, random forest is the most accurate. With an accuracy of 88.49% in bigram, decision tree is the most accurate. Among the finest fits are the unigram feature and random forest.