Big data is the latest industry buzzword to describe large volume of structured and unstructured data that can be difficult to process and analyze. Most of organization looking for the best approach to manage and analyze the large volume of data especially in making a decision. XML is chosen by many organization because of powerful approach during retrieval and storage processes. However, XML approach, the execution time for retrieving large volume of data are still considerably inefficient due to several factors. In this contribution, two databases approaches namely Extensible Markup Language (XML) and Java Object Notation (JSON) were investigated to evaluate their suitability for handling thousands records of publication data. The results showed JSON is the best choice for query retrieving speed and CPU usage. These are essential to cope with the characteristics of publication’s data. Whilst, XML and JSON technologies are relatively new to date in comparison to the relational database. Indeed, JSON technology demonstrates greater potential to become a key database technology for handling huge data due to increase of data annually.
Instant messenger is an IM application that allows communication in a secured manner. A secured private IM is needed to ensure data transmission between sender and recipient more secure. This paper presents a secure IM architecture. A secure architecture is divided into four modules; chat module, transceiver module, secure module, and routing module. In this research, hash algorithm was applied in secure module. The main function of hash algorithm is to encrypt and convert into hash value. The purpose of this encryption is to ensure unauthorized person cannot view the original data or information through the network. IM application was developed and tested. The result indicates this architecture and hash algorithm able to improve level of data security.
Purpose – The purpose of this paper is to first review the implementation of automatic identification and data capture) technologies in library/information science, focusing on barcode technology, radio frequency identification (RFID) and near field communication (NFC). This paper then presents S-Library, a new android-based application, to enable users to perform a wide range of information science-related transactions, such as borrowing, searching, returning and viewing transaction records. Design/methodology/approach – This paper presents the design process and the database and software components. For analysis, the authors used application testing, and also usability testing, with a questionnaire distributed to 343 users. Findings – The implementation of NFC technology means that S-Library has a number of technical advantages over other approaches. It was also shown with user acceptance testing that there was a high degree of user satisfaction with S-Library. Research limitations/implications – Although the findings combine technical assessment and usability testing and are extremely positive, further user evaluation could be performed. In addition, S-Library does not currently read existing RFID tags, which would improve the application further. Practical implications – The system proposed here shows that S-Library is a feasible approach taken to improve the library transaction experience and that it can replace and improve upon older technologies. Originality/value – This paper provides a first successful demonstration of a functioning and tested android and NFC-based library transaction system and shows that this approach generates a high degree of user reliability.
Multi-label classification is a technique used for mapping data from single labels to multiple labels. These multiple labels stand part of the same label set comprising inconsistent labels. The objective of multi-label classification is to create a classification model for previously unidentified samples. The accuracy of multi-label classification based on machine learning algorithms has been a particular study and discussion topic for researchers. This research aims to present a systematic literature review on multi-label classification based on machine learning algorithms. This study also discusses machine learning algorithm techniques and methods for multi-label classification. The findings would help researchers to explore and find the best accuracy of multi-label classification. The review result considered the Support Vector Machine (SVM) as the most accurate and appropriate machine learning algorithm in multi-label classification.
<p>Students’ Information System (SIS) in Universiti Sultan Zainal Abidin (UniSZA) handles thousands of records on the information of students, subject registration, etc. Efficiency of storage and query retrieval of these records is the matter of database management especially involving with huge data. However, the execution time for storing and retrieving these data are still considerably inefficient due to several factors. In this contribution, two database approaches namely Extensible Markup Language (XML) and JavaScript Object Notation (JSON) were investigated to evaluate their suitability for handling thousands records in SIS. The results showed JSON is the best choice for storage and query speed. These are essential to cope with the characteristics of students’ data. Whilst, XML and JSON technologies are relatively new to date in comparison to the relational database. Indeed, JSON technology demonstrates greater potential to become a key database technology for handling huge data due to an increase of data annually.</p>
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