Forthcoming and Online First Articles

International Journal of Intelligent Information and Database Systems

International Journal of Intelligent Information and Database Systems (IJIIDS)

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International Journal of Intelligent Information and Database Systems (4 papers in press)

Regular Issues

  • An adaptive fuzzy weight algorithm for the class imbalance learning problem   Order a copy of this article
    by Vo Duc Quang, Tran Dinh Khang 
    Abstract: In this study, we propose an adaptive fuzzy weight algorithm for the problem of two-class imbalanced learning. Initially, our algorithm finds a set of fuzzy weight values for data samples based on the distance from each sample to the centres of both minority and majority classes. Then, our algorithm iteratively adjusts the fuzzy weight values of sensitive samples on either positive or negative margins or class label noises. By doing so, our algorithm increases the influence of minority samples and decreases the influence of majority samples in forming a classifier model. Experimental results on four benchmark real-world imbalanced datasets including Transfusion, Ecoli, Yeast, and Abalone show that our algorithm outperforms the fuzzy SVM-CIL algorithm in terms of classification performance.
    Keywords: classification algorithm; class imbalance learning; CIL; fuzzy support vector machines; FSVM; weighted support vector machines; WSVM; support vector machine; SVM.
    DOI: 10.1504/IJIIDS.2023.10058648
  • A collaboration of an ontology and an autoregressive model to build an efficient chatbot model   Order a copy of this article
    by Thi Thanh Sang Nguyen, Dang Huu Trong Ho, Ngoc Tram Anh Nguyen, Pham Minh Thu Do 
    Abstract: Question-answer systems are now very popular and crucial to support human in automatically responding frequent questions in many fields. However, these systems depend on learning methods and training data. Therefore, it is necessary to prepare such a good dataset, but it is not an easy job. An ontology-based domain knowledge base is able to help to make nice question-answer pairs and reason semantic information effectively. This study proposes a novel chatbot model involving ontology to generate efficient responses automatically. Besides, an autoregressive model is also employed to complement responses. A case study of admissions advising at the International University VNU HCMC is taken into account in the proposed chatbot. Experimental results have shown that the collaboration of an ontology-based and autoregressive model-based chatbot is significantly effective.
    Keywords: ontology; chatbots; answer-question systems; domain knowledge base; deep learning; autoregressive model.
    DOI: 10.1504/IJIIDS.2023.10059348
  • Implementing domains in Neo4j   Order a copy of this article
    by Maja Cerjan, Kornelije Rabuzin, Martina Šestak 
    Abstract: Data growth has led to the need to apply new ways to process data. Graph databases are increasing their use, which can be seen over the years, with the Neo4j system being the most common. The biggest problem is the small number of implemented constraints that can be used. One of the shortcomings to be explored is the need to create domains, which are used when large amounts of data are manipulated and where the value needs to be limited or when multiple attributes have the same restrictions and data types. The creation of domains can be applied multiple times. This paper summarises the implementation of domains using Neo4j and the Java programming language.
    Keywords: NoSQL; domains; cypher; graph databases; Neo4j; constraints.
    DOI: 10.1504/IJIIDS.2023.10060491
  • Securing big graph databases: an overview of existing access control techniques   Order a copy of this article
    by Basmah Alzahrani, Asma Cherif, Suhair Alshehri, Abdessamad Imine 
    Abstract: Recently, the rapid evolution of technology has resulted in a significant increase in the volume of data generated by both users and organisations. This, in turn, has given rise to the big data phenomenon. However, traditional relational databases are ill-equipped to handle such vast and complex data. To address this challenge, the NoSQL big data management system has emerged as an efficient alternative. Within this system, the graph database has garnered significant attention from researchers due to its ability to handle complex relationships, such as those found in social networks. However, security remains a critical concern, particularly for sensitive and private data. Therefore, this survey seeks to explore recent solutions for securing graph databases, including techniques such as access control, view-based, and query rewriting approaches, as well as pattern matching algorithms for answering queries. As a result, our survey will contribute to filling the gap in existing research, as none of the previous surveys have examined these specific topics. Additionally, the survey provides recommendations for future research in this area.
    Keywords: big data; graph database; access control; view; graph pattern matching; GPM.