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3-L Model: A Model for Checking the Integrity Constraints of Mobile Databases
Hamidah Ibrahim,Zarina Dzolkhifli,Lilly Suriani Affendey,Praveen Madiraju 한국정보과학회 2009 Journal of Computing Science and Engineering Vol.3 No.4
In this paper we propose a model for checking integrity constraints of mobile databases called Three-Level (3-L) model, wherein the process of constraint checking to maintain the consistent state of mobile databases is realized at three different levels. Sufficient and complete tests proposed in the previous works together with the idea of caching relevant data items for checking the integrity constraints are adopted. This has improved the checking mechanism by preventing delays during the process of checking constraints and performing the update. Also, the 3-L model reduces the amount of data accessed given that much of the tasks are performed at the mobile host, and hence speeds up the checking process.
3-L Model: A Model for Checking the Integrity Constraints of Mobile Databases
Ibrahim, Hamidah,Dzolkhifli, Zarina,Affendey, Lilly Suriani,Madiraju, Praveen Korean Institute of Information Scientists and Eng 2009 Journal of Computing Science and Engineering Vol.3 No.4
In this paper we propose a model for checking integrity constraints of mobile databases called Three-Level (3-L) model, wherein the process of constraint checking to maintain the consistent state of mobile databases is realized at three different levels. Sufficient and complete tests proposed in the previous works together with the idea of caching relevant data items for checking the integrity constraints are adopted. This has improved the checking mechanism by preventing delays during the process of checking constraints and performing the update. Also, the 3-L model reduces the amount of data accessed given that much of the tasks are performed at the mobile host, and hence speeds up the checking process.
Mohd Rafiz Salji,Nur Izura Udzir,Mohd Izuan Hafez Ninggal,Nor Fazlida Mohd. Sani,Hamidah Ibrahim 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.12
Anomaly detection under Cloud computing environment plays an important role in detecting anomalous virtual machines (VMs) before real failures occur. In order to accurately characterize the new trend of VMs' performance, new samples are collected, detected, and selectively added into the training sample set. The newly added samples are used for updating the detection model, so as to improve detection accuracy. However, increasing number of training samples causes both much storage space and CPU time. To overcome this challenge, this article proposes an anomaly detection algorithm based on online learning Lagrangian SVM (termed OLLSVM) for detecting anomalous VMs. Online learning includes incremental learning and decremental learning. Single-sample and batch incremental learning algorithms are designed to update the detection model when adding a single sample or a set of samples. Similarly, single-sample and batch decremental learning algorithms are designed for deleting a single sample or a set of samples. The strategies for selecting sample(s) to be added or deleted are also designed. This article conducts experiments on Cloud datasets and KDD Cup datasets. The experimental results show that, compared with traditional Lagrangian SVM (LSVM) which retrains the detection model each time when adding or deleting sample(s), OLLSVM achieves almost similar high detection accuracy but much higher time efficiency.