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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.
Collective Interaction Filtering Approach for Detection of Group in Diverse Crowded Scenes
( Pei Voon Wong ),( Norwati Mustapha ),( Lilly Suriani Affendey ),( Fatimah Khalid ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.2
Crowd behavior analysis research has revealed a central role in helping people to find safety hazards or crime optimistic forecast. Thus, it is significant in the future video surveillance systems. Recently, the growing demand for safety monitoring has changed the awareness of video surveillance studies from analysis of individuals behavior to group behavior. Group detection is the process before crowd behavior analysis, which separates scene of individuals in a crowd into respective groups by understanding their complex relations. Most existing studies on group detection are scene-specific. Crowds with various densities, structures, and occlusion of each other are the challenges for group detection in diverse crowded scenes. Therefore, we propose a group detection approach called Collective Interaction Filtering to discover people motion interaction from trajectories. This approach is able to deduce people interaction with the Expectation-Maximization algorithm. The Collective Interaction Filtering approach accurately identifies groups by clustering trajectories in crowds with various densities, structures and occlusion of each other. It also tackles grouping consistency between frames. Experiments on the CUHK Crowd Dataset demonstrate that approach used in this study achieves better than previous methods which leads to latest results.