A Robust Mechanism for Categorizing Context-Aware Applications into Generations

Abstract

The hunt to categorize context-aware applications has been a prevalent issue to developers of context-aware applications. The previous categorizations were based on the functions of the applications. These mechanisms yielded limited results since many applications could not be categorized. This paper categorizes applications into four generations based on developmental trends through a literature survey. The first generation applications focused on data acquisition and used hardware sensors. The second generation applications focused on knowledge acquisition and used software sensors, semantic language and ontology-based modelling languages. The third generation applications focused on intelligent reasoning and used mechanisms to handle information uncertainty. The fourth generation applications deprecate cumbersome ruleset implementations and focus on artificial intelligence whilst taking into consideration the effect of the dynamics of users’ background and preference on contextual information. The study demonstrated that when applications, methods or technologies can be categorized over some time, it is better to classify them into generations.

Complementary Architecture of E-Learning Path Personalization and Optimization

“…The total ants/alumni (n) set at 300 was appropriate to find an optimal solution. The nodes in a cluster of the Spark executor were configured as [1,5,10,15,20,25,30,35,40,45,50].…”

“…The following ant colony algorithm settings are used in the experiment to determine the appropriate number of nodes per cluster: the total number of iterations Nc=100, values of pheromone trail α = 1.5, total ants n = 300, the pheromone evaporation coefficient = 0.5, the predicted heuristic factor = 2.0, and the heuristic information factor = 1.0. The nodes in a cluster of the Spark executor are configured as [1,5,10,15,20,25,30,35,40,45,50]. We execute the Spark-ACO algorithm 20 times for each executor value before calculating the average time.…”

“…It was therefore laudable for this study to focus on course generation mechanisms. Generally, a computational intelligence technique of evolutionary computing is suitable to provide the desired solution to the challenges of personalized learning paths as evolutionary computing seeks to optimize the objective function of scientific problems [5].…”