Estimating User Preferences by Managing Contextual History in Context Aware Systems

Abstract

<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 8.5pt;">Context awareness enables smart service discovery and adaptation for mobile and wireless hosts. The contextual data is acquired from sensors present in the smart space, which may be absent. The inherent noisy nature of wireless environments does not guarantee the gathering of correct data. A history module is thus required in conjunction with existing context-aware systems that overcomes these limitations by predicting the data. We present a modular approach that when coupled with existing context managers will be able to provide user preference on the basis of usage history.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 8.5pt;"></span></p

An interactive sensemaking framework for mobile visual analytics

“…Seifert et al have designed -TreasurePhone‖ to vary the access to privacy data base on contextual information like current locations and actions performed [27]. To overcome the noise at a single data collection in mobile context, Malik et al suggest using historical data as an estimation of users' preference to help predict the future action and provide services correspondingly [28]. While these works are valuable attempts for implement contextual information into mobile visual analytics, little theoretical guidance is provided.…”

Power Profiling of Context Aware Systems: A Contemporary Analysis and Framework for Power Conservation

“…Focused Techniques [8] Device power optimization Handheld mobile devices Software based energy efficiency [9] Enhancing energy efficient Mobile and cloud computing based devices Energy aware offloading techniques [10] Minimizing energy consumption Mobile cloud computing Effective task scheduling method [11] Cloud power optimization Cloud applications Energy consumption models [12] Power conservation Embedded systems Power management techniques [13] Energy efficiency Mobile devices Energy-aware profilers [14] Power consumption Context aware applications Energy profiler for sensor configuration [15] Energy efficiency Wearable sensors/healthcare application Approaches for context aware activity recognition [16] Power consumption Multimedia Content adaptation techniques [17] Energy profiling Software and hardware level DBMS [18] Power consumption and optimization P2P/Network communication File distribution and content streaming [19] Power efficiency Networks/mobile devices Power consumption in network communication [20] Energy XML. This history information can be purged to reduce the size as well as to identify the user preferences [35].…”

“…If the classification of the context is carried out as a predictive algorithm, it would support the power awareness task. Otherwise the system would use two different learning algorithms, i.e., one for classification of the context and other for sensor polling [35,37].…”

Integration of Situated Learning and Context Awareness System for Learning Basic Chinese

“…The situated learning theory emphasizes how students should blend into the learning environment by exploring and experiencing activities to gain applicable knowledge [27] [28]. Also context aware technology combines the surrounding environment with learning content to process language learning.…”