Learning automata: an introduction
Learning automata: an introduction
Project Aura: Toward Distraction-Free Pervasive Computing
IEEE Pervasive Computing
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules
IEEE Transactions on Knowledge and Data Engineering
Social Serendipity: Mobilizing Social Software
IEEE Pervasive Computing
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
SmokeScreen: flexible privacy controls for presence-sharing
Proceedings of the 5th international conference on Mobile systems, applications and services
On simulating episodic events against a background of noise-like non-episodic events
Proceedings of the 2010 Summer Computer Simulation Conference
Data Mining for Hierarchical Model Creation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A User-Centric Approach for Personalized Service Provisioning in Pervasive Environments
Wireless Personal Communications: An International Journal
| Hi-index | 0.00 |
Discovering and tracking of spatio-temporal patterns in noisy sequences of events is a difficult task that has become increasingly pertinent due to recent advances in ubiquitous computing, such as community-based social networking applications. The core activities for applications of this class include the sharing and notification of events, and the importance and usefulness of these functionalites increases as event-sharing expands into larger areas of one's life. Ironically, instead of being helpful, an excessive number of event notifications can quickly render the functionality of event-sharing to be obtrusive. Rather, any notification of events that provides redundant information to the application/user can be seen to be an unnecessary distraction. In this paper, we introduce a new scheme for discovering and tracking noisy spatio-temporal event patterns, with the purpose of suppressing reoccurring patterns, while discerning novel events. Our scheme is based on maintaining a collection of hypotheses, each one conjecturing a specific spatio-temporal event pattern. A dedicated Learning Automaton (LA) - the Spatio-Temporal Pattern LA (STPLA) - is associated with each hypothesis. By processing events as they unfold, we attempt to infer the correctness of each hypothesis through a real-time guided random walk. Consequently, the scheme we present is computationally efficient, with a minimal memory footprint. Furthermore, it is ergodic, allowing adaptation. Empirical results involving extensive simulations demonstrate the STPLA's superior convergence and adaptation speed, as well as an ability to operate successfully with noise, including both the erroneous inclusion and omission of events. Additionally, the results included, which involve a so-called "Presence Sharing" application, are both promising and in our opinion, impressive. It is thus our opinion that the proposed STPLA scheme is, in general, ideal for improving the usefulness of event notification and sharing systems, since it is capable of significantly, robustly and adaptively suppressing redundant information.