Browsing by Author "Walpola, MJ"

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    item: Conference-Abstract
    Identify whether the user is in a meeting or not by using naïve bayes classifier
    (Engineering Research Unit, Faculty of Engiennring, University of Moratuwa, 2016-04) Yogaraja, AA; Dharmasena, HSR; Gobinath, L; Vishnuvathsasarma, B; Walpola, MJ; Jayasekara, AGBP; Amarasinghe, YWR
    This paper is discussed about implementing a Naïve Bayes classifier to identify whether the user is in a meeting or not for the context aware systems. We implemented an application (AutoProfile App) to identify whether the user is in a meeting or not by using a Naïve Bayes classifier.
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    item: Conference-Abstract
    Integrating context-awareness with reminder tools
    (Engineering Research Unit, Faculty of Engiennring, University of Moratuwa, 2016-04) Ranasinghe, YS; Walpola, MJ; Jayasekara, AGBP; Amarasinghe, YWR
    People use reminders to recall tasks to-do. But sophisticated enough tools are unavailable. Context-Awareness in mobile computing is more engaging area in gathering information about the user’s current situation and would be ideal to integrate with the reminders to generate context-aware reminder where rich context is used to identify when a reminder should be presented to its recipient. Here we have proposed a model to integrate context-awareness with reminder’s decision making process and develop a Context-Aware reminder.
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    Mahasen : distributed storage resource broker
    (2014-06-18) Walpola, MJ; Perera, KDAKS; Kishanthan, T; Perera, HAS; Madola, DTHV; Perera, S
    Modern day systems are facing an avalanche of data, and they are being forced to handle more and more data intensive use cases. These data comes in many forms and shapes: Sensors (RFID, Near Field Communication, Weather Sensors), transaction logs, Web, social networks etc. As an example, weather sensors across the world generate a large amount of data throughout the year. Handling these and similar data require scalable, efficient, reliable and very large storages with support for efficient metadata based searching. This paper present Mahasen, a highly scalable storage for high volume data intensive applications built on top of a peer-to-peer layer. In addition to scalable storage, Mahasen also supports efficient searching, built on top of the Distributed Hash table (DHT).