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Outgoing Pega Lead System Architect with 12+ years of experience introducing partners to…

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  • AI4Process

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Publications

  • Relaxed Context-Aware Machine Learning Middleware (RCAMM) for Android: A Step towards Sustainability

    IEEE Conference on RAICS (Recent Advances in Intelligent Computational Systems)

    http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6745453

    Context Aware Computing is a promising approach of developing mobile applications that provide experiences and services in a manner that is fine-tuned based on the user's preferences. Applications such as Google Now, Apple Siri learn the User's activities from context related information and subsequently provide suggestions to the users in real-time. However, in almost all cases, application developers have to…

    http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6745453

    Context Aware Computing is a promising approach of developing mobile applications that provide experiences and services in a manner that is fine-tuned based on the user's preferences. Applications such as Google Now, Apple Siri learn the User's activities from context related information and subsequently provide suggestions to the users in real-time. However, in almost all cases, application developers have to develop the same set of mechanisms to consume the context information and storing it in an appropriate form rather than focusing on the parts of the application that consume the context information. This approach results in the repetition of the same task and multiple copies of data.
    This paper presents our work detailing the development of a middleware that handles context information collection and its storage. The work provides a framework that allows the developers to easily implement context aware applications that consume the services provided by the middleware. Applications will only have to react to context data (past and present) while the middleware takes care of everything else such as the background service for context information collection and storage, thus reducing the redundancy, increasing adaptability and flexibility, and simultaneously supporting developers in rapid prototyping of context-aware applications. Thus the paper presents our work towards building sustainable Android Framework which follows the principle of Reformat, Reduce, Regenerate, Reuse and Repurpose.

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  • Context Aware Machine Learning Framework for Android

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    Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and application themselves.
    A system is context-aware if it uses context to provide relevant information and/or services to the user, where relevancy depends on the user’s task.
    Sensing is a significant contributor to the current mobile computing…

    Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and application themselves.
    A system is context-aware if it uses context to provide relevant information and/or services to the user, where relevancy depends on the user’s task.
    Sensing is a significant contributor to the current mobile computing revolution. Today’s typical smart phone has more than eight sensors, including multiple microphones, cameras, accelerometers, gyroscopes, a GPS, a digital compass, and proximity sensors. These sensors not only provide natural user interaction with the device, but also offer tantalizing opportunities for context-aware computing.
    Currently, mobile applications are expected to manage their own context information. However, CondOS presents an alternative approach in which context aggregation and management is handled by the operating system. We use this approach to present “Contextual Intelligence Manager” i.e. CIM which uses a combination of the Middleware based and Server Based Architectures to provide efficient context management.

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  • Cluster Computing System

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    A computer cluster is a group of loosely coupled computers that work together closely so that in many respects it can be viewed as though it were a single computer. Clusters are commonly connected through fast local area networks. Clusters are usually deployed to improve speed and/or reliability over that provided by a single computer, while typically being much more cost-effective than single computers of comparable speed or reliability. Cluster computing has emerged as a result of convergence…

    A computer cluster is a group of loosely coupled computers that work together closely so that in many respects it can be viewed as though it were a single computer. Clusters are commonly connected through fast local area networks. Clusters are usually deployed to improve speed and/or reliability over that provided by a single computer, while typically being much more cost-effective than single computers of comparable speed or reliability. Cluster computing has emerged as a result of convergence of several trends including the availability of inexpensive high performance microprocessors and high speed networks, the development of standard software tools for high performance distributed computing. Clusters have evolved to support applications ranging from ecommerce, to high performance database applications. Clustering has been available since the 1980s. As of today, leading hardware and software companies offer clustering packages that are said to offer scalability as well as availability. Cluster computing can also be used as a relatively low-cost form of parallel processing for scientific and other applications that lend themselves to parallel operations.
    Clusters, built using commodity-off-the-shelf (COTS) hardware components and free, or commonly used, software, are playing a major role in solving large-scale science, engineering, and commercial applications. Cluster computing has emerged as a result of the convergence of several trends, including the availability of inexpensive high performance microprocessors and high speed networks, the development of standard software tools for high performance distributed computing, and the increasing need of computing power for computational science and commercial applications.

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