Since the dawn of human civilization, stories have been a popular medium of communication, both synchronously and asynchronously. Technically, a story is a time-ordered coherent sequence of events. In many applications, heterogeneous data is collected and organized so appropriate stories could be told. In this paper, we present a system that helps in generation of stories using a large database of events with associated multimodal data, called eventbase. We define storytelling as a two step process in which a storyteller can retrieve appropriate events and associated data, and then those are further filtered using preferences of the viewer. We develop this model using a measure of interestingness based on attributes of selected events and the preferences. Using an event system developed in our laboratory, we demonstrate the story telling process in Tolkien as one that generates multiple queries to select coherent interesting events to form a story.

Computational problems are increasingly relying on contextaware approaches for tractable solutions. Usually, these approaches statically link additional sources of information to those already present in the problem space. We have been building CueNet, a context discovery framework, which will dynamically discover the most relevant context for a given application problem. In this demonstration, we will show how the identities of people in personal photos can be discovered through contextual information. We present Picatrix: an event based photo browsing web interface. Users can select a photo, and see a live visualization of how our context discovery algorithm, seeded with the initial information, discovers context from different data sources, and uses it to tag the faces in the given photo.

Scrub is a troubleshooting tool for distributed applications that operate under strict SLOs common in production environments. It allows users to formulate queries on events occurring during execution in order to assess the correctness of the application's operation. Scrub has been in use for two years at Turn, where developers and users have relied on it to resolve numerous issues in its online advertisement bidding platform. This platform spans thousands of * This work was done when the author was at Turn Inc.

An image recognition problem is typically formulated as tagging a given set of images with labels from a predefined set. Context-aware approaches in problems like face recognition have utilized information about a user and the people she knows through different social networks. Traditionally, this context is statically linked to all of the available data. In this work, we propose a technique to dynamically discover which subset of all the available data is relevant context for the given recognition problem.In this dissertation, we propose the CueNet framework, to discover candidate labels for the person identification problem in personal photos. We describe our context model, and how it allows heterogeneous data sources to contribute useful context for the identification problem. We design algorithms to extract contextual information from these sources to discover a subset of candidates who could potentially appear in personal photos. Our early experiments show that CueNet is capable of removing upto 99% of irrelevant candidates, and was able to correctly tag 80% of frontal faces.

In this paper we address the problem of browser extensibility, needed to support the evolving nature of the web. Standards for supporting multimedia content by browsers are constantly updated, extended and introduced while the browser support is left behind.We present a novel approach to add browser support for content adhering to a new standard. Only a small set of requirements needs to be implemented as part of the browser's software, the rest is accounted by client-side code.Client-side code has the advantage that it can account for missing functionality with no changes to the browser, thus making it more dynamic, easier to implement, and enabling third-party developers to contribute. At the same time, it is risk-free for users accessing other content types. On the downside, client-side code suffers from degraded performance and potentially introduces security concerns.