PhD Thesis Proposal: Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Resource optimization in health care, manufacturing, and military operations requires the careful choreography of people and equipment to effectively fulfill the responsibilities of the profession. However, resource optimization is a computationally challenging problem, and poorly utilizing resources can have drastic consequences. Within these professions, there are human domain experts who are able to learn from experience to develop strategies, heuristics, and rules-of-thumb to effectively utilize the resources at their disposal. Manually codifying these heuristics within a computational tool is a laborious process and leaves much to be desired. Even with a codified set of heuristics, it is not clear how to best insert an autonomous decision-support system into the human decision-making process. The aim of this thesis is to develop an autonomous computational method for learning domain-expert heuristics from demonstration that can support the human decision-making process. We propose a new framework, called apprenticeship scheduling, which learns and embeds these heuristics within a scalable resource optimization algorithm for real-time decision-support. Our initial investigation, comprised of developing scalable methods for scheduling and studying shared control in human-machine collaborative resource optimization, inspires the development of our apprenticeship scheduling approach. We present a promising, initial prototype for learning heuristics from demonstration and outline a plan for our continuing work

Let Google index your media fragments

Current multimedia applications in Web 2.0 have generated a massive amount of multimedia resources, but most search results for multimedia resources still focus on the whole re-source level. Media fragments expose the inside content of multimedia resources for annotations, but they are yet fully explored and indexed by major search engines. W3C has published Media Fragment 1.0 as a standard way to describe media fragments on the Web. In this proposal, we make use of Google's Ajax Application Crawler to index media fragments represented by Media Fragment URIs. Each media fragment with related annotations will have an individual snapshot page, which could be indexed by the crawler. Initial evaluation has shown that the snapshot pages are successfully fetched by Googlebot and we are expecting more media fragments to be indexed using this method, so that the search for multimedia resources would be more efficient

Mobile Map Browsers: Anticipated User Interaction for Data Pre-fetching

When browsing a graphical display of geospatial data on mobile devices, users typically change the displayed maps by panning, zooming in and out, or rotating the device. Limited storage space on mobile devices and slow wireless communications, however, impede the performance of these operations. To overcome the bottleneck that all map data to be displayed on the mobile device need to be downloaded on demand, this thesis investigates how anticipated user interactions affect intelligent pre-fetching so that an on-demand download session is extended incrementally. User interaction is defined as a set of map operations that each have corresponding effects on the spatial dataset required to generate the display. By anticipating user interaction based on past behavior and intuition on when waiting for data is acceptable, it is possible to device a set of strategies to better prepare the device with data for future use. Users that engage with interactive map displays for a variety of tasks, whether it be navigation, information browsing, or data collection, experience a dynamic display to accomplish their goal. With vehicular navigation, the display might update itself as a result of a GPS data stream reflecting movement through space. This movement is not random, especially as is the case of moving vehicles and, therefore, this thesis suggests that mobile map data could be pre-fetched in order to improve usability. Pre-fetching memory-demanding spatial data can benefit usability in several ways, but in particular it can (1) reduce latency when downloading data over wireless connections and (2) better prepare a device for situations where wireless internet connectivity is weak or intermittent. This thesis investigates mobile map caching and devises an algorithm for pre-fetching data on behalf of the application user. Two primary models are compared: isotropic (direction-independent) and anisotropic (direction-dependent) pre-fetching. A prefetching simulation is parameterized with many trajectories that vary in complexity (a metric of direction change within the trajectory) and it is shown that, although anisotropic pre-fetching typically results in a better pre-fetching accuracy, it is not ideal for all scenarios. This thesis suggests a combination of models to accommodate the significant variation in moving object trajectories. In addition, other methods for pre-fetching spatial data are proposed for future research

The affects of caching in browser stage on the performance of web items delivery

Network congestion remains one of the main barriers to the continuing success of the Internet. Caching is a way to reduce traffic load on the server and network backbone, which improves the efficiency and scalability of web items delivery. Caching in computer networks might be performed in different stages. In this article, we investigate the load that web pages can put on a network and how caching can reduce the bandwidth requirements. This article concludes that caching in browser stage improves the delivery of web items