Mobile location-based applications rely heavily on network connections. When the mobile devices are offline, such applications become less accessible to users. A cache-based method is proposed to improve the offline accessibility for mobile location-based applications. The central idea is that when users are browsing information, the client program not only submits the current query window to the server, but also attempts to predict the most likely (from a probabilistic standpoint) query windows that would be submitted to the server in the future. The major challenge is the very large number of possible future query windows. This challenge is tackled by proposing a discretization technique that makes predictions over a finite subset of all possible query windows. A probabilistic model is proposed for prediction, which is trained using the query log recorded by the client, so that the prediction can be executed entirely on the client side. The advantage of this technique is that it requires no modification on the existing server side, so it can be adapted by most existing applications easily. The usability of the technique is demonstrated by prototyping it on top the NewsStand system so that the query window is constantly changing as users pan and zoom around the world using a gesturing interface, among others. Evaluation shows the prototype to be effective while decreasing the response time.

PhotoStand enables the use of a map query interface to retrieve news photos associated with news articles that are in turn associated with the principal locations that they mention collected as a result of monitoring the output of over 10,000 RSS news feeds, made available within minutes of publication, and stored in a PostgreSQL database. The news photos are ranked according to their relevance to the clusters of news articles associated with locations at which they are displayed. This work differs from traditional work in this field as the associated locations and topics (by virtue of the cluster with which the articles containing the news photos are associated) are generated automatically without any human intervention such as tagging, and that photos are retrieved by location instead of just by keyword as is the case for many existing systems. In addition, the clusters provide a filtering step for detecting near-duplicate news photos.

TweetPhoto utilizes a map query interface to display news photos from news articles that are extracted from the tweets of 2,000 Twitter users who have been determined to post news related content. These articles are then geotagged and clustered so that a set of locations are associated with a cluster and its associated images. For each of these locations, the images are scored based on the terms associated with the location and the image's caption. This work differs from traditional work in this area as all topic and location extraction is automated without the need for user entered content or GPS coordinates.

NewsStand is a Web-based mapping application that we have developed to enable searching for spatially-referenced information by using a map query interface. Previously, we adapted the Web version to execute on mobile devices such as smartphones where the main issue that was confronted was how to deal with the considerably smaller display screen while retaining access to the application through the browser. In the current work we discuss the issues that we encountered in converting the Web-based application to a native App primarily on the iPhone and iPod Touch. These issues involve how to compensate for the absence of a hovering action as well as how to integrate an interaction restriction to one hand coupled with use of the thumb as the pointing mechanism. In addition, a significant effort is devoted to the implementation of an intuitive mechanism for undoing the most recent actions. Other issues include the formulation of navigation shortcuts to avoid excessive traffic with the supporting database.