The advent of high speed input sensor and display technologies and the drive for faster interactive response suggests that human-computer interaction (HCI) task processing deadlines of a few milliseconds or less may be required in future handheld devices. At the same time, users will expect the same, if not better, battery life than today's devices under these more stringent response requirements. In this paper, we present a toolbox for exploring the design space of HCI event processors. We first describe the simulation platform for interactive environments that runs mobile user interface code with inputs recorded from human users. We validate it against a hardware platform from prior work. Given system-level constraints on latency, we demonstrate how this toolbox can be used to design a custom heterogeneous event processor that maximizes battery life. We show that our toolbox can pick design points that are 1.5-2.5× more energy-efficient than general-purpose big. LITTLE architectures.

Low power "helper" cores have been increasingly included on application processors to accomplish low intensity tasks such as music playing and motion sensing with minimum energy consumption. Recently, Guimbretière et al. [1] demonstrated that such helper cores can also be used to execute simple user interface tasks. We revisit this approach by implementing a similar system on an off-the-shelf application processor (TI OMAP4). Our study shows that in the case of high event rate interactions (pen inking and virtual keyboard), significant battery life gains (×1.7 and ×2.3 respectively) can be achieved with the helper core executing the interface. Having the helper core only dis-patch input events incurs a 18% penalty relative to the maximum savings rate, but allows for simplified deployment since it merely requires a change in toolkit infrastructure.

Investigators across many disciplines and organizations must sift through large collections of text documents to understand and piece together information. Whether they are fighting crime, curing diseases, deciding what car to buy, or researching a new field, inevitably investigators will encounter text documents. Taking a visual analytics approach, we integrate multiple text analysis algorithms with a suite of interactive visualizations to provide a flexible and powerful environment that allows analysts to explore collections of documents while sensemaking. Our particular focus is on the process of integrating automated analyses with interactive visualizations in a smooth and fluid manner. We illustrate this integration through two example scenarios: an academic researcher examining InfoVis and VAST conference papers and a consumer exploring car reviews while pondering a purchase decision. Finally, we provide lessons learned toward the design and implementation of visual analytics systems for document exploration and understanding.

Abstract Clustering plays an important role in many large‐scale data analyses providing users with an overall understanding of their data. Nonetheless, clustering is not an easy task due to noisy features and outliers existing in the data, and thus the clustering results obtained from automatic algorithms often do not make clear sense. To remedy this problem, automatic clustering should be complemented with interactive visualization strategies. This paper proposes an interactive visual analytics system for document clustering, called iVisClustering, based on a widely‐used topic modeling method, latent Dirichlet allocation (LDA). iVisClustering provides a summary of each cluster in terms of its most representative keywords and visualizes soft clustering results in parallel coordinates. The main view of the system provides a 2D plot that visualizes cluster similarities and the relation among data items with a graph‐based representation. iVisClustering provides several other views, which contain useful interaction methods. With help of these visualization modules, we can interactively refine the clustering results in various ways. Keywords can be adjusted so that they characterize each cluster better. In addition, our system can filter out noisy data and re‐cluster the data accordingly. Cluster hierarchy can be constructed using a tree structure and for this purpose, the system supports cluster‐level interactions such as sub‐clustering, removing unimportant clusters, merging the clusters that have similar meanings, and moving certain clusters to any other node in the tree structure. Furthermore, the system provides document‐level interactions such as moving mis‐clustered documents to another cluster and removing useless documents. Finally, we present how interactive clustering is performed via iVisClustering by using real‐world document data sets.