A technique for incorporating dynamic paths in lab-based mobile evaluations

Increasingly, lab evaluations of mobile applications are incorporating mobility. The inclusion of mobility alone, however, is insufficient to generate a realistic evaluation context since real-life users will typically be required to monitor their environment while moving through it. While field evaluations represent a more realistic evaluation context, such evaluations pose difficulties, including data capture and environmental control, which mean that a lab-based evaluation is often a more practical choice. This paper describes a novel evaluation technique that mimics a realistic mobile usage context in a lab setting. The technique requires that participants monitor their environment and change the route they are walking to avoid dynamically changing hazards (much as reallife users would be required to do). Two studies that employed this technique are described, and the results (which indicate the technique is useful) are discussed.

Random walk with restart over dynamic graphs

Random Walk with Restart (RWR) is an appealing measure of proximity between nodes based on graph structures. Since real graphs are often large and subject to minor changes, it is prohibitively expensive to recompute proximities from scratch. Previous methods use LU decomposition and degree reordering heuristics, entailing O(|V|^3) time and O(|V|^2) memory to compute all (|V|^2) pairs of node proximities in a static graph. In this paper, a dynamic scheme to assess RWR proximities is proposed: (1) For unit update, we characterize the changes to all-pairs proximities as the outer product of two vectors. We notice that the multiplication of an RWR matrix and its transition matrix, unlike traditional matrix multiplications, is commutative. This can greatly reduce the computation of all-pairs proximities from O(|V|^3) to O(|delta|) time for each update without loss of accuracy, where |delta| (<<|V|^2) is the number of affected proximities. (2) To avoid O(|V|^2) memory for all pairs of outputs, we also devise efficient partitioning techniques for our dynamic model, which can compute all pairs of proximities segment-wisely within O(l|V|) memory and O(|V|/l) I/O costs, where 1<=l<=|V| is a user-controlled trade-off between memory and I/O costs. (3) For bulk updates, we also devise aggregation and hashing methods, which can discard many unnecessary updates further and handle chunks of unit updates simultaneously. Our experimental results on various datasets demonstrate that our methods can be 1–2 orders of magnitude faster than other competitors while securing scalability and exactness.