We focus on the problem of answering OLAP queries via voice output. We present a holistic approach that combines query processing and result vocalization. We use the following key ideas to minimize processing overheads and maximize answer quality. First, our approach samples from the database to evaluate alternative speech fragments. OLAP queries are not fully evaluated. Instead, sampling focuses on result aspects that are relevant for voice output. To guide sampling, we rely on methods from the area of Monte-Carlo Tree Search. Second, we use pipelining to interleave query processing and voice output. The system starts providing the user with high-level insights while generating more fine-grained results in the background. Third, we optimize speech output to maximize the user's information gain under speaking time constraints. We use a maximum-entropy model to predict the user's belief about OLAP results, after listening to voice output. Based on that model, we select the most informative speech fragments (i.e., the ones minimizing the distance between user belief and actual data). We analyze formal properties of the proposed speech structure and analyze complexity of our algorithm. Also, we compare alternative vocalization approaches in an extensive user study.

Passenger comfort is a major factor influencing a commuter's decision to avail public transport. Existing studies suggest that factors like overcrowding, jerkiness, traffic congestion etc. correlate well to passenger's (dis)comfort. An online survey conducted with more than 300 participants from 12 different countries reveals that different personalized and context dependent factors influence passenger comfort during a travel by public transport. Leveraging on these findings, we identify correlations between comfort level and these dynamic parameters, and implement a smartphone based application, ComfRide, which recommends the most comfortable route based on user's preference honoring her travel time constraint. We use a 'Dynamic Input/Output Automata' based composition model to capture both the wide varieties of comfort choices from the commuters and the impact of environment on the comfort parameters. Evaluation of ComfRide, involving 50 participants over 28 routes in a state capital of India, reveals that recommended routes have on average 30% better comfort level than Google map recommended routes, when a commuter gives priority to specific comfort parameters of her choice.

IEEE 802.11ac supports Dynamic Bandwidth Channel Access (DBCA), where a wireless station dynamically selects the channel bandwidth based on the availability of the secondary channels. Although DBCA reduces the possibility of starvation due to non-availability of secondary channels, however, to the best of our knowledge, no existing works look into the performance benefits of IEEE 802.11ac DBCA based on theoretical modeling. In this paper, we develop a two dimensional Markov chain approach to model the performance of DBCA under various channel bonding conditions. We validate the proposed model based on a real testbed implementation. From the thorough analysis of the numerical results obtained from the model, we show that although DBCA improves channel utilization for secondary channels, it requires proper channel allocations and bonding level distributions across the wireless channels for reducing unfairness in the network. We observe that under certain circumstances, the secondary channel users can affect the throughput of primary channel users, which may introduce a short-term unfairness and a significant performance drop in the network.