Using the Default Option Bias to Influence Decision-Making While Driving

Gaining a better understanding of human–computer interaction in multiple-goal environments, such as driving, is critical as people increasingly use information technology to accomplish multiple tasks simultaneously. Extensive research shows that decision biases can be utilized as effective cues to guide user interaction in single-goal environments. This article is a first step toward understanding the effect of decision biases in multiple-goal environments. This study analyzed data from a field experiment during which a comparison was made between drivers’ decisions on parking lots in a single-goal environment and drivers’ decisions in a multiple-goal environment when being exposed to the default option bias. The article shows that the default option bias is effective in multiple-goal environments. The results have important implications for the design of human–computer interaction in multiple-goal environments

Fundamentals for Consistent Event Ordering In Distributed Shared Memory Systems

A large number of tasks in distributed systems can be traced down to the fundamental problem of attaining a consistent global view on a distributed computation. This problem has been addressed by a number of studies which focus on systems with message passing as their only means of interprocess communication. In the paper at hand we extend this restricted system model by additionally accounting for an abstract memory to be shared by the processes. We specify necessary and sufficient conditions for constructing a consistent global view on such systems and present helpful definitions, which are meant to be a solid formal base for further studies

Compiler Supported Interval Optimisation for Communication Induced Checkpointing

There exist mainly three different approaches of checkpoint-based recovery mechanisms for distributed systems: coordinated checkpointing, uncoordinated checkpointing and communication induced checkpointing. It can be shown that communication induced checkpointing theoretically has the least minimum overhead, but also that the effective overhead depends on the communication behaviour and the resulting forced checkpoints. If the placement of checkpoints and the communication pattern is disadvantageous, the overhead can get arbitrary large due to a high number of forced checkpoints. We introduce a compiler supported approach to avoid unfavourable combinations of communication behaviour and local checkpoint placement. We analyse the application statically and prepare the placement of voluntary checkpoints. These placement decisions are reviewed during runtime. With this approach we optimise the effective checkpoint-intevals of voluntary and forced checkpoints and thus reduce the overhead of communication induced checkpointing

Towards Identifying Contextual Factors on Parking Lot Decisions

The relevance of contextual factors that adapt in-car recommendations to the driver’s current situation is not yet fully understood. This paper presents a field study that has been conducted in order to identify relevant contextual factors of in-car parking lot recommender systems. Surprisingly, most contextual factors examined, i.e., weather, luggage, and traffic conditions, did not have a significant effect on the parking lot decision in the conducted field study. Only the urgency of the trip and the willingness to walk have significant effects on the decision outcome. Therefore, automobile manufacturers should focus on understanding the relevance of different contextual factors when developing user models for in-car recommender systems