Here is building some kind of in-car navigation system powered by Amazon's Alexa assistant. The new "one-stop solution for automakers," called Here Navigation On-Demand, will be sold to manufacturers as (shudder) software-as-a-service. In layman's terms, that means it will sit on top of existing infotainment platforms and operating systems. Details are light at the moment, but Here says it will be a truly "voice-first car navigation experience that keeps users focused on the road." We also know that the software will leverage Alexa Auto, the development kit that Amazon released last August, to give drivers personalized advice.
Your message has been sent. There was an error emailing this page. Vacuuming is one of the most loathed household chores. While it doesn't come with the ick factor of cleaning the toilet or the tedium of dusting, pushing and dragging a noisy, cumbersome vacuum is its own kind of torture. No wonder most of us only break it out the bare-minimum-recommended once a week.
Google Maps' augmented reality navigation is finally rolling out several months after its debut, although you might still have to wait a while. The company told the Wall Street Journal the walking-focused feature will be available shortly, but only to Local Guides (community reviewers) at first. The feature will need "more testing" before it's available to everyone else, Google said. Still, this suggests AR route-finding is much closer to becoming a practical reality. The core functionality remains the same.
Faceted navigation can effectively reduce user efforts of reaching targeted resources in databases, by suggesting dynamic facet values for iterative query refinement. A key issue is minimizing the navigation cost in a user query session. Conventional navigation scheme assumes that at each step, users select only one suggested value to figure out resources containing it. To make faceted navigation more flexible and effective, this paper introduces a multi-select scheme where multiple suggested values can be selected at one step, and a selected value can be used to either retain or exclude the resources containing it. Previous algorithms for cost-driven value suggestion can hardly work well under our navigation scheme. Therefore, we propose to optimize the navigation cost using the Minimum Description Length principle, which can well balance the number of navigation steps and the number of suggested values per step under our new scheme. An emperical study demonstrates that our approach is more cost-saving and efficient than state-of-the-art approaches.