Social navigation

In this chapter we present one of the pioneer approaches in supporting users in navigating the complex information spaces, social navigation support. Social navigation support is inspired by natural tendencies of individuals to follow traces of each other in exploring the world, especially when dealing with uncertainties. In this chapter, we cover details on various approaches in implementing social navigation support in the information space as we also connect the concept to supporting theories. The first part of this chapter reviews related theories and introduces the design space of social navigation support through a series of example applications. The second part of the chapter discusses the common challenges in design and implementation of social navigation support, demonstrates how these challenges have been addressed, and reviews more recent direction of social navigation support. Furthermore, as social navigation support has been an inspirational approach to various other social information access approaches we discuss how social navigation support can be integrated with those approaches. We conclude with a review of evaluation methods for social navigation support and remarks about its current state

Developing Future Smart Parking Solutions for Hangzhou\u27s IoT Town

With help from the Smart Cities Research Center of Zhejiang Province, this project aimed to assess and improve current smart parking solutions in Hangzhou, China. The team consulted industry experts and research students to gauge the direction of smart technology applicable to future parking solutions. The team analyzed results from interviews, customer surveys, and observations to infer needs for improved user experience. Research performed on future technologies allowed the team to offer a system framework recommendation with modern smart parking features for a characteristic town in Hangzhou. The project team discovered that a future smart parking system could integrate 5G, High-Frequency RFID, and non-contact payment methods to address the shortcomings of current smart parking systems

Virtual Reality as Navigation Tool: Creating Interactive Environments For Individuals With Visual Impairments

Research into the creation of assistive technologies is increasingly incorporating the use of virtual reality experiments. One area of application is as an orientation and mobility assistance tool for people with visual impairments. Some of the challenges are developing useful knowledge of the user’s surroundings and effectively conveying that information to the user. This thesis examines the feasibility of using virtual environments conveyed via auditory feedback as part of an autonomous mobility assistance system. Two separate experiments were conducted to study key aspects of a potential system: navigation assistance and map generation. The results of this research include mesh models that were fitted to the walk pathways of an environment, and collected data that provide insights on the viability of virtual reality based guidance systems

Personal Wayfinding Assistance

We are traveling many different routes every day. In familiar environments it is easy for us to find our ways. We know our way from bedroom to kitchen, from home to work, from parking place to office, and back home at the end of the working day. We have learned these routes in the past and are now able to find our destination without having to think about it. As soon as we want to find a place beyond the demarcations of our mental map, we need help. In some cases we ask our friends to explain us the way, in other cases we use a map to find out about the place. Mobile phones are increasingly equipped with wayfinding assistance. These devices are usually at hand because they are handy and small, which enables us to get wayfinding assistance everywhere where we need it. While the small size of mobile phones makes them handy, it is a disadvantage for displaying maps. Geographic information requires space to be visualized in order to be understandable. Typically, not all information displayed in maps is necessary. An example are walking ways in parks for car drivers, they are they are usually no relevant route options. By not displaying irrelevant information, it is possible to compress the map without losing important information. To reduce information purposefully, we need information about the user, the task at hand, and the environment it is embedded in. In this cumulative dissertation, I describe an approach that utilizes the prior knowledge of the user to adapt maps to the to the limited display options of mobile devices with small displays. I focus on central questions that occur during wayfinding and relate them to the knowledge of the user. This enables the generation of personal and context-specific wayfinding assistance in the form of maps which are optimized for small displays. To achieve personalized assistance, I present algorithmic methods to derive spatial user profiles from trajectory data. The individual profiles contain information about the places users regularly visit, as well as the traveled routes between them. By means of these profiles it is possible to generate personalized maps for partially familiar environments. Only the unfamiliar parts of the environment are presented in detail, the familiar parts are highly simplified. This bears great potential to minimize the maps, while at the same time preserving the understandability by including personally meaningful places as references. To ensure the understandability of personalized maps, we have to make sure that the names of the places are adapted to users. In this thesis, we study the naming of places and analyze the potential to automatically select and generate place names. However, personalized maps only work for environments the users are partially familiar with. If users need assistance for unfamiliar environments, they require complete information. In this thesis, I further present approaches to support uses in typical situations which can occur during wayfinding. I present solutions to communicate context information and survey knowledge along the route, as well as methods to support self-localization in case orientation is lost

Towards an aspect weaving BPEL engine

This position paper proposes the use of dynamic aspects and the visitor design pattern to obtain a highly configurable and extensible BPEL engine. Using these two techniques, the core of this infrastructural software can be customised to meet new requirements and add features such as debugging, execution monitoring, or changing to another Web Service selection policy. Additionally, it can easily be extended to cope with customer-specific BPEL extensions. We propose the use of dynamic aspects not only on the engine itself but also on the workflow in order to tackle the problems of Web Service hot deployment and hot fixes to long running processes. In this way, composing aWeb Service "on-the-fly" means weaving its choreography interface into the workflow

Probabilistic Model To Identify Movement Patterns In Geospatial Data

The task of trying to determine the movement pattern of objects based on available databases is a daunting one. Tracking the movement of these dynamic objects is important in different areas to understand the higher order patterns of movement that carry special meaning for a target application. However this is still a largely unsolved problem and recent work has focused on the relationships of moving point objects with stationary objects or landmarks on a map. Global Position System (GPS) is a widely used satellite-based navigation system. Popular use of these devices has produced large collections of data, some of which have been archived. These archived data sets and sometimes real time GPS data are now readily available over the internet and their analysis through computational methods can generate meaningful insights. These insights when applied appropriately can be used in everyday life. The purpose of this research is to make the case that automated analysis can provide insight that can otherwise be difficult to achieve due to the large volume and noisy characteristics of GPS data. We present experiments that have been performed on one of these archived databases which contain GPS traces of 536 yellow cabs in the San Francisco Bay area. Using data analysis, we determine the most visited tourist destinations within the San Francisco Bay area during the time period of the captured data. We also propose a probabilistic framework, which determines the probability of a new routing pattern using previous patterns. We use simulated routing patterns built on the same data format as that of the San Francisco cab data to predict the possible routes to be taken by a vehicle. All the probability calculations performed are done using Bayes’ theorem of conditional probability formula

Probabilistic Model To Identify Movement Patterns In Geospatial Data

The task of trying to determine the movement pattern of objects based on available databases is a daunting one. Tracking the movement of these dynamic objects is important in different areas to understand the higher order patterns of movement that carry special meaning for a target application. However this is still a largely unsolved problem and recent work has focused on the relationships of moving point objects with stationary objects or landmarks on a map. Global Position System (GPS) is a widely used satellite-based navigation system. Popular use of these devices has produced large collections of data, some of which have been archived. These archived data sets and sometimes real time GPS data are now readily available over the internet and their analysis through computational methods can generate meaningful insights. These insights when applied appropriately can be used in everyday life. The purpose of this research is to make the case that automated analysis can provide insight that can otherwise be difficult to achieve due to the large volume and noisy characteristics of GPS data. We present experiments that have been performed on one of these archived databases which contain GPS traces of 536 yellow cabs in the San Francisco Bay area. Using data analysis, we determine the most visited tourist destinations within the San Francisco Bay area during the time period of the captured data. We also propose a probabilistic framework, which determines the probability of a new routing pattern using previous patterns. We use simulated routing patterns built on the same data format as that of the San Francisco cab data to predict the possible routes to be taken by a vehicle. All the probability calculations performed are done using Bayes’ theorem of conditional probability formula