Detecting Stops from GPS Trajectories: A Comparison of Different GPS Indicators for Raster Sampling Methods

With the increasing prevalence of GPS tracking capabilities on smartphones, GPS trajectories have proven to be useful for an extensive range of research topics. Stop detection, which estimates activity locations, is fundamental for organizing GPS trajectories into semantically meaningful journeys. With previous methods overwhelmingly dependent on thresholds, contextual information or a pre-understanding of the GPS records, this paper addresses the challenge by contributing a ‘top-down’ raster sampling method which samples pre-calculated GPS indicators and clusters the raster cells with significantly different values as stops. We report a comparison of a set of precalculated GPS indicators with two baseline methods. By referencing a ground truth travel dairy, the raster sampling method demonstrates good and reliable capabilities on producing high accuracy, low redundancy and close proximity to the ground truth in three distinct travel use cases. This further indicates a good generic stop detection method

This Time It's Personal: from PIM to the Perfect Digital Assistant

Interacting with digital PIM tools like calendars, to-do lists, address books, bookmarks and so on, is a highly manual, often repetitive and frequently tedious process. Despite increases in memory and processor power over the past two decades of personal computing, not much has changed in the way we engage with such applications. We must still manually decompose frequently performed tasks into multiple smaller, data specific processes if we want to be able to recall or reuse the information in some meaningful way. "Meeting with Yves at 5 in Stata about blah" breaks down into rigid, fixed semantics in separate applications: data to be recorded in calendar fields, address book fields and, as for the blah, something that does not necessarily exist as a PIM application data structure. We argue that a reason Personal Information Management tools may be so manual, and so effectively fragmented, is that they are not personal enough. If our information systems were more personal, that is, if they knew in a manner similar to the way a personal assistant would know us and support us, then our tools would be more helpful: an assistive PIM tool would gather together the necessary material in support of our meeting with Yves. We, therefore, have been investigating the possible paths towards PIM tools as tools that work for us, rather than tools that seemingly make us work for them. To that end, in the following sections we consider how we may develop a framework for PIM tools as "perfect digital assistants" (PDA). Our impetus has been to explore how, by considering the affordances of a Real World personal assistant, we can conceptualize a design framework, and from there a development program for a digital simulacrum of such an assistant that is not for some far off future, but for the much nearer term

Context Based Knowledge Management in Healthcare: An EA Approached

While an enterprise architectural approach is appropriate for business and IT alignment it also has potential with respect to the design and implementation of healthcare applications by effective representation of healthcare processes. Our research is to develop an enterprise architectural framework for managing contextual knowledge by exploiting object location deduction technologies in healthcare processes that involve the movement of patients. Such a framework is intended to facilitate healthcare managers in adopting location deduction technologies for patient care resulting in improvements in clinical process management and healthcare services

Analyzing User Activities, Demographics, Social Network Structure and User-Generated Content on Instagram

Instagram is a relatively new form of communication where users can instantly share their current status by taking pictures and tweaking them using filters. It has seen a rapid growth in the number of users as well as uploads since it was launched in October 2010. Inspite of the fact that it is the most popular photo sharing application, it has attracted relatively less attention from the web and social media research community. In this paper, we present a large-scale quantitative analysis on millions of users and pictures we crawled over 1 month from Instagram. Our analysis reveals several insights on Instagram which were never studied before: 1) its social network properties are quite different from other popular social media like Twitter and Flickr, 2) people typically post once a week, and 3) people like to share their locations with friends. To the best of our knowledge, this is the first in-depth analysis of user activities, demographics, social network structure and user-generated content on Instagram.Comment: 5 page

Prediction Model for Offloading in Vehicular Wi-Fi Network

It cannot be denied that, the inescapable diffusion of smartphones, tablets and other vehicular network applications with diverse networking and multimedia capabilities, and the associated blooming of all kinds of data-hungry multimedia services that passengers normally used while traveling exert a big challenge to cellular infrastructure operators. Wireless fidelity (Wi-Fi) as well as fourth generation long term evolution advanced (4G LTE-A) network are widely available today, Wi-Fi could be used by the vehicle users to relieve 4G LTE-A networks. Though, using IEE802.11 Wi-Fi AP to offload 4G LTE-A network for moving vehicle is a challenging task since it only covers short distance and not well deployed to cover all the roads. Several studies have proposed the offloading techniques based on predicted available APs for making offload decision. However, most of the proposed prediction mechanisms are only based on historical connection pattern. This work proposed a prediction model which utilized historical connection pattern, vehicular movement and driver profile to predict the next available AP.  The proposed model is compared with the existing models to evaluate its practicability