Analysis of Log File Data to Understand Mobile Service Context and Usage Patterns

Several mobile acceptance models exist today that focus on user interface handling and usage frequency evaluation. Since mobile applications reach much deeper into everyday life, it is however important to better consider user behaviour for the service evaluation. In this paper we introduce the Behaviour Assessment Model (BAM), which is designed to gaining insights about how well services enable, enhance and replace human activities. More specifically, the basic columns of the evaluation framework concentrate on (1) service actuation in relation to the current user context, (2) the balance between service usage effort and benefit, and (3) the degree to which community knowledge can be exploited. The evaluation is guided by a process model that specifies individual steps of data capturing, aggregation, and final assessment. The BAM helps to gain stronger insights regarding characteristic usage hotspots, frequent usage patterns, and leveraging of networking effects showing more realistically the strengths and weaknesses of mobile services

Analysis of Log File Data to Understand Mobile Service Context and Usage Patterns

Several mobile acceptance models exist today that focus on user interface handling and usage frequency evaluation. Since mobile applications reach much deeper into everyday life, it is however important to better consider user behaviour for the service evaluation. In this paper we introduce the Behaviour Assessment Model (BAM), which is designed to gaining insights about how well services enable, enhance and replace human activities. More specifically, the basic columns of the evaluation framework concentrate on (1) service actuation in relation to the current user context, (2) the balance between service usage effort and benefit, and (3) the degree to which community knowledge can be exploited. The evaluation is guided by a process model that specifies individual steps of data capturing, aggregation, and final assessment. The BAM helps to gain stronger insights regarding characteristic usage hotspots, frequent usage patterns, and leveraging of networking effects showing more realistically the strengths and weaknesses of mobile service

Who you gonna call? Analyzing Web Requests in Android Applications

Relying on ubiquitous Internet connectivity, applications on mobile devices frequently perform web requests during their execution. They fetch data for users to interact with, invoke remote functionalities, or send user-generated content or meta-data. These requests collectively reveal common practices of mobile application development, like what external services are used and how, and they point to possible negative effects like security and privacy violations, or impacts on battery life. In this paper, we assess different ways to analyze what web requests Android applications make. We start by presenting dynamic data collected from running 20 randomly selected Android applications and observing their network activity. Next, we present a static analysis tool, Stringoid, that analyzes string concatenations in Android applications to estimate constructed URL strings. Using Stringoid, we extract URLs from 30, 000 Android applications, and compare the performance with a simpler constant extraction analysis. Finally, we present a discussion of the advantages and limitations of dynamic and static analyses when extracting URLs, as we compare the data extracted by Stringoid from the same 20 applications with the dynamically collected data

What's APPening to news? A mixed-method audience-centred study on mobile news consumption

News is increasingly being consumed on a multitude of media devices, including mobile devices. In recent years, mobile news consumption has permeated individuals’ news consumption repertoires. The main purpose of this study is twofold: (1) gain insight in how mobile news outlets infiltrated the broader news media repertoires of mobile device owners and (2) understand in what circumstances mobile news is consumed within these news media repertoires. The key is to understand how and why this widening agency in appropriating various places and social spaces in everyday life relates to general news media consumption (Peters, 2012). This two-phased study aims to illuminate how mobile device owners position their mobile news consumption in relation to other types of news media outlets. First, a guiding cluster analysis of a large-scale questionnaire (N = 1279) was preformed, indicating three types of news consumers. Second, in order to thicken the originally derived clusters, a mixed-method study was set up, combining objective data originating from mobile device logs with more subjective audience constructions through personal diaries and face-to-face interviews (N = 30). This study reveals the Janus-faced nature of mobile news. On the one hand, the majority of news consumers dominantly relies on traditional media outlets to stay informed, only to supplement with online mobile services in specific circumstances. Even then, there is at least a tendency to stick to trusted brand materials. On the other hand, these mobile news outlets/products do seem to increasingly infiltrate the daily lives of mobile audiences who were previously disengaged with news

MobGeoSen: facilitating personal geosensor data collection and visualization using mobile phones

Mobile sensing and mapping applications are becoming more prevalent because sensing hardware is becoming more portable and more affordable. However, most of the hardware uses small numbers of fixed sensors that report and share multiple sets of environmental data which raises privacy concerns. Instead, these systems can be decentralized and managed by individuals in their public and private spaces. This paper describes a robust system called MobGeoSens which enables individuals to monitor their local environment (e.g. pollution and temperature) and their private spaces (e.g. activities and health) by using mobile phones in their day to day life

CERN openlab Whitepaper on Future IT Challenges in Scientific Research

This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates

M-COMMERCE VS. E-COMMERCE: EXPLORING WEB SESSION BROWSING BEHAVIOR

With the growing popularity of mobile commerce (m-commerce), it becomes vital for both researchers and practitioners to understand m-commerce usage behavior. \ \ In this study, we investigate browsing behavior patterns based on the analysis of clickstream data that is recorded in server-side log files. We compare consumers\u27 browsing behaviors in the m-commerce channel against the traditional e-commerce channel. For the comparison, we offer an integrative web usage mining approach, combining visualization graphs, association rules and classification models to analyze the Web server log files of a large Internet retailer in Israel, who introduced m-commerce to its existing e-commerce offerings. \ \ The analysis is expected to reveal typical m-commerce and e-commerce browsing behavior, in terms of session timing and intensity of use and in terms of session navigation patterns. The obtained results will contribute to the emerging research area of m-commerce and can be also used to guide future development of mobile websites and increase their effectiveness. Our preliminary findings are promising. They reveal that browsing behaviors in m-commerce and e-commerce are different