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

Towards Ambient Assisted Cities and Citizens

Research on Smart Cities brings about ICT innovations towards aiding users in their daily activities, anticipating to their needs or suggesting service consumption towards a better vital experience. The city is a challenging environment for anybody elderly or with disabilities. Addressing these challenges so that elderly or disabled people encounter inclusive, friendlier, cities which are not frightening but supporting is the motivation of this work. Concretely, it lays out the ICT basis over which an ecosystem of user-centric urban apps aiding citizens in their daily activities may be assembled.This work has been supported by project grants IE11-316 (FUTURE INTERNET II) and PC2012-73 (DYNUI) from the Basque Government and CIP-ICT-PSP-2012-6 (IES CITIES) grant funded by European Commission

To trust, or not to trust: Highlighting the need for data provenance in mobile apps for smart cities ∗

The popularity of smartphones makes them the most suitable devices to ensure access to services provided by smart cities; furthermore, as one of the main features of the smart cities is the participation of the citizens in their governance, it is not unusual that these citizens generate and share their own data through their smartphones. But, how can we know if these data are reliable? How can identify if a given user and, consequently, the data generated by him/her, can be trusted? On this paper, we present how the IES Cities’ platform integrates the PROV Data Model and the related PROV-O ontology, allowing the exchange of provenance information about user-generated data in the context of smart cities. 1

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