Assessing Physical Location as a Potential Contextual Cue For Adaptive Mobile Contact Lists (original) (raw)

Proceedings of the 2009 international conference on Pervasive services - ICPS '09, 2009

Mobile context-aware applications experience a constantly changing environment with increased dynamicity. In order to work efficiently, the location of mobile users needs to be predicted and properly exploited by mobile applications. We propose a spatial context model, which deals with the location prediction of mobile users. Such model is used for the classification of the users' trajectories through Machine Learning (ML) algorithms. Predicting spatial context is treated through supervised learning. We evaluate our model in terms of prediction accuracy w.r.t. specific prediction parameters. The proposed model is also compared with other ML algorithms for location prediction. Our findings are very promising for the efficient operation of mobile context-aware applications.

Social functions of location in mobile telephony

Personal and Ubiquitous Computing, 2006

Location appears to be one of the most important aspects of context in mobile communication. It is a complex piece of information involving several levels of detail. Location intertwines with other relevant aspects of context: the parties' present activity, relative time and identities. The analysis of mobile conversations provides insights into the functions of ''location'' for mobile users. Most mobile calls involve a sequence in which location is reported. Location is made relevant by the parties' activities. Location telling takes place in five different activity contexts during mobile calls. Location may be an index of interactional availability, a precursor for mutual activity, part of an ongoing activity, or it may bear emergent relevance for the activity or be presented as a social fact. Typically, joint activities make relevant spatio-temporal location such as distance in minutes from the meeting point via the vehicle used. For users, location does not appear to be relevant in purely geographical terms.

Location: a socially dynamic property-a study of location telling in mobile phone calls

2003

Farther Than You May Think: An Empirical Investigation of the Proximity of Users to Their Mobile Phones

Lecture Notes in Computer Science, 2006

Implicit in much research and application development for mobile phones is the assumption that the mobile phone is a suitable proxy for its owner's location. We report an in-depth empirical investigation of this assumption in which we measured proximity of the phone to its owner over several weeks of continual observation. Our findings, summarizing results over 16 different subjects of a variety of ages and occupations, establish baseline statistics for the proximity relationship in a typical US metropolitan market. Supplemental interviews help us to establish reasons why the phone and owner are separated, leading to guidelines for developing mobile phone applications that can be smart with respect to the proximity assumption. We show it is possible to predict the proximity relationship with 86% confidence using simple parameters of the phone, such as current cell ID, current date and time, signal status, charger status and ring/vibrate mode.

A Location-Aware Mobile Call Handling Assistant

21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07), 2007

With the near ubiquity of mobile phones, people are reachable almost anywhere and at any time. At the same time we see an increasing need for people to limit their interactions on the mobile phone, to control (within socially acceptable bounds) when they are reachable and by whom. The user's location plays a significant role herein. Present mobile phones typically provide static means for managing reachability, such as manual profiles. In this paper, we present a location-aware call handling assistant as a dynamic solution. The assistant runs on mobile devices and enables users to manage calls based on their current context (in particular, their location and activity, the date and time, and the caller and caller's group). The system exploits Bluetooth technology for location determination and for user modelling, a new rule-based technique, Prioritized Ripple Down Rules, which gives the user a high level of confidence in the system's behaviour. We discuss the results of a small-scale user study in which the mobile call handling assistant was simulated on a PDA.

Exploiting contextual information to improve call prediction

2019

With the increase in contact list size of mobile phone users, the management and retrieval of contacts has becomes a tedious job. In this study, we analysed some important dimensions that can effectively contribute in predicting which contact a user is going to call at time t. We improved a state of the art algorithm, that uses frequency and recency by adding temporal information as an additional dimension for predicting future calls. The proposed algorithm performs better in overall analysis, but more significantly there was an improvement in the prediction of top contacts of a user as compared to the base algorithm.

Advanced Location Prediction Techniques in Mobile Computing

Context-awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify and predict context in order to act efficiently, beforehand, for the benefit of the user. Firstly, we propose an efficient spatial context classifier and a short-term predictor for the future location of a mobile user in cellular networks. Secondly, we propose a novel adaptive mobility prediction algorithm, which deals with location context representation and trajectory prediction of moving users. Thirdly, we propose a short-memory adaptive location predictor that realizes mobility prediction in the absence of extensive historical mobility information. Fourthly, we assume the existence of a pattern base and try to compare the movement pattern of a certain user with stored information in order to predict future locations. Our findings, compared with other schemes, are very promising for the location prediction problem and the adoption of proactive context-aware applications and services.

Predicting the Location of Mobile Users A Machine Learning Approach

Social linking and physical proximity in a mobile location-based service

Proceedings of the 1st international workshop on Mobile location-based service - MLBS '11, 2011

In this paper we collected and examined the indoor location traces of users of an indoor location-based social network service called Find & Connect deployed at an academic conference, to explore the relation between users' physical proximity and the connecting properties of their social links. We define a parameter called encounter to represent the physical proximity between users, and also select two kinds of social links that exist in the online social graph formed during the conference, i.e., friendship and sharing common friends. Using these parameters, we present a correlation study of encounter duration, frequency and distribution with the formation and strength of the social links. Results show that, on average, an increasing encounter duration between users leads to a high possibility of the establishments of social links, while afterwards this increment of encounter duration slows down after establishments of social links. We also find users that are highly sociable (with regards to the number of friends and common friends) indicate a higher proximity interaction with their friends, and similarity of a pair of users suggests more and longer encounters between them. This means, for two kinds of social links we select, there is a strong relation between social linking and physical proximity.

Predicting the next location change and time of change for mobile phone users

Proceedings of the Third ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems, 2014

Predicting the next location of people from their mobile phone logs has become an active research area. Due to two main reasons this problem is very challenging: the log data is very large and there are variety of granularity levels for specifying the spatial and the temporal attributes. In this work, we focus on predicting the next location change of the user and when this change occurs. Our method has two steps, namely clustering the spatial data into larger regions and grouping temporal data into time intervals to get higher granularity levels, and then, applying sequential pattern mining technique to extract frequent movement patterns to predict the change of the region of the user and its time frame. We have validated our results with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, and we have obtained very high accuracy results.

Assessing Physical Location as a Potential Contextual Cue For Adaptive Mobile Contact Lists (original) (raw)

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