On the Feature Discovery for App Usage Prediction in Smartphones

With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a smartphone. Such an Apps usage prediction framework is a crucial prerequisite for fast App launching, intelligent user experience, and power management of smartphones. By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations. The IF feature is derived by constructing the proposed App Usage Graph (abbreviated as AUG) that models App usage transitions. In light of AUG, we are able to discover usage relations among Apps. Since users may have different usage behaviors on their smartphones, we further propose one personalized feature selection algorithm. We explore minimum description length (MDL) from the training data and select those features which need less length to describe the training data. The personalized feature selection can successfully reduce the log size and the prediction time. Finally, we adopt the kNN classification model to predict Apps usage. Note that through the features selected by the proposed personalized feature selection algorithm, we only need to keep these features, which in turn reduces the prediction time and avoids the curse of dimensionality when using the kNN classifier. We conduct a comprehensive experimental study based on a real mobile App usage dataset. The results demonstrate the effectiveness of the proposed framework and show the predictive capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape

Semantic Activity Classification Using Locomotive Signatures from Mobile Phones

We explore the use of mobile phone-generated sensor feeds to determine the high-level (i.e., at the semantic level), indoor, lifestyle activities of individuals, such as cooking & dining at home and working & having lunch at the work- place. We propose and evaluate a 2-Tier activity extraction framework (called SAMMPLE1) where features of the low-level accelerometer data are first used to identify individual locomotive micro-activities (e.g., sitting or standing), and the micro-activity sequence is subsequently used to identify the discriminatory characteristics of individual semantic activities. Using 152 days of real-life behavioral traces from users, our approach achieves an average accuracy of 77.14%, an improvement of 16.37% from the traditional 1-Tier approach, which directly uses statistical features of the accelerometer stream, towards such activity classification tasks

Classification and prediction of whereabouts patterns from the Reality Mining dataset

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Mobile data in handcuffs : how limited mobile data affect people’s behavior on mobile data usage and megabyte allocation in different locations

In the era of mobile data, there have been many changes regarding human behavior on data consumption. Having abundant or “unlimited” data is becoming more and more common. However, what would happen to the behaviors of people if they were under extreme conditions of data limitations. This study shows the behavioral outcome of the participants of this survey when exposed to limitations regarding data consumption. Some of the most chosen applications in terms of importance when travelling abroad and when staying home were mobility applications, communication, social media, and restaurant search downloads. There exist many different types of research on mobile data but very few highlight the importance of behavioral change and data prioritization. Under the umbrella of mobile data, many different businesses are being disrupted due to the mass interconnectivity of people and differentiated networks. Using real-life data, this study shows us that people do prioritize their applications when exposed to certain data limitations in different scenarios. The outcomes of the survey show us businesses could be affected by mobile data and the pulling of information that participants have. The exposure to limited data further increased this disruption as people only require applications that were necessary and most of these were related to some specific industries such as tourism and gastronomy. This study shows us that people in general, prefer to download information with an average mean of 70.2 megabytes rather than upload information with an average mean of 29.8 with a certain pattern in application prioritization.Em tempos modernos com o crescimento da importância do mundo digital, tem havido muitas mudanças no comportamento dos utilizadores de dados móveis. Cada vez mais, os consumidores têm acesso a tarifários de dados móveis ilimitados. Porém, o que aconteceria se o uso de dados viesse a ser limitado? Este estudo analisa as mudanças nos comportamentos dos participantes quando confrontados com limitações no tarifário. Parte das aplicações mais importantes para o consumidor são aplicações dos setores da mobilidade, comunicação, redes sociais e pesquisas de restaurantes. Existem diferentes investigações na área dos dados móveis, mas poucas dos mesmas realçam a importância das mudanças no comportamento e na priorização dos dados. Com o crescimento dos dados móveis, múltiplos negócios têm sido perturbados pela interconectividade dos consumidores e pela diferenciação das redes. Através de dados reais, este estudo mostra como as pessoas priorizam as suas aplicações quando expostas a limitações nos dados móveis. Os resultados da nossa pesquisa mostram-nos que, de facto, certos negócios podem ser afetados pelos dados móveis e pela sondagem de informação sujeita aos mesmos. A exposição à limitação de dados continuou a aumentar esta distorção, visto que os consumidores só usaram as aplicações que consideram mais importantes, a maioria dos quais relacionados com indústrias específicas como o turismo e a gastronomia. O estudo concluí que os consumidores no geral preferem ter velocidades de download de 70.2 megabytes por segundo do que velocidades de upload de 29.8 megabytes

A Picture of Present Ubicomp Research Exploring Publications from Important Events in the Field

In this work we use a dataset of papers published in top conferences focused on ubiquitous computing (ubicomp) to provide an overview and analysis of recent ubiquitous computing research performed internationally and in Brazil. The contributions of this study are twofold. First, we extracted useful information from our dataset such as representativeness of authors and institutions, and the formation of communities. Second, we analyzed all papers published between 2010 and 2011 in all top international conferences, creating a taxonomy of recent ubicomp research performed internationally. Afterthat we mapped SBCUP papers (Brazilian ubicomp conference) according to this taxonomy, which enables the comparison of international and national research. This study is useful to guide novices in the field and it also provides experienced researchers with facts enabling the discussion of ubicomp research.Key words: Ubiquitous computing, scientific network, collaboration network, Pervasive, Percom, Ubicomp, SBCUP, taxonomy, characterization

A Learning-based Approach to Exploiting Sensing Diversity in Performance Critical Sensor Networks

Wireless sensor networks for human health monitoring, military surveillance, and disaster warning all have stringent accuracy requirements for detecting and classifying events while maximizing system lifetime. to meet high accuracy requirements and maximize system lifetime, we must address sensing diversity: sensing capability differences among both heterogeneous and homogeneous sensors in a specific deployment. Existing approaches either ignore sensing diversity entirely and assume all sensors have similar capabilities or attempt to overcome sensing diversity through calibration. Instead, we use machine learning to take advantage of sensing differences among heterogeneous sensors to provide high accuracy and energy savings for performance critical applications.;In this dissertation, we provide five major contributions that exploit the nuances of specific sensor deployments to increase application performance. First, we demonstrate that by using machine learning for event detection, we can explore the sensing capability of a specific deployment and use only the most capable sensors to meet user accuracy requirements. Second, we expand our diversity exploiting approach to detect multiple events using a distributed manner. Third, we address sensing diversity in body sensor networks, providing a practical, user friendly solution for activity recognition. Fourth, we further increase accuracy and energy savings in body sensor networks by sharing sensing resources among neighboring body sensor networks. Lastly, we provide a learning-based approach for forwarding event detection decisions to data sinks in an environment with mobile sensor nodes

Multi-Dimensional-Personalization in mobile contexts

During the dot com era the word "personalisation” was a hot buzzword. With the fall of the dot com companies the topic has lost momentum. As the killer application for UMTS or the mobile internet has yet to be identified, the concept of Multi-Dimensional-Personalisation (MDP) could be a candidate. Using this approach, a recommendation of mobile advertisement or marketing (i.e., recommendations or notifications), online content, as well as offline events, can be offered to the user based on their known interests and current location. Instead of having to request or pull this information, the new service concept would proactively provide the information and services – with the consequence that the right information or service could therefore be offered at the right place, at the right time. The growing availability of "Location-based Services“ for mobile phones is a new target for the use of personalisation. "Location-based Services“ are information, for example, about restaurants, hotels or shopping malls with offers which are in close range / short distance to the user. The lack of acceptance for such services in the past is based on the fact that early implementations required the user to pull the information from the service provider. A more promising approach is to actively push information to the user. This information must be from interest to the user and has to reach the user at the right time and at the right place. This raises new requirements on personalisation which will go far beyond present requirements. It will reach out from personalisation based only on the interest of the user. Besides the interest, the enhanced personalisation has to cover the location and movement patterns, the usage and the past, present and future schedule of the user. This new personalisation paradigm has to protect the user’s privacy so that an approach supporting anonymous recommendations through an extended "Chinese Wall“ will be described