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Life Log Management Based on Machine Learning Technique

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Life Log Management Based on Machine Learning Technique Life Log Management based on Machine Learning Technique Keum-Sung Hwang, Student Member, and Sung-Bae Cho, Senior Member, IEEE Abstract?Mobile devices have already shown great potential attracted the attention of researchers and developers. Recent in terms of providing customized services to users because they research conducted by Nokia is a good example [1]. can record meaningful and private information continually for In recent years, researchers have started making progress in long periods of time, so the research for understanding and effectively integrating context and content for personal managing the life log of human has received increasing attention information management. The integration of content and in recent years. In this paper, we propose a novel method for life log management based on the machine learning, which context derives understanding of personal information, so summarizes and manages the experiences of human. The method many context-aware computing studies such as intelligent uses an effective probabilistic network model for analyzing calling services [2], messaging services [3], analysis and various kinds of log data in mobile environments, which were management of mobile life logs [4][5][6] have been actively modularized to decrease complexity. We also propose a mobile investigated. life browser, which visualizes and searches human's mobile life The human memory model consists of short-term memory based on the contents and context of personal information. The mobile life browser is for searching the personal information and long-term memory (LTM). The LTM has implicit effectively collected on his/her mobile device and for supporting memory and explicit memory that can be recalled consciously. the concept-based searching method by using concept networks To exploit the explicit memory that includes semantic and Bayesian networks. memory (general knowledge memory) and episodic memory (specific experience), we need to extract well-refined retrieval Index Terms-- Life Log, Mobile Application, Context-aware cues. Horvitz, et al. tried to detect and estimate the retrieval Computing. cues called landmarks by discovering a given human's cognitive activity model from PC log data based on the I. INTRODUCTION Bayesian approach [7]. It showed that the probabilistic model ith the growth of technology for digital and personal is good for recognizing everyday-life of humans. Whandheld devices such as smart phones, the availability Mobile devices present some limitations such as relatively of ubiquitous devices to produce, store, view and insufficient memory capacity, lower CPU power exchange personal data of humans increases. Enormous (data-processing speed), smaller screen size, awkward input amount of contextual information of everyday-life, which is interface, and limited battery life compared to desktop PCs. In called life log, can be collected automatically and continuously by the diverse devices we use. The life log shows addition, they have to operate in the dynamic and changeable great potential in terms of diverse intelligent and customized real world, which means that they require more robustness services. against uncertainties occurring on mobile environment [8]. The personal mobile device such as cell phone is one of the In this paper, we propose a modular model of Bayesian most utilizable for the life log services. The personal mobile networks for effective analysis of mobile life log data and devices have better characteristics than desktop computers. extraction of semantic information and memory landmarks First of all, mobile devices can collect and manage various which are used as a special event for helping recall it [7]. We kinds of user information, for example, by logging user's calls, also studied and developed a mobile life browser by making a SMS (short message service), photography, music-playing database from the data collected on mobile devices and by and GPS (global positioning system) information. Also, analyzing and summarizing the log data and by visualizing the mobile devices can be customized to fit any given user's personal everyday-life efficiently. The mobile life browser preferences. Furthermore, mobile devices can collect enables us to manage personal life log data and supports to everyday information effectively. Such features allow for the develop diverse additional services by utilizing the possibility of diverse and convenient services, so they have information easily and supports the concept-based searching method by using concept networks and Bayesian networks. This research was supported by the MKE (Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research II. BACKGROUNDS Center) support program supervised by the IITA (Institute of Information Technology Assessment), IITA-2008-(C1090-0801-0011). A. Life Log K.-S. Hwang is with the Department of Computer Science, Yonsei Numerous sources are available for logging information. University, Seoul, Korea (telephone: +82-2-2123-4803, e-mail: yellowg@ cs.yonsei.ac.kr). For example, ContextPhone is context-logging software for S.-B. Cho is with the Department of Computer Science, Yonsei Nokia 60 smart phones [9], and its source is available to the University, Seoul, Korea (telephone: +82-2-2123-2720, e-mail: sbcho@ public. It collects information on a wide range of topics by cs.yonsei.ac.kr). logging photographs taken, music downloads, that was designed by human and used for the application of short-message-service use, multimedia messaging service use, this paper. It shows a DAG structure, node name, state name and Bluetooth use; monitoring the phone¡s battery level; and inferred probabilities. Since the problem of understanding storing call logs; and recording applications in use. However, of human's intention and inference of contexts accompany because ContextPhone is based on the Symbian operating much uncertainty, it is known that the probabilistic models system (OS), we also developed a logging module for the such as Bayesian network have advantage for solving it [15]. Windows mobile environment. In this paper, the Bayesian network model is also utilized for MyLifeBits is a Microsoft research project to create a understanding the user's life and extracting meaningful "lifetime store of everything." It includes full-text search, text information. and audio annotations, and hyperlinks. SenseCam is one of hardware developed for the project [10]. It stores photographs of everyday-life by monitoring the brightness and temperature of environment continuously. B. Life Browser Life browser is a tool for searching personal information effectively and is a term used for the first time by E. Horvitz of Microsoft Research [11]. Horvitz, et al. proposed a method that detected and estimated landmarks, which are used as a special event for helping recall it, by discovering a given Fig.1. An example of ConceptNet. human's cognitive activity model from PC log data based on previor work/when landmarks where Previor:Eating Coffee-shop the Bayesian approach [7]. Their approach visualized Yes 0% Yes 0% Teaing Ordinary-place No 100% No 100% Yes 2% Eating-out Yes 0% Previor:Stroll everyday-PC-life of humans with hierarchical structure and No 98% Yes 83% No 100% Yes 0% High-class-restaurant Lunch-time No 17% No 100% Eating(Fastfood) Yes 0% showed a good performance for recognizing and learning. Yes 100% Yes 20% No 100% No 0% No 80% Fastfood-shop LifeLog proposed by MIT reality mining group was a tool Eating Yes 0% Dinner-time Eating(Western) Korean-restaurant Yes 93% No 100% Yes 0% Yes 0% for visualizing the log information gathered by the logging Yes 90% No 7% No 100% No 10% No 100% software developed by Helsinki University. It was developed Breakfast-time Meal-time Eating(Eastern) Restaurant Yes 0% Yes 90% Yes 18% Yes 100% for simple automatic diary [12]. The Lifeblog software of No 100% No 10% No 82% No 0% Nokia afforded new opportunities to capture, manipulate and Fig. 2. The landmark inference BN ¡Activity in restaurant¡ designed and used communicate daily events and thoughts that assisted in in this paper. This is one of 39 BNs deigned. The node of gray box is making meaning in individual and collective contexts [1]. landmark node. In [13], the authors proposed a method for identifying semantic information on mobile devices and developed III. THE MOBILE LIFE BROWSER AniDiary software making cartoon diary of mobile users The overall process of landmark extraction from the mobile automatically. The preliminary investigation is the foundation log data and life log visualization is shown in Fig. 3. Various of the present work. mobile log data is preprocessed in advance, and then the C. ConceptNet landmark-reasoning module detects the landmarks. The preprocessing module is operated by the techniques of pattern ConceptNet developed by MIT Media Laboratory is a recognition and simple rule reasoning. The BN reasoning very-large semantic network of common sense knowledge and module performs probabilistic inference [13]. practical textual reasoning toolkit [14]. It aggregates The map-based visualization module shows the map-based 1,600,000 common sense knowledge extracted by heuristic browser interface to the user and supports concept-based rules from 700,000 sentences of Open Mind Common Sense searching capability, because it is developed to operate on (OMCS) project. The knowledge elements are linked with web environment with using open source API and database temporal, spatial, physical, social, and mental relationships
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