Browsing by Author "Lee, Wang-Chien"

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A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining
(2018-07-01)
Shuai, Hong-Han
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Shen, Chih-Ya
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Yang, De-Nian
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Lan, Yi-Feng Carol
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Lee, Wang-Chien
The explosive growth in popularity of social networking leads to the problematic usage. An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental status cannot be directly observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires in Psychology. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the accuracy. To increase the scalability of STM, we further improve the efficiency with performance guarantee. Our framework is evaluated via a user study with 3,126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results manifest that SNMDD is promising for identifying online social network users with potential SNMDs.
A Novel System for Extracting Useful Correlation in Smart Home Environment
(2013)
Chen, Yi-Cheng
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Peng, Wen-Chih
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Lee, Wang-Chien
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資訊工程學系
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Department of Computer Science
Owing to the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much needed to discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance rather than mining the usage correlation among appliances. In this paper, a novel system, namely, Correlation Pattern Mining System (CPMS), is developed to capture the usage patterns and correlations among appliances. With several new optimization techniques, CPMS can reduce the search space effectively and efficiently. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.
CIM: Community-Based Influence Maximization in Social Networks
(2014-04-01)
Chen, Yi-Cheng
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Zhu, Wen-Yuan
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Peng, Wen-Chih
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Lee, Wang-Chien
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Lee, Suh-Yin
Given a social graph, the problem of influence maximization is to determine a set of nodes that maximizes the spread of influences. While some recent research has studied the problem of influence maximization, these works are generally too time consuming for practical use in a large-scale social network. In this article, we develop a new framework, community-based influence maximization (CIM), to tackle the influence maximization problem with an emphasis on the time efficiency issue. Our proposed framework, CIM, comprises three phases: (i) community detection, (ii) candidate generation, and (iii) seed selection. Specifically, phase (i) discovers the community structure of the network; phase (ii) uses the information of communities to narrow down the possible seed candidates; and phase (iii) finalizes the seed nodes from the candidate set. By exploiting the properties of the community structures, we are able to avoid overlapped information and thus efficiently select the number of seeds to maximize information spreads. The experimental results on both synthetic and real datasets show that the proposed CIM algorithm significantly outperforms the state-of-the-art algorithms in terms of efficiency and scalability, with almost no compromise of effectiveness.
Clustering and aggregating clues of trajectories for mining trajectory patterns and routes
(2015-04-01)
Hung, Chih-Chieh
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Peng, Wen-Chih
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Lee, Wang-Chien
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交大名義發表
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National Chiao Tung University
In this paper, we propose a new trajectory pattern mining framework, namely Clustering and Aggregating Clues of Trajectories (CACT), for discovering trajectory routes that represent the frequent movement behaviors of a user. In addition to spatial and temporal biases, we observe that trajectories contain silent durations, i.e., the time durations when no data points are available to describe the movements of users, which bring many challenging issues to trajectory pattern mining. We claim that a movement behavior would leave some clues in its various sampled/observed trajectories. These clues may be extracted from spatially and temporally co-located data points from the observed trajectories. Based on this observation, we propose clue-aware trajectory similarity to measure the clues between two trajectories. Accordingly, we further propose the clue-aware trajectory clustering algorithm to cluster similar trajectories into groups to capture the movement behaviors of the user. Finally, we devise the clue-aware trajectory aggregation algorithm to aggregate trajectories in the same group to derive the corresponding trajectory pattern and route. We validate our ideas and evaluate the proposed CACT framework by experiments using both synthetic and real datasets. The experimental results show that CACT is more effective in discovering trajectory patterns than the state-of-the-art techniques for mining trajectory patterns.
Energy-Aware Set-Covering Approaches for Approximate Data Collection in Wireless Sensor Networks
(2012-11-01)
Hung, Chih-Chieh
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Peng, Wen-Chih
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Lee, Wang-Chien
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資訊工程學系
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Department of Computer Science
To conserve energy, sensor nodes with similar readings can be grouped such that readings from only the representative nodes within the groups need to be reported. However, efficiently identifying sensor groups and their representative nodes is a very challenging task. In this paper, we propose a centralized algorithm to determine a set of representative nodes with high energy levels and wide data coverage ranges. Here, the data coverage range of a sensor node is considered to be the set of sensor nodes that have reading behaviors very close to the particular sensor node. To further reduce the extra cost incurred in messages for selection of representative nodes, a distributed algorithm is developed. Furthermore, maintenance mechanisms are proposed to dynamically select alternative representative nodes when the original representative nodes run low on energy, or cannot capture spatial correlation within their respective data coverage ranges. Using experimental studies on both synthesis and real data sets, our proposed algorithms are shown to effectively and efficiently provide approximate data collection while prolonging the network lifetime.
Energy-efficient and cost-effective web API invocations with transfer size reduction for mobile mashup applications
(2014-04-01)
Huang, Chen-Che
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Huang, Jiun-Long
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Tsai, Chin-Liang
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Wu, Guan-Zhong
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Chen, Chia-Min
Recently, the proliferation of smartphones and the extensive coverage of wireless networks have enabled numerous mobile users to access Web resources with smartphones. Mobile mashup applications are very attractive to smartphone users due to specialized services and user-friendly GUIs. However, to offer new services through the integration of Web resources via Web API invocations, mobile mashup applications suffer from high energy consumption and long response time. In this paper, we propose a proxy system and two techniques to reduce the size of data transfer, thereby enabling mobile mashup applications to achieve energy-efficient and cost-effective Web API invocations. Specifically, we design an API query language that allows mobile mashup applications to readily specify and obtain desired information by instructing a proxy to filter unnecessary information returned from Web API servers. We also devise an image multi-get module, which results in mobile mashup applications with smaller transfer sizes by combining multiple images and adjusting the quality, scale, or resolution of the images. With the proposed proxy and techniques, a mobile mashup application can rapidly retrieve Web resources via Web API invocations with lower energy consumption due to a smaller number of HTTP requests and responses as well as smaller response bodies. Experimental results show that the proposed proxy system and techniques significantly reduce transfer size, response time, and energy consumption of mobile mashup applications.
Exploring Pattern-Aware Travel Routes for Trajectory Search
(2013-06-01)
Wei, Ling-Yin
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Peng, Wen-Chih
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Lee, Wang-Chien
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資訊工程學系
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Department of Computer Science
With the popularity of positioning devices, Web 2.0 technology, and trip sharing services, many users are willing to log and share their trips on the Web. Thus, trip planning Web sites are able to provide some new services by inferring Regions-Of-Interest (ROIs) and recommending popular travel routes from trip trajectories. We argue that simply providing some travel routes consisting of popular ROIs to users is not sufficient. To tour around a wide geographical area, for example, a city, some users may prefer a trip to visit as many ROIs as possible, while others may like to stop by only a few ROIs for an in-depth visit. We refer to a trip fitting the former user group as an in-breadth trip and a trip suitable for the latter user group as an in-depth trip. Prior studies on trip planning have focused on mining ROIs and travel routes without considering these different preferences. In this article, given a spatial range and a user preference of depth/breadth specified by a user, we develop a Pattern-Aware Trajectory Search (PATS) framework to retrieve the top K trajectories passing through popular ROIs. PATS is novel because the returned travel trajectories, discovered from travel patterns hidden in trip trajectories, may represent the most valuable travel experiences of other travelers fitting the user's trip preference in terms of depth or breadth. The PATS framework comprises two components: travel behavior exploration and trajectory search. The travel behavior exploration component determines a set of ROIs along with their attractive scores by considering not only the popularity of the ROIs but also the travel sequential relationships among the ROIs. To capture the travel sequential relationships among ROIs and to derive their attractive scores, a user movement graph is constructed. For the trajectory search component of PATS, we formulate two trajectory score functions, the depth-trip score function and the breadth-trip score function, by taking into account the number of ROIs in a trajectory and their attractive scores. Accordingly, we propose an algorithm, namely, Bounded Trajectory Search (BTS), to efficiently retrieve the top K trajectories based on the two trajectory scores. The PATS framework is evaluated by experiments and user studies using a real dataset. The experimental results demonstrate the effectiveness and the efficiency of the proposed PATS framework.
Indexing spatial data in cloud data managements
(2014-12-01)
Wei, Ling-Yin
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Hsu, Ya-Ting
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Peng, Wen-Chih
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Lee, Wang-Chien
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資訊工程學系
With the proliferation of smart phones and location-based services, the amount of data with spatial information, referred to as spatial data, is dramatically increasing. Cloud computing plays an important role handling large-scale data analysis, and several cloud data managements (CDMs) have been developed for processing data. CDMs usually provide key-value storage, where each key is used to access its corresponding value. However, user-generated spatial data are usually distributed non-uniformly. In this paper, we present a novel key design based on an R+-tree (KR+-index) for retrieving skewed spatial data efficiently. In the experiments, we implement the KR+-index on Cassandra, and study its performance using spatial data. Experiments show that the KR+-index outperforms the-state-of-the-art methods. (C) 2013 Elsevier B.V. All rights reserved.
Mining Community Structures in Peer-to-Peer Environments
(2008)
Yu, Ching-Hua
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Peng, Wen-Chih
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Lee, Wang-Chien
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交大名義發表
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National Chiao Tung University
Most social networks exhibit community structures, in which nodes are tightly connected to each other within a community but only loosely connected to nodes in other communities. Researches on community mining have received a lot of attention; however, most of them are based on a centralized system model and thus not applicable to the distributed model of P2P networks. In this paper, we propose a distributed: community mining algorithm, namely Asynchronous Clustering and Merging scheme (ACM), for computing environments. Due to the dynamic and distributed nature of P2P networks, The ACM scheme employs an asynchronous strategy such that local clustering is executed without requiring an expensive global clustering to be performed in asynchronous fashion. Experimental results show that ACM is able to discover community structures with high quality while outperforming the existing approaches.
Newsfeed Filtering and Dissemination for Behavioral Therapy on Social Network Addictions
(2018-01-01)
Shuai, Hong-Han
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Lien, Yen-Chieh
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Yang, De-Nian
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Lan, Yi-Feng
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Lee, Wang-Chien
While the popularity of online social network (OSN) apps continues to grow, little attention has been drawn to the increasing cases of Social Network Addictions (SNAs). In this paper, we argue that by mining OSN data in support of online intervention treatment, data scientists may assist mental healthcare professionals to alleviate the symptoms of users with SNA in early stages. Our idea, based on behavioral therapy, is to incrementally substitute highly addictive newsfeeds with safer, less addictive, and more supportive newsfeeds. To realize this idea, we propose a novel framework, called Newsfeed Substituting and Supporting System (N3S), for newsfeed filtering and dissemination in support of SNA interventions. New research challenges arise in 1) measuring the addictive degree of a newsfeed to an SNA patient, and 2) properly substituting addictive newsfeeds with safe ones based on psychological theories. To address these issues, we first propose the Additive Degree Model (ADM) to measure the addictive degrees of newsfeeds to different users. We then formulate a new optimization problem aiming to maximize the efficacy of behavioral therapy without sacrificing user preferences. Accordingly, we design a randomized algorithm with a theoretical bound. A user study with 716 Facebook users and 11 mental healthcare professionals around the world manifests that the addictive scores can be reduced by more than 30%. Moreover, experiments show that the correlation between the SNA scores and the addictive degrees quantified by the proposed model is much greater than that of state-of-the-art preference based models.
On Finding Socially Tenuous Groups for Online Social Networks
(2017-01-01)
Shen, Chih-Ya
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Huang, Liang-Hao
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Yang, De-Nian
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Shuai, Hong-Han
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Lee, Wang-Chien
Existing research on finding social groups mostly focuses on dense subgraphs in social networks. However, finding socially tenuous groups also has many important applications. In this paper, we introduce the notion of k-triangles to measure the tenuity of a group. We then formulate a new research problem, Minimum k-Triangle Disconnected Group (MkTG), to find a socially tenuous group from online social networks. We prove that MkTG is NP-Hard and inapproximable within any ratio in arbitrary graphs but polynomial-time tractable in threshold graphs. Two algorithms, namely TERA and TERA-ADV, are designed to exploit graph theoretical approaches for solving MkTG on general graphs effectively and efficiently. Experimental results on seven real datasets manifest that the proposed algorithms outperform existing approaches in both efficiency and solution quality.
On VR Spatial Query for Dual Entangled Worlds
(2019-01-01)
Ko, Shao-Heng
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Lin, Ying-Chun
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Lai, Hsu-Chao
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Lee, Wang-Chien
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Yang, De-Nian
With the rapid advent of Virtual Reality (VR) technology and virtual tour applications, there is a research need on spatial queries tailored for simultaneous movements in both the physical and virtual worlds. Traditional spatial queries, designed mainly for one world, do not consider the entangled dual worlds in VR. In this paper, we first investigate the fundamental shortest-path query in VR as the building block for spatial queries, aiming to avoid hitting boundaries and obstacles in the physical environment by leveraging Redirected Walking (RW) in Computer Graphics. Specifically, we first formulate Dual-world Redirected-walking Obstacle-free Path (DROP) to find the minimum-distance path in the virtual world, which is constrained by the RW cost in the physical world to ensure immersive experience in VR. We prove DROP is NP-hard and design a fully polynomial-time approximation scheme, Dual Entangled World Navigation (DEWN), by finding Minimum Immersion Loss Range (MIL Range). Afterward, we show that the existing spatial query algorithms and index structures can leverage DEWN as a building block to support kNN and range queries in the dual worlds of VR. Experimental results and a user study with implementation in HTC VIVE manifest that DEWN outperforms the baselines with smoother RW operations in various VR scenarios.
Parallelizing Itinerary-Based KNN Query Processing in Wireless Sensor Networks
(2010-05-01)
Fu, Tao-Yang
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Peng, Wen-Chih
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Lee, Wang-Chien
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資訊工程學系
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Department of Computer Science
Wireless sensor networks have been proposed for facilitating various monitoring applications (e. g., environmental monitoring and military surveillance) over a wide geographical region. In these applications, spatial queries that collect data from wireless sensor networks play an important role. One such query is the K-Nearest Neighbor (KNN) query that facilitates collection of sensor data samples based on a given query location and the number of samples specified (i.e., K). Recently, itinerary-based KNN query processing techniques, which propagate queries and collect data along a predetermined itinerary, have been developed. Prior studies demonstrate that itinerary-based KNN query processing algorithms are able to achieve better energy efficiency than other existing algorithms developed upon tree-based network infrastructures. However, how to derive itineraries for KNN query based on different performance requirements remains a challenging problem. In this paper, we propose a Parallel Concentric-circle Itinerary-based KNN (PCIKNN) query processing technique that derives different itineraries by optimizing either query latency or energy consumption. The performance of PCIKNN is analyzed mathematically and evaluated through extensive experiments. Experimental results show that PCIKNN outperforms the state-of-the-art techniques.
Processing k nearest neighbor queries in location-aware sensor networks
(2007-12-01)
Xu, Yingqi
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Fu, Tao-Yang
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Lee, Wang-Chien
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Winter, Julian
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交大名義發表
Efficient search for k nearest neighbors to a given location point (called a KNN query) is an important problem arising in a variety of sensor network applications. In this paper, we investigate in-network query processing strategies under a KNN query processing framework in location-aware wireless sensor networks. A set of algorithms, namely the geo-routing tree, the KNN boundary tree and the itinerary-based KNN algorithms, are designed in accordance with the global infrastructure-based, local infrastructure-based and infrastructure-free strategies, respectively. They have distinctive performance characteristics and are desirable under different contexts. We evaluate the performance of these algorithms under several sensor network scenarios and application requirements, and identify the conditions under which the various approaches are preferable. (C) 2007 Elsevier B.V. All rights reserved.
Significant Correlation Pattern Mining in Smart Homes
(2015-05-01)
Chen, Yi-Cheng
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Peng, Wen-Chih
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Huang, Jiun-Long
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Lee, Wang-Chien
;
資訊工程學系
Owing to the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much needed to discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance rather than mining the usage correlation among appliances. In this article, a novel algorithm, namely Correlation Pattern Miner (CoPMiner), is developed to capture the usage patterns and correlations among appliances probabilistically. CoPMiner also employs four pruning techniques and a statistical model to reduce the search space and filter out insignificant patterns, respectively. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.
Social-Aware VR Configuration Recommendation via Multi-Feedback Coupled Tensor Factorization
(2019-01-01)
Lai, Hsu-Chao
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Shuai, Hong-Han
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Yang, De-Nian
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Huang, Jiun-Long
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Lee, Wang-Chien
Recent technological advent in virtual reality (VR) has attracted a lot of attention to the VR shopping, which thus far is designed for a single user. In this paper, we envision the scenario of VR group shopping, where VR supports: 1) flexible display of items to address diverse personal preferences, and 2) convenient view switching between personal and group views to foster social interactions. We formulate the Multiview-Enabled Configuration Recommendation (MECR) problem to rank a set of displayed items for a VR shopping user. We design the Multiview-Enabled Configuration Ranking System (MEIRS) that first extracts discriminative features based on Marketing theories and then introduces a new coupled tensor factorization model to learn the representation of users, Multi-View Display (MVD) configurations, and multiple feedback with content features. Experimental results manifest that the proposed approach outperforms personalized recommendations and group recommendations by at least 30.8% in large-scale datasets and 63.3% in the user study in terms of hit ratio and mean average precision.