Browsing by Author "Chen, Ming-Syan"

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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 QoS-aware and energy-conserving transcoding proxy using on-demand data broadcasting
(2007-08-01)
Huang, Jiun-Long
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Chen, Ming-Syan
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資訊工程學系
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Department of Computer Science
Most research works in transcoding proxies in mobile computing environments are on the basis of the traditional client-server architecture and do not employ the data broadcast technique. In addition, the issues of QoS provision and energy conservation are also not addressed in the prior studies. In view of this, we design in this paper a QoS-aware and energy-conserving transcoding proxy by utilizing the on-demand broadcasting technique. We first propose a QoS-aware and energy-conserving transcoding proxy architecture, abbreviated as QETP, and model it as a queuing network consisting of three queues. By analyzing the queuing network, three lemmas are derived to estimate the load of these queues. We then propose a version decision policy and a service admission control scheme to provide QoS in QETP. The derived lemmas are used to guide the execution of the proposed version decision policy and service admission control scheme to achieve the given QoS requirement. In addition, we also propose a data indexing method to reduce the power consumption of clients. To measure the performance of the proposed architecture, three experiments are conducted. Experimental results show that the average access time reduction of the proposed scheme over the traditional client-server architecture ranges from 45 percent to 75 percent. Experimental results also show that the proposed scheme is more scalable than the traditional client-server architecture and is able to effectively control the system load to attain the given QoS requirements. In addition, the proposed scheme is able to greatly reduce the average tuning time of clients at the cost of a slight increase ( around 5 percent in our experiments) in average access time.
Concurrent Packet Recovery for Distributed Uplink Multiuser MIMO Networks
(2016-12-01)
Shen, Wei-Liang
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Lin, Kate Ching-Ju
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Cheng, Wan-Jie
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Qiu, Lili
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Chen, Ming-Syan
While recent works on multiuser MIMO (MU-MIMO) mainly focus on boosting the throughput by enabling concurrent transmissions, less attention is paid to recovering concurrent erroneous streams. We however notice that uplink MU-MIMO is especially vulnerable to errors because error in any stream corrupts most of the other concurrent streams. Even worse, carrier sense and rate adaptation become more challenging in MU-MIMO, which further decreases reliability. Existing systems however recover errors by retransmitting all the streams in a corrupted packet, thereby taking away the significant performance benefit of MU-MIMO. To effectively harness the ideal gain of MU-MIMO, we develop Concurrent Packet Recovery (CPR), a recovery protocol customized for MU-MIMO. It has two distinctive features: (i) It judiciously selects the minimum number of streams to be retransmitted to support successful decoding; (ii) during retransmission, it utilizes the full degrees of freedom by allowing new streams to be sent in parallel. Our evaluation via testbed experiments and trace-driven simulations shows that CPR can efficiently recover both normal losses and collisions. For three-antenna AP scenarios, the throughput gain is up to 31.1 percent when no hidden terminal exists, and is 3.16x when 20 percent pairs of contending clients are hidden terminals.
Distributed and scalable sequential pattern mining through stream processing
(2017-11-01)
Chen, Chun-Chieh
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Shuai, Hong-Han
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Chen, Ming-Syan
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電機工程學系
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Department of Electrical and Computer Engineering
Scalability is a primary issue in existing sequential pattern mining algorithms for dealing with a large amount of data. Previous work, namely sequential pattern mining on the cloud (SPAMC), has already addressed the scalability problem. It supports the MapReduce cloud computing architecture for mining frequent sequential patterns on large datasets. However, this existing algorithm does not address the iterative mining problem, which is the problem that reloading data incur additional costs. Furthermore, it did not study the load balancing problem. To remedy these problems, we devised a powerful sequential pattern mining algorithm, the sequential pattern mining in the cloud-uniform distributed lexical sequence tree algorithm (SPAMC-UDLT), exploiting MapReduce and streaming processes. SPAMC-UDLT dramatically improves overall performance without launching multiple MapReduce rounds and provides perfect load balancing across machines in the cloud. The results show that SPAMC-UDLT can significantly reduce execution time, achieves extremely high scalability, and provides much better load balancing than existing algorithms in the cloud.
Exploring group moving pattern for an energy-constrained object tracking sensor network
(2007)
Tsai, Hsiao-Ping
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Yang, De-Nian
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Peng, Wen-Chih
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Chen, Ming-Syan
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交大名義發表
In this paper, we investigate and utilize the characteristic of the group movement of objects to achieve energy conservation in the inherently resource-constrained wireless object tracking sensor network (OTSN). We propose a novel mining algorithm that consists of a global mining and a local mining to leverage the group moving pattern. We use the VMM model together with Probabilistic Suffix Tree (PST) in learning the moving patterns, as well as Highly Connected Component (HCS) that is a clustering algorithm based on graph connectivity for moving pattern clustering in our mining algorithm. Based on the mined out group relationship and the group moving patterns, a hierarchically predict ion-based query algorithm and a group data aggregation algorithm are proposed. Our experiment results show that the energy consumption in terms of the communication cost for our system is better than that of the conventional query/update based OTSN, especially in the case that on-tracking objects have the group moving characteristics.
Flow Classification for Software-Defined Data Centers Using Stream Mining
(2019-01-01)
Chao, Shou-Chieh
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Lin, Kate Ching-Ju
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Chen, Ming-Syan
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資訊工程學系
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Department of Computer Science
Traffic management is known to be important to effectively utilize the high bandwidth provided by datacenters. Recent works have focused on identifying elephant flows and rerouting them to improve network utilization. These approaches however require either a significant monitoring overhead or hardware/end-host modifications. In this paper, we propose FlowSeer, a fast, low-overhead elephant flow detection and scheduling system using data stream mining. Our key idea is that the features from flows' first few packets allow us to train the streaming classification models that can accurately and quickly predict the rate and duration of any initiated flow. With these predicted information, FlowSeercan adapt routing polices of elephant flows to their demands and dynamic network conditions. Another nice property of FlowSeeris its capability of enabling the controller and switches to perform cooperative prediction. Most of decisions can be made by switches locally, thereby reducing both detection latency and signaling overhead. FlowSeerrequires less than 100 flow table entries at each switch to enable cooperative prediction, and hence can be implemented on off-the-shelf switches. The evaluation via both experiments in realistic virtual networks and trace-driven simulations shows that FlowSeerimproves the throughput by multiple times over Hedera, which pulls flow statistics, and performs comparably to Mahout, which needs end-host modification.
Highly Parallel Sequential Pattern Mining on a Heterogeneous Platform
(2018-01-01)
Hsieh, Yu-Heng
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Chen, Chun-Chieh
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Shuai, Hong-Han
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Chen, Ming-Syan
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電子工程學系及電子研究所
Sequential pattern mining can be applied to various fields such as disease prediction and stock analysis. Many algorithms have been proposed for sequential pattern mining, together with acceleration methods. In this paper, we show that a heterogeneous platform with CPU and GPU is more suitable for sequential pattern mining than traditional CPU-based approaches since the support counting process is inherently succinct and repetitive. Therefore, we propose the PArallel SequenTial pAttern mining algorithm, referred to as PASTA, to accelerate sequential pattern mining by combining the merits of CPU and GPU computing. Explicitly, PASTA adopts the vertical bitmap representation of database to exploits the GPU parallelism. In addition, a pipeline strategy is proposed to ensure that both CPU and GPU on the heterogeneous platform operate concurrently to fully utilize the computing power of the platform. Furthermore, we develop a swapping scheme to mitigate the limited memory problem of the GPU hardware without decreasing the performance. Finally, comprehensive experiments are conducted to analyze PASTA with different baselines. The experiments show that PASTA outperforms the state-of-the-art algorithms by orders of magnitude on both real and synthetic datasets.
Mining top-k frequent patterns in the presence of the memory constraint
(2008-08-01)
Chuang, Kun-Ta
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Huang, Jiun-Long
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Chen, Ming-Syan
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資訊工程學系
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Department of Computer Science
We explore in this paper a practicably interesting mining task to retrieve top-k (closed) itemsets in the presence of the memory constraint. Specifically, as opposed to most previous works that concentrate on improving the mining efficiency or on reducing the memory size by best effort, we first attempt to specify the available upper memory size that can be utilized by mining frequent itemsets. To comply with the upper bound of the memory consumption, two efficient algorithms, called MTK and MTK_Close, are devised for mining frequent itemsets and closed itemsets, respectively, without specifying the subtle minimum support. Instead, users only need to give a more human-understandable parameter, namely the desired number of frequent (closed) itemsets k. In practice, it is quite challenging to constrain the memory consumption while also efficiently retrieving top-k itemsets. To effectively achieve this, MTK and MTK_Close are devised as level-wise search algorithms, where the number of candidates being generated-and-tested in each database scan will be limited. A novel search approach, called delta-stair search, is utilized in MTK and MTK_Close to effectively assign the available memory for testing candidate itemsets with various itemset-lengths, which leads to a small number of required database scans. As demonstrated in the empirical study on real data and synthetic data, instead of only providing the flexibility of striking a compromise between the execution efficiency and the memory consumption, MTK and MTK_Close can both achieve high efficiency and have a constrained memory bound, showing the prominent advantage to be practical algorithms of mining frequent patterns.
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.
Power-law relationship and self-similarity in the itemset support distribution: analysis and applications
(2008-08-01)
Chuang, Kun-Ta
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Huang, Jiun-Long
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Chen, Ming-Syan
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資訊工程學系
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Department of Computer Science
In this paper, we identify and explore that the power-law relationship and the self-similar phenomenon appear in the itemset support distribution. The itemset support distribution refers to the distribution of the count of itemsets versus their supports. Exploring the characteristics of these natural phenomena is useful to many applications such as providing the direction of tuning the performance of the frequent-itemset mining. However, due to the explosive number of itemsets, it is prohibitively expensive to retrieve lots of itemsets before we identify the characteristics of the itemset support distribution in targeted data. As such, we also propose a valid and cost-effective algorithm, called algorithm PPL, to extract characteristics of the itemset support distribution. Furthermore, to fully explore the advantages of our discovery, we also propose novel mechanisms with the help of PPL to solve two important problems: (1) determining a subtle parameter for mining approximate frequent itemsets over data streams; and (2) determining the sufficient sample size for mining frequent patterns. As validated in our experimental results, PPL can efficiently and precisely identify the characteristics of the itemset support distribution in various real data. In addition, empirical studies also demonstrate that our mechanisms for those two challenging problems are in orders of magnitude better than previous works, showing the prominent advantage of PPL to be an important pre-processing means for mining applications.
User-Centric Network MIMO With Dynamic Clustering
(2017-06-01)
Lin, Kate Ching-Ju
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Shen, Wei-Liang
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Chen, Ming-Syan
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Tan, Kun
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資訊工程學系
Recent advances have demonstrated the potential of network MIMO (netMIMO), which combines a practical number of distributed antennas as a virtual netMIMO AP (nAP) to improve spatial multiplexing of an WLAN. Existing solutions, however, either simply cluster nearby antennas as static nAPs, or dynamically cluster antennas on a per-packet basis so as to maximize the sum rate of the scheduled clients. To strike the balance between the above two extremes, in this paper, we present the design, implementation and evaluation of FlexNEMO, a practical two-phase netMIMO clustering system. Unlike previous perpacket clustering approaches, FlexNEMO only clusters antennas when client distribution and traffic pattern change, as a result being more practical to be implemented. A medium access control protocol customized for uplink transmissions is then designed to allow the clients at the center of nAPs to have a higher probability to gain uplink access opportunities, but still ensure long-term fairness among clients. By combining on-demand clustering and priority-based access control, FlexNEMO not only improves antenna utilization, but also optimizes the channel condition for every individual client. We evaluated our design via both test bed experiments on USRPs and trace-driven emulations. The results demonstrate that FlexNEMO can deliver 94.7% and 93.7% throughput gains over static antenna clustering in a 4-antenna test bed and 16-antenna emulation, respectively.