Hong-Han Shuai

MobiUP: An Upsampling-Based System Architecture for High-Quality Video Streaming on Mobile Devices

Nowadays, mobile video streaming enables people to access digital content, such as online TV shows, music videos, sports reports, and news programs, anytime, anywhere. However, current streaming services in mobile networks are subject to the available wireless bandwidth shared among many users and can only provide videos with limited resolutions. Moreover, on recently developed high-resolution mobile devices, such as iPhone, Google Nexus One, Nokia N97, and SonyEricsson X10, the resolution of video streaming is much lower than the devices can actually support. As a result, existing video upsampling schemes usually introduce visual artifacts. In response to the above problem, we bridge the resolution gap between streaming videos and client screens, and propose a novel upsampling-based system architecture, called MobiUP, to enable high-quality video streaming onto mobile devices. To avoid modifying existing codecs for video streaming, MobiUP upsamples videos with decoded frames and appends a limited amount of metadata to the streaming videos for facilitating high-quality and real-time conversion from low resolution to high fullscreen resolution on the client side. In other words, the proposed upsampling architecture complements current systems. Therefore, MobiUP is generic and flexible, and it can be implemented easily on mobile devices for practical use. The implementation results demonstrate that, although the appended metadata is less than 8% of the total transmitted data, it improves the quality of the upsampled video significantly. Meanwhile, the computation time of MobiUP Client is close to that of bilinear upsampling algorithms implemented on mobile devices.

When Social Influence Meets Item Inference

Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in the form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines.

Distributed and scalable sequential pattern mining through stream processing

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.

Revenue maximization for telecommunications company with social viral marketing

Viral marketing, a marketing strategy that leverages the influence power in intimate relationship, has become more prevalent due to the popularity of online social networking services in recent years. Consumers are more likely to make a purchase based on social media referrals. Since marketing through social media and traditional channels may target on different audiences, how to maximize the revenue of a telecommunications company by employing different advertising ways and selecting initial users for advertisements is a critical problem. Therefore, in this paper, we formulate a new research problem, namely Cost-Aware Multi-wAy Influence maXimization (CAMAIX) to address the need mentioned above. We design a 1/2-approximation algorithm with various pruning and budget allocation strategies to solve CAMAIX efficiently. We conduct extensive experiments on a large-scale real dataset from a telecommunications company. The results show that our proposed algorithm outperforms the baseline algorithms in both solution quality and efficiency.

Data Acquisition for Probabilistic Nearest-Neighbor Query

Management of uncertain data in spatial queries has drawn extensive research interests to consider the granularity of devices and noises in the collection and the delivery of data. Most previous works usually model and handle uncertain data to find the required results directly. However, it is more difficult for users to obtain useful insights when data uncertainty dramatically increases. In this case, users are usually willing to invest more resources to improve the result by reducing the data uncertainty in order to obtain more interesting observations with the existing schemes. In light of this important need, this paper formulates a new problem of selecting a given number of uncertain data objects for acquiring their attribute values to improve the result of the Probabilistic k-Nearest-Neighbor (k-PNN) query. We prove that better query results are guaranteed to be returned with data acquisition, and we devise several algorithms to maximize the expected improvement. We first explore the optimal single-object acquisition for 1-PNN to examine the fundamental problem structure and then propose an efficient algorithm that discovers crucial properties to simplify the probability derivation in varied situations. We extend the proposed algorithm to achieve the optimal multi-object acquisition for 1-PNN by deriving an upper bound to facilitate efficient pruning of unnecessary sets of objects. Moreover, for data acquisition of k-PNN, we extract the k-PNN answers with sufficiently large probabilities to trim the search space and properly exploit the result of single-object acquisition for estimating the gain from multi-object acquisition. The experimental results demonstrate that the probability of k-PNN can be significantly improved even with only a small number of objects for data acquisition.

On Finding Socially Tenuous Groups for Online Social Networks

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.

Newsfeed Filtering and Dissemination for Behavioral Therapy on Social Network Addictions

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.

A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining

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.