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Articles 4561 - 4590 of 8495
Full-Text Articles in Computer Sciences
Geometric Approaches For Top-K Queries [Tutorial], Kyriakos Mouratidis
Geometric Approaches For Top-K Queries [Tutorial], Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
Top-k processing is a well-studied problem with numerous applications that is becoming increasingly relevant with the growing availability of recommendation systems and decision-making software. The objective of this tutorial is twofold. First, we will delve into the geometric aspects of top-k processing. Second, we will cover complementary features to top-k queries, with strong practical relevance and important applications, that have a computational geometric nature. The tutorial will close with insights in the effect of dimensionality on the meaningfulness of top-k queries, and interesting similarities to nearest neighbor search.
Semantic Visualization For Short Texts With Word Embeddings, Van Minh Tuan Le, Hady W. Lauw
Semantic Visualization For Short Texts With Word Embeddings, Van Minh Tuan Le, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Semantic visualization integrates topic modeling and visualization, such that every document is associated with a topic distribution as well as visualization coordinates on a low-dimensional Euclidean space. We address the problem of semantic visualization for short texts. Such documents are increasingly common, including tweets, search snippets, news headlines, or status updates. Due to their short lengths, it is difficult to model semantics as the word co-occurrences in such a corpus are very sparse. Our approach is to incorporate auxiliary information, such as word embeddings from a larger corpus, to supplement the lack of co-occurrences. This requires the development of a …
Toward Accurate Network Delay Measurement On Android Phones, Weichao Li, Daoyuan Wu, Rocky K. C. Chang, Ricky K. P. Mok
Toward Accurate Network Delay Measurement On Android Phones, Weichao Li, Daoyuan Wu, Rocky K. C. Chang, Ricky K. P. Mok
Research Collection School Of Computing and Information Systems
Measuring and understanding the performance of mobile networks is becoming very important for end users and operators. Despite the availability of many measurement apps, their measurement accuracy has not received sufficient scrutiny. In this paper, we appraise the accuracy of smartphone-based network performance measurement using the Android platform and the network round-trip time (RTT) as the metric. We show that two of the most popular measurement apps-Ookla Speedtest and MobiPerf-have their RTT measurements inflated. We build three test apps that cover three common measurement methods and evaluate them in a testbed. We overcome the main challenge of obtaining a complete …
Hibs-Ksharing: Hierarchical Identity-Based Signature Key Sharing For Automotive, Zhuo Wei, Yanjiang Yang, Yongdong Wu, Jian Weng, Robert H. Deng
Hibs-Ksharing: Hierarchical Identity-Based Signature Key Sharing For Automotive, Zhuo Wei, Yanjiang Yang, Yongdong Wu, Jian Weng, Robert H. Deng
Research Collection School Of Computing and Information Systems
Equipped with various sensors and intelligent systems, modern cars turn into entities with connectivity, autonomy, and safety. Car rental/car sharing is an innovative transportation concept and integral in today's urban living. It enables users to access a fleet of vehicles located throughout cities. Complementing public transportation, the car-sharing service helps people to meet their transportation needs economically and in an environmentally responsible manner. When a customer wants to rent a car from a rental company or an owner wants to share a private car with his/her friends or family members, the customer or the user should gain admission to the …
Smartphone Sensing Meets Transport Data: A Collaborative Framework For Transportation Service Analytics, Yu Lu, Archan Misra, Wen Sun, Huayu Wu
Smartphone Sensing Meets Transport Data: A Collaborative Framework For Transportation Service Analytics, Yu Lu, Archan Misra, Wen Sun, Huayu Wu
Research Collection School Of Computing and Information Systems
We advocate for and introduce TRANSense, a framework for urban transportation service analytics that combines participatory smartphone sensing data with city-scale transportation-related transactional data (taxis, trains etc.). Our work is driven by the observed limitations of using each data type in isolation: (a) commonly-used anonymous city-scale datasets (such as taxi bookings and GPS trajectories) provide insights into the aggregate behavior of transport infrastructure, but fail to reveal individual-specific transport experiences (e.g., wait times in taxi queues); while (b) mobile sensing data can capture individual-specific commuting-related activities, but suffers from accuracy and energy overhead challenges due to usage artefacts and lack …
Secure Encrypted Data Deduplication With Ownership Proof And User Revocation, Wenxiu Ding, Zheng Yan, Robert H. Deng
Secure Encrypted Data Deduplication With Ownership Proof And User Revocation, Wenxiu Ding, Zheng Yan, Robert H. Deng
Research Collection School Of Computing and Information Systems
Cloud storage as one of the most important cloud services enables cloud users to save more data without enlarging its own storage. In order to eliminate repeated data and improve the utilization of storage, deduplication is employed to cloud storage. Due to the concern about data security and user privacy, encryption is introduced, but incurs new challenge to cloud data deduplication. Existing work cannot achieve flexible access control and user revocation. Moreover, few of them can support efficient ownership proof, especially public verifiability of ownership. In this paper, we propose a secure encrypted data deduplication scheme with effective ownership proof …
Integrating Apache Spark And R For Big Data Analytics On Solving Geographic Problems, Mengqi Zhang, Tin Seong Kam
Integrating Apache Spark And R For Big Data Analytics On Solving Geographic Problems, Mengqi Zhang, Tin Seong Kam
Research Collection School Of Computing and Information Systems
With the advent ofdigital technology and smart devices, a flood of digital data is beinggenerated every day. This huge amount of data not only records the historyactivities but also provides future valuable information for organizations andbusinesses. However, the true values of these data will not be fullyappreciated until they have been processed, analyzed and the analysis resultsbeen communicated to decision makers in a business friendly manner.In view of thisneed, big data has been one of the major research focus in the academicresearch community especially in the field of computer science and the softwarevendor as well as the big data service …
Predicting Potential Alzheimer Medical Condition In Elderly Using Iot Sensors - Case Study, Zhi Hao Kevin Chong, Yu Xuan Tee, Ling Jing Toh, Shi Jia Phang, Jie Ying Liew, Bertran Queck, Swapna Gottipati
Predicting Potential Alzheimer Medical Condition In Elderly Using Iot Sensors - Case Study, Zhi Hao Kevin Chong, Yu Xuan Tee, Ling Jing Toh, Shi Jia Phang, Jie Ying Liew, Bertran Queck, Swapna Gottipati
Research Collection School Of Computing and Information Systems
Ageing population would cause profound problems and the impact is already being felt today in many developed countries such as Singapore. The main concern for the Government is to help the citizens with active ageing through home ownership and good healthcare. With Internet of Things (IoT) gaining traction globally, Singapore is set to take advantage of this technology and leverage it to extend its capabilities towards a graceful Ageing-In-Place for the elderly. This ties in nicely with the expertise of SHINE Seniors project by SMU-iCity Lab, which integrates IT with healthcare in ways that creates innovative IT health solutions that …
Modeling Trajectories With Recurrent Neural Networks, Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, Wei Wang
Modeling Trajectories With Recurrent Neural Networks, Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, Wei Wang
Research Collection School Of Computing and Information Systems
Modeling trajectory data is a building block for many smart-mobility initiatives. Existing approaches apply shallow models such as Markov chain and inverse reinforcement learning to model trajectories, which cannot capture the long-term dependencies. On the other hand, deep models such as Recurrent Neura lNetwork (RNN) have demonstrated their strength of modeling variable length sequences. However, directly adopting RNN to model trajectories is not appropriate because of the unique topological constraints faced by trajectories. Motivated by these findings, we design two RNN-based models which can make full advantage of the strength of RNN to capture variable length sequence and meanwhile to …
Deepfacade: A Deep Learning Approach To Facade Parsing, Hantang Liu, Jialiang Zhang, Jianke Zhu, Steven C. H. Hoi
Deepfacade: A Deep Learning Approach To Facade Parsing, Hantang Liu, Jialiang Zhang, Jianke Zhu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
The parsing of building facades is a key component to the problem of 3D street scenes reconstruction, which is long desired in computer vision. In this paper, we propose a deep learning based method for segmenting a facade into semantic categories. Man-made structures often present the characteristic of symmetry. Based on this observation, we propose a symmetric regularizer for training the neural network. Our proposed method can make use of both the power of deep neural networks and the structure of man-made architectures. We also propose a method to refine the segmentation results using bounding boxes generated by the Region …
Mechanism Design For Strategic Project Scheduling, Pradeep Varakantham, Na Fu
Mechanism Design For Strategic Project Scheduling, Pradeep Varakantham, Na Fu
Research Collection School Of Computing and Information Systems
Organizing large scale projects (e.g., Conferences, IT Shows, F1 race) requires precise scheduling of multiple dependent tasks on common resources where multiple selfish entities are competing to execute the individual tasks. In this paper, we consider a well studied and rich scheduling model referred to as RCPSP (Resource Constrained Project Scheduling Problem). The key change to this model that we consider in this paper is the presence of selfish entities competing to perform individual tasks with the aim of maximizing their own utility. Due to the selfish entities in play, the goal of the scheduling problem is no longer only …
On Return Oriented Programming Threats In Android Runtime, Akshaya Venkateswara Raja, Jehyun Lee, Debin Gao
On Return Oriented Programming Threats In Android Runtime, Akshaya Venkateswara Raja, Jehyun Lee, Debin Gao
Research Collection School Of Computing and Information Systems
Android has taken a large share of operating systems forsmart devices including smartphones, and has been an attractive target to theattackers. The arms race between attackers and defenders typically occurs ontwo front lines — the latest attacking technology and the latest updates to theoperating system (including defense mechanisms deployed). In terms of attackingtechnology, Return-Oriented Programming (ROP) is one of the most sophisticatedattack methods on Android devices. In terms of the operating system updates,Android Runtime (ART) was the latest and biggest change to the Android family.In this paper, we investigate the extent to which Android Runtime (ART) makesReturn-Oriented Programming (ROP) attacks …
Real-Time Influence Maximization On Dynamic Social Streams, Yanhao Wang, Qi Fan, Yuchen Li, Kian-Lee Tan
Real-Time Influence Maximization On Dynamic Social Streams, Yanhao Wang, Qi Fan, Yuchen Li, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
Influence maximization (IM), which selects a set of k users(called seeds) to maximize the influence spread over a social network, is a fundamental problem in a wide range of applications such as viral marketing and network monitoring.Existing IM solutions fail to consider the highly dynamic nature of social influence, which results in either poor seed qualities or long processing time when the network evolves.To address this problem, we define a novel IM query named Stream Influence Maximization (SIM) on social streams.Technically, SIM adopts the sliding window model and maintains a set of k seeds with the largest influence value over …
Seeing Through The Same Lens: Introspecting Guest Address Space At Native Speed, Siqi Zhao, Xuhua Ding, Wen Xu, Dawu Gu
Seeing Through The Same Lens: Introspecting Guest Address Space At Native Speed, Siqi Zhao, Xuhua Ding, Wen Xu, Dawu Gu
Research Collection School Of Computing and Information Systems
Software-based MMU emulation lies at the heart of out-of-VM live memory introspection, an important technique in the cloud setting that applications such as live forensics and intrusion detection depend on. Due to the emulation, the software-based approach is much slower compared to native memory access by the guest VM. The slowness not only results in undetected transient malicious behavior, but also inconsistent memory view with the guest; both undermine the effectiveness of introspection. We propose the immersive execution environment (ImEE) with which the guest memory is accessed at native speed without any emulation. Meanwhile, the address mappings used within the …
Flexible Online Task Assignment In Real-Time Spatial Data, Yongxin Tong, Libin Wang, Zimu Zhou, Bolin Ding, Lei Chen, Jieping Ye, Ke Xu
Flexible Online Task Assignment In Real-Time Spatial Data, Yongxin Tong, Libin Wang, Zimu Zhou, Bolin Ding, Lei Chen, Jieping Ye, Ke Xu
Research Collection School Of Computing and Information Systems
The popularity of Online To Offline (O2O) service platforms has spurred the need for online task assignment in real-time spatial data, where streams of spatially distributed tasks and workers are matched in real time such that the total number of assigned pairs is maximized. Existing online task assignment models assume that each worker is either assigned a task immediately or waits for a subsequent task at a fixed location once she/he appears on the platform. Yet in practice a worker may actively move around rather than passively wait in place if no task is assigned. In this paper, we define …
Recommendation Vs Sentiment Analysis: A Text-Driven Latent Factor Model For Rating Prediction With Cold-Start Awareness, Kaisong Song, Wei Gao, Shi Feng Feng, Daling Wang, Kam-Fai Wong, Chengqi Zhang
Recommendation Vs Sentiment Analysis: A Text-Driven Latent Factor Model For Rating Prediction With Cold-Start Awareness, Kaisong Song, Wei Gao, Shi Feng Feng, Daling Wang, Kam-Fai Wong, Chengqi Zhang
Research Collection School Of Computing and Information Systems
Review rating prediction is an important research topic. The problem was approached from either the perspective of recommender systems (RS) or that of sentiment analysis (SA). Recent SA research using deep neural networks (DNNs) has realized the importance of user and product interaction for better interpreting the sentiment of reviews. However, the complexity of DNN models in terms of the scale of parameters is very high, and the performance is not always satisfying especially when user-product interaction is sparse. In this paper, we propose a simple, extensible RS-based model, called Text-driven Latent Factor Model (TLFM), to capture the semantics of …
The Role Of Knowledge Sharing Via Organizational Social Media In The Workplace, Murad A. Moqbel, Fiona Fui-Hoon Nah
The Role Of Knowledge Sharing Via Organizational Social Media In The Workplace, Murad A. Moqbel, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Although a plethora of studies have been recently conducted on organizational social media, little research has specifically examined how organizational social media can contribute to organizational workers’ performance through knowledge sharing. The objective of this research is to fill this gap by investigating the role of knowledge sharing in organizational social media use and its effect on in-role and innovative performance through the lens of social capital and social cognitive theories. Hypotheses were developed and a survey study is proposed.
Impact Of Artificial Intelligence, Robotics, And Automation On Higher Education, Keng Siau
Impact Of Artificial Intelligence, Robotics, And Automation On Higher Education, Keng Siau
Research Collection School Of Computing and Information Systems
What is the future of higher education in the AI age? Higher education is expected to be challenged by AI, Robotics, and Automation on multiple fronts. First and foremost, AI, robotics, and automation are replacing and will continue to replace jobs and revolutionalize every nation’s economy and disrupt economic development in the world. Millions of job are expected to be replaced by machines (Zhao & Siau, 2017). Many manufacturing jobs have already been replaced by robots and middle class jobs may be taken over by AI in the near future (Siau & Yang, 2017).The short and long term impact on …
A Research Stream On Sentiment Analysis, B. Yuan, Keng Siau
A Research Stream On Sentiment Analysis, B. Yuan, Keng Siau
Research Collection School Of Computing and Information Systems
Sentiment analysis (SA) is a powerful mining technique to study online reviews and comments (Lee & Siau, 2001; Adeborna & Siau, 2014; Zhao & Siau, 2017). It is an advanced text mining technique and the goal of SA is to recognize and extract meaningful information from data using natural language processing (NLP) and computational linguistics.SA has been applied to areas such as marketing, online social media, customer service, education, and even energy fields (Yuan & Siau, 2017). For example, SA can be used to identify the attitude of customers according to polarity of the reviews and comments that they left …
Using Cognitive Maps Of Mental Models To Evaluate Learning Challenges: A Case Study, Z. Shen, Keng Siau
Using Cognitive Maps Of Mental Models To Evaluate Learning Challenges: A Case Study, Z. Shen, Keng Siau
Research Collection School Of Computing and Information Systems
Mental models are organized knowledge structures that individuals form to make sense of the world around them. Cognitive maps are the externalized portrayals of mental models in graphical format. Mental models and cognitive maps have been used as an instructional design method, an assessment tool, and a learning strategy in college education. In this paper, we propose a novel use of mental models and cognitive maps as a device to elicit students’ challenges in learning course materials. Our case study in an Information Systems class illustrates how cognitive maps are constructed from students’ mental models, how learning challenges are identified …
Embedding-Based Representation Of Categorical Data By Hierarchical Value Coupling Learning, Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu, Hang Gao
Embedding-Based Representation Of Categorical Data By Hierarchical Value Coupling Learning, Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu, Hang Gao
Research Collection School Of Computing and Information Systems
Learning the representation of categorical data with hierarchical value coupling relationships is very challenging but critical for the effective analysis and learning of such data. This paper proposes a novel coupled unsupervised categorical data representation (CURE) framework and its instantiation, i.e., a coupled data embedding (CDE) method, for representing categorical data by hierarchical value-to-value cluster coupling learning. Unlike existing embedding- and similarity-based representation methods which can capture only a part or none of these complex couplings, CDE explicitly incorporates the hierarchical couplings into its embedding representation. CDE first learns two complementary feature value couplings which are then used to cluster …
Learning Homophily Couplings From Non-Iid Data For Joint Feature Selection And Noise-Resilient Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Huan Liu
Learning Homophily Couplings From Non-Iid Data For Joint Feature Selection And Noise-Resilient Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Huan Liu
Research Collection School Of Computing and Information Systems
This paper introduces a novel wrapper-based outlier detection framework (WrapperOD) and its instance (HOUR) for identifying outliers in noisy data (i.e., data with noisy features) with strong couplings between outlying behaviors. Existing subspace or feature selection-based methods are significantly challenged by such data, as their search of feature subset(s) is independent of outlier scoring and thus can be misled by noisy features. In contrast, HOUR takes a wrapper approach to iteratively optimize the feature subset selection and outlier scoring using a top-k outlier ranking evaluation measure as its objective function. HOUR learns homophily couplings between outlying behaviors (i.e., abnormal behaviors …
Personas For Content Creators Via Decomposed Aggregate Audience Statistics, Jisun An, Haewoon Kwak, Bernard J. Jansen
Personas For Content Creators Via Decomposed Aggregate Audience Statistics, Jisun An, Haewoon Kwak, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
We propose a novel method for generating personas based on online user data for the increasingly common situation of content creators distributing products via online platforms. We use non-negative matrix factorization to identify user segments and develop personas by adding personality such as names and photos. Our approach can develop accurate personas representing real groups of people using online user data, versus relying on manually gathered data.
Fair Deposits Against Double-Spending For Bitcoin Transactions, Xingjie Yu, Shiwen M. Thang, Yingjiu Li, Robert H. Deng
Fair Deposits Against Double-Spending For Bitcoin Transactions, Xingjie Yu, Shiwen M. Thang, Yingjiu Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
In Bitcoin network, the distributed storage of multiple copies of the blockchain opens up possibilities for double spending, i.e., a payer issues two separate transactions to two different payees transferring the same coins. To detect the doublespending and penalize the malicious payer, decentralized non-equivocation contracts have been proposed. The basic idea of these contracts is that the payer locks some coins in a deposit when he initiates a transaction with the payee. If the payer double spends, a cryptographic primitive called accountable assertions can be used to reveal his Bitcoin credentials for the deposit. Thus, the malicious payer could be …
Can Syntax Help? Improving An Lstm-Based Sentence Compression Model For New Domains, Liangguo Wang, Jing Jiang, Hai Leong Chieu, Chen Hui Ong, Dandan Song, Lejian Liao
Can Syntax Help? Improving An Lstm-Based Sentence Compression Model For New Domains, Liangguo Wang, Jing Jiang, Hai Leong Chieu, Chen Hui Ong, Dandan Song, Lejian Liao
Research Collection School Of Computing and Information Systems
In this paper, we study how to improve thedomain adaptability of a deletion-basedLong Short-Term Memory (LSTM) neuralnetwork model for sentence compression.We hypothesize that syntactic informationhelps in making such modelsmore robust across domains. We proposetwo major changes to the model: usingexplicit syntactic features and introducingsyntactic constraints through Integer LinearProgramming (ILP). Our evaluationshows that the proposed model works betterthan the original model as well as a traditionalnon-neural-network-based modelin a cross-domain setting.
Accelerating Dynamic Graph Analytics On Gpus, Mo Shan, Yuchen Li, Bingsheng He, Kian-Lee Tan
Accelerating Dynamic Graph Analytics On Gpus, Mo Shan, Yuchen Li, Bingsheng He, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
As graph analytics often involves compute-intensive operations,GPUs have been extensively used to accelerate the processing. However, in many applications such as social networks, cyber security, and fraud detection, their representative graphs evolve frequently and one has to perform are build of the graph structure on GPUs to incorporate the updates. Hence, rebuilding the graphs becomes the bottleneck of processing high-speed graph streams. In this paper,we propose a GPU-based dynamic graph storage scheme to support existing graph algorithms easily. Furthermore,we propose parallel update algorithms to support efficient stream updates so that the maintained graph is immediately available for high-speed analytic processing …
Projection-Free Distributed Online Learning In Networks, Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang
Projection-Free Distributed Online Learning In Networks, Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang
Research Collection School Of Computing and Information Systems
The conditional gradient algorithm has regained a surge of research interest in recent years due to its high efficiency in handling large-scale machine learning problems. However, none of existing studies has explored it in the distributed online learning setting, where locally light computation is assumed. In this paper, we fill this gap by proposing the distributed online conditional gradient algorithm, which eschews the expensive projection operation needed in its counterpart algorithms by exploiting much simpler linear optimization steps. We give a regret bound for the proposed algorithm as a function of the network size and topology, which will be smaller …
Object Detection Meets Knowledge Graphs, Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar
Object Detection Meets Knowledge Graphs, Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar
Research Collection School Of Computing and Information Systems
Object detection in images is a crucial task in computer vision, with important applications ranging from security surveillance to autonomous vehicles. Existing state-of-the-art algorithms, including deep neural networks, only focus on utilizing features within an image itself, largely neglecting the vast amount of background knowledge about the real world. In this paper, we propose a novel framework of knowledge-aware object detection, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm. The framework employs the notion of semantic consistency to quantify and generalize knowledge, which improves object detection through a re-optimization process to achieve …
Managing Sensor Systems For Early Detection Of Mild Cognitive Impairment In Community Elderly: Lessons Learned And Future Work, Boon Thai Ng, Hwee-Pink Tan, Hwee Xian Tan
Managing Sensor Systems For Early Detection Of Mild Cognitive Impairment In Community Elderly: Lessons Learned And Future Work, Boon Thai Ng, Hwee-Pink Tan, Hwee Xian Tan
Research Collection School Of Computing and Information Systems
The aging population is a pertinent issue faced by governments globally. One of the most common and costly health issues associated with the aging population is cognitive decline, leading up to dementia. In this paper, we describe a non-intrusive, continuous and scalable system for early detection of Mild Cognitive Impairment (MCI) in the elderly, which enables early medical interventions to be provided. We focus on the system design and feature extraction of the sensor system, to validate our hypothesis of the use of sensor systems for early detection of MCI. Lessons learned from deploying the sensor system is presented, together …
On Efficiently Finding Reverse K-Nearest Neighbors Over Uncertain Graphs, Yunjun Gao, Xiaoye Miao, Gang Chen, Baihua Zheng, Deng Cai, Huiyong Cui
On Efficiently Finding Reverse K-Nearest Neighbors Over Uncertain Graphs, Yunjun Gao, Xiaoye Miao, Gang Chen, Baihua Zheng, Deng Cai, Huiyong Cui
Research Collection School Of Computing and Information Systems
Reverse k-nearest neighbor (RkNN) query on graphs returns the data objects that take a specified query object q as one of their k-nearest neighbors. It has significant influence in many real-life applications including resource allocation and profile-based marketing. However, to the best of our knowledge, there is little previous work on RkNN search over uncertain graph data, even though many complex networks such as traffic networks and protein–protein interaction networks are often modeled as uncertain graphs. In this paper, we systematically study the problem of reversek-nearest neighbor search on uncertain graphs (UG-RkNN search for short), where graph edges contain uncertainty. …