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Research Collection School Of Computing and Information Systems

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Full-Text Articles in Databases and Information Systems

Topical Keyphrase Extraction From Twitter, Xin Zhao, Jing Jiang, Jing He, Yang Song, Palakorn Achananuparp, Ee Peng Lim, Xiaoming Li Jun 2011

Topical Keyphrase Extraction From Twitter, Xin Zhao, Jing Jiang, Jing He, Yang Song, Palakorn Achananuparp, Ee Peng Lim, Xiaoming Li

Research Collection School Of Computing and Information Systems

Summarizing and analyzing Twitter content is an important and challenging task. In this paper, we propose to extract topical keyphrases as one way to summarize Twitter. We propose a context-sensitive topical PageRank method for keyword ranking and a probabilistic scoring function that considers both relevance and interestingness of keyphrases for keyphrase ranking. We evaluate our proposed methods on a large Twitter data set. Experiments show that these methods are very effective for topical keyphrase extraction.

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Query Weighting For Ranking Model Adaptation, Peng Cai, Wei Gao, Aoying Zhou, Kam-Fai Wong Jun 2011

Query Weighting For Ranking Model Adaptation, Peng Cai, Wei Gao, Aoying Zhou, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

We propose to directly measure the importance of queries in the source domain to the target domain where no rank labels of documents are available, which is referred to as query weighting. Query weighting is a key step in ranking model adaptation. As the learning object of ranking algorithms is divided by query instances, we argue that it’s more reasonable to conduct importance weighting at query level than document level. We present two query weighting schemes. The first compresses the query into a query feature vector, which aggregates all document instances in the same query, and then conducts query weighting …

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Regret Minimizing Audits: A Learning-Theoretic Basis For Privacy Protection, Jeremiah Blocki, Nicolas Christin, Anupam Datta, Arunesh Sinha Jun 2011

Regret Minimizing Audits: A Learning-Theoretic Basis For Privacy Protection, Jeremiah Blocki, Nicolas Christin, Anupam Datta, Arunesh Sinha

Research Collection School Of Computing and Information Systems

Audit mechanisms are essential for privacy protection in permissive access control regimes, such as in hospitals where denying legitimate access requests can adversely affect patient care. Recognizing this need, we develop the first principled learning-theoretic foundation for audits. Our first contribution is a game-theoretic model that captures the interaction between the defender (e.g., hospital auditors) and the adversary (e.g., hospital employees). The model takes pragmatic considerations into account, in particular, the periodic nature of audits, a budget that constrains the number of actions that the defender can inspect, and a loss function that captures the economic impact of detected and …

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Towards Human-Like Social Multi-Agents With Memetic Automaton, Liang Feng, Yew-Soon Ong, Ah-Hwee Tan, Xian-Shun Chen Jun 2011

Towards Human-Like Social Multi-Agents With Memetic Automaton, Liang Feng, Yew-Soon Ong, Ah-Hwee Tan, Xian-Shun Chen

Research Collection School Of Computing and Information Systems

Memetics is a new science that has attracted increasing attentions in the recent decades. Beyond the formalism of simple hybrids, adaptive hybrids and memetic algorithms, the notion of memetic automaton as an adaptive entity that is self-contained and uses memes as building blocks of information is recently conceptualized in the context of computational intelligence as potential tools for effective problem-solving [1]. Taking this cue, this paper embarks a study on Memetic Multiagent system (MeM) towards human-like social agents with memetic automaton. Particularly, we introduce a potentially rich meme-inspired design and operational model, with Darwin’s theory of natural selections and Dawkins’ …

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Getting To Know Social Media Analytics, Tin Seong Kam May 2011

Getting To Know Social Media Analytics, Tin Seong Kam

Research Collection School Of Computing and Information Systems

Over the last five years, the unprecedented development and use of social mediating technologies such as blog, wiki, Facebook, and Tweeter have engendered radically new ways of working, playing, and creating meaning, leaving an indelible mark on nearly every domain imaginable. Despite the growing ubiquity of social mediating technologies, their potential has hardly been tapped. Effectively using data collected from social mediating technologies by the business community is far from trivial. This is mainly due to the general lack of awareness on Social Network Analysis (SNA) techniques and technologies among the business analysts and practitioners. This presentation aims to provide …

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Continuous Nearest Neighbor Search In The Presence Of Obstacles, Yunjun Gao, Baihua Zheng, Gang Chen, Chun Chen, Qing Li May 2011

Continuous Nearest Neighbor Search In The Presence Of Obstacles, Yunjun Gao, Baihua Zheng, Gang Chen, Chun Chen, Qing Li

Research Collection School Of Computing and Information Systems

Despite the ubiquity of physical obstacles (e.g., buildings, hills, and blindages, etc.) in the real world, most of spatial queries ignore the obstacles. In this article, we study a novel form of continuous nearest-neighbor queries in the presence of obstacles, namely continuous obstructed nearest-neighbor (CONN) search, which considers the impact of obstacles on the distance between objects. Given a data setP, an obstacle set O, and a query line segment q, in a two-dimensional space, a CONN query retrieves the nearest neighbor p ∈ P of each point p′ on q according to the obstructed distance, the shortest path between …

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Double Updating Online Learning, Peilin Zhao, Steven C. H. Hoi, Rong Jin May 2011

Double Updating Online Learning, Peilin Zhao, Steven C. H. Hoi, Rong Jin

Research Collection School Of Computing and Information Systems

In most kernel based online learning algorithms, when an incoming instance is misclassified, it will be added into the pool of support vectors and assigned with a weight, which often remains unchanged during the rest of the learning process. This is clearly insufficient since when a new support vector is added, we generally expect the weights of the other existing support vectors to be updated in order to reflect the influence of the added support vector. In this paper, we propose a new online learning method, termed Double Updating Online Learning, or DUOL for short, that explicitly addresses this problem. …

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Comparing Twitter And Traditional Media Using Topic Models, Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee Peng Lim, Hongfei Yan, Xiaoming Li Apr 2011

Comparing Twitter And Traditional Media Using Topic Models, Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee Peng Lim, Hongfei Yan, Xiaoming Li

Research Collection School Of Computing and Information Systems

Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to …

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Two-Layer Multiple Kernel Learning, Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi Apr 2011

Two-Layer Multiple Kernel Learning, Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Multiple Kernel Learning (MKL) aims to learn kernel machines for solving a real machine learning problem (e.g. classification) by exploring the combinations of multiple kernels. The traditional MKL approach is in general “shallow” in the sense that the target kernel is simply a linear (or convex) combination of some base kernels. In this paper, we investigate a framework of Multi-Layer Multiple Kernel Learning (MLMKL) that aims to learn “deep” kernel machines by exploring the combinations of multiple kernels in a multi-layer structure, which goes beyond the conventional MKL approach. Through a multiple layer mapping, the proposed MLMKL framework offers higher …

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Efficient Topological Olap On Information Networks, Qiang Qu, Feida Zhu, Xifeng Yan, Jiawei Han, Philip Yu, Hongyan Li Apr 2011

Efficient Topological Olap On Information Networks, Qiang Qu, Feida Zhu, Xifeng Yan, Jiawei Han, Philip Yu, Hongyan Li

Research Collection School Of Computing and Information Systems

We propose a framework for efficient OLAP on information networks with a focus on the most interesting kind, the topological OLAP (called “T-OLAP”), which incurs topological changes in the underlying networks. T-OLAP operations generate new networks from the original ones by rolling up a subset of nodes chosen by certain constraint criteria. The key challenge is to efficiently compute measures for the newly generated networks and handle user queries with varied constraints. Two effective computational techniques, T-Distributiveness and T-Monotonicity are proposed to achieve efficient query processing and cube materialization. We also provide a T-OLAP query processing framework into which these …

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A Family Of Simple Non-Parametric Kernel Learning Algorithms From Pairwise Constraints, Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi Apr 2011

A Family Of Simple Non-Parametric Kernel Learning Algorithms From Pairwise Constraints, Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Previous studies of Non-Parametric Kernel Learning (NPKL) usually formulate the learning task as a Semi-Definite Programming (SDP) problem that is often solved by some general purpose SDP solvers. However, for N data examples, the time complexity of NPKL using a standard interior-point SDP solver could be as high as O(N6.5), which prohibits NPKL methods applicable to real applications, even for data sets of moderate size. In this paper, we present a family of efficient NPKL algorithms, termed "SimpleNPKL", which can learn non-parametric kernels from a large set of pairwise constraints efficiently. In particular, we propose two efficient SimpleNPKL algorithms. One …

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Weight-Based Boosting Model For Cross-Domain Relevance Ranking Adaptation, Peng Cai, Wei Gao, Kam-Fai Wong, Aoying Zhou Apr 2011

Weight-Based Boosting Model For Cross-Domain Relevance Ranking Adaptation, Peng Cai, Wei Gao, Kam-Fai Wong, Aoying Zhou

Research Collection School Of Computing and Information Systems

Adaptation techniques based on importance weighting were shown effective for RankSVM and RankNet, viz., each training instance is assigned a target weight denoting its importance to the target domain and incorporated into loss functions. In this work, we extend RankBoost using importance weighting framework for ranking adaptation. We find it non-trivial to incorporate the target weight into the boosting-based ranking algorithms because it plays a contradictory role against the innate weight of boosting, namely source weight that focuses on adjusting source-domain ranking accuracy. Our experiments show that among three variants, the additive weight-based RankBoost, which dynamically balances the two types …

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Predicting Item Adoption Using Social Correlation, Freddy Chong-Tat Chua, Hady W. Lauw, Ee Peng Lim Apr 2011

Predicting Item Adoption Using Social Correlation, Freddy Chong-Tat Chua, Hady W. Lauw, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Users face a dazzling array of choices on the Web when it comes to choosing which product to buy, which video to watch, etc. The trend of social information processing means users increasingly rely not only on their own preferences, but also on friends when making various adoption decisions. In this paper, we investigate the effects of social correlation on users’ adoption of items. Given a user-user social graph and an item-user adoption graph, we seek to answer the following questions: 1) whether the items adopted by a user correlate to items adopted by her friends, and 2) how to …

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Mkboost: A Framework Of Multiple Kernel Boosting, Hao Xia, Steven C. H. Hoi Apr 2011

Mkboost: A Framework Of Multiple Kernel Boosting, Hao Xia, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Multiple kernel learning (MKL) has been shown as a promising machine learning technique for data mining tasks by integrating with multiple diverse kernel functions. Traditional MKL methods often formulate the problem as an optimization task of learning both optimal combination of kernels and classifiers, and attempt to resolve the challenging optimization task by various techniques. Unlike the existing MKL methods, in this paper, we investigate a boosting framework of exploring multiple kernel learning for classification tasks. In particular, we present a novel framework of Multiple Kernel Boosting (MKBoost), which applies boosting techniques for learning kernel-based classifiers with multiple kernels. Based …

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Confidence Weighted Mean Reversion Strategy For On-Line Portfolio Selection, Bin Li, Steven C. H. Hoi, Peilin Zhao, Vivek Gopalkrishnan Apr 2011

Confidence Weighted Mean Reversion Strategy For On-Line Portfolio Selection, Bin Li, Steven C. H. Hoi, Peilin Zhao, Vivek Gopalkrishnan

Research Collection School Of Computing and Information Systems

On-line portfolio selection has been attracting increasing attention from the data mining and machine learning communities. All existing on-line portfolio selection strategies focus on the first order information of a portfolio vector, though the second order information may also be beneficial to a strategy. Moreover, empirical evidences show that the stock price relatives may follow the mean reversion property, which has not been fully exploited by existing strategies. This article proposes a novel on-line portfolio selection strategy named ``Confidence Weighted Mean Reversion'' (CWMR). Inspired by the mean reversion principle in finance and confidence weighted online learning technique in machine learning, …

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Learning Feature Dependencies For Noise Correction In Biomedical Prediction, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang Apr 2011

Learning Feature Dependencies For Noise Correction In Biomedical Prediction, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

The presence of noise or errors in the stated feature values of biomedical data can lead to incorrect prediction. We introduce a Bayesian Network-based Noise Correction framework named BN-NC. After data preprocessing, a Bayesian Network (BN) is learned to capture the feature dependencies. Using the BN to predict each feature in turn, BN-NC estimates a feature's error rate as the deviation between its predicted and stated values in the training data, and allocates the appropriate uncertainty to its subsequent findings during prediction. BN-NC automatically generates a probabilistic rule to explain BN prediction on the class variable using the feature values …

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Corn: Correlation-Driven Nonparametric Learning Approach For Portfolio Selection, Bin Li, Steven C. H. Hoi, Vivekanand Gopalkrishnan Apr 2011

Corn: Correlation-Driven Nonparametric Learning Approach For Portfolio Selection, Bin Li, Steven C. H. Hoi, Vivekanand Gopalkrishnan

Research Collection School Of Computing and Information Systems

Machine learning techniques have been adopted to select portfolios from financial markets in some emerging intelligent business applications. In this article, we propose a novel learning-to-trade algorithm termed CO Relation-driven Nonparametric learning strategy (CORN) for actively trading stocks. CORN effectively exploits statistical relations between stock market windows via a nonparametric learning approach. We evaluate the empirical performance of our algorithm extensively on several large historical and latest real stock markets, and show that it can easily beat both the market index and the best stock in the market substantially (without or with small transaction costs), and also surpass a variety …

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Ir-Tree: An Efficient Index For Geographic Document Search, Zhisheng Li, Ken C. K. Lee, Baihua Zheng, Wang-Chien Lee, Dik Lun Lee, Xufa Wang Apr 2011

Ir-Tree: An Efficient Index For Geographic Document Search, Zhisheng Li, Ken C. K. Lee, Baihua Zheng, Wang-Chien Lee, Dik Lun Lee, Xufa Wang

Research Collection School Of Computing and Information Systems

Given a geographic query that is composed of query keywords and a location, a geographic search engine retrieves documents that are the most textually and spatially relevant to the query keywords and the location, respectively, and ranks the retrieved documents according to their joint textual and spatial relevances to the query. The lack of an efficient index that can simultaneously handle both the textual and spatial aspects of the documents makes existing geographic search engines inefficient in answering geographic queries. In this paper, we propose an efficient index, called IR-tree, that together with a top-k document search algorithm facilitates four …

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Utility-Oriented K-Anonymization On Social Networks, Yazhe Wang, Long Xie, Baihua Zheng, Ken C. K. Lee Apr 2011

Utility-Oriented K-Anonymization On Social Networks, Yazhe Wang, Long Xie, Baihua Zheng, Ken C. K. Lee

Research Collection School Of Computing and Information Systems

"Identity disclosure" problem on publishing social network data has gained intensive focus from academia. Existing k-anonymization algorithms on social network may result in nontrivial utility loss. The reason is that the number of the edges modified when anonymizing the social network is the only metric to evaluate utility loss, not considering the fact that different edge modifications have different impact on the network structure. To tackle this issue, we propose a novel utility-oriented social network anonymization scheme to achieve privacy protection with relatively low utility loss. First, a proper utility evaluation model is proposed. It focuses on the changes on …

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Modeling Link Formation Behaviors In Dynamic Social Networks, Viet-An Nguyen, Cane Wing-Ki Leung, Ee Peng Lim Mar 2011

Modeling Link Formation Behaviors In Dynamic Social Networks, Viet-An Nguyen, Cane Wing-Ki Leung, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Online social networks are dynamic in nature. While links between users are seemingly formed and removed randomly, there exists some interested link formation behaviors demonstrated by users performing link creation and removal activities. Uncovering these behaviors not only allows us to gain deep insights of the users, but also pave the way to decipher how social links are formed. In this paper, we propose a general framework to define user link formation behaviors using well studied local link structures (i.e., triads and dyads) in a dynamic social network where links are formed at different timestamps. Depending on the role a …

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Artificial Cognitive Memory - Changing From Density Driven To Functionality Driven, Luping Shi, Kaijun Yi, Kiruthika Ramanathan, Rong Zhao, Ning Ning, Ding Ding, Tow Chong Chong Feb 2011

Artificial Cognitive Memory - Changing From Density Driven To Functionality Driven, Luping Shi, Kaijun Yi, Kiruthika Ramanathan, Rong Zhao, Ning Ning, Ding Ding, Tow Chong Chong

Research Collection School Of Computing and Information Systems

Increasing density based on bit size reduction is currently a main driving force for the development of data storage technologies. However, it is expected that all of the current available storage technologies might approach their physical limits in around 15 to 20 years due to miniaturization. To further advance the storage technologies, it is required to explore a new development trend that is different from density driven. One possible direction is to derive insights from biological counterparts. Unlike physical memories that have a single function of data storage, human memory is versatile. It contributes to functions of data storage, information …

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Pgtp: Power Aware Game Transport Protocol For Multi-Player Mobile Games, Bhojan Anand, Jeena Sebastian, Soh Yu Ming, Akhihebbal L. Ananda, Mun Choon Chan, Rajesh Krishna Balan Feb 2011

Pgtp: Power Aware Game Transport Protocol For Multi-Player Mobile Games, Bhojan Anand, Jeena Sebastian, Soh Yu Ming, Akhihebbal L. Ananda, Mun Choon Chan, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Applications on the smartphones are able to capitalize on the increasingly advanced hardware to provide a user experience reasonably impressive. However, the advancement of these applications are hindered battery lifetime of the smartphones. The battery technologies have a relatively low growth rate. Applications like mobile multiplayer games are especially power hungry as they maximize the use of the network, display and CPU resources. The PGTP, presented in this paper is aware of both the transport requirement of these multiplayer mobile games and the limitation posed by battery resource. PGTP dynamically controls the transport based on the criticality of game state …

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Mining Social Images With Distance Metric Learning For Automated Image Tagging, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Ying He Feb 2011

Mining Social Images With Distance Metric Learning For Automated Image Tagging, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Ying He

Research Collection School Of Computing and Information Systems

With the popularity of various social media applications, massive social images associated with high quality tags have been made available in many social media web sites nowadays. Mining social images on the web has become an emerging important research topic in web search and data mining. In this paper, we propose a machine learning framework for mining social images and investigate its application to automated image tagging. To effectively discover knowledge from social images that are often associated with multimodal contents (including visual images and textual tags), we propose a novel Unified Distance Metric Learning (UDML) scheme, which not only …

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A Two-View Learning Approach For Image Tag Ranking, Jinfeng Zhuang, Steven C. H. Hoi Feb 2011

A Two-View Learning Approach For Image Tag Ranking, Jinfeng Zhuang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Tags of social images play a central role for text-based social image retrieval and browsing tasks. However, the original tags annotated by web users could be noisy, irrelevant, and often incomplete for describing the image contents, which may severely deteriorate the performance of text-based image retrieval models. In this paper, we aim to overcome the challenge of social tag ranking for a corpus of social images with rich user-generated tags by proposing a novel two-view learning approach. It can effectively exploit both textual and visual contents of social images to discover the complicated relationship between tags and images. Unlike the …

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Searching Patterns For Relation Extraction Over The Web: Rediscovering The Pattern-Relation Duality, Yuan Fang, Kevin Chen-Chuan Chang Feb 2011

Searching Patterns For Relation Extraction Over The Web: Rediscovering The Pattern-Relation Duality, Yuan Fang, Kevin Chen-Chuan Chang

Research Collection School Of Computing and Information Systems

While tuple extraction for a given relation has been an active research area, its dual problem of pattern search- to find and rank patterns in a principled way- has not been studied explicitly. In this paper, we propose and address the problem of pattern search, in addition to tuple extraction. As our objectives, we stress reusability for pattern search and scalability of tuple extraction, such that our approach can be applied to very large corpora like the Web. As the key foundation, we propose a conceptual model PRDualRank to capture the notion of precision and recall for both tuples and …

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Distance Metric Learning From Uncertain Side Information For Automated Photo Tagging, Lei Wu, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Nenghai Yu Feb 2011

Distance Metric Learning From Uncertain Side Information For Automated Photo Tagging, Lei Wu, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Nenghai Yu

Research Collection School Of Computing and Information Systems

Automated photo tagging is an important technique for many intelligent multimedia information systems, for example, smart photo management system and intelligent digital media library. To attack the challenge, several machine learning techniques have been developed and applied for automated photo tagging. For example, supervised learning techniques have been applied to automated photo tagging by training statistical classifiers from a collection of manually labeled examples. Although the existing approaches work well for small testbeds with relatively small number of annotation words, due to the long-standing challenge of object recognition, they often perform poorly in large-scale problems. Another limitation of the existing …

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Evolution Of Developer Collaboration On The Jazz Platform: A Study Of A Large Scale Agile Project, Subhajit Datta, Renuka Sindhgatta, Bikram Sengupta Feb 2011

Evolution Of Developer Collaboration On The Jazz Platform: A Study Of A Large Scale Agile Project, Subhajit Datta, Renuka Sindhgatta, Bikram Sengupta

Research Collection School Of Computing and Information Systems

Collaboration is a key aspect of the agile philosophy of software development. As a software system matures over iterations, trends of developer collaboration can offer valuable insights into project dynamics. In this paper, we study evolution of developer collaboration for a large scale agile project on the Jazz platform. We construct networks of collaboration based on developer affiliations across comments on work items and file changes; and then compare parameters of such networks with established results from networks of scientific collaborations. The comparisons illuminate interesting facets of developer collaboration on the Jazz platform. Such perception helps deeper understanding of the …

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Fraud Detection In Online Consumer Reviews, Nan Hu, Ling Liu, Vallabh Sambamurthy Feb 2011

Fraud Detection In Online Consumer Reviews, Nan Hu, Ling Liu, Vallabh Sambamurthy

Research Collection School Of Computing and Information Systems

Increasingly, consumers depend on social information channels, such as user-posted online reviews, to make purchase decisions. These reviews are assumed to be unbiased reflections of other consumers' experiences with the products or services. While extensively assumed, the literature has not tested the existence or non-existence of review manipulation. By using data from Amazon and Barnes & Noble, our study investigates if vendors, publishers, and writers consistently manipulate online consumer reviews. We document the existence of online review manipulation and show that the manipulation strategy of firms seems to be a monotonically decreasing function of the product's true quality or the …

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Fraud Detection In Online Consumer Reviews, Nan Hu, Ling Liu, Vallbh Sambamurthy Feb 2011

Fraud Detection In Online Consumer Reviews, Nan Hu, Ling Liu, Vallbh Sambamurthy

Research Collection School Of Computing and Information Systems

Increasingly, consumers depend on social information channels, such as user-posted online reviews, to make purchase decisions. These reviews are assumed to be unbiased reflections of other consumers' experiences with the products or services. While extensively assumed, the literature has not tested the existence or non-existence of review manipulation. By using data from Amazon and Barnes & Noble, our study investigates if vendors, publishers, and writers consistently manipulate online consumer reviews. We document the existence of online review manipulation and show that the manipulation strategy of firms seems to be a monotonically decreasing function of the product's true quality or the …

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Real-Time Traffic Estimation Using Data Expansion, Roger Lederman, Laura Wynter Jan 2011

Real-Time Traffic Estimation Using Data Expansion, Roger Lederman, Laura Wynter

Research Collection School Of Computing and Information Systems

This paper presents a method for estimating missing real-time traffic volumes on a road network using both historical and real-time traffic data. The method was developed to address urban transportation networks where a non-negligible subset of the network links do not have real-time link volumes, and where that data is needed to populate other real-time traffic analytics. Computation is split between an offline calibration and a real-time estimation phase. The offline phase determines link-to-link splitting probabilities for traffic flow propagation that are subsequently used in real-time estimation. The real-time procedure uses current traffic data and is efficient enough to scale …

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