Preface ——

APWeb 2016

The 18th Asia Pacific Web Conference

The Asia Pacific Web Conference, a leading international conference on research, development and applications of Web technologies, database systems, information management and software engineering, is aiming at attracting professionals of different communities such as industry and academic from not only Asia Pacific countries but also other continents. The objective is to share the experience in the area of the World Wide Web with the underlying technologies and applications.

Important Dates:

  • Camera-Ready Deadline: July 1, 2016

  • Author Registration Deadline: July 31, 2016

  • Conference Time: Sept 23 – Sept 25, 2016

About Suzhou

The long history of Suzhou City has left behind many attractive scenic spots and historical sites with beautiful and interesting legends. The elegant classical gardens, the old-fashioned houses and delicate bridges hanging over flowing waters in the drizzling rain, the beautiful lakes with undulating hills in lush green, and the exquisite arts and crafts, etc. have made Suzhou a renowned historical and cultural city full of eternal and poetic charm. 

Suzhou is best known for its gardens: Humble Administrator's Garden, Lingering Garden, the Surging Wave Pavilion, and the Master of Nets Garden. These gardens weave together the best of traditional Chinese architecture, painting and arts. 

Suzhou is also known as the "Venice of the East". The city is sandwiched between Taihu Lake and Grand Canal. Network of cannels, criss-crossed with hump-backed bridges, give Suzhou an image of City on the water.

Previous APWeb

Previous APWeb conferences were held in Beijing (1998), Hong Kong (1999), Xi'an (2000), Changsha (2001), Xi'an (2003), Hangzhou (2004), Shanghai (2005), Harbin (2006), Huangshan (2007), Shenyang (2008), Suzhou (2009), Busan (2010), Beijing (2011), Kunming (2012), Sydney (2013), Changsha(2014) and Guangzhou (2015).

APWeb 2016 will take place at Suzhou, China. It is a city with a long history on the lower reaches of the Yangtze River and on the shores of Lake Taihu in the province of Jiangsu, China.

Image - Night Views of Suzhou Jinji Lake Bridge
Section ——


Professor Zhi-Hua Zhou

Professor Zhi-Hua Zhou 

Nanjing University

Title: Big Data Incremental Learning


Abstract: Traditional learning approaches usually try to collect all available data and then train a model. In big data applications, however, the data usually come in an accumulation or streaming way. Thus, it is more desirable to do incremental learning rather than training a new model from scratch when receiving new data. It is noteworthy that some important losses used in machine learning are quite challenging for incremental optimization. Moreover, in addition to new training samples, new classes may also occur.  In this talk we will introduce some studies along this direction.

Short Biography:  Zhi-Hua Zhou is a Professor and Founding Director of the LAMDA Group at Nanjing University. He authored the book "Ensemble Methods: Foundations and Algorithms", and published more than 100 papers in top-tier journals and conference proceedings. His work have received more than 22,000 citations, with a h-index of 71. He also holds 14 patents and has good experiences in industrial applications. He has received various awards, including the National Natural Science Award of China, the IEEE CIS Outstanding Early Career Award, the Microsoft Professorship Award, 12 international journal/conference paper/competition awards, etc. He serves as the Executive Editor-in-Chief of Frontiers of Computer Science, Associate Editor-in-Chief of Science China, and Associate Editor of ACM TIST, IEEE TNNLS, etc. He founded ACML (Asian Conference on Machine Learning) and served as General Chair of ICDM’16, PAKDD’14, etc., Program Chair of IJCAI’15 Machine Learning track, SDM’13, etc. He also serves as Advisory Committee member for IJCAI 2015-2016, and Steering Committee Member of PAKDD and PRICAI. He is a Fellow of the AAAI, IEEE, IAPR, IET/IEE, CCF, and an ACM Distinguished Scientist.

Professor Yufei Tao

Professor Yufei Tao 

The University of Queensland

Title: Small and Sweet MapReduce Algorithms

Abstract:  MapReduce has grown into a matured and powerful paradigm for large-scaled parallel computing. This keynote will introduce principles for designing algorithms on this paradigm that are both (i) small, i.e., they can be implemented in a real system with reasonable efforts, and (ii) sweet, i.e., they possess strong theoretical performance guarantees. Assuming little prior knowledge, we will start with the definition of the massively parallel computation (MPC) model, which has nowadays become a popular model in the database community for studying MapReduce algorithms. We will then move on to discuss MPC algorithms that can solve several fundamental database problems (particular, sorting and joins) optimally. The talk will end with several open problems exciting in the eyes of the speaker.

Short Biography: Yufei Tao is a Professor at the School of Information Technology and Electrical Engineering, the University of Queensland (UQ). Prior to joining UQ, he held professorial positions at the City University of Hong Kong, the Chinese University of Hong Kong (CUHK), and the Korea Advanced Institute of Science and Technology (KAIST). He served as an associate editor of ACM Transactions on Database Systems (TODS) from 2008 to 2015, and of IEEE Transactions on Knowledge and Data Engineering (TKDE) from 2012 to 2014. He was a PC chair of International Conference on Data Engineering (ICDE) 2014, and of International Symposium on Spatial and Temporal Databases (SSTD) 2011. He was a keynote speaker at International Conference on Database Theory (ICDT) 2016, and a winner of the SIGMOD best paper award in 2013 and 2015.

Professor Cyrus Shahabi

Professor Cyrus Shahabi

University of Southern California (USC)

Title: Inference of Social Relationships from Location Data

Abstract: For decades, social scientists have been studying people's social behaviors by utilizing sparse datasets obtained by observations and surveys.  These studies received a major boost in the past decade due to the availability of web data (e.g., social networks, blogs and review web sites). However, due to the nature of the utilized dataset, these studies were confined to behaviors that were observed mostly in the virtual world. Differing from all the earlier work, here, we aim to study social behaviors by observing people's behaviors in the real world.  This is now possible due to the availability of large high-resolution spatio-temporal location data collected by GPS-enabled mobile devices through mobile apps (Google's Map/Navigation/Search/Chrome, Facebook, Foursquare, WhatsApp, Twitter) or through online services, such as geo-tagged contents (tweets from Twitter, pictures from Instagram, Flickr or Google+ Photo), etc.

In particular, we focus on inferring two specific social measures: 1) pair-wise strength -- the strength of social connections between a pair of users, and 2) pair-wise influence - the amount of influence that an individual exerts on another, by utilizing the available high-fidelity location data representing people's movements.

Finally, we argue that due to the sensitivity of location data and user privacy concerns, these inferences cannot be largely carried out on individually contributed data without privacy guarantees. Hence, we discuss open problems in protecting individuals'location information while enabling these inference analyses.

Short Biography: Cyrus Shahabi is a Professor of Computer Science and Electrical Engineering and the Director of the Information Laboratory (InfoLAB) at the Computer Science Department and also the Director of the NSF's Integrated Media Systems Center (IMSC) at the University of Southern California (USC). He is also the director of Informatics at USC' Viterbi School of Engineering. He was the CTO and co-founder of a USC spin-off, Geosemble Technologies, which was acquired in July 2012. Since then, he founded another company, ClearPath (recently rebranded as TallyGo), focusing on predictive path-planning for car navigation systems. He received his B.S. in Computer Engineering from Sharif University of Technology in 1989 and then his M.S. and Ph.D. Degrees in Computer Science from the University of Southern California in May 1993 and August 1996, respectively. He authored two books and more than two hundred research papers in the areas of databases, GIS and multimedia with more than 12 US Patents. 

Dr. Shahabi was an Associate Editor of IEEE Transactions on Parallel and Distributed Systems (TPDS) from 2004 to 2009, IEEE Transactions on Knowledge and Data Engineering (TKDE) from 2010-2013 and VLDB Journal from 2009-2015. He is currently on the editorial board of the ACM Transactions on Spatial Algorithms and Systems (TSAS) and ACM Computers in Entertainment. He is the founding chair of IEEE NetDB workshop and also the general co-chair of SSTD'15, ACM GIS 2007, 2008 and 2009. He chaired the nomination committee of ACM SIGSPATIAL for the 2011-2014 terms. He is a PC co-Chair of BigComp'2016 and MDM'2016. In the past, he has been PC co-chair of DASFAA 2015, IEEE MDM 2013 and IEEE BigData 2013, and regularly serves on the program committee of major conferences such as VLDB, ACM SIGMOD, IEEE ICDE, ACM SIGKDD, IEEE ICDM, and ACM Multimedia.

Dr. Shahabi is a fellow of IEEE, and a recipient of the ACM Distinguished Scientist award in 2009, the 2003 U.S. Presidential Early Career Awards for Scientists and Engineers (PECASE), the NSF CAREER award in 2002, and the 2001 Okawa Foundation Research Grant for Information and Telecommunications. 

Section ——

Distinguished Lecture Series

Professor Chen Li

Professor Chen Li 

University of California, Irvine

Title: Real-Time Analytics and Visualization on Large-Scale Spatial-Temporal-Textual Data

Abstract: We are developing a system called Cloudberry to support analytics and visualization on large data sets with spatial, temporal, and textual attributes, such as social media data and query logs. It supports aggregation queries on various types of attributes, and allows efficient data exploration at different granularities (e.g., state, county, and city). It also supports real-time analytics, which can allow applications to monitor “what’s happening now.” To achieve a high speed, it includes an intelligent middleware for view materialization and cache management. As a general-purpose solution for large data sets, it uses the Apache AsterixDB big data management system that provides rich features and high performance, such as various indexes and data feeds. In this talk, we will give an overview of the system, our initial results, and open challenges in this direction. A live demonstration using tweets is available at .

Short Biography: Chen Li is a professor in the Department of Computer Science at UC Irvine. He received his Ph.D. degree in Computer Science from Stanford University, and his M.S. and B.S. in Computer Science from Tsinghua University, China, respectively.  His research interests are in the field of data management, including data cleaning, data integration, data-intensive computing, and text analytics.  He was a recipient of an NSF CAREER Award, several test-of-time publication awards, and many other grants and industry gifts. He was once a part-time Visiting Research Scientist at Google.  He founded a company SRCH2 to develop an open source search engine with high performance and advanced features.


Professor Jianliang Xu 

Hong Kong Baptist University

Title: Towards Interactive and Social-Aware Spatial Query Services

Abstract: Location-based service (LBS) have been gaining in prominence, with about 40% of world's population using smartphones today. As such, there is a growing need to continuously advance the spatial database research for emerging LBS applications, which pose new challenges as well as new opportunities. For example, the convergence of location data and social media has enabled a new class of geo-social queries that combine location and social factors in query processing. In addition, to enhance system usability and user experience, it is important to support instantaneous and interactive responses to queries. In this talk, we will present several of our recent efforts on geo-social queries and "why-not"/"what-if" interactive queries that are aimed to improve the functionality, usability, and performance of spatial query services. We will also discuss some possible future research directions.

Short Biography: Jianliang Xu is a Professor in the Department of Computer Science, Hong Kong Baptist University (HKBU). He received the BEng degree from Zhejiang University and the PhD degree from Hong Kong University of Science and Technology. He held visiting positions at Pennsylvania State University and Fudan University. His current research interests include data management, database security & privacy, and location-aware computing. He has published more than 150 technical papers in these areas, most of which appeared in leading journals and conferences including SIGMOD, VLDB, ICDE, TODS, TKDE and VLDBJ, with an h-index of 38 (Google Scholar). He was a recipient of IEEE ICDE Outstanding Reviewer Award (2010) and HKBU Faculty Performance Award for Outstanding Young Researcher (2012). He has served as a program co-chair/vice chair for a number of major international conferences including IEEE ICDCS 2012, IEEE CPSNA 2015 and WAIM 2016. He is an Associate Editor of IEEE Transactions on Knowledge and Data Engineering (TKDE).

Section ——



Professor Kun-Ta Chuang 

National Cheng Kung University

Title : Data Science for Epidemic Computing

Abstract: The control of epidemic spread is the critical challenge for the authority in recent decades. When people are moving to live in the urban area, the crowded situation inevitably increases the outbreak probability of some contagious diseases such as flu and dengue fever. For the need to prevent the out-of-control infections, it is necessary to develop new technologies, predicting and evaluating the prevention result along with the dynamic deployment of intervention strategies over time.

In this tutorial, we will introduce some mechanisms from data science and discuss their extension applied in the outbreak control during the spread of dengue fever in Taiwan 2015. We will also discuss the intervention procedure in Taiwan and show the way to incorporate data mining idea for epidemic computing into the process of decision making in the government side. The audience will know the basic concept of public health and learn the way to devise new computational algorithms for this critical challenge.  

Short BiographyKun-Ta Chuang currently serves as an assistant professor in Department of Computer Science and Information Engineering in National Cheng Kung University. He was a senior engineer at EDA giant Synopsys during 2006-2011. He received the Ph.D. degree from Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan in 2006. His research interests include data mining, web technology, mobile data management, and cloud computing.


Professor Hyeonsang Eom  

Seoul National University

Title: Workload-Aware Resource Management Technologies for Improving Server Performance

Abstract: Datacenters where various sorts of servers may run have been becoming larger and more heterogeneous, possibly being highly distributed. It is crucial to manage many heterogeneous resources effectively to efficiently and cost-effectivity provide services; it is necessary to allocate “right” resources to Virtual Machines (VMs) in virtualized datacenters in order to decrease the cost of the operation while meeting the SLAs (Service Level Agreements) such as meeting the latency requirement. One of the most effective ways to allocate “right” resources to a VM would be to do it considering the characteristics of the VM such as the memory intensiveness of the workload executed in the VM. However, the existing schedulers do not consider these kinds of characteristics, including the NOVA scheduler of OpenStack and DRS (Distributed Resource Scheduler) of VMWare. In this tutorial, I explain some workload-aware schedulers, and our workload-aware one that schedules VMs on OpenStack clusters of nodes, considering the characteristics of workload executed in the VMs. Our experimental study with Redis and Memcached possibly caching the data and links of Web servers shows that our memory-intensiveness-aware scheduler may outperform the default scheduler of OpenStack and DRS as well in terms of throughput and latency.

Short Biography: Hyeonsang Eom received the BS degree in computer science and statistics from Seoul National University (SNU), Seoul, Korea, in 1992, and the MS and PhD degrees in computer science from the University of Maryland at College Park, Maryland, USA, in 1996 and 2003, respectively. He is currently an associate professor in the Department of Computer Science and Engineering at SNU, where he has been a faculty member since 2005. He was an intern in the data engineering group at Sun Microsystems, California, USA, in 1997, and a senior engineer in the Telecommunication R&D Center at Samsung Electronics, Korea, from 2003 to 2004. His research interests include distributed systems, cloud computing, operating systems, high performance storage systems, energy efficient systems, fault-tolerant systems, security, and information dynamics.

Image - The 15th Asia-Pacific Web Conference
Section ——


2nd International Workshop on Web Data Mining and Applications (WDMA 2016)

1st International Workshop on Graph Analytics and Query Processing (GAP 2016)

1st International Workshop on Spatio-temporal Data Management and Analytics (SDMA’2016)

Section ——

Accepted Papers

Research Full Paper

  1. Probabilistic Nearest Neighbor Query in Traffic-Aware Spatial Networks

  2. NERank: Bringing Order to Named Entities from Texts

  3. FTS: A Practical Model for Feature-based Trajectory Synthesis

  4. Distributed Text Representation with Weighting Scheme Guidance for Sentiment Analysis

  5. A Real Time Wireless Interactive Multimedia System

  6. Forecasting Career Choice for College Students Based on Campus Big Data

  7. Efficient Group Top-k Spatial Keyword Query Processing

  8. Online Prediction for Forex with an Optimized Experts Selection Model

  9. Flexible and Adaptive Stream Join Algorithm

  10. Practical Study of Subclasses of Regular Expressions in DTD and XML Schema

  11. Finding Frequent Items in Time Decayed Data Streams

  12. Fast Rare Category Detection Using Nearest Centroid Neighborhood

  13. Scalable Private Blocking Technique for Privacy-Preserving Record Linkage

  14. Learn to recommend local event using heterogeneous social networks

  15. Latent Semantic Diagnosis in Traditional Chinese Medicine

  16. Top-k Temporal Keyword Query over Social Media Data

  17. Correlation-based Weighted k-Labelsets for Multi-Label Classification

  18. Classifying Relation via Bidirectional Recurrent Neural Network based on Local Information

  19. Psychological Stress Detection from Online Shopping

  20. When a Friend Online is More Than a Friend in Life: Intimate Relationship Prediction in Microblogs

  21. Community Inference with Bayesian Symmetric Non-Negative Matrix Factorization

  22. Confidence-learning based collaborative filtering with heterogeneous implicit feedbacks

  23. Spica: A Path Bundling Model for Rational Route Recommendation

  24. Mining Co-locations from Continuously Distributed Uncertain Spatial Data

  25. An Online Approach for Direction-Based Trajectory Compression with Error Bound Guarantee

  26. A Target-dependent Sentiment Analysis Method for Micro-blog Streams

  27. Maximizing the influence ranking under limited Cost in social network

  28. A Data Grouping CNN Algorithm for Short-term Traffic Flow Forecasting

  29. Repair Singleton IDs on the Fly

  30. Making Cold Data Identification Efficient in Non-Volatile Memory Systems

  31. Star-Scan : A Stable Clustering by Statistically Finding Centers and Noises

  32. Aggregating Crowd Wisdom with Instance Grouping Methods

  33. FHSM: Factored Hybrid Similarity Methods for Top-N Recommender Systems

  34. Accelerating Path Nearest Neighbor Search in Spatial Networks

  35. Man-O-Meter: Modeling and Assessing the Evolution of Language Usage of Individuals on Microblogs

  36. Modeling for Noisy Labels of Crowd Workers

  37. Online Streaming Feature Selection using Sampling Techniques and Correlations between Features

  38. CoDS:Co-training with Domain Similarity for Cross-domain Image Sentiment Classification

  39. Personalized Resource Recommendation Based on Regular Tag and User Operation

  40. Academic Paper Recommendation Based on Community Detection in Citation-Collaboration Networks

  41. Budget Minimization with Time and Influence Constraints in Social Network

  42. Feature Selection via Vectorizing Feature's Discriminative Information

  43. Incomplete Data Classfication Based on Multiple Views

  44. Preference Join on Heterogeneous Data

  45. A Label Inference Method based on Maximal Entropy Random Walk over Graphs

  46. An Adaptive kNN Using Listwise Approach for Implicit Feedback

  47. Accelerating Time Series Shapelets Discovery with Key Points

  48. Quantifying the Effect of Sentiment on Topic Evolution in Chinese Microblog

  49. Efficient Evaluation of Shortest Travel-time Path Queries in Road Networks by Optimizing Waypoints in Route Requests through Spatial Mashups

  50. Improving Recommendation Accuracy for Travelers by Exploiting POI Correlations

  51. Fuzzy Keywords Query

  52. Towards Efficient Influence Maximization for Evolving Social Networks

  53. Detecting Community Pacemakers of Burst Topic in Twitter

  54. Dynamic User Attribute Discovery on Social Media

  55. Improving Temporal Recommendation Accuracy and Diversity via Long and Short-Term Preference Transfer and Fusion Models

  56. Measuring Directional Semantic Similarity with Multi-Features

  57. Discovering Companion Vehicles from Live Streaming Traffic Data

  58. EPLA:Efficient Personal Location Anonymity

  59. The Competition of User Attentions Among Social Network Services: a Social Evolutionary Game Approach

  60. An Adaptive Partition-Based Caching Approach for Efficient Range Queries on Key-value Data

  61. Mechanism Analysis of Competitive Information Synchronous Dissemination in Social Networks

  62. A Context-aware Method for Top-k Recommendation in Smart TV

  63. A Topic-Specific Contextual Expert Finding Method in Social Network 

  64. Real-time Anomaly Detection over ECG Data Stream Based on Component Spectrum

  65. Near-Duplicate Web Video Retrieval and Localization Using Improved Edit Distance

  66. Finding Latest Influential Research Papers through Modeling Two Views of Citation Links

  67. Handling Estimation Inaccuracy in Query Optimization

  68. An Approach for Cross-Community Content Recommendation: A Case Study on Docker

  69. Multi-label Chinese Microblog Emotion Classification via Convolutional Neural Network

  70. Mining Recent High Expected Weighted Itemset from Uncertain Databases

  71. Context-aware Chinese Microblog Sentiment Classification with Bidirectional LSTM

  72. A Secure and Robust Covert Channel Based on Secret Sharing Scheme

  73. A Hybrid Method for POI Recommendation: Combining Check-in Count, Geographical Information and Reviews

  74. A Workload-Driven Vertical Partitioning Approach Based on Streaming Framework

  75. Time-Constrained Sequenced Route Query in Indoor Spaces

  76. Discovering Approximate Functional Dependencies from distributed big data

  77. B-mine: Frequent Pattern Mining and Its Application to Knowledge Discovery from Social Networks

  78. An Efficient Online Event Detection Method for Microblogs via User Modeling

Research Short Paper

  1. Microblog Sentiment Analysis Based on Sentiment Features

  2. FVBM: A Filter-Verification-Based Method for Finding Top-k Closeness Centrality on Dynamic Social Networks

  3. Online Hot Topic Detection from Web News Based on Bursty Term Identification

  4. Grouped Team Formation in Social Networks

  5. Profit Maximizing Route Recommendation for Vehicle Sharing Requests

  6. Ontology-Based Interactive Post-Mining of Interesting Co-location Patterns

  7. AALRSMF: An Adaptive Learning Rate Schedule for Matrix Factorization

  8. A Graph Clustering Algorithm for Citation Networks 

  9. A Distributed Frequent Itemsets Mining Algorithm Using Sparse Boolean Matrix on Spark

  10. A Simple Stochastic Gradient Variational Bayes for the Correlated Topic Model

  11. Reasoning with Large Scale OWL 2 EL Ontologies based on MapReduce

  12. Purchase and Redemption Prediction based on Multi-task Gaussian Process and Dimensionality Reduction

  13. RORS: Enhanced Rule-based OWL Reasoning on Spark

  14. A Hadoop-based Database Querying Approach for Non-expert Users

  15. A  Collaborative Join Scheme on a MIC-based Heterogeneous Platform

  16. Pairwise Expansion: A New Topdown Search for mCK Queries Problem over Spatial Web

  17. Mentioning the optimal users in the appropriate time on Twitter

  18. Historical Geo-Social Query Processing

  19. WS-Rank: Bringing Sentences into Graph for Keyword Extraction

  20. Efficient Community Maintenance for Dynamic Social Networks

  21. Open Sesame! Web Authentication Cracking via Mobile App Analysis

  22. K-th Order Skyline Queries in Bicriteria Networks

  23. A K-Motifs Discovery Approach for Large Time-Series Data Analysis

  24. User Occupation Prediction on Microblogs

  25. Similarity Recoverable, Format-preserving String Encryption

Industry Full Paper

  1. Combo-Recommendation based on Potential Relevance of Items

Demo Paper

  1. OPGs-Rec: Organized-POI-Groups Based Recommendation

  2. A Demonstration of Encrypted Logistics Information System

  3. PCMiner:An Extensible Framework for Analysing and Detecting Protein Complexes

  4. MASM:A Novel Movie Analysis System based on Microblog

  5. A Text Retrieval System Based on Distributed Representations

  6. A System for Searching Renting Houses Based on Relaxed Query Answering

  7. TagTour: a Personalized Tourist Resource Recommendation System

  8. A Chronic Disease Analysis System Based on Dirty Data Mining

  9. ADDS: An Automated Disease Diagnosis-aided System


  11. Factorization machine based business credit scoring by leveraging Internet data

  12. OICRM:An Ontology-based Interesting Co-location Rule Miner

  13. CB-CAS: a CAS-Based Cross-Browser SSO System

  14. A Demonstration of QA System based on Knowledge Base

  15. An Alarming and Prediction System for Infections Disease Baced on Combined Modles

  16. Co-location Detector: A System to Find Interesting Spatial Co-locating Relationships

  17. KEIPD:Knowledge Extraction and Inference System for Personal Documents

Workshop Papers

International Workshop on Web Data Mining and Applications (WDMA 2016)

  1. Maximizing the Cooperative Influence Spread in a Social Network Oriented to Viral Marketing    

  2. A Multi-Model Based Approach for Big Data Analytics: the Case on Education Grant Distribution    

  3. Sentiment Target Extraction Based on CRFs  with Multi-features for Chinese Microblog    

  4. EMD-DSJoin: Efficient Similarity Join over Probabilistic Data Streams Based on Earth Mover’s Distance    

  5. Sentiment Analysis on User Reviews  through Lexicon and Rule-based Approach    

  6. Social Link Prediction Based on the Nodes' Information Transfer    

  7. An Improved ML-kNN Approach Based on Coupled Similarity    

  8. A Novel Recommendation Method Based on User’s Interest and Heterogeneous Information    

  9. Knee point-driven bottleneck detection algorithm for cloud service system    

  10. Confirmatory Analysis on  Influencing Factors When Mention Users in Twitter    

  11. A stock recommendation strategy based on M-LDA model    

  12. Short-term Forecasting and Application about Indoor Cooling Load Based on EDA-PSO-BP Algorithm     

International Workshop on Graph Analytics and Query Processing (GAP 2016)

  1. Identifying Relevant  Subgraphs in Large networks    

  2. User-dependent Multi-relational Community Detection in Social Networks    

  3. Compressing Streaming Graph Data Based on Triangulation    

International Workshop on Spatio-temporal Data Management and Analytics (SDMA’2016)

  1. Scene Classification in High Resolution Remotely Sensed Images based on PCANet    

  2. Finding  Top-k Places for Group Social Activities    

  3. Temporal Spatial-Keyword Search On Databases Using SQL     

  4. Features of Rumor Spreading on WeChat Moments    

  5. Distance-Based Continuous Skylines On Geo-Textual Data    

  6. Improving Urban Traffic Evacuation Capability in Emergency Response by Using Smart Phones    

  7. Context Enhanced Keyword Extraction for Sparse Geo-entity Relation from Web Texts    

  8. A Stacked Generalization Framework for City Traffic Related Geospatial Data Analysis    

  9. Detection   of Statistically Signi cant Bus Delay  Aggregation by Spatial-Temporal Scanning    

  10. Acquisition and Representation of Knowledge for Academic Field    

  11. Using Learning Features to Find Similar Trajectories    

  12. An Algorithm for Mining Moving Flock Patterns from Pedestrian Trajectories    

Image - A google data center
Conference Program
Section ——

Conference Program

Program at a Glance



  Detailed Program can be found in Program-v17.pdf. Should you find anything unclear or incorrect, please let us know. 

Section ——

Conference Venue


Suzhou Xi'an Jiaotong-Liverpool International Conference Center(苏州西交利物浦国际会议中心)


Location: No. 99 Renai Road, Suzhou City, Jiangsu Provice, P.R.China


Tel: +86-512-86665555



The conference venue is 20 minutes drive from Suzhou Railway Station (苏州火车站) and 30 minutes drive from Suzhou Highspeed Train Station (苏州高铁北站). Taxi is the most convenient way to get there.