Professor Christian S. Jensen

    Aalborg University

    Tuesday, March 28th, 9:00 - 10:10 (Chair: Lei Chen)

    Title: Trajectory-Based Routing in Spatial Networks 


Abstract: We are witnessing an unprecedented wave of digitization of societal processes. As a result, data is increasingly available that captures the states of societal processes at an unprecedented level of detail, in turn enabling us to better understand and improve the processes. Road transportation is one such process. Here, the proliferation of vehicle trajectory data enables the accurate capture of the time-varying state of traffic. The speaker argues that when the underlying data is in the form of trajectories, traditional vehicle routing, where a road network is modeled as a graph and weights such as travel times are assigned to edges, is obsolete. Instead, new approaches are needed. One promising approach is to associate weights with arbitrary graph paths. This approach presents new challenges and opportunities to routing. Another promising approach is to return routes that mirror the routes seen in historical trajectories. The speaker will describe setting and will cover different uses of trajectories for vehicle routing.

Short Biography: Christian S. Jensen is Obel Professor of Computer Science at Aalborg University, Denmark, and he was recently with Aarhus University for three years and spent a one-year sabbatical at Google Inc., Mountain View. His research concerns data management and data-intensive systems, and its focus is on temporal and spatio-temporal data management. Christian is an ACM and an IEEE Fellow, and he is a member of Academia Europaea, the Royal Danish Academy of Sciences and Letters, and the Danish Academy of Technical Sciences. He has received several national and international awards for his research. He is Editor- in-Chief of ACM Transactions on Database Systems.

blob.png    Dr. Divesh Srivastava

    AT&T Labs-Research

    Wednesday, March 29th, 9:10 – 10:20 (Chair:  Peter Scheuermann)

    Title: The Confounding Problem of Private Data Release


Abstract: In our Big Data era, as data-driven decision making sweeps through all aspects of society, the demands to make useful data available are growing ever louder. For example, the ubiquity of GPS-enabled devices has resulted in a wealth of data about the movements of individuals and populations, which can be analyzed for useful information to aid in city and traffic planning, disaster preparedness, and so on. But the problem of releasing such data without disclosing confidential information, such as the places people visit, is a subtle and difficult one. Is “private data release” an oxymoron?   This talk will delve into the motivations of private data release, explore the challenges, and outline some of the historical and recent approaches developed in response to this confounding problem. 

Short Biography:  Dr. Divesh Srivastava is the head of Database Research at AT&T Labs-Research. He is an ACM fellow and the managing editor of the Proceedings of the VLDB Endowment (PVLDB). His research interests and publications span a variety of topics in data management. He received his Ph.D. from the University of Wisconsin, Madison, USA, and his Bachelor of Technology from the Indian Institute of Technology, Bombay, India.

    Professor Victor Chang

   Xi’an Jiaotong-Liverpool University, Suzhou

   Thursday, March 30th  9:00 – 10:10 (Chair: Torben B. Pedersen)

    Title:  Interplay between Cloud, IoT and Data Science: methods, examples and research contributions

Abstract: Emerging technologies in Cloud, Internet of Things (IoT) and Data Science can provide a state-of-the-art for modern services. IoT is the platform to connect to different devices and collect data. Data Science provides techniques and methods to analyze and interpret and visualize data. Cloud is the service to provide security, storage and software as the service. Interplay takes the form of architectural fusion, data fusion, service integration and security fusion. Interplay of fusion services can offer to improve the quality of services and experiences by speeding up the requests, reducing complexity, enhancing security, offering real-time data protection and using methods involved with Big Data, Cloud, AIs and deep learning to perform tasks and complete .This keynote presents the best examples to overcome challenges and provide real solutions in healthcare, finance, education, weather studies, social networks, security & privacy, smart cities and integration as a service. Results will be explained in details and research contributions for each discipline be summed up. Recommendations are made as a result of successful deliveries, ways to resolve problems and real case studies with positive impacts.

Short Biography:  Prof. Victor Chang is an Associate Professor (Reader) and Director of PhD at IBSS, Xi’an Jiaotong-Liverpool University, Suzhou, China, after working as a Senior Lecturer at Leeds Beckett University, UK, for 3.5 years. Within 4 years, he completed Ph.D. (CS, Southampton) and PGCert (Higher Education, Fellow, Greenwich) while working for several projects at the same time. Before becoming an academic, he has achieved 97% on average in 27 IT certifications. He won a European Award on Cloud Migration in 2011, IEEE Outstanding Service Award in 2015,  best papers in 2012 and 2015, the 2016 European award: Best Project in Research, Outstanding Young Scientist award in India on the behalf of ICRTCCM 2017 and numerous awards since 2012. He is widely regarded as a leading expert on Big Data/Cloud/IoT/security. He is a visiting scholar/PhD examiner at several universities, an Editor-in-Chief of IJOCI & OJBD journals, Editor of FGCS, founding chair of two international workshops and founding Conference Chair of IoTBDS and COMPLEXIS since Year 2016. He was involved in different projects worth more than £12.5 million. He has published 3 books as sole authors and the editor of 2 books on Cloud Computing and related technologies. He has given and will give 10 keynotes at international conferences.



    Professor  Sanjay Kumar Madria

    Missouri University of Science and Technology

    Tuesday, March 28th  10:30 – 12:00

    Title: Incentive-based Dynamic Content Management in Mobile Crowdsourcing for Smart City Applications

Abstract: Ever-increasing prevalence of social networking using mobile devices has catalyzed the growth of interesting and innovative new-age mobile crowdsourcing applications, which work at the intersection of human-centric computation (e.g., economic incentive management and social computing) and dynamic management of information as well as content in mobile networks. The prevalence and proliferation of mobile devices coupled with popularity of social media and increasingly technology-savvy users have fuelled the growth of mobile crowdsourcing and participatory sensing. In particular, participatory sensing can occur in various ways by means of devices (e.g., mobile phones, PDAs, laptops and various types of sensors) or by including humans in the loop or both. Notably, participatory sensing can also potentially act as a key enabling technology for various applications involving smarter cities initiatives.

Incidentally, large-scale collection of city-related event data is crucial to effective planning and decision-making for improving city management. Examples of city-related event data include traffic congestion, illegal parking, accidents, dysfunctional streetlights, broken pavements, potholes, planned road construction works, and waterlogging, and garbage collection. Notably, existing sensor-based data collection mechanisms cannot always take human judgment and the context of the event into consideration, and the costs of deploying them across all city locations would be prohibitively expensive. Hence, event data collection can be used to complement sensor-based data collection. Since mobile devices often come equipped with various kinds of sensors, resident-driven data collection is also well aligned with current technological trends. However, incentives need to be provided to users for encouraging them to contribute better quality event data.

Short Biography: 

Sanjay Kumar Madria received his Ph.D. in Computer Science from Indian Institute of Technology, Delhi, India in 1995. He is a full professor in the Department of Computer Science at the Missouri University of Science and Technology (formerly, University of Missouri-Rolla, USA). He has published over 235 Journal and conference papers in the areas of mobile data management, big data, sensor computing, cloud and cyber security. He won five IEEE best papers awards including IEEE MDM 2011, IEEE MDM 2012 and IEEE SRDS 2015.  He has also been awarded JSPS (Japanese Society for Promotion of Science) visiting scientist fellowship in 2006 and ASEE (American Society of Engineering Education) fellowship at AFRL from 2008 to 2016.  In 2012, he got awarded NRC Fellowship by National Academies, USA. He has received faculty excellence and research awards in years 2007, 2009, 2011, 2013 and 2015 from his university for excellence in research. He is an ACM Distinguished Scientist, and IEEE Senior Member as well as IEEE Golden Core Awardee. His research is supported by NSF, ARO, AFRL, ARL, NIST and AFRL among others. 

blob.png    Professor Verena Kantere

    The University of Geneva & The National Technical University of Athens

    Tuesday, March 28th  13:30 – 15:00

    Title: Tutorial on Data Analytics in Multi-Engine Environments


Abstract: The performance of analytics on Big Data collections is the focus of a lot of research and is becoming a leading requirement in many business domains and scientific disciplines. Data analytics includes techniques, algorithms and tools for the inspection of data collections in order to extract patterns, generalizations and other useful information. Much of the recent research is focused on the employment of Hadoop for efficient data analysis, which takes advantage of the MapReduce paradigm and distributed storage, and, furthermore, Spark mostly for real-time analysis, which takes advantage of in-memory processing. The success and effectiveness of such analysis depend on numerous challenges related to the data itself, the nature of the analytics tasks, as well as the data processing platforms over which the analysis is performed.

Data analytics may vary significantly in terms of the properties of data, type of analysis and processing system and may require cross-platform analysis with migration of both data and processing. For example, data may be structured, semi-structured, or unstructured residing in files, and may have different inter-dependencies, such as relational constraints, tree-like or graph dependencies. The type of analysis may include stream processing, information retrieval, query processing, mining, clustering, integration, and other. The underlying processing systems may be traditional relational DBMSs, but also RDF stores, NoSQL databases, graph databases etc.

The above diversity across data, processing and systems has recently spawned an interest in the research community for the creation of transparent all-inclusive solutions for data analytics in multi-engine environments, which enable the inter-operability of the involved systems, and focus in the construction of inter-system optimizers. The challenges for the creation of such solutions lie in the consolidation of different engine capabilities for data processing, such that the integrated system adapts its operation depending on the input workload and the data location and type, and the processing load of the engines. Research to tackle this challenge includes the definition of versatile programming models, engine performance modeling and monitoring, planning and optimization techniques, parallel deployment and execution on multiple engines, workflow management and visualization techniques.

Short Biography:  Verena Kantere is a Maˆıtre d’Enseignement et de Recherche (equivalent to Associate Professor) at the Centre Universitaire d’Informatique (CUI) of the University of Geneva (UniGe) and an Assistant Professor at the School of Electrical and Computer Engineering in the National Technical University of Athens, working towards the provision and exchange of data services in cloud environments, focusing on the management of Big Data and performance of Big Data analytics, by developing methods, algorithms and fully fledged systems. Before coming to the UniGe she was a tenure-track junior assistant professor at the Department of Electrical Engineering and Information Technology at the Cyprus University of Technology (CUT). She has received a Diploma and a Ph.D. from the National Technical University of Athens, (NTUA) and a M.Sc. from the Department of Computer Science at the University of Toronto (UofT), where she also started her PhD studies. After the completion of her PhD studies she worked as a postdoctoral researcher at the Ecole Polytechnique Federale de Lausanne (EPFL). She is currently leading the research on Adaptive Analytics in the ASAP ( EU project. She has created and co-chaired several workshops, the most recent being the Workshop on Multi-Engine Data Analytics (collocated with EDBT 2016), she has given 30 invited talks in international conferences, universities and research institutes and has served in the PC of 40 international conferences and workshops.

Maxim Filatov is a PhD student working in CUI of the University of Geneva (UniGe), under the supervision of Prof. Verena Kantere. Prior to Geneva, Maxim obtained his Masters degree in Mechanics at the Computational Mechanics department of Lomonosov Moscow State University, where he also started his PhD studies. During this period he also worked as a researcher in RD of Roxar and TimeZYX on the development of reservoir simulation software. He developed methods, algorithms and fully-fledged systems for modeling of multiphase flow, focusing on the performance aspect. His PhD research is focused on the management of Big Data and performance of Big Data analytics. He is currently working on Adaptive Analytics in the ASAP ( FP7 EU project.

blob.png    Dr. Yu Zheng

    Microsoft Research

    Wednesday, March 29th  10:40 – 12:10

    Title: Urban Computing: Enabling Urban Intelligence with Big Data 


Abstract: Urban computing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in cities to tackle urban challenges, e.g. air pollution, energy consumption and traffic congestion. Urban computing connects unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods, to create win-win-win solutions that improve urban environment, human life quality, and city operation systems. Urban computing is an inter-disciplinary field where computer science meets urban planning, transportation, economy, the environment, sociology, and energy, etc., in the context of urban spaces. The vison of urban computing has been leading to better cities that matter to billions of people.

Though the concept of urban computing has been proposed for a few years, there are still quite a few questions open. For example, what are the core research problems of urban computing? What are the challenges of the research theme? What are the key methodologies for urban computing? What are the representative applications in this domain, and how does an urban computing system work?

Short Biography:  Dr. Yu Zheng is a research manager from Microsoft Research, passionate about using big data to tackle urban challenges. Zheng currently serves as the Editor-in-Chief of ACM Transactions on Intelligent Systems and Technology, and has served as chair on over 10 prestigious international conferences—most recently, as the program co-chair of ICDE 2014. In 2013, he was named one of the Top Innovators under 35 by MIT Technology Review (TR35) and featured by Time Magazine for his research on urban computing. In 2016, Zheng was named ACM Distinguished Scientist for his contribution to spatio-temporal data mining and urban computing. Zheng is also a Chair Professor at Shanghai Jiao Tong University, an Adjunct Professor at Hong Kong University of Science and Technology, and Hong Kong Polytechnic University.


The 4th International Workshop on Big Data Management and Service (BDMS 2017)

The 2st Workshop on Big Data Quality Management (BDQM 2017)

The 4th International Workshop on Semantic Computing and Personalization (SeCoP)

The 1st International Workshop on Data Management and Mining on MOOCs (DMMOOC 2017)


Title: How DB meets AI
Coordinator: Prof. Xuemin Lin (University of New South Wales)


Prof. Christian S. Jensen (Aalborg University)

Dr. Divesh Srivastava (AT&T Labs-Research)

Prof. Xiaoyang Wang (Fudan University)

Dr. Yu Zheng (Microsoft Research)

Prof. Xiaofang Zhou (University of Queensland)

Conference Program

DASFAA 2017 Conference Program at Glance

DASFAA 2017 Conference Program Final.pdf

DASFAA 2017 Workshop Day (March 27th, 2017)

8:30 – 17:00

Registration (Lobby, 1F)

9:00 – 10:00

DMMOOC   Keynote

(Xie Xiu Hall, 1F, 携秀厅)

BDMS   Session 1

(Fu Cui   Hall, 2F, 浮翠厅)

  Invited Talk & Session 1

Bo Ya   Hall, 3F, 博雅厅)

10:00 – 10:30

Coffee break

10:30 – 12:00

DMMOOC   Session 1

(Xie Xiu Hall, 1F, 携秀厅)

BDMS   Session 2

(Fu Cui   Hall, 2F, 浮翠厅)

BDQM   Session 2

Bo Ya   Hall, 3F, 博雅厅)

12:00 – 13:30


13:30 – 14:30

DMMOOC Session   2

(Xie Xiu Hall, 1F, 携秀厅)

BDMS   Session 3

(Fu Cui   Hall, 2F, 浮翠厅)

SeCoP   Session 1

Bo Ya   Hall, 3F, 博雅厅)

14:30 – 15:00

Coffee break

15:00 – 16:40

DMMOOC   Session 3

(Xie Xiu Hall, 1F, 携秀厅)

BDMS   Session 4

(Fu Cui   Hall, 2F, 浮翠厅)

SeCoP   Session 2

Bo Ya   Hall, 3F, 博雅厅)

18:00 – 20:00



DASFAA 2017 Conference Day 1 (March 28th, 2017)

8:30 – 17:00

Registration (Lobby, 1F)

8:30 – 9:00

      Opening Ceremony and Welcome
       Professor Xiaofang Zhou

9:00 – 10:10

Keynote Speech: Trajectory-Based Routing in Spatial Networks

Professor Christian S. Jensen

                                                                         (Chair: Lei Chen)

(Yuan Xiang Hall, 3F, 远香堂)

10:10 – 10:30

Coffee Break

10:30 – 12:00

Research Session 1

Semantic web and knowledge management

(Xie Xiu Hall, 1F, 携秀厅)

Industry Session 1 Big Data

(Fu Cui Hall, 2F, 浮翠厅)

Tutorial 1

Incentive-based Dynamic Content Management in Mobile Crowdsourcing for Smart City Applications

(Yuan Xiang Hall, 3F, 远香堂)

12:00 – 13:30



13:30 – 15:00

Research Session 2

Indexing and Distributed Systems

(Xie Xiu Hall, 1F, 携秀厅)

Research Session 3

Network Embedding

(Fu Cui Hall, 2F, 浮翠厅)

Tutorial 2

Data Analytics in Multi-Engine Environments

(Yuan Xiang Hall, 3F, 远香堂)

15:00 – 15:20

Coffee Break

15:20 – 16:50

Research Session 4

Trajectory and Time Series Data Processing

(Xie Xiu Hall, 1F, 携秀厅)

Research Session 5

Data Mining

(Yuan Xiang Hall, 3F, 远香堂)


DASFAA   2017 Conference Day 2 (March 29th, 2017)

8:30 – 17:00

Registration (Lobby, 1F)

8:30 – 9:10

Award Ceremony:10 year best paper speech

                                                         Chair: Professor Masaru Kitsuregawa

(Yuan Xiang Hall, 3F, 远香堂)

9:10 – 10:20

Keynote Speech: The   Confounding Problem of Private Data Release

Dr. Divesh   Srivastava

                                                            (Chair: Peter Scheuermann)

(Yuan Xiang Hall, 3F, 远香堂)

10:20 – 10:40

Coffee Break

10:40 – 12:10

Research Session 6

Query Processing   and Optimization (I)

(Xie Xiu Hall, 1F, 携秀厅)

Research Session 7

Text Mining

(Fu Cui Hall, 2F, 浮翠厅)

Tutorial 3

Urban Computing: Enabling Urban Intelligence with Big Data 

(Yuan Xiang Hall, 3F, 远香堂)

12:10 – 13:30


13:30 – 15:20

Research Session 8   Recommendation

(Xie Xiu Hall, 1F, 携秀厅)

Research session 9

Security, Privacy,   Senor and Cloud

(Fu Cui Hall, 2F, 浮翠厅)

Industry Session 2

Social Networks and   Graphs

(Yuan Xiang Hall, 3F, 远香堂)

15:20 – 15:40

Coffee Break

15:40 – 17:20

Research Session 10   Social Network Analytics (I)

(Xie Xiu Hall, 1F, 携秀厅)

Research Session 11  

Map Matching and   Spatial Keywords

(Fu Cui Hall, 2F, 浮翠厅)

Panel: How DB   meets AI 

Chair: Prof. Xuemin Lin

(Yuan Xiang Hall, 3F, 远香堂)

18:00 – 21:00


(Chun   Han Hall, 2F, 春酣厅)

DASFAA 2017 Conference Day 3 (March 30th, 2017)

9:00 – 10:10

Keynote Speech: Interplay between Cloud, IoT and Data Science:  

                                                       Methods, Examples and Research contributions

Professor Victor Chang

                                                                  (Chair: Torben Pedersen)

(Yuan Xiang Hall, 3F, 远香堂)

10:10 – 10:30

Coffee Break

10:30 – 12:00

Research Session 12

Query Processing and Optimization (II)

(Xie Xiu Hall, 1F, 携秀厅)

Research Session 13

Search and Information Retrieval

(Fu Cui Hall, 2F, 浮翠厅)

Research Session 14

String and Sequence Processing

(Yuan Xiang Hall, 3F, 远香堂)

12:00 – 13:30



13:30 – 15:00

Research Session 15

Stream Data Processing

(Xie Xiu Hall, 1F, 携秀厅)

Research Session 16

Social Network Analytics (II)

(Fu Cui Hall, 2F, 浮翠厅)

Research Session 17

Graph and Network Data Processing

Bo Ya Hall, 3F, 博雅厅)

15:00 – 15:20

Coffee Break

15:20 – 16:50

Research Session 18 

Spatial Databases

(Xie Xiu Hall, 1F, 携秀厅)

Research Session 19 

Real Time Data Processing

(Fu Cui Hall, 2F, 浮翠厅)


Bo Ya Hall, 3F, 博雅厅)

*Xie Xiu Hall, Fu Cui Hall, Bo Ya Hall are in Yuan Zhong Building(园中楼)

*Chun Han Hall is in Shan Shui Building(山水楼)

DASFAA 2017 Conference Program Final.pdf

Program of The 1st International Workshop on Data Management and Mining on MOOCs (DMMOOC 2017)

Workshop Opening: 9:00 – 9:05

Keynote (TBD): 9:05 – 10:00

Session 1: 10:00 – 12:00

1.         MOOCon: A Framework for Semi-supervised Concept Extraction from MOOC Content
Zhuoxuan Jiang, Yan Zhang and Xiaoming Li

2.         Exploring N-gram Features in Clickstream Data for MOOC Learning Achievement Prediction
Xiao Li, Ting Wang and Huaimin Wang

3.         Predicting Student Examinee Rate in Massive Open Online Courses
Wei Lu, Tongtong Wang, Min Jiao, Xiaoying Zhang, Shan Wang, Xiaoyong Du and Hong Chen

4.         Task Assignment of Peer Grading in MOOCs
Yong Han, Wenjun Wu and Yanjun Pu

Session 2: 13:30 – 14:30

1.         Predicting Honors Student Performance Using RBFNN and PCA Method
Moke Xu, Yu Liang and Wenjun Wu

2.         Towards Economic Models for MOOC Pricing Strategy Design
Yongzheng Jia, Zhengyang Song, Xiaolan Bai and Wei Xu

3.         Using Pull-Based Collaborative Development Model in Software Engineering Courses: An Case Study
Yao Lu, Xinjun Mao, Gang Yin, Tao Wang and Yu Bai

Session 3: 15:00 – 16:40

1.         DKG: An Expanded Knowledge Base for Online Course
Haimeng Duan, Yuanhao Zheng, Lei Shi, Chonghong Jin, Hongwei Zeng and Jun Liu

2.         What Decides the Dropout in MOOCs
Xiaohang Lu, Shengqing Wang, Junjie Huang, Wenguang Chen and Zengwang Yan

3.         A Method of Constructing the Mapping Knowledge Domains in Chinese Based on the Online Courses
Zhengzhou Zhu, Yang Li, Youming Zhang and Zhonghai Wu

4.         Social Friendship-Aware Courses Arrangement in MOOCs
Yuan Liang

5.         Quality-Aware Crowdsourcing Curriculum Recommendation in MOOCs
Yunpeng Gao

6.         Crowdsourcing based Teaching Assistant Arrangement for MOOC
Dezhi Sun and Bo Liu

7.         Quantitative Analysis of Learning Data in A Programming Course
Yu Bai, Liqian Chen, Gang Yin, Xinjun Mao, Ye Deng, Tao Wang and Huaimin Wang

Program of The 4th International Workshop on Big Data Management and Service (BDMS 2017)

Session 1: 9:00-10:00

1.         Automatically Classify Chinese Judgment Documents Utilizing Machine Learning Algorithms
Miaomiao Lei, Jidong Ge, Zhongjin Li, Chuanyi Li, Yemao Zhou, Xiaoyu Zhou, and Bin Luo (Nanjing University)

2.         Time-Aware and Topic-based Reviewer Assignment
Hongwei Peng, Haojie Hu, Keqiang Wang, and Xiaoling Wang (East China Normal University)

3.         A Novel Approach for Author Name Disambiguation Using Ranking Confidence
Xueqin Lin, Jia Zhu, Yong Tang, Fen Yang, Bo Peng, and Weiling Li (South China Normal University)

Session 2: 10:30-11:30

1.         LFLogging: A Latch-Free Logging Scheme for PCM-Based Big Data Management Systems
Wenqiang Wang, Peiquan Jin, Shouhong Wan, Lihua Yue (University of Science and Technology of China)

2.         Adaptive Bayesian network Structure Learning from Big Datasets
Yan Tang, Qidong Zhang, Huaxin Liu, and Wangsong Wang (Hohai University)

3.         A Partitioning Scheme for Big Dynamic Trees
Atsushi Sudoh, Tatsuo Tsuji, Ken Higuchi (University of Fukui)

Session 3: 13:30-14:30

1.         Online Formation of Large Tree-structured Team
Cheng Ding, Fan Xia, Gopakumar, Weining Qian, and Aoying Zhou (East China Normal University)

2.         Cell-based DBSCAN Algorithm using Minimum Bounding Rectangle Criteria
Tatsuhiro Sakai, Keiichi Tamura, and Hajime Kitakami (Hiroshima City University)

3.         Optimization Factor Analysis of Large-Scale Join Queries on Different Platforms
Chao Yang, Qian Wang, Qing Yang, Huibing Zhang, Jingwei Zhang, and Ya Zhou (Guilin University of Electronic Technology)

Session 4: 15:00-16:20

1.         RTMatch: Real-Time Location Prediction based on Trajectory Pattern Matching
Dong Zhenjiang (Shanghai Jiao Tong University), Deng Jia, Jiang Xiaohui, Wang Yongli (Nanjing University of Science and Technology)

2.         Discovering Interesting Co-location Patterns Interactively Using Ontologies
Xuguang Bao and Lizhen Wang (Yunnan University)

3.         Which Mapping Service Should We Select in China?
Detian Zhang, Jia-ao Wang, and Fei Chen (Jiangnan University)

4.         An Online Prediction Framework for Dynamic Service-generated QoS Big Data
Jianlong Xu (Shantou University), Changsheng Zhu (Shantou University), Qi Xie (Southwest University for Nationalities)


Program of the 2nd Workshop on Big Data Quality Management (BDQM 2017)

9:00-9:40 Invited Speech:  Exacting Data from Texts (Prof Xiaochun Yang, Northeastern University, China)

Session 1 (9:40-10:10)

1.         A New Schema Design Method for Multi-Tenant Database
Yaoqiang Xu, Jiacai Ni

2.         Capture Missing Values with Inference on Knowledge Base
Zhixin Qi, Hongzhi Wang, Fanshan Meng, Jianzhong Li, Hong Gao

Session 2 (10:30-11:15)

1.         Weakly-Supervised Named Entity Extraction using Word Representations
Kejun Deng, Dongsheng Wang, Junfei Liu

2.         Efficient Web-based Data Imputation with Graph Model
Yiwen Tang, Hongzhi Wang, Shiwei Zhang, Huijun Zhang, Ruoxi Shi, Jianzhong Li, Hong Gao

3.         RDF Data Assessment Based On Metrics and Improved PageRank Algorithm
Kai Wei, Pingfang Tian, Li Huang, Jinguang Gu


Program of The 4th International Workshop on Semantic Computing and Personalization (SeCoP 2017)

Session 1: 13:30 – 14:30

1.         Reader's Choice - A Recommendation Platform
Sayar Kumar Dey and Günter Fahrnberger

2.         Accelerating Convolutional Neural Networks Using Fine-Tuned Backpropagation Progress
Yulong Li, Zhenhong Chen, Yi Cai, Dongping Huang and Qing Li

3.         A Personalized Learning Strategy Recommendation Approach for Programming Learning
Peipei Gu, Junxia Ma, Wei Chen, Lujuan Deng and Lan Jiang

Session 2: 15:00 – 16:00

1.         Wikipedia Based Short Text Classification Method
Junze Li, Yi Cai, Zhiwei Cai, Hofung Leung and Kai Yang

2.         An Efficient Boolean Expression Index by Compression
Jin Tao, Chenxi Zhang and Weixiong Rao

Conference Program

Research Session 1 Semantic web and knowledge management  (Chair: Qing Xie)

1.     A General Fine-Grained Truth Discovery Approach for Crowdsourced Data Aggregation

Yang Du* (USTC), Hongli Xu (USTC), Yu'e Sun (USTC), Liusheng Huang (USTC)

2.     Learning the Structures of Online Asynchronous Conversations

Jun Chen* (Tsinghua University), Chaokun Wang (Tsinghua University), Heran Lin (Tsinghua University), Weiping Wang (Tsinghua University), Zhipeng Cai (Tsinghua University), Jianmin Wang (Tsinghua University)

3.     A Question Routing Technique using Deep Neural Network for Communities of Question Answering

Amr Azzam* (Cairo University), Neamat Tazi (Cairo University), Ahmad Hossny (University of Adelaide)

4.     Category-Level Transfer Learning from Knowledge Base to Microblog Stream for Accurate Event Detection

Weijing Huang (Peking University), Tengjiao Wang (), Wei Chen* (Peking university), Yazhou Wang (Peking University)

Research session 2 Indexing and Distributed Systems (Chair: Lei Duan)

1.     AngleCut: A Ring-Based Hashing Scheme for Distributed Metadata Management

Jiaxi Liu (Shanghai Jiao Tong University), Renxuan Wang (Shanghai Jiao Tong University), Xiaofeng Gao* (Shanghai Jiao Tong University), Xiaochun Yang (Northeastern University), Guihai Chen (Shanghai Jiao Tong University)

2.     An Efficient Bulk Loading Approach of Secondary Index in Distributed Log-Structured Data Stores

Yanchao Zhu (ECNU), Zhao Zhang* (ECNU), Peng Cai (ECNU), Weining Qian (East China Normal University), Aoying Zhou (East China Normal University)

3.     Performance Comparison of Distributed Processing of Large Volume of Data on top of Xen and Docker-based Virtual Clusters

Haejin Chung* (Dankook university), Yunmook Nah ( Dankook University)

4.     An Adaptive Data Partitioning Scheme for Accelerating Exploratory Spark SQL Queries

Chenghao Guo* (Fudan University), Zhigang Wu (Fudan University), Zhenying He (Fudan University), Xiaoyang Wang (Fudan University)


Research Session 3 Network Embedding  (Chair: Xuan Zhou)

1.   Semi-Supervised Network Embedding

Chaozhuo Li* (Beihang University), Zhoujun Li (Beihang University), Senzhang Wang (Nanjing University of Aeronautics and Astronautics), Yang Yang (Beihang University), Xiaoming Zhang (Beihang University), Jianshe Zhou (Capital Normal University)

2.   CirE: Circular Embeddings of Knowledge Graphs

Zhijuan Du* (Renmin University of China), Zehui Hao (Renmin University of China), Xaiofeng Meng (Renmin University of China), Qiuyue Wang (Renmin University of China)

3.   PPNE: Property Preserving Network Embedding

Chaozhuo Li* (Beihang University), Senzhang Wang (Nanjing University of Aeronautics and Astronautics), Dejian Yang (Beihang University), Yang Yang (Beihang University), Zhoujun Li (Beihang University), Xiaoming Zhang (Beihang University), Jianshe Zhou (Beihang University)

4.   HINE: Heterogeneous Information Network Embedding

Yuxin Chen* (Peking University), chenguang Wang (Peking University)

Research Session 4 Trajectory and Time Series Data Processing (Chair: Xiaochun Yang)

1.     DT-KST: Distributed Top-k Similarity Query on Big Trajectory Streams

Zhigang Zhang (East China Normal University), yilin Wang (East China Normal University), Jiali Mao (East China Normal University), Shaojie Qiao (Chengdu University of Information Technology), cheqing Jin* (East China Normal University), aoying Zhou (East China Normal University)

2.     A Distributed Multi-level Composite Index for KNN Processing on Long Time Series

Xiaqing Wang (Fudan University), Zicheng Fang (Fudan University), Peng Wang* (Fudan University), Ruiyuan Zhu (Fudan University), Wei Wang (Fudan University)

3.     Outlier Trajectory Detection: A Trajectory Analytics based Approach

Zhongjian Lv (Soochow University), Jiajie Xu* (Soochow University), Pengpeng Zhao (Soochow University), Lei Zhao (Soochow University), Xiaofang Zhou (School of ITEE, University of Queensland), guangfeng liu (Soochow University)

4.     Clustering Time Series Utilizing a Dimension Hierarchical Decomposition Approach

Qiuhong Li (Fudan University), Peng Wang* (Fudan University), Yang Wang (Fudan University), Wei Wang (Fudan University), Yimin Liu (Third Affiliated Hospital of Second Military Medical University), Jiaye Wu (Fudan University), Danyang Dou (Fudan University)

Research Session 5 Data Mining (Chair: Hao Huang)

1.         Fast Extended One-Versus-Rest Multi-label SVM Classification Algorithm Based on Approximate Extreme Points

Zhongwei Sun* (Ocean University of China), zhongwen Guo (Ocean University of China), Xupeng Wang (Ocean University of China), Jing Liu (Ocean University of China), Shiyong Liu (Ocean University of China)

2.         Efficiently Discovering Most-Specific Mixed Patterns From Large Data Trees

Xiaoying Wu (Wuhan University, China), Dimitri Theodoratos* (New Jersey Institute of Technology, USA)

3.         Max-Cosine Matching Based Neural Models for Recognizing Textual Entailment

Zhipeng Xie* (Fudan University), Junfeng Hu (Fudan University)

4.         An Intelligent Field-aware Factorization Machine Mode

Cairong Yan* (Donghua University), Qinglong Zhang (), Xue Zhao (), Yongfeng Huang (Donghua University)

Research Session 6 Query Processing and Optimization (I)(Chair: Yunjun Gao)

1.     Beyond Skylines: Explicit Preferences

Markus Endres* (University of Augsburg), Timotheus Preisinger (University of Augsburg)

2.     Optimizing Window Aggregate Functions in Relational Database Systems
Guangxuan Song* (East China Normal University), Jiansong Ma (East China Normal University), Xiaoling Wang (East China Normal University), cheqing Jin (East China Normal University), Yu Cao (East China Normal University)

3.     Query Optimization on Hybrid Storage

Anxuan Yu (Renmin University of China), Qingzhong Meng (Renmin University of China), Xuan Zhou* (Renmin University of China), Binyu Shen (Renmin University of China), Yansong Zhang (Renmin University of China)

4.     Efficient Batch Grouping in Relational Datasets

Jizhou Sun* (Harbin Institute of Technology), Jianzhong Li (Harbin Institute of Technology), Hong Gao (Harbin Institute of Technology)

Research Session 7 Text Mining (Chair: Yunmook Nah)

1.     Memory-Enhanced Latent Semantic Model: Short Text Understanding for Sentiment Analysis
Fei Hu (Southwest University), Xiaofei Xu (Southwest University), Jingyuan Wang (Southwest University), Zhanbo Yang (Southwest University), Li Li* (Southwest University)

2.     Supervised Intensive Topic Models for Emotion Detection over Short Text

Yanghui Rao* (Sun Yat-sen University), Jianhui Pang (Sun Yat-sen University), Haoran Xie (The Education University of Hong Kong), An Liu (School of Computer Science and Technology, Soochow University, China), Tak-Lam Wong (The Education University of Hong Kong), Qing Li ( City University of Hong Kong), Fu Lee Wang (Caritas Institute of Higher Education)

3.     Leveraging Pattern Associations for Word Embedding Models
Qian Liu (Beijing Institute of Technolog), Heyan Huang (BIT), Yang Gao* (BIT), Xiaochi Wei (BIT), Ruiying Geng (Beijing University of Technology)

4.   Multi-Granularity Neural Sentence Model for Measuring Short Text Similarity
Jiangping Huang *(Wuhan University), Shuxin Yao(Carnegie Mellon University), Chen Lyu(Wuhan University), and Donghong Ji(Wuhan University)

Research Session 8 Recommendation (Chair: Cheqing Jin)

1.         Leveraging Kernel Incorporated Matrix Factorization for Smartphone Application Recommendation
Chenyang Liu* (Shanghai JiaoTong University), Jian Cao (Shanghai JiaoTong University), Jing He (Victoria University)

2.         Preference integration in context-aware recommendation
Lin Zheng* (Wuhan University), Fuxi Zhu (Wuhan University)

3.         Jointly Modeling Heterogeneous Temporal Properties in Location Recommendation
Saeid Hosseini* (University Of Queensland), Hongzhi Yin (University Of Queensland), Meihui Zhang ( SUTD), Xiaofang Zhou (School of ITEE, University of Queensland, Brisbane, Australia), Shazia Sadiq (University Of Queensland)

4.         Location-Aware News Recommendation Using Deep Localized Semantic Analysis
Cheng Chen (BUPT), Thomas Lukasiewicz (University of Oxford), Xiangwu Meng* (BUPT), Zhenghua Xu (University of Oxford)

5.         Review-Based Cross-Domain Recommendation through Joint Tensor
Tianhang Song (Shandong University), Zhaohui Peng* (Shandong University), Senzhang Wang (Shandong University), Wenjing Fu (Shandong University), Xiaoguang Hong (Shandong University), Philip Yu (University of Illinois at Chicago)


Research session 9 Security, Privacy, Senor and Cloud (Chair: Günter Fahrnberger)

1.        A Local-clustering-based Personalized Differential Privacy Framework for User-based Collaborative Filtering
Yongkai Li* (Wuhan University), Shubo Liu (Wuhan University),
Jun Wang (Wuhan University), Mengjun Liu (Wuhan University)

2.        Fast Multi-Dimensional Range Queries on Encrypted Cloud Databases
Jialin Chi* (ISCAS), Cheng Hong (ISCAS), Min Zhang (ISCAS), Zhenfeng Zhang (ISCAS)

3.        When differential privacy meets randomized perturbation: a hybrid approach for privacy-preserving recommender system
Xiao Liu* (Soochow University), An Liu (Soochow University), Xiangliang Zhang (Soochow University), Zhixu Li (Soochow University guangfeng liu (Soochow University), Lei Zhao (Soochow University), Xiaofang Zhou (School of ITEE, University of Queensland, Brisbane, Australia)

4.        Supporting Cost-Efficient Multi-Tenant Database Services with Service Level Objectives(SLOs)
Yifeng Luo (Fudan University), Junshi Guo (), Jiaye Zhu (Fudan University), Jihong Guan (Tongji University), Shuigeng Zhou* (Fudan University)

5.        Recovering Missing Values from Corrupted Spatio-Temporal Sensory Data via Robust Low-Rank Tensor Completion
Wenjie Ruan* (The University of Adelaide), Peipei Xu (University of Electronic Science and Technology of China (UESTC), China), Michael Sheng (Macquarie University), Nickolas Falkner (The University of Adelaide), Xue Li (University of Queensland), Wei Emma Zhang (The University of Adelaide)

Research Session 10 Social Network Analytics (I)(Chair: P. KRISHNA REDDY)

1.     Group-Level Influence Maximization with Budget Constraint

Qian Yan (Wuhan University), Hao Huang* (Wuhan University), Yunjun Gao ( Zhejiang University), Wei Lu (Renmin university of china), Qinming He (Zhejiang University)

2.     Correlating Stressor Events for Social Network based Adolescent Stress Prediction

Qi Li* (Tsinghua University), Liang Zhao (Xi’an Jiaotong University), Yuanyuan Xue (Tsinghua University), Li Jin (Tsinghua University), Mostafa Alli (Tsinghua Univ.), Ling Feng (Tsinghua University)

3.     Emotion detection in online social network based on multi-label learning

Xiao Zhang (Nanjing University), Wenzhong Li* (Nanjing University), Sanglu Lu (Nanjing University)

Research Session 11 Map Matching and Spatial Keywords(Chair: Bolong Zheng)

1.     HIMM: An HMM-Based Interactive Map-Matching System

Xibo ZHOU* (HKUST), Ye Ding (HKUST), Haoyu Tan (HKUST), Qiong Luo (HKUST), Lionel Ni (HKUST)

2.     HyMU:A Hybrid Map Updating Framework

Tao Wang (East China Normal University), Jiali Mao (East China Normal University), cheqing Jin* (East China Normal University)

3.     Multi-objective Spatial Keyword Query with Semantics

Jing Chen (Soochow University), Jiajie Xu* (School of Computer Science and Technology, Soochow University, China), Zhixu Li (School of Computer Science and Technology, Soochow University, China), An Liu (School of Computer Science and Technology, Soochow University, China), Chengfei Liu (), Zhiming Ding (Beijing University of Technology)

Research Session 12 Query Processing and Optimization (II)(Chair: Saeid Hosseini)

1.     RSkycube: Efficient Skycube Computation by Reusing Principle

Kaiqi Zhang* (Harbin Institute of Technology), Hong Gao (Harbin Institute of Technology), Xixian Han (Harbin Institute of Technology), Donghua Yang (Harbin Institute of Technology), Zhipeng Cai (Harbin Institute of Technology), Jianzhong Li (Harbin Institute of Technology)

2.     Similarity Search Combining Query Relaxation and Diversification

Ruoxi Shi (Harbin Institute of Technology), Hongzhi Wang* (), Tao Wang (, Yutai Hou (, Yiwen Tang (, Jianzhong Li (), Hong Gao ( HIT)

3.     An Unsupervised Approach for Low-Quality Answer Detection in Community Question-Answering

Haocheng Wu* (USTC), Zuohui Tian (Harbin Institute of Technology), Wei Wu (Microsoft Research), Enhong Chen (University of Science and Technology of China, Hefei, China)

4.     Approximate OLAP on Sustained Data Streams

Salman Shaikh* (University of Tsukuba), Hiroyuki Kitagawa (University of Tsukuba)

Research Session 13 Search and Information Retrieval(Chair: Wei Wang)

1.     Hierarchical Semantic Representations of Online News Comments for Emotion Tagging using Multiple Information Sources

Chao Wang (Nankai University), Ying Zhang* (Nankai University), Wei Jie (School of Computing and Engineering, University of West London, UK), Christian Sauer (School of Computing and Engineering, University of West London, UK), Xiaojie Yuan (Nankai University)

2.     Towards a Query-less News Search Framework on Twitter

Xiaotian Hao* (HKUST, CSE department), Ji Cheng (HKUST, CSE department), Jan Vosecky (HKUST, CSE department), Wilfred Ng (HKUST, CSE department)

3.     Semantic Definition Ranking

Zehui Hao* (Renmin University of China), Zhongyuan Wang (Facebook), Xiaofeng Meng (Renmin University of China), Jun Yan (Microsoft Research Asia), Qiuyue Wang (Renmin University of China)

4.     An Improved Approach for long tail advertising in Sponsored Search

Amar Budhiraja* (IIIT-Hyderabad), P. Krishna Reddy (IIIT-Hyderabad)

Research Session 14 String and Sequence Processing(Chair: Jia Zhu)

1.     Locating Longest Common Subsequences with Limited Penalty

Bin Wang* (Northeastern University), Shiying Luo (NEU), Xiaochun Yang (Northeastern University)

2.     Top-k String Auto-Completion with Synonyms

Pengfei Xu* (University of Helsinki), Jiaheng Lu (University of Helsinki)

3.     Efficient Regular Expression Matching on Compressed Strings

Yutong Han* (Northeastern University ), Bin Wang (Northeastern University), Xiaochun Yang (Northeastern University), Huaijie Zhu (Northeastern University )

4.     Mining Top-k Distinguishing Temporal Sequential Patterns from Event Sequences

Lei Duan* (Sichuan University), Li Yan (Sichuan University), Guozhu Dong (Wright State University), Jyrki Nummenmaa (Wright State University), Hao Yang (Sichuan University)

Research Session 15 Stream Data Processing (Chair: Ming Gao)

1.        Soft Quorums: A High Availability Solution for Service Oriented Stream Systems

Chunyao Song* (Nankai University), Tingjian Ge (University of Massachusetts, Lowell), Cindy Chen (University of Massachusetts, Lowell), Jie Wang (University of Massachusetts, Lowell)

2.        StroMAX: Partitioning-based Scheduler for Real-time Stream Processing System

Jiawei Jiang* (Peking University), Zhipeng Zhang (Peking University), Bn Cui ( Peiking University), Yunhai Tong (Peking University), Ning Xu (Peking University)

3.        Partition-based Clustering with Sliding Windows for Data Streams

Jonghem Youn* (Seoul National University), Jihun Choi (Seoul National University), Junho Shim (Sookmyung Women’s University), Sang-goo Lee (Seoul National University)

4.        CBP: A New Parallelization Paradigm for Massively Distributed Stream Processing

Qingsong Guo* (North University of China), Yongluan Zhou (University of Southern Denmark)

Research Session 16 Social Network Analytics (II)(Chair: Qun Chen)

1.     Measuring and Maximizing Influence via Random Walk in Social Activity Networks

Pengpeng Zhao (USTC), Yongkun Li* (U of Sci and Tech of China), Hong Xie (National University of Singapore), Zhiyong Wu (University of Science and Technology of China), Yinlong Xu (University of Science and Technology of China), John C. S. Lui (The Chinese University of Hong Kong)

2.     Adaptive Overlapping Community Detection with Bayesian NonNegative Matrix Factorization

Shi Xiaohua* (Shanghai jiaotong university), Lu Hongtao (Shanghai jiaotong university), Guanbo Jia (Shanghai jiaotong university)

3.     A Unified Approach for Learning Expertise and Authority in Digital Libraries


Research Session 17 Graph and Network Data Processing(Chair: Xin Wang)

1.     Efficient Local Clustering Coefficient Estimation in Massive Graphs

Hao Zhang (Wuhan University), Yuanyuan Zhu* (Wuhan University), Lu Qin (UTS), Hong Cheng (Chinese University of Hong Kong), Jeffrey Xu Yu ( Chinese University of Hong Kong)

2.     Efficient Processing of Growing Temporal Graphs

Huanhuan Wu* (CUHK), Yunjian Zhao (CUHK), James Cheng (CUHK), Da Yan (CUHK)

3.     Effective k-Vertex Connected Component Detection in Large-Scale Networks

Yuan Li* (Northeastern University, China), Yuhai Zhao (Northeastern University, China), Guoren Wang (Northeastern University, China), Feida Zhu (Singapore Management University), Yubao Wu (Georgia State University), Shengle Shi (Northeastern University, China)

Research Session 18  Spatial Databases(Chair: Xike Xie)

1.     Efficient Landmark-Based Candidate Generation for kNN Queries on Road Networks

Tenindra Abeywickrama* (Monash University), Muhammad Cheema (Monash University)

2. MinSum Based Optimal Location Query in Road Networks

Lv Xu (Sun Yat-Sen University), ganglin Mai (Sun Yat-Sen University), Zitong Chen (The Chinese University of Hong Kong), Yubao Liu* (Sun Yat-Sen University), Genan Dai (Sun Yat-Sen University)

3. Efficiently Mining High Utility Co-location Patterns from Spatial Data Sets with Instance-Specific Utilities

Lizhen Wang* (Yunnan University), Wanguo Jiang (Yunnan University), Hongmei Chen (Yunnan University)

Research Session 19  Real Time Data Processing(Chair: Chunyao Song)

1.     Supporting Real-time Analytic Queries in Big and Fast Data Environments

Guangjun Wu* (IIE, CAS), Xiaochun Yun (IIE), Chao Li (National Computer Network & Information Security Administration Center), shupeng Wang (IIE), Yipeng Wang (Institute of Information Engineering), xiaoyu Zhang (Institute of Information Engineering), Siyu Jia (Institute of Information Engineering), Guangyan Zhang (Department of Computer Science and Technology, Tsinghua University)

2.     Boosting Moving Average Reversion Strategy for Online Portfolio Selection

Xiao Lin* (Tsinghua University), Min Zhang (Tsinahua University), Yongfeng Zhang (University of Massachusetts Amherst)

3.     Continuous Summarization over Microblog Threads

Liangjun Song (RMIT university), Ping Zhang (Wuhan University), Zhifeng Bao* (RMIT University), Timos Sellis (Swinburne University of Technology)

4.     Drawing Density Core-sets from Incomplete Relational Data

Yongnan Liu* (Harbin Institute of Technology), Jianzhong Li (Harbin Institute of Technology), Hong Gao (Harbin Institute of Technology)

Industry Session 1 Big Data(Chair: Han Su)

1.     Co-training an Improved Recurrent Neural Network with Probability Statistic Models for Named Entity Recognition

Yueqing Sun (Wuhan University of Technology), Lin Li* (Wuhan University of Technology), Zhongwei Xie (Wuhan University of Technology), Qing Xie (Wuhan University of Technology), Xin Li (iFLYTEK Big Data Research Institute), Guandong Xu (University of Technology)

2.     EtherQL: A Query Layer for Blockchain System

Yang Li (Soochow University), Kai Zheng* (Soochow University), Ying Yan (Microsoft Research), Xiaofang Zhou (University of Queensland)

3.     Optimizing Scalar User-Defined Functions in In-Memory Column-Store Database System

Cheol Ryu (SAP Labs Korea), Sunho Lee (SAP Labs Korea), Kihong Kim (SAP Labs Korea), Kunsoo Park* (SAP Labs Korea), Yongsik Kwon (SAP Labs Korea), Sang Cha (SAP Labs Korea), Changbin Song (SAP Labs Korea), Emanuel Ziegler (SAP SE Germany), Stephan Muench (SAP SE Germany)

4.     GPS-Simulated Trajectory Detection

Han Su* (University of Electronic Science and Technology of China), Wei Chen (University of Electronic Science and Technology of China), Rong Liu (University of Electronic Science and Technology of China), Min Nie (Xundao Inc.), Bolong Zheng (University of

Queensland), Zehao Huang (University of Queensland), Defu Lian (University of Electronic Science and Technology of China)

Industry Session 2 Social Networks and Graphs(Chair: Wen Hua)

1.     Edge Influence Computation in Dynamic Graphs

Louie Qin* (University of Huddersfield), Michael Sheng (Macquarie University), Simon Parkinson (University of Huddersfield), Nickolas Falkner (The University of Adelaide)

2.   Predicting Academic Performance via Semi-Supervised Learning with Constructed Campus Social Network

Huaxiu Yao (Pennstate University), Min Nie (University of Electronic Science and Technology of China), Han Su (University of Electronic Science and Technology of China), Hu Xia (University of Electronic Science and Technology of China), Defu Lian* (University of Electronic Science)

3.   Social User Profiling: A Social-Aware Topic Modeling Perspective

Ma Chao ( University of Science and Tec), Chen Zhu (Baidu Inc,), Yanjie Fu (), Hengshu Zhu* (Baidu), Guiquan Liu (University of Science and Technology of China, Hefei, China), Enhong Chen (University of Science and Technology of China, Hefei, China)

4.     Cost-effective Data Partition for Distributed Stream Processing System

Xiaotong Wang* (East China Normal University), Junhua Fang (East China Normal University), Yuming Li (East China Normal University), Rong Zhang (, aoying Zhou (East China Normal University)

5.     A Graph-based Push Service Platform

Huifeng Guo* (Harbin Institute of Technology), Ruiming Tang (Noah’s Ark Lab, Huawei), Yunming Ye (Harbin Institute of Technology), Zhenguo Li (Harbin Institute of Technology), Xiuqiang He (Noah’s Ark Lab, Huawei)

Demo Session

1.     DKGBuilder: An Architecture for Building a Domain Knowledge Graph from Scratch

Yan Fan* (East China Normal University), Chengyu Wang (East China Normal University), Guomin Zhou (East China Normal University), Xiaofeng He (East China Normal University)

2.     CLTR: Collectively Linking Temporal Records Across Heterogeneous Sources

Yanyan Zou* (SUTD), Kasun Perera (SUTD)

3.     PhenomenaAssociater: Linking Multi-Domain Spatio temporal datasets

Prathamesh Walkikar* (UMBC), Vandana Janeja (UMBC)

4.     VisDM:A Data Stream Visualization Platform
Lars Melander (Uppsala University), Kjell Orsborn(Uppsala University),  Tore Risch(Uppsala University), Daniel Wedlund (Uppsala University),

Conference Venue

Nanlin Hotel 苏州南林饭店

Suzhou Nanlin Hotel is located in Suzhou's famous 'entertainment bar street' (Shiquan Street), adjacent to the bustling commercial district, which has convenient transportation and elegant environment. Nanlin Hotel has convenient access to major highways, commercial centers, tourist attractions, Suzhou Industrial Park and Suzhou High-tech Industrial Development Zone.

The hotel has beautiful scenery. And it is integrated with the great scenery of the park. In the park grow lush ancient trees. The flowers and trees contend in beauty and fascination all the year round, sending out bursts of fragrance. Rockeries, waterfalls, pavilions, flowers and trees formed a beautiful picture of nature. Guests will feel like in Suzhou gardens when staying here. 


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