WAMDM means " Web And Mobile Data Management", Which is Professor Xiaofeng Meng's research lab and is affiliated with the Key Laboratory for Data Engineering and Knowledge Engineering MOE and the Department of Computer Science, School of Information, Renmin University of China.

The research vision in WAMDM is how database techniques would fit into the Web and Mobile computing environments. The research style in our lab is having two tracks - research and system - in order to ensure that the research is actually applied. Innovative data systems research is our goal.

WAMDM Lab has been conducting database related research for many years, and is considered one of the best database groups in the country. It's projects range from the Web Data Management, XML Data Management to Mobile Data Management in the past ten years. Now we are focusing on Cloud data management, databases for new storage hardware, privacy protection for mobile Web, social computing etc. in the new decade.

The site contains information on the projects that are currently in progress and the people in the group. You can also find information on the weekly seminar and annual report. In addition, this site hosts the following webpages:


[Conferences & Others]
BDSC2020  BDSC2019  BDSC2018   BDSC2017   NCSC2012 ChinaPrivacy 2018  ChinaPrivacy 2017  ChinaPrivacy 2016
HardBD 2018  HardBD 2016  HardBD 2015  HardBD 2014 CloudDM 2016  CloudDM 2015  CloudDB 2014
WAIM 2013 PSBD 2017
XLDB Asia 2012 HardBD 2013  FlashDB2012    FlashDB2011
CloudDB2013 CloudDB2012 CloudDB2011 CloudDB2010 CloudDB2009 NDBC2010
MDM2008 WISA2018  WISA2007
CCF BigData 2013 Database Society of China Computer Federation

Hot Events  
Lab Seminars
  • 2019-03-29 Knowledge Representation for Emotion Intelligence(ICDE2019 PhD Symposium预) byShuo Wang
  • Abstract: Emotion intelligence (EI) is a traditional topic for psychology, sociology, biology and medical science. Because emotion is related with the personality, interpersonal effect, social function, disease treatment, etc. Analyzing the emotion from the Web data by computer technology becomes more and more popular, and the scientists from the non-computer domains need more helpful computing models to deal with professional problems that are not traditional for computer science. Knowledge representation is a basic and possible solution as a bridge between emotion intelligence and artific ial intelligence. For the sentiment words, word embedding can map the words to vectors that represent the semantic context of the words. Sentiment embedding based on the word embedding can capture both semantics and the emotion information. We have introduced two kinds of improving embedding methods (MEC and Emo2Vec) for the sentiment words embedding. For emotion structure based on the psychology of emotion, knowledge graph can represent the cognitive relations between different emotion types. The same emotional expressions can affect the reaction and behaviors of the recipient in different ways due to factors such as social relations, information processing, time pressure, etc. Knowledge graph can represent these complicated situations as the relations between the entities and attributes. Based on this graph, we make the inference or prediction of the emotion influence on decision making.
  • 2019-03-29 EMT: A Tail-Oriented Method for Specific Domain Knowledge Graph Completion(PAKDD预) byYi Zhang
  • Abstract: The basic unit of knowledge graph is triplet, including head entity, relation and tail entity. Centering on knowledge graph, knowledge graph completion has attracted more and more attention and made great progress. However, these models are all verified by open domain data sets. When applied in specific domain case, they will be challenged by practical data distributions. For example, due to poor presentation of tail entities caused by their relation-oriented feature, they can not deal with the completion of enzyme knowledge graph. Inspired by question answering and rectilinear propagation of lights, this paper puts forward a tail-oriented method - Embedding for Multi-Tails knowledge graph (EMT). Specifically, it first represents head and relation in question space; then, finishes projection to answer one by tail-related matrix; finally, gets tail entity via translating operation in answer space. To overcome time-space complexity of EMT, this paper includes two improved models: EMTv and EMTs. Taking some optimal translation and composition models as baselines, link prediction and triplets classification on an enzyme knowledge graph sample and Kinship proved our performance improvements, especially in tails prediction.
    Invited Talks
    • Prof Meng . Keynote Speech . “Big data and social computing” . China National Conference on Big Data & Social Computing (BDSC2017). 2017-08-12.Lanzhou.[News]
    • Prof Meng . Keynote Speech . “Issues and challenges of Large scale privacy protection”. China Privacy Protection Conference(ChinaPrivacy2017).2017-08-04.Guiyang.[News]
    • Prof Meng . Keynote Speech . “Practice and Prospect of scientific big data management systems”. Conference on scientific data(2017). 2017-08-02 .Yunnan.[News]
    • Prof Meng . Keynote Speech . “Privacy protection in big data governance” . RenMim University of China . 2016-11-7 .Beijing.[News]
    • Prof Meng . Keynote Speech . “Data open and privacy protection” . Information Security Technology Conference & Expo 2016 .2016-10-14 .Qingdao.[News]
    • Prof Meng . Keynote Speech . “The challenges of scientific big data management systems” . Conference on scientific data(2016) .2016-06-25.Shanghai.[News]
    • Prof Meng . Keynote Speech . “Big data privacy protection” .International Conference on Cryptography and Cloud Computing Security (CCCS2016) . Guangzhou University. 2016-06-17.Guangzhou.[News]
    • Prof Meng . Keynote Speech . “Challenge and opportunity of big data management systems” . Database Technology Conference China (DTCC 2016 ).2016-05-12. Beijing.[News]
    • Prof Meng . Keynote Speech . “Big data management: issues and Reflections” . Jingdezhen Ceramic Institute .2016-05-06.Jingdezhen.[News]
    • Prof Meng . Invited talk . “Big data fusion” . Jiangxi University of Finance And Economics . 2016-05-05 . Nanchang .[News]
    • Prof Meng . Invited Talk . Big data storage and privacy. Big data Forum . Beijing University of Techonolgy . 2016-01-18.Beijing.[News]
    • Prof Meng. Keynote speech. “Location Big Data Privacy Protection”. the 2015 International CAE Symposium on Ubiquitous Syrveying . 2015-11-06.Wuhan.[News]
    • Prof Meng .Keynote Speech. “Big data privacy management”. NSFC Shuangqing Forum rity. 2015-05-06.Bon State Secueijing.[News]
    • Prof. Meng. Keynote speech. "Big Data Privacy Management". CNCC 2014 Open Data and Privacy Management Forum . 2014-10-25. Zhengzhou. [News]
  • [Sep,15,2017]Prof. Meng Gave the Invited Talk about Research and Impact at Springer Nature YOCSEF club in Beijing[Detail]
  • [Aug,22,2017]Beijing International Book Fair "Big Data Management Book Series" copyright export signing ceremony[Detail]
  • [Jan 05,2017] Large-scale Industry-specific Knowledge Graph Building Seminar Hosted by WAMDM[Detail]
  • [Dec 09,2016] Big Data Management Based on New Hardware Seminar Hosted by WAMDM[Detail]
  • [Nov 07,2016] The First China Privacy Protection Conference Hosted by WAMDM[Detail]
  • [Dec 02,2015] Big Data Architecture Seminar Hosted by WAMDM Lab[Detail]
  • [Nov 06,2015] Prof Meng Gave the keynote speech on the 2015 International CAE Symposium on Ubiquitous Syrveying, Mapping and Big Data of Locations[Detail]
  • [Oct 29,2015] Prof Meng Visiting Purdue University[Detail]
  • [July 17,2015] Prof Meng gave an invited talk at Tianjin University of Technology[Detail]
  • [May 14,2015] Big Data Privacy Management Seminar Hosted by WAMDM Lab[Detail]
  • [Apr 08,2015] Complex Correlation Data Management Seminar Hosted by WAMDM Lab[Detail]
  • [Jan 20,2015] Prof Elisa Bertino from Purdue University visiting WAMDM Lab[Detail]
  • [Oct 30,2014] Prof Meng Chaired "Open Data and Privacy Management Forum" on CNCC2014[Detail]
  • [Oct 28,2014] Big Data paper selected in Frontrunner 5000 (Top Articles from Outstanding S&T Journals in China)[Detail]
  • [May 07,2014] Prof Meng gave Keynote speech on 2014 High Education Information Summit. Xi'an, May 7, 2014[Detail]
  • [Apr 16,2014] CCF@U193:Prof Meng gave invited talk in Beijing Technology and Business University[Detail]
  • [Jan 06,2014]Prof. Meng elected 2013 CCF Fellow [Detail]
  • [more]
    Recent and Selected Publications
  • C. Yang, Z. Du, X. Meng, et al. A Frequency Scaling based Performance Indicator Framework for Big Data Systems[C]. Accepted for the 24th International Conference on Database Systems for Advanced Applications (DASFAA), 2019, Chiang Mai, Thailand.
  • Yi Zhang, Zhijuan Du, Xiaofeng Meng. EMT: A Tail-Oriented Method for Specific Domain Knowledge Graph Completion[C]. Accepted for Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD), 2019,Macau, China.
  • Shuo Wang,Xiaofeng Meng. Konwledge Representation for Emotion Intelligence. Accepted for the 35th IEEE International Conference on Data Engineering (ICDE) ,2019.Macau SAR, China.(PhD Symposium)
  • Zhiqiang Duan, Chen Yang, Yongjie Du, Xiaofeng Meng, et al.SciDetector: Scientific Event Discovery by Tracking Variable Source Data Streaming[C].Accepted for International Conference on Data Engineering (ICDE) ,2019,SAR, China.(Demo)
  • Q. Ye, H. Hu, X. Meng, et al. PrivKV: Key-Value Data Collection with Local Differential Privacy[C]. Proceedings of IEEE Symposium on Security and Privacy (S&P), IEEE, pages: 294-308, 2019, San Francisco, USA.[PDF]
  • C. Yang, X. Meng, Z. Du. Cloud based Real-Time and Low Latency Scientific Event Analysis[C]. Proceedings of the IEEE International Conference on Big Data (BigData), pages: 498-507,2018,Seattle, WA, USA.[PDF]
  • C. Yang, X. Meng, Z. Du. AstroServ: A Distributed Database for Serving Large-Scale Full Life-Cycle Astronomical Data[C]. Proceedings of the first International Conference on Big Scientific Data Management (BigSDM), vol:abs/1811.10861, 2018, Beijing, China.[PDF]
  • C. Yang, X. Meng, Z. Du. Data Management in Time-Domain Astronomy: Requirements and Challenges[C]. Proceedings of the first International Conference on Big Scientific Data Management (BigSDM), vol:abs/1811.10855,2018, Beijing, China.[PDF]
  • Y. Du, Chen Yang, Xiaofeng Meng. Real-Time Query Enabled by Variable Precision in Astronomy[C]. Proceedings of the first International Conference on Big Scientific Data Management (BigSDM), 2018, Beijing, China.[PDF]
  • Z. Duan, C. Yang, X. Meng. Continuous Cross Identification in Large-scale Dynamic Astronomical Data Flow[C]. Proceedings of the first International Conference on Big Scientific Data Management (BigSDM), 2018, Beijing, China.[PDF]
  • S. Wang, X. Meng. Multi-Emotion Category Improving Embedding for Sentiment Classification[C]. Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM (CIKM), pages: 1719-1722, 2018, Turin, Italy.[PDF]
  • X. Meng, Z. Hao, J. Li, Y. Zhang, et al. ScholarSpace: Academic Search in China[C]. Proceedings of ACM Woodstock conference (WOODSTOCK’18). ACM, 2018, New York, USA.[PDF]
  • Y. Zhang, Z. Hao, J. Li, X. Meng, & S. Wang. KGBuilder: A System for Large-Scale Scientific Domain Knowledge Graph Building[C]. Proceedings of 3rd international workshop on Biomedical Informatics with Optimization and Machine Learning (Boom), in conjunction with 27th International Joint Conference on Artificial Intelligence. (IJCAI), 2018, Stockholm, Sweden.[PDF]
  • Q. Wang, X. Meng. Bacteria and Biotope Entity Recognition Using A Dictionary-Enhanced Neural Network Model[C]. Proceedings of the BioNLP 2018 workshop, pages:147-150, 2018, Melbourne.[PDF]
  • S. Wang, Z. Hao, X. Meng, et al. Scholar Graph: A Chinese Knowledge Graph of Chinese Scholars[C]. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018), 2018, Miyazaki, Japan.[PDF]
  • E Huo , Xiaofeng Meng . A trajectory data publishing method satisfying differential privacy [J].Journal of Computer Science,Vol41 (2): 400-412, 2018.[PDF]
  • Xiaojian Zhang , Kaizhong Jin , Xiaofeng Meng . Privacy space segmentation method based on adaptive grid [J]. Computer research and development,Vol55 (6): 1143-1156, 2018.[PDF]
  • Xiaojian Zhang , Congcong Fu , Xiaofeng Meng . Differential Privacy Protection for Face Image Publishing [J].Vol23 (9): 1305-1315, 2018.[PDF]
  • Qingqing Ye , Xiaofeng Meng , Minjie Zhu , et al. Review of Localized Differential Privacy [J]. Journal of Software, Vol29 (7):1981-2005, 2018.[PDF]
  • Chunkai Wang , Xiaofeng Meng . On-line connection method for inclined data streams [J]. Journal of Software, Vol29 (3): 869-882, 2018.[PDF]
  • C. Wang, X. Meng, et al. Automating Characterization Deployment in Distributed Data Stream Management Systems[J]. Proceedings of IEEE Transactions on Knowledge and Data Engineering(TKDE),pages:2669--2681,2017,San Diego, California, USA. (Full Paper).[PDF]
  • C. Yang, Q. Guo, X. Meng, et al. Revisiting Performance in Big Data Systems: A Resource Decoupling Approach. Proceedings of ACM Symposium on Cloud Computing(SoCC),pages:639,2017, Santa Clara , CA.(Poster).[PDF]
  • C. Wang, X. Meng. Partitioning Road Network Streams Based on Runtime Correlation Discovery[C]. Proceedings of the 18th IEEE International Conference on the Mobile Data Management (MDM), pages: 272-277,2017, Daejeon, South Korea.[PDF]
  • Y. Li, X. Meng, Zhang Q, et al. Common patterns of online collective attention flow[J]. Science China Information Sciences, Vol 60(5): 059102:1-3, 2017.[PDF]
  • Z. Weng, Q. Guo, C. Wang, et al. AdaStorm: Resource Efficient Storm with Adaptive Configuration[C]. Proceedings of the 33rd International Conference on Data Engineering (ICDE) , pages: 1363-1364, 2017, San Diego, CA.[PDF]
  • Z. Du, Z. Hao, X. Meng, et al. CirE: Circular Embeddings of Knowledge Graphs[C]. Proceedings of the International Conference on Database Systems for Advanced Applications (DASFFA), pages:148-162, 2017, Suzhou, China.[PDF]
  • Z. Hao, Z. Wang, X. Meng, et al. Semantic Definition Ranking[C]. Proceedings of the International Conference on Database Systems for Advanced Applications (DASFFA), pages:153-168, 2017, Suzhou, China.[PDF]
  • S. Guo, X. Meng. Density Peaks Clustering with Differential Privacy[C]. Proceedings of the 8th Biennial Conference on Innovative Data Systems Research(CIDR),2017, Chaminade, CA.
  • Wang C, Meng X, Guo Q, et al. OrientStream: A Framework for Dynamic Resource Allocation in Distributed Data Stream Management Systems[C].Proceedings of the 25th ACM International on Conference on Information and Knowledge Management(CIKM),pages: 2281-2286, 2016,Indianapolis,IN.[PDF]
  • Ma Y, Meng X, Wang S. Parallel similarity joins on massive high‐dimensional data using MapReduce[J]. Concurrency and Computation: Practice and Experience, Vol 28(1): 166-183, 2016.
  • J.Wang,Z. Guo, X.Meng. An Efficient Design and Implementation of Multi-Level Cache for Database Systems [C]. Proceedings of the20th International Conferenceon Database Systems for Advanced Applications (DASFAA),pages: 160-174, 2015,Hanoi, Vietnam.[PDF]
  • J.Wang,Z. Guo, X. Meng. SASS: A High-Performance Key-Value Store Design for Massive Hybrid Storag[C]. Proceedings of the20th International Conferenceon Database Systems for Advanced Applications (DASFAA),pages: 145-159,2015,Hanoi, Vietnam.[PDF]
  • L.Wang, X.Meng, H. Hu, et al. Bichromatic Reverse Nearest Neighbor Query without Information Leakage. [C]. Proceedings of the20th International Conferenceon Database Systems for Advanced Applications (DASFAA),pages:609-624,2015,Hanoi, Vietnam.[PDF]
  • Y. Ma, X. Meng. Set similarity join on massive probabilistic data using MapReduce. Distributed and Parallel Databases. Vol 32(3): 447-464, 2014.[PDF]
  • Y. Fan, W. Lai, X. Meng. Optimizing Database Operators by Exploiting Internal Parallelism of Solid State Drives. IEEE Data Eng. Bull. Vol 37(2): 12-18, 2014.[PDF]
  • X. Zhang, R. Chen, J. Xu, X. Meng. Towards Accurate Histogram Publication under Differential Privacy. Accepted for publication in Proceedings of the 14th SIAM International Conference on Data Mining (SDM 2014): 587-595, Philadelphia, Pennsylvania, USA. (Full Paper)[PDF]
  • J. Zhou, Z. Bao, W. Wang, J. Zhao, X. Meng: Efficient Query Processing for XML Keyword Queries based on the IDList Index. Accepted by VLDBJ. Vol 23(1): 25-50, 2014.[PDF]
  • Z. Huo, X. Meng, R. Zhang: Feel Free to Check In: Privacy Alert against Hidden Location Inference Attacks in GeoSN. In Processings of the 18th International Conference on Database Systems for Advanced Applications (DASFAA 2013): 377-491. April 22-25, 2013, Wuhan, China. (Regular paper)[PDF]
  • X. Zhang, X. Meng, R. Chen: Differential Private Set-Valued Data Release against Incremental Updates. In Proceedings of the 18th International Conference on Database Systems for Advanced Applications (DASFAA 2013): 392-406. April 22-25, 2013, Wuhan, China. (Regular paper)[PDF]
  • Y. Gan, X. Meng, Y. Shi: COLA: A Cloud-based System for Online Aggregation (Demonstration). In Proceedings of the 29th International Conference on Data Engineering(ICDE2013): 1368-1371, April 8-12, 2013, Brisbane, Australia.[PDF]
  • W. Cao, D. SHASHA: AppSleuth: a Tool for Database Tuning at the Application Level. In Proceedings of the 16th International Conference on Extending Database Technology (EDBT2013): 589-600. March 18-22, Genoa, Italy.[PDF]
  • D. SHASHA, W .Cao: Tuning in Action (Demonstration) In Proceedings of the 16th International Conference on Extending Database Technology (EDBT 2013): 737-740. March 18-22, Genoa, Italy.[PDF]
  • [more]