Beng Chin OOI

Department of Computer Science
School of Computing
National University of Singapore
Computing 1, Computing Drive, Singapore 117417

ooibc AT
Tel: +65-6516 6465
Office: COM1, #03-22


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Beng Chin is a Distinguished Professor of Computer Science and Director of IDMI at the National University of Singapore (NUS), and an adjunct Chang Jiang Professor at Zhejiang University. He obtained his BSc (1st Class Honors) and PhD from Monash University, Australia, in 1985 and 1989 respectively. His research interests include database system architectures, performance issues, indexing techniques and query processing, in the context of multimedia, spatio-temporal, distributed/parallel/P2P/in-memory/cloud database systems and applications.

Beng Chin has served as a PC member for international conferences such as ACM SIGMOD, VLDB, IEEE ICDE, WWW, and SIGKDD, and as Vice PC Chair for ICDE'00,04,06, co-PC Chair for SSD'93 and DASFAA'05, PC Chair for ACM SIGMOD'07, Core DB PC chair for VLDB'08, and PC co-Chair for IEEE ICDE'12. He was an editor of VLDB Journal and IEEE Transactions on Knowledge and Data Engineering, Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (TKDE)(2009-2012), and a co-chair of the ACM SIGMOD Jim Gray Best Thesis Award committee. He is serving as a co-Editor-in-Chief of Elsevier's Journal of Big Data Research, an editor of IEEE Transactions on Cloud Computing and Springer's Distributed and Parallel Databases, and a PC co-Chair of IEEE Big Data'15 (2015). He is also serving as a Trustee Board Member and President of VLDB Endowment, and an Advisory Board Member of ACM SIGMOD. He co-founded yzBigData.

Beng Chin is the recipient of ACM SIGMOD 2009 Contributions award, a co-winner of the 2011 Singapore President's Science Award, the recipient of 2012 IEEE Computer Society Kanai award, 2013 NUS Outstanding Researcher Award, and 2014 IEEE TCDE CSEE Impact Award. He is a recipient of VLDB'14 Best Paper award. He is a fellow of the ACM and IEEE.

Beng Chin's ongoing large system projects include:

  1. SINGA: a distributed Deep Learning platform. SINGA is an (Apache Incubator) open source, distributed training platform for deep learning models, and is designed with three goals, namely, usability, scalability and extensibility. As in other big data analytic platforms such as Hadoop and epiC, we make the programming model of SINGA easy to follow. Specifically, users construct their models by combining operators called Layers and SINGA's runtime takes care of (and is optimized for) the distributed execution and communication between nodes. Scalability is achieved by partitioning both the training data and the model, and distributing the training over multiple nodes. We make the code of SINGA modular and extensible to support different types of deep learning models and different training/optimization algorithms. Co-Space is an earlier system designed for supporting cross-domain retrieval that led to the development of SINGA.
  2. CIIDAA: a Comprehensive IT Infrastructure for Data-intensive Applications and Analysis is an CRP project funded by NRF (NRF-CRP8-2011-08). The main objective is to use cloud computing to address the Big Data problem. For specific applications, this approach has been shown to be effective, and systems such as Hadoop have become very popular. However, they have limitations ( see ACM Computing survey paper on MapReduce), and are suitable only for a class of applications that have a structure amenable to fine-grain asynchronous parallelization. Furthermore, there remain many challenges in actually using cloud computing systems in practice, including issues of resource contention across multiple jobs being run concurrently. The aim of this project is to develop a platform for supporting predictive real-time analytics in the area of web consumers (collaborating with Starhub) and healthcare (collaborating with NUH, National University Health System).
  3. epiC: an Elastic, Power-aware, data-Intensive Cloud platform. The objectives are to design and implement an efficient multi-tenancy cloud system for supporting high throughout low latency transactions and high performance reliable query processing, with online and interactive analytics capability. and the system will be released as open source soon. Related earlier project: UTab.
  4. LogBase, is a distributed log-structured data management system, that adopts the log-only storage to handle high append and write load, such as Urban/Sensor information processing. Indexing, transaction management and query processing are the key issues being investigated, and source codes are being released as open source in phases. LogBase is related to an ongoing research on database support for Energy and Environmental Sustainability Solutions for Megacities.
  5. CDAS: a Crowdsourcing Data Analytics System that has been designed to improve the quality of query results and effectively reduce the processing cost at the same time. It is being built as a crowdsourcing system that provides primitive operators to facilitate composition of crowdsourcing tasks. Other key issues such as privacy and applicability, and various applications are being investigated.
With the ubiquity of Big Data and fusion of applications and technologies, the projects are related in many aspects. Beng Chin approaches research problems and system design with the philosophy that all algorithms and structures should be simple, elegant and yet efficient so that they can be easily grafted into existing systems and they are implementable, maintainable and scalable in actual applications. A good example would be his approach towards the design of new indexes; they are mainly B+-tree based -- simple and elegant in design, and efficient, robust and scalable in performance (eg. TP-index[ICDE1994], ST B-tree[DKE1995], iMinMax[PODS2000], iDistance[VLDB2001, TODS2005], B^x-tree[VLDB2004], GiMP[TODS2005], ST^2B-tree[SIGMOD2008, TODS2010], B^{ed}-tree[SIGMOD2010], String Indexing[TKDE2014]).