2017 Australasian Database Conference

Bio: Mingsheng Ying was Cheung Kong Chair Professor at Tsinghua University and Director of the Scientific Committee, the National Key Laboratory of Intelligent Technology and Systems, China. Since 2008, he is Distinguished Professor and Research Director of the Centre for Quantum Software and Information, University of Technology Sydney, Australia. He is also Deputy Director for Research of the Institute of Software, Chinese Academy of Sciences.
Ying’s research interests include quantum computation and quantum information, programming theory, and logical foundations of artificial intelligence. In particular, he developed Hoare logic for quantum programs and proved its (relative) completeness (TOPLAS’11). He defined the notion of invariants for quantum programs (POPL’17). He initiated the research line of model checking quantum Markov chains (CONCUR’12-14, TOCL’14). Ying is the author of Foundations of Quantum Programming (Morgan Kaufmann 2016).

Bio: Divesh Srivastava is the head of Database Research at AT&T Labs-Research. He is a Fellow of the Association for Computing Machinery (ACM) and the managing editor of the Proceedings of the VLDB Endowment (PVLDB). He has served as a trustee of the VLDB Endowment, as an associate editor of the ACM Transactions on Database Systems (TODS), as an associate Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE), and as a general or program committee co-chair of many conferences. He has presented keynote talks at several international conferences, and 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.

Bio: Masaru Kitsuregawa received his Information Engineering Ph.D. degree from the University of Tokyo in 1983. Since then he joined the Institute of Industrial Science, the University of Tokyo, and is currently a professor. He is also a professor at Earth Observation Data Integration & Fusion Research Initiative of the University of Tokyo since 2010. He also serves Director General of National Institute of Informatics since 2013. Dr. Kitsuregawa's research interests include Database Engineering, and he had been principal researcher of Funding Program for World-Leading Innovative R&D on Science and Technology, MEXT Grant-in-Aids Program for "Info-Plosion", and METI's Information Grand Voyage Project. He had served President of Information Processing Society of Japan from 2013 to 2015. He served as a committee member for a number of international conferences, including ICDE Steering Committee Chair. He is an IEEE Fellow, ACM Fellow, IEICE Fellow and IPSJ Fellow, and he won ACM SIGMOD E.F.Codd Contriutions Award, Medal with Purple Ribbon, 21st Century Invention Award, and C&C Prize.

Title: Big Data for solving societal problem

Abstract: Big data has been focused since 2012 after Obama initiative. In Japan we have launched Info-plosion project much beforehand and tried to solve societal problems using big data, such as healthcare, disaster prevention, transportation etc. Will talk about 20 Peta Byte earth observation system and 200B records health care information system etc.

Bio: Guoliang Li is an Associate Professor of Department of Computer Science, Tsinghua University, Beijing, China. His research interests include crowdsourced data management, large-scale data cleaning and integration, and big spatio-temporal data analytics. He has regularly served as the PC members of many premier conferences, such as SIGMOD, VLDB, KDD, ICDE, WWW, IJCAI, and AAAI. He was a PC co-chair of WAIM 2014, WebDB 2014, NDBC 2016, and an area chair of CIKM 2016-2017. He is an associated editor of some premier journals, such as TKDE, Big Data Research, and FCS. He has published more than 80 papers in premier conferences and journals, such as SIGMOD, VLDB, ICDE, SIGKDD, SIGIR, ACM TODS, VLDB Journal, and TKDE. His papers have been cited more than 3600 times. He received IEEE TCDE Early Career Award, and best paper awards/nominations at DASFAA 2014 and APWeb 2014.

Title: Hybrid Human-Machine Big Data Integration

Abstract: Data integration cannot be completely addressed by automated processes. We proposed a hybrid human-machine method that harnesses human ability to address this problem. The framework first uses machine algorithms to identify possible matching pairs and then utilizes the crowd to compute actual matching pairs from these candidate pairs. In this talk, I will introduce our two systems on hybrid human-machine big data integration. (1) DIMA: A distributed in-memory system on big-data integration that can use SQL to integrate heterogenous data. DIAM can be used to identify candidate matching pairs in big data integration. (2) CDB: A crowd-powered database system that provides declarative programming interfaces and allows users to utilize an SQL-like language for posing crowdsourced queries. CDB can be used to refine the candidate pairs in big data integration.

Bio: Professor Man-Hong Yung grew up and was educated in Hong Kong. He received a Bachelor degree of physics in 2002 and a Master's degree of physics in 2004 from the Chinese University of Hong Kong. In the summer of 2003, he went to the Institute for Quantum Information in the California Institute of Technology (Caltech) to learn the theory of quantum information. In 2004, he was invited to the Harvard University to participate in the projects on quantum information research. Then he went to the University of Illinois at Urbana-Champaign (UIUC) for the Ph.D. study, under the supervision of Nobel Laureates Prof. Anthony Leggett (Physics 2003). His PhD thesis is on the crossing field of physics and information science. After graduation, he went to the Harvard University as a postdoc fellow and mainly worked on quantum information and physical chemistry. During this period, he also served as a Visiting Scientist of MIT Center for Excitonics and Executive Board Member of MIT China energy and Environment Research Association. Professor Man-Hong Yung returned China in September 2013 and worked as an Assistant Professor at the Institute for Interdisciplinary Information Sciences of Tsinghua University. Then he left Tsinghua University and worked as an Associate Professor at the Department of Physics in the Southern University of Science and Technology in 2016. The research interests of Professor Man-Hong Yung mainly focus on the design of quantum algorithms and quantum simulation, and series of significant contributions have been made to this field. He has many publications as the first author, and corresponding author in the top journals of science, such as the Nature Photonics, Physical Review Letters, PNAS ,Nature Communications (4 papers) , Science Advances and so on. He was invited to write review articles for Ann. Rev. Phys. Chem and Advances in Chemical Physics. As one of the representative works, the study of quantum thermal insulation [Li & Yung (co-authorship) NJP 2014] was chosen by the IOP Publishing as the "IOP Select" and was selected by the New Journal of Physics as the "Highlights of 2014". In addition to hold the post of the reviewers as different magazines, Prof. Yung was invited to join the editorial board of Scientific Reports and review the articles in the field of quantum physics.

Bio: Zhengfeng Ji is currently a Professor of the Centre for Quantum Software and Information (QSI), Faculty of Engineering and Information Technology (FEIT), University of Technology Sydney. He received BEng and PhD from the Department of Computer Science and Technology, Tsinghua University, Beijing, China in 2002 and 2007. His current research interests include quantum algorithms, quantum complexity theory and (post-)quantum cryptography.

Bio: Ce Wang is a PhD student of Professor Hui Zhai at Institute For Advanced Study, Tsinghua University. He graduated from Department of Physics, Tsinghua University in 2014.

Bio: Dr Chris Ferrie is a Senior Lecturer in the Centre for Quantum Software and Information, University of Technology Sydney (UTS). He holds a UTS Chancellor’s Research Fellowship and an ARC DERCA Fellowship. He obtained his PhD from the Institute for Quantum Computing in 2012. His research is in the characterisation and control of quantum devices using machine learning. He is also the author of the book Quantum Physics for Babies.

Title: Quantum Privacy-Preserving Perceptron

Abstract: With the extensive applications of machine learning, the issue of private or sensitive data in the training examples becomes more and more serious: during the training process, personal information or habits may be disclosed to unexpected persons or organisations, which can cause serious privacy problems or even financial loss. In this paper, we present a quantum privacy-preserving algorithm for machine learning with perceptron. There are mainly two steps to protect original training examples. Firstly when checking the current classifier, quantum tests are employed to detect data user's possible dishonesty. Secondly when updating the current classifier, private random noise is used to protect the original data. The advantages of our algorithm are: (1) it protects training examples better than the known classical methods; (2) it requires no quantum database and thus is easy to implement. Moreover, it can be simply generalized to deal with other machine learning tasks (for instance, association rule mining and decision tree learning) and preserve both parties' privacy.

Title: Information theory for data management

Abstract: This tutorial explores the use of information theory as a tool to express and quantify notions of information content and information transfer for representing and analyzing data. We do so in an application-driven way, using a variety of data management applications, including database design, data integration and data anonymization.

Title: Crowdsourcing for Data Management

Abstract: In this session we will introduce the concept of micro-task crowdsourcing and human computation presenting examples of hybrid human-machine systems for entity linking, data integration and search. Such systems are examples of how the use of human intelligence at scale in combination with machine-based algorithms can outperform traditional data management systems. In this context, we will then discuss efficiency and effectiveness challenges of micro-task crowdsourcing platforms including spam, quality control, task assignment models, and job scheduling.

Title: Contextual Intent Tracking for Personal Assistants

Abstract: A new paradigm of recommendation is emerging in intelligent personal assistants such as Apple's Siri, Google Now, and Microsoft Cortana, which recommends "the right information at the right time" and proactively helps you "get things done". This type of recommendation requires precisely tracking users' contemporaneous intent, i.e., what type of information (e.g., weather, stock prices) users currently intend to know, and what tasks (e.g., playing music, getting taxis) they intend to do. Users' intent is closely related to context, which includes both external environments such as time and location, and users' internal activities that can be sensed by personal assistants. The relationship between context and intent exhibits complicated co-occurring and sequential correlation, and contextual signals are also heterogeneous and sparse, which makes modeling the context-intent relationship a challenging task. To solve the intent tracking problem, we propose the Kalman filter regularized PARAFAC2 (KP2) nowcasting model, which compactly represents the structure and co-movement of context and intent. The KP2 model utilizes collaborative capabilities among users, and learns for each user a personalized dynamic system that enables efficient nowcasting of users' intent. Extensive experiments using real-world data sets from a commercial personal assistant show that the KP2 model significantly outperforms various methods, and provides inspiring implications for deploying large-scale proactive recommendation systems in personal assistants.

Title: Euclidean DBSCAN Revisited: From Static to Dynamic

Abstract: DBSCAN is a highly successful density-based clustering method for multi-dimensional points. Its seminal paper won the Test-of-Time Award at KDD 2014, and it has over 9000 citations at Google Scholar. Although DBSCAN has received extensive applications, its computational hardness was unsolved until the recent work at SIGMOD 2015. This talk focuses on the problem of computing DBSCAN clusters on a set of n points in d-dimensional space from scratch (assuming no existing indexes) under the Euclidean distance. More specifically, we first show the DBSCAN problem is “hard” in three or higher dimensional space. Motivated by this, we propose a relaxed version of the problem called ρ-approximate DBSCAN, which returns the same clusters as DBSCAN, unless the clusters are “unstable”. The ρ-approximate problem is “easy” regardless of the constant dimensionality. This talk further investigate the algorithmic principles for dynamic clustering by DBSCAN. Surprisingly, we prove that the ρ-approximate version suffers from the very same hardness when the dataset is fully dynamic. We also show that this issue goes away as soon as tiny further relaxation is applied, yet still ensuring the same quality of ρ-approximate DBSCAN.

Title: Big Data Meets the Microgrid: Challenges and Opportunities

Abstract: A microgrid is a discrete energy system consisting of distributed energy sources (including demand management, storage, and generation) and loads capable of operating in parallel with, or independently from, the main power grid. It paves a way to effectively integrate various sources of distributed generation, especially Renewable Energy Sources (RES), and meanwhile provides a good solution for supplying power in case of an emergency by having the ability to change between islanded mode and grid-connected mode. On the other hand, control and protection are big challenges in this type of network configuration, which draws our attention to utilize data analytics techniques for enabling smarter control in the microgrid ecosystem. With an extensive range of sensors installed in the microgrid, it has been continuously generating streaming machine-to-machine (M2M) data to support dynamic optimization of power generation, consumption and storage. Microgrids bring significant new challenges and opportunities to data management and data analytics, from data acquisition, data quality control, data compression, data fusion, data disaggregation, data mining, and data prediction. In this talk we will introduce this emerging area to the data management community, with an emphasis on the challenges and some promising research topics on large scale mcirogrid data analytics.