Invited Talk: Speed, Stream, Structure: Scalable Analytics for Mining Complex Data
Abstract
Big data applications are commonly featured with large scale complex data with increasing volumes and dynamic changing relationships. Finding similarities and building predictive models for complex data are two fundamental challenges. This talk will present several works we have recently proposed to tackle the learning speed, stream data, and structure relationships for scalable data mining: (1) Speed: how to efficiently calculate the similarity between large scale text documents, by taking semantic context information into consideration; (2) Stream: how to build effective classification models for graph stream networks; and (3) Structure: how to represent an object, e.g., an image, graphs with structure relationships for learning and classification. For the speed challenge, we will propose a context-preserving hashing to calculate similarities between texts with preserved context information. For the stream challenge, we will use graph hashing and factorization to build graph classification models for large scale dynamic networks. For the structure challenge, we will propose a multi-graph representation model and design specific learning algorithms for multi-graph classification.
Biography of Prof. Zhu
Prof. Xingquan Zhu is an associate professor in the Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University. His research interests are in the areas of data analytics, machine learning, and bioinformatics. He was the recipient of an ARC Future Fellowship in 2010, and has received two Best Paper Awards and one Best Student Paper Award. Dr. Zhu is an associate editor of the IEEE Transactions on Knowledge and Data Engineering (2014-date, 2008-2012), and is serving on the Editorial Board of the International Journal of Social Network Analysis and Mining SNAM (2010-date) and Network Modeling Analysis in Health Informatics and Bioinformatics Journal (2014-date). He was the program committee co-chair for the 14th IEEE International Conference on Bioinformatics and BioEngineering (BIBE-2014), IEEE International Conference on Granular Computing (GRC-2013), 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2011), and the 9th International Conference on Machine Learning and Applications (ICMLA-2010). He also served as a conference co-chair for ICMLA-2012.
Abstract
Big data applications are commonly featured with large scale complex data with increasing volumes and dynamic changing relationships. Finding similarities and building predictive models for complex data are two fundamental challenges. This talk will present several works we have recently proposed to tackle the learning speed, stream data, and structure relationships for scalable data mining: (1) Speed: how to efficiently calculate the similarity between large scale text documents, by taking semantic context information into consideration; (2) Stream: how to build effective classification models for graph stream networks; and (3) Structure: how to represent an object, e.g., an image, graphs with structure relationships for learning and classification. For the speed challenge, we will propose a context-preserving hashing to calculate similarities between texts with preserved context information. For the stream challenge, we will use graph hashing and factorization to build graph classification models for large scale dynamic networks. For the structure challenge, we will propose a multi-graph representation model and design specific learning algorithms for multi-graph classification.
Biography of Prof. Zhu
Prof. Xingquan Zhu is an associate professor in the Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University. His research interests are in the areas of data analytics, machine learning, and bioinformatics. He was the recipient of an ARC Future Fellowship in 2010, and has received two Best Paper Awards and one Best Student Paper Award. Dr. Zhu is an associate editor of the IEEE Transactions on Knowledge and Data Engineering (2014-date, 2008-2012), and is serving on the Editorial Board of the International Journal of Social Network Analysis and Mining SNAM (2010-date) and Network Modeling Analysis in Health Informatics and Bioinformatics Journal (2014-date). He was the program committee co-chair for the 14th IEEE International Conference on Bioinformatics and BioEngineering (BIBE-2014), IEEE International Conference on Granular Computing (GRC-2013), 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2011), and the 9th International Conference on Machine Learning and Applications (ICMLA-2010). He also served as a conference co-chair for ICMLA-2012.
Invited Talk: Advance in Distributed Storage Coding and Systems
Abstract
The demand for reliable storage and seamless access of massive amounts of data has inspired a whole new class of large-scale distributed storage systems. Typically, these complex, multi-tiered systems often run on clusters of thousands of individually unreliable commodity devices and have to tolerate multiple failures, might caused by software, hardware, network connectivity and power issues. In such systems, recovery from failures is now part of regular operation rather than a rare exception. Hence, ensuring the data reliability calls for the introduction of redundancy and efficient repair paradigms. This talk will focus on the advances of redundancy schemes in distributed storage systems and introduce a novel coding technique, termed as BASIC codes, for efficient data recovery.
Biography of Prof. Li
Dr. Hui Li is now a full professor with Dept. of Computer Engineering, ShenZhen Graduate School, Peking University. He received his Bachelor Degree of Engineering from TsingHua University in 1986, and Ph.D of Information Engineering from the Chinese University of HongKong in 2000. His current research interesting includes: distributed storage coding and system, network coding theory and it applications on future network architecture.
Abstract
The demand for reliable storage and seamless access of massive amounts of data has inspired a whole new class of large-scale distributed storage systems. Typically, these complex, multi-tiered systems often run on clusters of thousands of individually unreliable commodity devices and have to tolerate multiple failures, might caused by software, hardware, network connectivity and power issues. In such systems, recovery from failures is now part of regular operation rather than a rare exception. Hence, ensuring the data reliability calls for the introduction of redundancy and efficient repair paradigms. This talk will focus on the advances of redundancy schemes in distributed storage systems and introduce a novel coding technique, termed as BASIC codes, for efficient data recovery.
Biography of Prof. Li
Dr. Hui Li is now a full professor with Dept. of Computer Engineering, ShenZhen Graduate School, Peking University. He received his Bachelor Degree of Engineering from TsingHua University in 1986, and Ph.D of Information Engineering from the Chinese University of HongKong in 2000. His current research interesting includes: distributed storage coding and system, network coding theory and it applications on future network architecture.
Talk: Big Education--A Case of Big Data in eLearning
Abstract
This talk will be in two parts. The first part will be related to online learning as in machine learning and the second part will be dealing with topics in education analytics for online learning in Massive Open Online Course (MOOC), University Open Online Course (UOOC), Small Personal Online Course (SPOC), flipped classroom, etc.
Online learning is a promising technique for big data analytics, especially for learning from streaming data. One important property of online learning is that it can adaptively update the parameters of learning models when a new sample appears. This can avoid retraining from scratch. In the first part of the talk, I will give a novel online learning model on how to adaptively seek nonlinear classifiers when two classes of data are imbalanced. Formulation, algorithms, theory, and experimental results are presented accordingly.
Big Education is the convergence of Big Data in education as these are two hot topics of intense research and discussion in recent years. In the second part of the talk, I will introduce a new project that is being funded by the Hong Kong SAR Government named, Knowledge and Education Exchange Platform (KEEP). The KEEP portal is a knowledge aggregator and technology integrator that provides access to online educational resources for producing positive teaching and learning experiences to the educators and students.
Biography of Prof. King
Prof. Irwin King's research interests include machine learning, social computing, web intelligence, data mining, and multimedia information processing. In these research areas, he has over 210 technical publications in journals and conferences. In addition, he has contributed over 20 book chapters and edited volumes. Moreover, Prof. King has over 30 research and applied grants. One notable patented system he has developed is the VeriGuide System, previously known as the CUPIDE (Chinese University Plagiarism IDentification Engine) system, which detects similar sentences and performs readability analysis of text-based documents in both English and in Chinese to promote academic integrity and honesty.
Prof. King is the Book Series Editor for ``Social Media and Social Computing" with Taylor and Francis (CRC Press). He is also an Associate Editor of the ACM Transactions on Knowledge Discovery from Data (ACM TKDD) and a former Associate Editor of the IEEE Transactions on Neural Networks (TNN) and IEEE Computational Intelligence Magazine (CIM). He is a member of the Editorial Board of the Open Information Systems Journal, Journal of Nonlinear Analysis and Applied Mathematics, and Neural Information Processing Letters and Reviews Journal (NIP-LR). He has also served as Special Issue Guest Editor for Neurocomputing, International Journal of Intelligent Computing and Cybernetics (IJICC), Journal of Intelligent Information Systems (JIIS), and International Journal of Computational Intelligent Research (IJCIR). He is a senior member of IEEE and a member of ACM, International Neural Network Society (INNS), and Asian Pacific Neural Network Assembly (APNNA). Currently, he is serving the Neural Network Technical Committee (NNTC) and the Data Mining Technical Committee under the IEEE Computational Intelligence Society (formerly the IEEE Neural Network Society). He is also a member of the Board of Governors of INNS and a Vice-President and Governing Board Member of APNNA. He also serves INNS as the Vice-President for Membership in the Board of Governors.
Prof. King is an associate dean of engineering faculty and a professor at the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He received his B.Sc. degree in Engineering and Applied Science from California Institute of Technology, Pasadena and his M.Sc. and Ph.D. degree in Computer Science from the University of Southern California, Los Angeles.
Abstract
This talk will be in two parts. The first part will be related to online learning as in machine learning and the second part will be dealing with topics in education analytics for online learning in Massive Open Online Course (MOOC), University Open Online Course (UOOC), Small Personal Online Course (SPOC), flipped classroom, etc.
Online learning is a promising technique for big data analytics, especially for learning from streaming data. One important property of online learning is that it can adaptively update the parameters of learning models when a new sample appears. This can avoid retraining from scratch. In the first part of the talk, I will give a novel online learning model on how to adaptively seek nonlinear classifiers when two classes of data are imbalanced. Formulation, algorithms, theory, and experimental results are presented accordingly.
Big Education is the convergence of Big Data in education as these are two hot topics of intense research and discussion in recent years. In the second part of the talk, I will introduce a new project that is being funded by the Hong Kong SAR Government named, Knowledge and Education Exchange Platform (KEEP). The KEEP portal is a knowledge aggregator and technology integrator that provides access to online educational resources for producing positive teaching and learning experiences to the educators and students.
Biography of Prof. King
Prof. Irwin King's research interests include machine learning, social computing, web intelligence, data mining, and multimedia information processing. In these research areas, he has over 210 technical publications in journals and conferences. In addition, he has contributed over 20 book chapters and edited volumes. Moreover, Prof. King has over 30 research and applied grants. One notable patented system he has developed is the VeriGuide System, previously known as the CUPIDE (Chinese University Plagiarism IDentification Engine) system, which detects similar sentences and performs readability analysis of text-based documents in both English and in Chinese to promote academic integrity and honesty.
Prof. King is the Book Series Editor for ``Social Media and Social Computing" with Taylor and Francis (CRC Press). He is also an Associate Editor of the ACM Transactions on Knowledge Discovery from Data (ACM TKDD) and a former Associate Editor of the IEEE Transactions on Neural Networks (TNN) and IEEE Computational Intelligence Magazine (CIM). He is a member of the Editorial Board of the Open Information Systems Journal, Journal of Nonlinear Analysis and Applied Mathematics, and Neural Information Processing Letters and Reviews Journal (NIP-LR). He has also served as Special Issue Guest Editor for Neurocomputing, International Journal of Intelligent Computing and Cybernetics (IJICC), Journal of Intelligent Information Systems (JIIS), and International Journal of Computational Intelligent Research (IJCIR). He is a senior member of IEEE and a member of ACM, International Neural Network Society (INNS), and Asian Pacific Neural Network Assembly (APNNA). Currently, he is serving the Neural Network Technical Committee (NNTC) and the Data Mining Technical Committee under the IEEE Computational Intelligence Society (formerly the IEEE Neural Network Society). He is also a member of the Board of Governors of INNS and a Vice-President and Governing Board Member of APNNA. He also serves INNS as the Vice-President for Membership in the Board of Governors.
Prof. King is an associate dean of engineering faculty and a professor at the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He received his B.Sc. degree in Engineering and Applied Science from California Institute of Technology, Pasadena and his M.Sc. and Ph.D. degree in Computer Science from the University of Southern California, Los Angeles.