第122回計算科学コロキウムを、8月3日（金）に開催いたします。
多数のご来聴をお願いいたします。

場所：筑波大学　計算科学研究センター　国際ワークショップ室
日時：2018年8月3日（金）13:30-14:30
講演タイトル：Graph Processing: The Integration of RDBMS and Graph System
講演者氏名　：Professor Jeffrey Xu Yu
Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong

使用言語　　：英語

Abstract:
To support analytics on massive graphs such as online social networks, RDF, Semantic Web, etc. many new graph algorithms are designed to query graphs for a specific problem, and many distributed graph processing systems are developed to support graph querying by programming. In this talk, first, we focus on RDBM, which has been well studied over decades to manage large datasets. We revisit the issue how RDBMS can support graph processing at the SQL level. Our work is motivated by the fact that there are many relations stored in RDBMS that are closely related to a graph in real applications and need to be used together to query the graph, and RDBMS is a system that can query and manage data while data may be updated over time. To support graph processing, we propose 4 new relational algebra operations. The 4 new relational algebra operations can be defined by the 6 basic relational algebra operations with group-by-&-aggregation. We revisit SQL recursive queries and show that the 4 operations with others are ensured to have a fixpoint, following the techniques studied in Datalog, and enhance the recursive WITH clause in SQL’99. Such enhanced recursive WITH Clause can be supported by major RDBMSs. Second, we discuss how to translate such newly introduced operations into a graph system to achieve efficiency.

Bio:
Dr. Jeffrey Xu Yu is a Professor in the Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. His current main research interests include graph mining, graph query processing, graph pattern matching, keywords search in databases, and online social networks. Dr. Yu served as an Information Director and a member in ACM SIGMOD executive committee (2007-2011), an associate editor of IEEE Transactions on Knowledge and Data Engineering (2004-2008), and an associate editor in VLDB Journal (2007-2013). Currently he serves as an associate editor of ACM Transactions on Database Systems (TODS), WWW Journal, Data Science and Engineering, the International Journal of Cooperative Information Systems, the Journal on Health Information Science and Systems (HISS), and Journal of Information Processing. Dr. Yu served/serves in many organization committees and program committees in international conferences/workshops including PC Co-chair of APWeb’04, WAIM’06, APWeb/WAIM’07, WISE’09, PAKDD’10, DASFAA’11, ICDM’12, NDBC’13, ADMA’14, CIKM’15 and Bigcomp17, and conference general Co-chair of APWeb’13 and ICDM’18.

世話人：北川 博之　

第121回計算科学コロキウムを、6月11日（月）に開催いたします。
多数のご来聴をお願いいたします。

場所：筑波大学　計算科学研究センター　会議室B
日時：2018年6月11日（月）11:15-12:15
講演タイトル：Statistics and Machine Learning: Common Goals, Practices, and Interactions!
講演者氏名：Professor Sam Behseta
Director of Center for the Computational and Applied Mathematics (CCAM) California State University
使用言語：英語

Abstract:
Ever since the advent of correlation and simple linear regression at the turn of the 20th century, Statistics has been known as a data driven discipline within the spectrum of mathematical sciences. Subsequently, the flow of statistical approaches typically has encompassed several stages, including data collection, model fitting, and parameter interpretation. While earlier statistical attempts were mostly devoted to the establishment of a theoretical framework for inference, the growth and popularity of computational techniques in recent years have significantly shifted modern statistical methodologies from theory to applications, and thus have made them far more computationally driven. In particular, robust computational techniques such as Hamiltonian Markov Chain Monte Carlo techniques have emboldened the applications of Bayesian modeling for scientific problem solving. In this talk, I will discuss some areas in which there is a considerable overlap between statistical modeling of Big Data and Machine Learning. For example, I will draw upon the role of mixture models, Dirichlet process priors, Gaussian processes, and dimensionality reduction methods in Statistics to demonstrate their dual functionality in the current approaches for classification and prediction, as practiced in Machine Learning. I will then suggest a potentially unifying infrastructure for both disciplines, where the two cultures can further collaborate to offer more robust solutions for solving scientific inquiries.

世話人：北川 博之　

第120回計算科学コロキウムを、5月18日（金）に開催いたします。
多数のご来聴をお願いいたします。

場所：筑波大学　計算科学研究センター　会議室A
日時：2018年5月18日（金）14:00–15:30
講演タイトル：Keeping-up with the Flood of Data in Extreme Scale Simulations
講演者氏名：Franck Cappello (Senior Computer Scientist, Argonne National Laboratory)
使用言語：英語

Abstract:
Many extreme-scale scientific simulations are already generating more data than can be communicated, stored, and analyzed.
The data flood will get even worse with exascale systems. In this talk, we will discuss two projects of the U.S. Exascale Computing Project (ECP) addressing this problem in the contexts of checkpoint/restart and simulation I/O. The first project is VeloC, a multilevel checkpoint/restart environment reducing dramatically the checkpoint time perceived by the application. With minimal code modifications (generally about 10-20 lines of codes), the VeloC environment leverages the different levels of the storage hierarchy to perform fast checkpointing and asynchronous checkpoint movements. We will present early performance results of this environment that is co-designed with ECP applications and that will serve as the main checkpoint/restart library for ECP simulations. The second project, EZ, develops and improves the SZ lossy compressor for scientific data. Scientific data reduction is necessary in order to dramatically accelerate I/O and reduce the data storage footprint. Lossy compression seems the only practical and effective direction to reduce significantly scientific datasets for a broad spectrum of applications. Care must be taken, however, to keep the information that matters for the scientists. We will introduce the latest version of the SZ compressor and discuss its performance for several ECP datasets.

Bio:
Franck is senior computer scientist at Argonne National Laboratory and adjunct associate professor in the department of computer science at University of Illinois at Urbana Champaign. He is the director of the Joint-Laboratory on Extreme Scale Computing gathering seven of the leading high-performance computing institutions in the world: Argonne National Laboratory (ANL), National Center for Scientific Applications (NCSA), Inria, Barcelona Supercomputing Center (BSC), Julich Supercomputing center (JSC), Riken CCS and UTK/ICL. Franck is an expert in fault tolerance for high-performance parallel computing. Recently he started investigating lossy compression for scientific datasets to respond to the pressing needs of scientists performing large scale simulations and experiments for significant data reduction. Franck is member of the editorial board of IEEE Transactions on Parallel and Distributed Computing and has served the steering committees of ACM HPDC and IEEE CCGRID. He is fellow of the IEEE.

世話人：朴 泰祐