日本データベース学会の皆様、 既に御存じの方も多いかと思いますが、東京大学の喜連川優先生がACM SIGMODの 2009 SIGMOD Edgar F. Codd Innovations Award を受賞されました！ 喜連川先生のご実績は国内では知らない方はいないと存じますが、国際的にも 極めて名誉ある賞を受賞され、日本のデータベースコミュニティとしてこの上 なく喜ばしく、心よりお祝い申し上げます。 ACM SIGMOD 日本支部長 横田 治夫 At Mon, 08 Jun 2009 19:40:47 -0500 [<200906090040.n590el48007545@vimes.cs.wisc.edu>] David Maier <maier@cs.pdx.edu> wrote:
It is a great pleasure and honor to announce the winners of the following ACM SIGMOD awards, which will be presented at the ACM SIGMOD Conference in Providence. Please join us in congratulating this year's award winners! More details about these awards can be found at SIGMOD Online. (http://www.sigmod.org/sigmod/NEWS/09/sigmod-award-recipients.4jun2009.html).
Sincerely,
The 2009 ACM SIGMOD Awards Committee Rakesh Agrawal Peter Buneman Laura Haas David Maier (2009 Chair) Gerhard Weikum
========================================================================== 2009 SIGMOD Edgar F. Codd Innovations Award for innovative and highly significant contributions of enduring value to the development, understanding, or use of database systems and databases.
Masaru Kitsuregawa is the recipient of the 2009 SIGMOD Edgar F. Codd Innovations Award for contributions to high-performance database technology.
Kitsuregawa made major contributions to the development of hash-join algorithms, which significantly improved the performance of join operations in relational database systems. That work has influenced related research in areas such as query execution, plan optimization and dynamic query-workload balancing, as well as the development of commercial database products. He implemented the hash-based approach on a variety of platforms, including the Functional Disk System and multi- node PC clusters, demonstrating its substantial advantages through detailed evaluations. He has also applied hash-based strategies to parallel association mining and showed its effectiveness there. His contributions in the hardware area include a high-speed sorting system with a sophisticated memory management algorithm. That work was eventually commercialized in collaboration with colleagues, and won the Datamation sort benchmark in 2000. ============================================================================ 2009 SIGMOD Contributions Award for outstanding and sustained services to and promotion of the database field through activities such as education, conference organization, journals, standards, and research funding.
Beng Chin Ooi
Beng Chin Ooi is the recipient of the 2009 SIGMOD Contributions Award for his sustained and selfless contributions to the database community in promoting and pursuing high standards of database research at both the international and regional level.
Beng Chin Ooi has been consistently involved in activities that push the frontier of database research. He has taken leadership roles on technical programs for ICDE, SSD, SIGMOD and VLDB. He has helped ensure a high standard for database journal publications as an editor for The VLDB Journal, IEEE Transactions on Knowledge and Data Engineering, International Journal of Geographical Information Science, and Geoinformatca. Currently, he is the editor-in-chief of IEEE Transactions on Knowledge and Data Engineering, and an editor for the Distributed and Parallel Databases journal. He also serves on the SIGMOD Doctoral Dissertation Award committee and on the board of the VLDB Endowment. Ooi has contributed greatly to promoting database research, especially in the Asia-Pacific region, by building a eminent database group at the National University of Singapore that supports high quality research throughout the region through direct collaborations and participation in regional conferences. ============================================================================= 2009 SIGMOD Test of Time Award for the paper from the 1999 SIGMOD Conference that has had the paper that has had the most impact (research, products, methodology) over theintervening decade.
Jeffrey Scott Vitter and Mai Wang Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets
This influential paper showed that aggregates over sparse, high-dimensional arrays can be approximated with wavelets to give a compact data-cube representation that supports queries at interactive speeds. Prior histogram- based methods had prohibitive I/O costs for massive data sets of high dimensionality. The method is more accurate than random sampling and supports progressive refinement of answers when additional accuracy is desired. The paper inspired significant follow-on work by others, in the OLAP domain and also more broadly in approximate query processing, selectivity estimation, indexing of images and time series, and data-stream processing. _______________________________________________ Please do not post msgs that are not relevant to the database community at large. Go to www.cs.wisc.edu/dbworld for guidelines and posting forms. To unsubscribe, go to https://lists.cs.wisc.edu/mailman/listinfo/dbworld