★講演会のご案内★ ６月１７日午後６時１５分〜 東大生研にて

[Miyuki Nakano <miyuki [at] tkl.iis.u-tokyo.ac.jp>]

日本データベース学会の皆様、

以下の講演会が6月17日(月)・東大生研にて行われます。
ふるってご参加ください。
　　　　　　　　　　　　　　　　　東京大学生研
　　　　　　　　　　　　　　　　　中野美由紀

　☆☆☆　6月17日(月)　講演会のご案内　　　☆☆☆

主催　最先端研究開発支援プログラム（FIRST）: 超巨大データベース時代に
　　　向けた最高速データベースエンジンの開発と当該エンジンを核とする
　　　戦略的社会サービスの実証・評価
後援　電子情報通信学会　データ工学研究専門委員会
後援　ACM SIGMOD Japan Chapter


日時　6月17日（月）　午後6時15分〜7時45分
場所　東京大学生産技術研究所　E棟　5階　会議室Ａ／Ｂ（Ew-501, 502）
      http://www.iis.u-tokyo.ac.jp/map/index.html

18:15-19:00
Speaker :　Prof. Ling Liu (Georgia Tech)
Title: Semantic Hash Partitioning for Parallel Processing of
Heterogeneous Graphs

19:00-19:45
Speaker: 	Prof. Calton Pu (Georgia Institute of Technology)
Title: 	Big Data, IoT, and Clouds: Research Opportunities in Disaster
Management

参加費　無料

皆様のご参加をお待ちしております。

                        FIRST :　研究代表者　　　　　　　喜連川　優
                        電子情報通信学会データ工学研究会　委員長
　　　　　　　　　　　　　　　　　　　　　　　　　　　　 木俵　豊
　　　　　　　　　　

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To: first_lecture [at] tkl.iis.u-tokyo.ac.jp

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プログラム

18:15 - 19:00
Speaker :　Prof. Ling Liu (Georgia Institute of Technology)
Title: Semantic Hash Partitioning for Parallel Processing of
Heterogeneous Graphs

Abstract:
As the size and variety of information networks continue to grow in many
scientific and engineering domains, we wit- ness a growing demand for
efficient processing of large heterogeneous graphs using a cluster of
compute nodes in the Cloud. One open issue is how to effectively
partition a large graph to process complex graph operations efficiently.
In this paper, we present VB-Partitioner ? a distributed data
partitioning model and algorithms for efficient processing of graph
operations over large-scale graphs in the Cloud. Our VB-Partitioner has
three salient features. First, it introduces vertex blocks (VBs) and
extended vertex blocks (EVBs) as the building blocks for semantic
partitioning of large graphs. Second, VB-Partitioner utilizes vertex
block grouping algorithms to place those vertex blocks that have high
correlation in graph structure into the same partition. Third,
VB-Partitioner employs a VB-partition guided query partitioning model to
speed up the parallel processing of graph pattern queries by reducing
the amount of inter-partition query processing. We conduct extensive
experiments on several real-world graphs with millions of vertices and
billions of edges. Our results show that VB-Partitioner significantly
outperforms the popular random block-based data partitioner in terms of
query latency and scalability over large-scale graphs.

19:00-19:45
Speaker: 	Prof. Calton Pu, Georgia Institute of Technology
Title: 	Big Data, IoT, and Clouds: Research Opportunities in Disaster
Management
Abstract:
The ongoing convergence of evolution of devices (Internet of Things),
deployment of large shared infrastructures (computing clouds), and
accumulation of Big Data (sensors and social networks) has created
exciting new research challenges. We will describe some of these
challenges in the three phases of disaster management: preparedness
beforehand, emergency response, and recovery afterwards. With concrete
scenarios, e.g., using Twitter for effective information exchange during
emergencies, we will discuss the research challenges in quality of
service (e.g., real-time response time, high availability and robustness
despite widespread failures), and quality of information (e.g.,
security, privacy, and robustness despite misinformation). These
research challenges require integration and synthesis of results from
several related areas, e.g., sensor networks and social networks as Big
Data producers, and sophisticated models running on clouds as Big Data
consumers. Such integration of heterogeneous and open data sources will
require automated methods for reconciling syntactic and semantic
differences among semi-structured sources as well as filtering out
noises and misinformation in order to achieve the quality of service and
quality of information requirements of a mission-critical application
such as disaster management. We will use a concrete multi-hazard
scenario (landslides) to illustrate these research challenges and some
promising solutions.



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中野　美由紀	東京大学　生産技術研究所　戦略情報融合国際研究センタ
Miyuki NAKANO	Institute of Industrial Science, Univ. of Tokyo
　　　　　　　　Center for Information Fusion
miyuki [at] tkl.iis.u-tokyo.ac.jp