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 　　　　　　　　　　 ----------------------------------------------------------------- To: first_lecture@tkl.iis.u-tokyo.ac.jp 　講演会　参加申し込み 6月17日（月）の講演会に参加 ・名前　　　 ・ご所属 ------------------------------------------------------------------ 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. -- ----------------------------------------------------------------------- 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@tkl.iis.u-tokyo.ac.jp