中国计算机学会
China Computer Federation
CCF 上海 “海外青年之星计算机主题论坛”（FORCS’2016）
复旦大学分论坛
于 2016 年 11 月 4 日 (星期五) 9:00-16:30
在上海市张衡路 825 号复旦大学张江校区行政楼第二会议室举行
敬请光临
主办方：中国计算机学会
承办：CCF 上海、复旦大学计算机科学技术学院、复旦大学大数据学院
9:00-9:30 会议注册、报到
9:30-9:45 开幕式
9:45-9:55 合影
9:55-11:30 学术报告（主持人：王晓阳院长）
特邀讲者：黄欣博士，
报告题目：Community Search over Big Graphs
特邀讲者：王飞博士
报告题目：医疗大数据
11:30-13:00 午餐&自由交流
13:00-14:30 学术报告（主持人：王晓阳院长）
特邀讲者：熊鹏程博士
报告题目：从 HiveQL 到标准 SQL: 大数据分析平台 Apache Hive 最新进展
特邀讲者：刘金飞
报告题目：Finding Pareto Optimal Groups: Group-based Skyline
14:30-14:45 茶歇
14:45-15:00 复旦大学计算机科学技术学院和大数据学院情况介绍
（主持人：许晓茵书记）
15:00-16:00 复旦大学人事政策介绍及青年教师联谊活动（主持人：许晓茵书记）
FORCS'2016 论坛联合主席：
王晓阳 CCF 上海主席，复旦⼤大学计算机科学技术学院和软件学院院长
谷大武 CCF 上海副主席，上海交通大学计算机系教授
报名⽅方式：
如参会，请于 10 月 30 日 22：00 前以 “FORCS'2016 复旦大学分论坛报名+姓名” 为主题回复下附回执至 sqwang@fudan.edu.cn; xujn@fudan.edu.cn，以便提供会务。
联系人：王顺箐，电话：13564992634
徐敬楠，电话：15021149392
会场方位示意图
地址：上海市浦东区张衡路 825 号行政楼第二会议室
路线：1. 地铁二号线在张江高科站下，坐有轨电车到张江校区北门（蔡伦路校门）进；
2. 坐出租车在张衡路 825 号门口进，右侧的楼房即是行政楼。
CCF 上海 “海外青年之星计算机主题论坛” (FORCS'2016)
由中国计算机学会（CCF）主办，CCF 上海活动中⼼心与上海交通⼤大学、复旦⼤大学等⾼高校联合承办的 “2016 海外青年之星计算机主题论坛” (FORCS'2016) 将于 2016 年 11 ⽉月 1-4 ⽇日在上海召开。论坛旨在整合 CCF 与⾼高校资源，汇聚海外计算机领域的优秀青年学者，结合国内在沪⾼高校在计算机领域的⼈人才需求及引进政策，为海外计算机领域的优秀青年学者搭建⼀一流的学术交流平台。
讲者简介
特邀讲者：黄欣博士
讲者简介：Xin Huang is a postdoctoral fellow of Computer Science at University of British Columbia, Canada. He received the Ph.D. degree from the Department of Systems Engineering and Engineering Management at the Chinese University of Hong Kong. His research interests include data management, data mining, and social network analysis. He has recently been actively working on the topics of community discovery and graph query processing. His work is published in several top-tier database conference/journal including SIGMOD, VLDB, ICDE and VLDB journal. He serves on program committees for conferences including ICDE’17, WWW’17, SDM’17, and etc.
报告题目：Community Search over Big Graphs: Models, Algorithms, and Opportunities
报告内容摘要：Communities naturally exist in many real-world networks such as social, biological, collaboration, and communication networks and serve as an invaluable tool for organizing and understanding their structure. Recently, community search has attracted significantly increasing attention, from simple and static graphs to evolving, attributed, location-based graphs. Different from the well-studied problem of community detection that finds all communities in an entire network, community search is to find the cohesive communities w.r.t. the query nodes. In this talk, we survey the state-of-the-art of community search on various kinds of networks across different application areas such as desely-connected community search, attributed community search, and querying geo-social groups. We first highlight the challenges posed by the community search problems. We continue the presentation of their principles, methodologies, algorithms, and applications, and give a comprehensive comparison of the state-of-the-art techniques. Finally, this talk concludes by offering future directions for research in this important and growing area.
特邀讲者：王飞博士
讲者简介：美国康奈尔大学威尔医学院助理教授，IBM 沃森研究中心顾问，法国液化空气集团 (Air liquide) 研究顾问，数字医疗新兴企业 RxPredict 研究顾问。他于 2008 年在清华大学自动化系获得博士学位，并曾在佛罗里达国际大学计算机系以及康奈尔大学统计系分别从事过博士后研究。其博士学位论文 “图上的半监督学习算法研究” 获得了 2011 年全国优秀博士论文奖。主要研究方向包括数据挖掘， 机器学习及其在医疗信息学中的应用。王飞博士已经在相关方向的顶级国际会议和杂志上发表了近 150 篇学术论文，引用超过 3800 次，H 指数 33。在相关顶级国际学术会议上做过 11 次专题报告 (Tutorial)。其论文（或指导学生的论文）曾获得 ICHI2016 最佳论文奖，ICDM 2015 最佳学生论文奖，AMIA 2014 转化生物信息学峰会的 Marco Romani 最佳论文提名，ICDM 2010 的最佳研究论文提名奖，SDM 2011 以及 2015 最佳研究论文候选。王飞博士还申请了超过 40 项相关方向的专利，其中 5 项已在美国获得授权。王博士目前是数据挖掘顶级刊物 Data Mining and Knowledge Discovery 的执行编委 (Action Editor)，模式识别顶级刊物 Pattern Recognition 的编委 (Editorial Board)，以及智能健康刊物 Journal of Health Informatics Research 和 Smart Health 的编委 (Associate Editor)。
报告题目：医疗大数据
报告内容摘要：在计算机科学高速发展的今天，大数据分析技术正在慢慢渗透到日常生活的各个领域。医疗健康与每个人的生活息息相关。如何利用大数据分析来改善当前的医疗现状，当大家拥有更加健康的生活，已经逐渐成为国际上研究的热点。近些年来已经有越来越多的国内以及国外企业投身于这一领域。本报告将从分析我国现有的医疗状况开始，介绍大数据技术将如何帮助提高人们的医疗健康水平。具体内容包括总结归纳健康大数据的种类，目前国际健康大数据技术研究的热点方与挑战，归纳目前已有的相关企业，以及展望未来可能的发展方向。
特邀讲者：熊鹏程博士
讲者简介：Pengcheng Xiong has extensive research and development experiences in centralized/distributed RDBMS internals, Hive and etc. He has dozens of publications in database related conferences and journals with thousands of citations. He is now a staff engineer and program manager on the next-generation Hive optimizer in Hortonworks, Inc. He serves and contributes as an Apache Hive PMC member. He holds a PhD from Georgia Tech.
报告题目：从 HiveQL 到标准 SQL: 大数据分析平台 Apache Hive 最新进展
报告内容摘要：Although Hive becomes de facto standard for SQL queries in Hadoop, many potential users hesitate to adopt it due to its lack of full support of SQL compliance. In this talk, we will give overview of the major advancements in Apache Hive towards full support of SQL compliance that we have already achieved in the recent years. More specifically, we will talk about the keywords and quoted identifiers, primary key and foreign keys, and newly added set operators like union, intersect, except and minus. We will not only show concrete examples of how to use them, but also share the experience and lessons that we have gained during the research and development process.
特邀讲者：刘金飞
讲者简介：Jinfei Liu is currently a fourth-year Ph.D. student in the department of mathematics and computer science at Emory University. He is interested in data science, with a current focus on skyline queries and data security & privacy. He has published over twenty papers including VLDB, CIKM, and IPL. He contributed to the fastest skyline computation algorithm from theoretical perspective. In addition, he has served as reviewers for many conferences and journals, e.g., VLDB, ICDE, CIKM, TKDE, and DKE.
报告题目：Finding Pareto Optimal Groups: Group-based Skyline
报告内容摘要：Skyline computation, aiming at identifying a set of skyline points that are not dominated by any other point, is particularly useful for multi-criteria data analysis and decision making. Traditional skyline computation, however, is inadequate to answer queries that need to analyze not only individual points but also groups of points. To address this gap, in this talk, I will show how to generalize the original skyline definition to the novel group-based skyline, which represents Pareto optimal groups that are not dominated by other groups. In order to compute G-Skyline groups consisting of k points efficiently, I will present a novel structure that represents the points in a directed skyline graph and captures the dominance relationships among the points based on the first k skyline layers. Finally, I will present two heuristic algorithms to efficiently compute the G-Skyline groups: the point-wise algorithm and the unit group-wise algorithm, using various pruning strategies.
