2018年7月28日，由深圳大学组织，广东省大数据协同创新中心在深圳举行了发展与建设研讨会，参与人员来自中大、华工、暨大、中科院深圳研究院、华南师大、香港城大、浸会大学、南方科大、港大、澳门大学等协同单位和拟新增协同单位的专家学者。

       研讨会期间，广东省大数据协同创新中心还与深圳大学大数据系统计算技术国家工程实验室、CCF YOCSEF深圳等联合举办了以“大数据与人工智能技术"为主题的“青年计算科技校企协同创新技术沙龙”。研讨会邀请了5位知名专家学者。 华南师范大学计算机学院院长汤庸、香港城市大学电脑科学系多媒体软件工程研究中心创始主任李青、香港大学Reynold Cheng、西北工业大学教授深圳大学兼职特聘教授聂飞平、腾讯AI安全负责人成杰峰分别就《社交网络中的数据智能应用》、《Event Cube: a Conceptual Framework for Multi-sourced Event Management and Multi-dimensional Analysis》、《Meta Paths and Meta Structures: Analysing Large Heterogeneous Information Networks》、《大规模结构化学习及其高效聚类算法》、《腾讯知识图谱的AI应用》主题做报告。

      华南师范大学汤庸在"社交网络中的数据智能应用"报告中，主要介绍学者社交网络SCHOLAT中大数据应用案例及其数据智能研究情况，得到学术界和工业界与会人员积极反响。

广东省“大数据协同创新中心”发展与建设研讨会会场

“大数据与人工智能技术"沙龙会场

“大数据与人工智能技术"海报

题目：社交网络中的数据智能应用
特邀讲者：汤庸，华南师范大学，计算机学院院长
      学者网创始人，华南师范大学学位委员会副主席、计算机学院院长，二级教授、博士导师，获武汉大学学士和硕士学位、中国科技大学博士学位。中国计算机学会首批杰出会员，青工委荣誉委员、YOCSEF广州首届主席，目前是协同计算专业委员会副主任，广东省计算机学会常务副会长。享受国务院政府特殊津贴，入选首批教育部新世纪优秀人才计划，获宝钢教育奖、丁颖科技奖、南粤教坛新秀、中山大学教学名师等，主持的教学科研成果获省部级一等奖4项、二等奖5项等。更多信息请见SCHOLAT个人主页：www.scholat.com/ytang。
      报告提要：主要以华南师范大学自主研发的面向学者的社交网络——学者网(scholat.com)为背景，介绍了个人学术空间管理、学术团队网站、课程教学网站及学术圈的创建，以及学术搜索和推荐服务等学者社交网络及大数据应用。

题目：Event Cube: a Conceptual Framework for Multi-sourced Event Management and Multi-dimensional Analysis
特邀讲者：李青，香港城市大学电脑科学系，多媒体软件工程研究中心创始主任
       Qing Li is a Professor at the Department of Computer Science, and the Director of the Engineering Research Centre on Multimedia Software at the City University of Hong Kong, where he joined as a faculty member since Sept 1998. He received his B.Eng. from Hunan University (Changsha), and M.Sc. and Ph.D. degrees from the University of Southern California (Los Angeles), all in computer science. His research interests include multi-modal data management, conceptual data modeling, social media and Web services, and e-learning systems. He has authored/co-authored over 400 publications in these areas. He is actively involved in the research community and has served as an associate editor of a number of major technical journals including IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Internet Technology (TOIT), Data and Knowledge Engineering (DKE), World Wide Web (WWW), and Journal of Web Engineering, in addition to being a Conference and Program Chair/Co-Chair of numerous major international conferences. He also sits in the Steering Committees of DASFAA, ER, ACM RecSys, IEEE U-MEDIA, and ICWL. Prof. Li is a Fellow of IET (UK), a senior member of IEEE (US) and a distinguished member of CCF (China).
       报告提要： The publicly available data such as the massive and dynamically updated news and social media data streams (a.k.a. big data) covers the various aspects of social activities, personal views and Expressions, which points to the importance of understanding and discovering the
knowledge patterns underlying the big data, and the need of developing methodologies and techniques to discover real-world events from such big data, to manage and to analyze the discovered events in an efficient and elegant way. In this talk we introduce techniques of discovering events from the multi-modal big data and building an event cube model to support event queries and analysis, by addressing the tasks of data cleansing, data fusion, event detection and modeling. Preliminary experimental results on some of the tasks will be reported. We further explore and connect the important events discovered in a multimodal collection of inputs from various public sources, uncover their co-occurrence and track down the spatial and temporal dependency to answer the challenging questions of "how" and "why". A novel event cube (EC) model is devised to support various queries and analysis tasks of events; such events include those discovered by techniques of untargeted event detection (UED) and targeted event detection (TED) from multi-sourced data. More specifically, based on essential event elements of 5W1H, the EC model is developed to organize the discovered events from multiple dimensions, and to operate on the events at various levels of granularity, which facilitates analyzing and mining hidden/inherent relationships among the events effectively.

题目：Meta Paths and Meta Structures: Analysing Large Heterogeneous Information Networks
特邀讲者：Reynold Cheng，香港大学，计算机系副教授
       Dr. Reynold Cheng obtained his MSc and PhD from Department of Computer Science of Purdue University in 2003 and 2005 respectively. He was an Assistant Professor in HKU since 2008-11. He was granted an Outstanding Young Researcher Award 2011-12 by HKU. He is the Chair of the Department Research Postgraduate Committee of HKU. He is an editorial board member of TKDE, DAPD and IS, and was a guest editor for TKDE, DAPD, and Geoinformatica. He is an area chair of ICDE 2017, a senior PC member for DASFAA 2015, PC
co-chair of APWeb 2015, area chair for CIKM 2014, area chair for Encyclopedia of Database Systems, program co-chair of SSTD 2013, and a workshop co-chair of ICDE 2014. He received an Outstanding Service Award in the CIKM 2009 conference.
       报告提要： A heterogeneous information network (HIN) is a graph model in which objects and edges are annotated with types. Large and complex databases, such as YAGO and DBLP, can be modeled as HINs. A fundamental problem in HINs is the computation of closeness, or relevance, between two HIN objects. Relevance measures, such as PCRW, PathSim, and HeteSim, can be used in various applications, including information retrieval, entity resolution, and product recommendation. These metrics are based on the use of meta paths, essentially a sequence of node classes and edge types between two nodes in a HIN. In this tutorial, we will give a detailed review of meta paths, as well as how they are used to define relevance. In a large and complex HIN, retrieving meta paths manually can be complex, expensive, and error-prone. Hence, we will explore systematic methods for finding meta paths. In particular, we will study a solution based on the Query-by-Example (QBE) paradigm, which allows us to discovery meta paths in an effective and efficient manner.
We further generalise the notion of a meta path to "meta structure", which is a directed acyclic graph of object types with edge types connecting them. Meta structure, which is more expressive than the meta path, can describe complex relationship between two HIN objects (e.g., two papers in DBLP share the same authors and topics). We develop three relevance measures based on meta structure. Due to the computational complexity of these measures, we also study an algorithm with data structures proposed to support their evaluation. Finally, we will examine solutions for performing query recommendation based on meta paths. We will also discuss future research directions in HINs.

题目：大规模结构化学习及其高效聚类算法
特邀讲者：聂飞平，西北工业大学教授，深圳大学兼职特聘教授，青年**
        聂飞平，西北工业大学教授，深圳大学兼职特聘教授，中组部青年****。2008年至2009年曾在新加坡南洋理工大学从事研究工作，之后在美国德州大学阿灵顿分校先后担任研究助理教授，研究副教授，研究教授，2015年入选中组部青年****。主要研究兴趣为模式识别与机器学习中的理论和方法设计，并将所设计的方法成功应用于图像分割与标注、多媒体信息理解与检索、生物信息学等领域的实际问题中。已在TPAMI、IJCV、TNNLS、ICML、NIPS、SIGKDD等国际顶尖期刊和会议上发表学术论文百余篇，其中在中国计算机学会（CCF）推荐的A类期刊和会议上发表论文100余篇。据Google Scholar统计，论文总引用为10000余次，H指数为52。常年应邀担任相关领域顶级期刊和会议的审稿专家或程序委员会委员，并同时应邀担任IEEE Transactions on Neural Networks and Learning Systems、Information Science等多个国际一流SCI期刊的编委。
        报告提要：数据聚类是机器学习和数据挖掘研究中的一个基本问题。在数十年的研究中，已经提出了很多聚类方法，而基于图的聚类方法是其中最有效的方法之一。传统的图聚类方法需要用户事先给定一个图，然后采用松弛技巧将问题转化为一个可解的问题。由于一般的图不具有结构性，因此得到的解是连续的，需要利用离散技术得到最终的聚类结果，从而使得聚类结果十分依赖于初始化。针对这些问题，我们提出了一种结构化图学习方法，通过学习一个具有结构的图，使得我们可以直接得到聚类结果，不再依赖于初始化。该新方法具有性能优越，稳定等优点，并且其中的结构化图学习思想可以应用在其他基于图的机器学习方法中，具有很大的应用价值和启发性。

题目：腾讯知识图谱的AI应用
特邀讲者：成杰峰，腾讯专家级研究员，腾讯AI安全负责人
       成杰峰，香港中文大学博士，香港大学博士后。曾任中国科学院副教授，华为诺亚方舟实验室（香港）研究员，现任职腾讯专家研究员、腾讯AI安全负责人。研发了一系列分析大规模巨型图的核心技术。负责研发了华为VENUS图计算系统、腾讯安全AI引擎、腾讯云星图知识图谱的研发。曾主持过包括国家自然科学基金项目、广东省科技重大专项等多项国家、省重点项目；在TKDE、JVLDB、VLDB、ICDE、KDD、CIKM等国际顶刊/会上发表40余篇学术论文，累计引用千余次，产生了20余项国内国际专利。
       报告提要：将介绍腾讯云星图知识图谱在AI方面的一些应用。

相关链接：

http://csse.szu.edu.cn/cn/view?11450