服務計算與大數據系列學術報告會

發布時間:2020-01-09

報告題目1:大數據時代的人工智能

報 告 人:竇萬春  教授(南京大學)

報告時間:2020111 9:00-9:50

報告地點:理工D318

 

報告題目2Finding All You Need: Web APIs Recommendation in Web of Things through Keywords Search

報 告 人:齊連永 教授(曲阜師范大學)

報告時間:20201119:50-10:40

報告地點:理工D318

 

報告題目3Collaborative Quantification and Placement of Edge Servers for Internet of Vehicles

報 告 人:許小龍 助理教授(南京信息工程大學)

報告時間:202011110:40-11:30

報告地點:理工D318

 

科學技術處

202019

 

報告摘要1過去十余年,計算機領域中以“云、物、移、大、智”為代表的各種新技術迅猛發展。如何從技術體系的角度,理解這些技術內在的關聯,進而從技術衍變的角度實現上述單項技術的應用集成,直接影響到復雜系統的應用開發。本報告從大數據和人工智能出發,在回顧計算機領域重大研究主題的基礎上,給出了一個集成思路,并對未來的發展提出了自己的見解。

 

報告摘要2The increasing number of web APIs registered in various service sharing communities (e.g., ProgrammableWeb) has provided a promising way to quickly build various apps with diverse functions. Generally, an app developer can manually discover, select and compose a set of appropriate web APIs to build a new app satisfying the developer's functional and non-functional business requirements, economically and conveniently. However, the above manual web APIs selection process is usually time-consuming and cumbersome as most app developers often do not have much background knowledge of candidate web APIs. Moreover, the manually selected web APIs cannot always guarantee to be integrated successfully as the compatibilities between different web APIs are often varied and not validated. In view of these challenges, we define a weighted APIs correlation graph (W-ACG) in this paper to model the APIs functions and compatibilities. Furthermore, we propose a novel web APIs recommendation approach named K-CAR (Keywords-based and Compatibility-aware APIs Recommendation) based on the defined W-ACG. Through analyzing the input keywords describing the functions expected by an app developer, K-CAR can return the app developer a set of optimal web APIs that are not only functional-qualified but also compatibility-guaranteed. Extensive experiments are deployed on 18,478 real-world web APIs and 6,146 real-world apps to evaluate the usefulness and efficiency of K-CAR.

 

報告摘要3Facing serious challenges in bandwidth and latency, currently adopted cloud computing is no longer effective for performing the real-time tasks from Internet of Vehicles (IoV) in the smart cities. An emerging computing paradigm, i.e., edge computing, is proposed to complement cloud computing by offloading the tasks to the edge of the network. Generally, the task offloading is implemented based on the premise that edge servers (ESs) are appropriately quantified and located. However, the quantification of the ESs is often offered according to the empirical knowledge, lacking analysis on the real traffic condition in IoV. Thus, the quantity and locations of the ESs need to be thoroughly discussed ahead, otherwise additional latency and network congestion will occur. In this talk, I will address the abovementioned problem, and show a designed collaborative method for the quantification and placement of the ESs in IoV.

 


返回原圖
/

卧龙吟赚钱技巧 江西时时彩几点 明利配资 北京时时彩开奖官网 云南11选5真准网最大遗漏 安徽11选五前三直选遗漏 广东快乐十分开奖结果走势图 重庆幸运农场开奖官网 湖北福彩30选5开奖详情 配资公司怎么赚钱 运输龙头股票代码 加拿大快乐8晚上几点开奖 郑州期货配资网站 如何计算股票涨跌幅 福建22选5开奖走势图 上海十一选五基本走势图表 股票投资软件顶级杨方配资