报 告 人：窦万春 教授（南京大学）
报告题目2：Finding All You Need: Web APIs Recommendation in Web of Things through Keywords Search
报 告 人：齐连永 教授（曲阜师网投彩票手机版范大学）
报告题目3：Collaborative Quantification and Placement of Edge Servers for Internet of Vehicles
报 告 人：许小龙 助理教授（南京信息工程大学）
报告摘要2：The 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.
报告摘要3：Facing 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.