CHORUS - Top-k Composition of Browsing Scripts for efficient social-enabled Usage of Web-based Services

Web提供了对数十亿个网站的大量信息和功能的访问，这些网站是高度分布的，并由各自的提供商自主维护。对于日益复杂的用例，终端用户需要从属于不同网站的各种网页中提取、组合和聚合信息。通常需要的信息位于网络深处，也就是说，只有在与网站进行一定的交互之后才能访问它。此外，当这种复杂的任务反复出现时，它甚至会消耗更多的时间。目前的技术和研究状况对最终用户有效地从各个网站汇编所需的信息提供的支持很少。搜索引擎通常专注于推荐用户可能或应该访问的网站，而不是回答用户的整体信息需求。语义网和关联数据等方法对数据采取静态视图，并依赖于提供者之间的合作。最初为测试网站开发的Web自动化脚本，允许最终用户捕获他们的浏览活动作为可执行的过程，并与其他最终用户共享它们。这种技术非常有希望将最终用户带入面向提供者的web。然而，为了充分发挥最终用户脚本的潜力，特别是可重用性特性，需要有效的脚本搜索和组合技术。在这个项目中，我们将开发浏览脚本top-k组合的方法。我们的方法将基于脚本的形式语义、它们的功能和非功能属性、用户偏好和结构化查询，同时仍然支持语法异构以实现互操作性。特别是，我们将开发根据用户偏好对脚本和脚本组合进行排序的方法，以及脚本的检索和组合，以满足结构化查询中指定的需求。现有的排名机制侧重于精确计算排名，因此必须在表达性上妥协，以获得可接受的性能，与之相反，我们将允许通过模糊规则来指定模糊偏好。模糊规则比现有的偏好语言具有更强的表达能力，并且存在有效的模糊规则推理方法。我们将研究是否以及如何从组件脚本的排名中以组合方式计算脚本组合的排名。为了有效地检索给定查询的匹配脚本，我们将通过适当的索引增强naïve模型检查技术。为了实现高效的脚本构图，我们将开发基于平面空间规划的方法。合成方法将利用检索方法。在这两种方法中，我们的目标都是只计算top-k而不是完整的答案列表。

The Web provides access to a huge amount of information and functionality with billions of websites that are highly distributed and maintained autonomously by their respective providers. For increasingly sophisticated use cases an end user needs to extract, combine, and aggregate information from various web pages belonging to different websites. Often required information lies deep in the web, that is, it is accessible only after performing certain interactions with the web site. Furthermore, it is even more time consuming when such complex tasks are recurring. Current technologies and state of research offer little support for end users in efficiently compiling together the required information from various web sites. Search engines usually focus on recommending websites that a user may or should visit, but not on answering the overall information need of the user. Approaches such as Semantic Web and Linked Data take a static view on the data and rely on cooperation of providers. Web automation scripts, initially developed for test websites, allow end users to capture their browsing activities as executable processes and share them with other end users. Such a technique is very promising for bringing end users into a provider oriented web. However, in order to benefit the full potential of end user scripts, especially the reusability feature, efficient script search and composition techniques are required.In this project we will develop methods for top-k composition of browsing scripts. Our methods will be based on formal semantics of the scripts, their functional and non-functional properties, user preferences and structured queries while still supporting syntactic heterogeneity for the purpose of interoperability. In particular, we will develop methods for ranking scripts and script compositions according to user preferences as well as retrieval and composition of scripts to full the requirement specified in a structured query. In contrast to existing ranking mechanisms that focus on precise computation of a rank and therefore have to compromise on the expressivity to still an acceptable performance, we will allow specification of vague preference by means of fuzzy rules. Fuzzy rules are more expressive than the existing preference languages and there exist efficient inference methods for fuzzy rules. We will investigate whether and how the rank of a script composition can be computed in a compositional fashion from the ranks of the component scripts. In order to retrieve matching scripts for a given query efficiently, we will augment naïve model-checking techniques by appropriate indexes. In order to achieve efficient script composition, we will develop methods based on Plan Space Planning. The composition method will make use of the retrieval method. In both the methods we aim at computing only top-k instead of complete list of answers.