Web连接器:简化Web数据收集的统一API包装器

IF 2.6 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI:10.14778/3611540.3611616

Weiyuan Wu, Pei Wang, Yi Xie, Yejia Liu, George Chow, Jiannan Wang

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引用次数: 0

摘要

从Web API(如Twitter API、Yelp Fusion API、Spotify API和DBLP API)收集结构化数据是数据科学生命周期中的一项常见任务，但它需要数据科学家具备高级编程技能。为了简化web数据收集并降低进入门槛，开发了API包装器，将API调用包装成易于使用的函数。然而，现有的API包装器没有标准化，这意味着用户必须下载和维护多个API包装器，并学习如何使用它们，而开发人员必须花费大量时间为任何新网站创建API包装器。在这个演示中，我们展示了Web Connector，它统一了API包装器来克服这些限制。首先，Web Connector具有易于使用的编程接口，旨在提供类似于从关系数据库读取数据的用户体验。其次，Web Connector新颖的系统架构只需要最小的努力，就可以使用现有的API描述文件为最终用户获取数据。第三，Web Connector包含一个半自动API描述文件生成器，它利用示例生成的概念来创建新的API包装器，而无需编写代码。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Web Connector: A Unified API Wrapper to Simplify Web Data Collection

Collecting structured data from Web APIs, such as the Twitter API, Yelp Fusion API, Spotify API, and DBLP API, is a common task in the data science lifecycle, but it requires advanced programming skills for data scientists. To simplify web data collection and lower the barrier to entry, API wrappers have been developed to wrap API calls into easy-to-use functions. However, existing API wrappers are not standardized, which means that users must download and maintain multiple API wrappers and learn how to use each of them, while developers must spend considerable time creating an API wrapper for any new website. In this demo, we present the Web Connector, which unifies API wrappers to overcome these limitations. First, the Web Connector has an easy-to-use program-ming interface, designed to provide a user experience similar to that of reading data from relational databases. Second, the Web Connector's novel system architecture requires minimal effort to fetch data for end-users with an existing API description file. Third, the Web Connector includes a semi-automatic API description file generator that leverages the concept of generation by example to create new API wrappers without writing code.

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来源期刊

Proceedings of the Vldb Endowment Computer Science-General Computer Science

CiteScore

7.70

自引率

0.00%

发文量

期刊介绍： The Proceedings of the VLDB (PVLDB) welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission’s topic of the journal’s editorial board. Finally, the submission’s contributions should build on work already published in data management outlets, e.g., PVLDB, VLDBJ, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation.