They are often preferred in web data extraction because they are easy to prepare and have short expressions. Furthermore, FSM wrappers can work as a filter to reduce the number of training pages and advance the learning curve for wrapper generation. 
We discuss research challenges for extending our approach to a general method applicable to a yet larger number of cases.ĭiv>Web data extraction is a key component in many business intelligence tasks, such as data transformation, exchange, and analysis. This system works in the vast majority of test cases and produces very fast and extremely resource-efficient wrappers. We present the first algorithm and system performing such an automated translation on suitably restricted types of web sites. In this paper, we demonstrate the principal feasibility of automatically translating browser-based wrappers into "browserless" wrappers. However, creating and maintaining browserless wrappers of high precision requires specialists, and is prohibitively labor-intensive at scale.
In contrast, it is magnitudes more resource-efficient to use a "browserless" wrapper which directly accesses a web server through HTTP requests, and takes the desired data directly from the raw replies.
Such scrapers (or wrappers) are therefore expensive to execute, in terms of time and network traffic. Most modern web scrapers use an embedded browser to render web pages and to simulate user actions.
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