Discovering Land Cover Web Map Services from the Deep Web with JavaScript Invocation Rules

Abstract

Automatic discovery of isolated land cover web map services (LCWMSs) can potentially help in sharing land cover data. Currently, various search engine-based and crawler-based approaches have been developed for finding services dispersed throughout the surface web. In fact, with the prevalence of geospatial web applications, a considerable number of LCWMSs are hidden in JavaScript code, which belongs to the deep web. However, discovering LCWMSs from JavaScript code remains an open challenge. This paper aims to solve this challenge by proposing a focused deep web crawler for finding more LCWMSs from deep web JavaScript code and the surface web. First, the names of a group of JavaScript links are abstracted as initial judgements. Through name matching, these judgements are utilized to judge whether or not the fetched webpages contain predefined JavaScript links that may prompt JavaScript code to invoke WMSs. Secondly, some JavaScript invocation functions and URL formats for WMS are summarized as JavaScript invocation rules from prior knowledge of how WMSs are employed and coded in JavaScript. These invocation rules are used to identify the JavaScript code for extracting candidate WMSs through rule matching. The above two operations are incorporated into a traditional focused crawling strategy situated between the tasks of fetching webpages and parsing webpages. Thirdly, LCWMSs are selected by matching services with a set of land cover keywords. Moreover, a search engine for LCWMSs is implemented that uses the focused deep web crawler to retrieve and integrate the LCWMSs it discovers. In the first experiment, eight online geospatial web applications serve as seed URLs (Uniform Resource Locators) and crawling scopes; the proposed crawler addresses only the JavaScript code in these eight applications. All 32 available WMSs hidden in JavaScript code were found using the proposed crawler, while not one WMS was discovered through the focused crawler-based approach. This result shows that the proposed crawler has the ability to discover WMSs hidden in JavaScript code. The second experiment uses 4842 seed URLs updated daily. The crawler found a total of 17,874 available WMSs, of which 11,901 were LCWMSs. Our approach discovered a greater number of services than those found using previous approaches. It indicates that the proposed crawler has a large advantage in discovering LCWMSs from the surface web and from JavaScript code. Furthermore, a simple case study demonstrates that the designed LCWMS search engine represents an important step towards realizing land cover information integration for global mapping and monitoring purposes.

Geographic Information Metadata—An Outlook from the International Standardization Perspective

“…Researchers have proposed various ways of overcoming this challenge. One approach is to enhance web crawler functionality so that it can discover geospatial metadata in geoportals [55,56] or enhance a search engine's capabilities for assessing the fitness of use of geospatial datasets [57]. Another approach is to include geospatial metadata in HTML pages, which are readily accessible by web crawlers [58,59].…”

Employing Search Engine Optimization (SEO) Techniques for Improving the Discovery of Geospatial Resources on the Web

“…In contrast, others have proposed and developed special-purpose web crawlers that discover and understand geospatial resources in a geoportal [6,7], but this approach does not expose the resources for mainstream information retrieval. The research also contributes to online information retrieval generally by demonstrating the application of SEO techniques in the geospatial domain.…”

“…Such efforts are focused on enhancing the web crawler's capabilities to discover and understand geospatial resources in a geoportal [6,7]. They do not exploit the ability of such resources to make themselves visible to web search engines.…”

Bolegweb Platform – Contribution to the Web Communities

“…An ability to automatically generate standardized metadata from the content of a harvested data-publishing server would significantly facilitate maintenance and management of the description of large volumes of data as reported in previous research works (Florczyk et al, 2012;Kalantari et al, 2016;Giuliani et al, 2016). In addition, many research activities (Abargues et al, 2009;López-Pellicer et al, 2011;Kliment et al, 2013;Hou et al, 2016) have reported that the mainstream web provides significant number of valuable GI resources of several types (e.g. OGC services, KML data, etc.).…”