8,700 research outputs found
Web Data Extraction, Applications and Techniques: A Survey
Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of applications. Many
approaches to extracting data from the Web have been designed to solve specific
problems and operate in ad-hoc domains. Other approaches, instead, heavily
reuse techniques and algorithms developed in the field of Information
Extraction.
This survey aims at providing a structured and comprehensive overview of the
literature in the field of Web Data Extraction. We provided a simple
classification framework in which existing Web Data Extraction applications are
grouped into two main classes, namely applications at the Enterprise level and
at the Social Web level. At the Enterprise level, Web Data Extraction
techniques emerge as a key tool to perform data analysis in Business and
Competitive Intelligence systems as well as for business process
re-engineering. At the Social Web level, Web Data Extraction techniques allow
to gather a large amount of structured data continuously generated and
disseminated by Web 2.0, Social Media and Online Social Network users and this
offers unprecedented opportunities to analyze human behavior at a very large
scale. We discuss also the potential of cross-fertilization, i.e., on the
possibility of re-using Web Data Extraction techniques originally designed to
work in a given domain, in other domains.Comment: Knowledge-based System
Inductive Verification of Data Model Invariants for Web Applications ∗
Modern software applications store their data in remote cloud servers. Users interact with these applications using web browsers or thin clients running on mobile devices. A key issue in dependability of these applications is the correctness of the actions that update the data store, which are triggered by user requests. In this paper, we present techniques for automatically checking if the actions of an application preserve the data model invariants. Our approach first automatically data store, from a given application using instrumented execution. The abstract data store identifies the sets of objects and relations (associations) used by the application, and the actions that update the data store by deleting or creating objects or by changing the relations among the objects. We show that checking invariants of an abstract data store corresponds to inductive invariant verification, and can be done using a mapping to First Order Logic (FOL) and using a FOL theorem prover. We implemented this approach for the Rails framework and applied it to three open source applications. We found four previously unknown bugs and reported them to the developers, who confirmed and immediately fixed two of them
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