A survey on semi-structured web data manipulations by non-expert users

Abstract

Since the emergence of web 2.0, data started floating all over the web, through online and offline applications, and across all application domains. Web data (semi-structured data loaded through web browsers and applications communicating via internet protocols such as HTTP), in particular XML-based data, is being used for simple commercial information display (i.e., XHTML/HTML in commercial websites), instant messaging (e.g., XMPP for messaging in Whatsapp, Skype, Gtalk etc.), financial transactions (i.e., CDF3 in ecommerce), medical record processing and storage (e.g., HL7 for electronic medical records), social media (e.g., XHTML/HTML in facebook, LinkedIn, Google Plus, etc.), and others. This phenomenon rendered web data manipulation (i.e., monitoring, modifying, controlling, etc.) by IT (information technology) experts, computer technicians and engineers utterly difficult seeing its exponential growth rate in volume and diversity. Not to mention the dynamicity of the data which is continuously changing on the clock and its heterogeneity (e.g., HTML/HTML5, XML, XHTML, RDF, OWL, etc.).

Consequently, the manipulation of web data and in particular XML data (since XML has become one of the most essential data types used in computer communications) has shifted from the hands of computer scientists and programmers towards public computer users in all application domains.

This has brought a new criterion into the web data manipulation research field, web data manipulation by non-experts. In this paper, we study and analyze existent techniques for manipulating semi-structured web data, particularly XML data, from a non-expert point of view while relating it to traditional manipulation techniques defined in the literature (i.e., filtering, adaptation, data extraction, transformation, access control, encryption, etc.). Web data manipulation techniques by non-experts were categorized under 3 major titles: (i) XML-oriented visual languages dealing with XML data extraction and transformations, (ii) Mashups tackling mainly XML restructuring with value manipulations, and (iii) Dataflow visual programming languages targeting non-expert manipulations and providing means to visually manipulate scientific data. A full analysis was conducted which allowed existent approaches/techniques to be compared and evaluated providing an overview of the current requirements on this subject.