Provenance in Web Feed Mash-Up Systems

Introduction

A new breed of applications and services that integrates data from several sources which we refer to as mash-ups has grown significantly all across the Internet. Mash-ups are web-based applications that combine data and content from more than one source into an aggregated service presented in a single display. Several types of mash-ups have been developed such as mapping and image mash-ups. One of the most popular mash-ups comes in the form of web feed mash-ups (news mash-ups) relying on syndication technologies (e.g. RSS (RSS Advisory Board, 2016) and Atom (IETF, 2016)). This kind of mash-ups aggregates feeds derived from multiple news websites and then presents them over the web. Visiting many separate websites can take a lot of time to find out if content has been updated. With web feed mash-ups, updated information from many sites can be consolidated into a single view, reducing the time and effort needed to regularly check websites for updates. Furthermore, as information is combined from multiple sites, feed mash-ups are utilized as tools that create personalized and unique information catering to the user's particular interests.

Although using web feeds in mash-ups offers several advantages, there are still some limitations in existing applications. According to the nature of this kind of mash-ups, web feeds aggregated generally come from several diverse sources (e.g. news website, blogs and web services). Furthermore, they are usually executed by a number of transformations or feed processing units in real-time. However, the description of the source of data that are aggregated in mash-ups is not usually apparent. The details pertaining to the feed items and the processing operations involved in the production of feed mash-up results are not often provided as well. Therefore, it is difficult to know exactly how feed results in data mash-ups are generated. In particular, it is difficult for users to make determinations about whether information is trusted or not, considering that source feeds have to pass through several transformations before the feed results are presented.

As a result, it is crucial to ascertain automatically whether or not to trust each individual feed mash-up result by examining the process that created, aggregated, and delivered it. We derive a strong requirement for precisely tracing the sFource feeds that caused a given result feed, which we refer to as “provenance tracking in web feed mash-ups.” The existence of such functionality would allow feed mash-up results to be verified and validated, so that users can have confidence and trust in the results provided by web feed mash-ups.

To address provenance tracking in web feed mash-ups, we propose a novel provenance mechanism that supports dynamic provenance collection and provenance query in web feed mash-ups. We introduce a provenance architecture for web feed mash-ups extending the PASOA provenance architecture (Groth et al., 2006). This provenance architecture applies the service-orientated architecture (SOA) as a core principle. To allow provenance information to be recorded and interchanged with other systems, PROV (Gil & Miles, 2016) - W3C data model for provenance interchange on the web - is utilized as the provenance model for our provenance solution. The use of PROV enables an implement of our provenance architecture to dynamically record provenance assertions - assertions pertaining to provenance recorded by feed processing units during the execution of data mash-ups. Therefore, dynamic provenance tracking in web feed mash-ups can be achieved.

This paper will make the following key contributions:

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  It presents a novel provenance tracking mechanism which can precisely express relationships for every feed mash-up result in a web feed mash-up system.

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  It introduces a provenance architecture for feed mash-ups that describes the structure of the provenance system, system components and interactions between components.

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  It proposes a provenance query algorithm for feed mash-ups which utilizes a graph traversal technique to obtain the provenance of a particular feed mesh-up result.

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  It introduces a provenance storage optimization method for web feed mash-ups

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  It presents the performance characteristics of our provenance solution in terms of the storage consumption for provenance collection.