Dereferencing a URI returns a representation of the current state of the resource identified by that URI. But, on the Web representations of prior states of a resource are also available, for example, as resource versions in Content Management Systems or archival resources in Web Archives such as the Internet Archive. This paper introduces a resource versioning mechanism that is fully based on HTTP and uses datetime as a global version indicator. The approach allows "follow your nose" style navigation both from the current time-generic resource to associated time-specific version resources as well as among version resources. The proposed versioning mechanism is congruent with the Architecture of the World Wide Web, and is based on the Memento framework that extends HTTP with transparent content negotiation in the datetime dimension. The paper shows how the versioning approach applies to Linked Data, and by means of a demonstrator built for DBpedia, it also illustrates how it can be used to conduct a time-series analysis across versions of Linked Data descriptions.
There is a relationship between the described work and efforts that research the problem of provenance of Linked Data, specifically those provenance aspects concerned with the time intervals in which specific data is valid. For example, @cite_7 , is concerned with provenance graphs that allow expressing such validity information, whereas @cite_8 focuses on applications to support preserving link integrity over time. Our proposal introduces a native HTTP approach that allows leveraging the results of these efforts at Web scale.
@cite_7 @cite_8
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The openness of the Web and the ease to combine linked data from dierent sources creates new challenges. Systems that consume linked data must evaluate quality and trustworthiness of the data. A common approach for data quality assessment is the analysis of provenance information. For this reason, this paper discusses provenance of data on the Web and proposes a suitable provenance model. While traditional provenance research usually addresses the creation of data, our provenance model also represents data access, a dimension of provenance that is particularly relevant in the context of Web data. Based on our model we identify options to obtain provenance information and we raise open questions concerning the publication of provenance-related metadata for linked data on the Web. As increasing amounts of valuable information are produced and persist digitally, the ability to determine the origin of data becomes important. In science, medicine, commerce, and government, data provenance tracking is essential for rights protection, regulatory compliance, management of intelligence and medical data, and authentication of information as it flows through workplace tasks. In this paper, we show how to provide strong integrity and confidentiality assurances for data provenance information. We describe our provenance-aware system prototype that implements provenance tracking of data writes at the application layer, which makes it extremely easy to deploy. We present empirical results that show that, for typical real-life workloads, the run-time overhead of our approach to recording provenance with confidentiality and integrity guarantees ranges from 1 -13 . A major challenge in data-driven biomedical research lies in the collection and representation of data provenance information to ensure that findings are reproducibile. In order to communicate and reproduce multi-step analysis workflows executed on datasets that contain data for dozens or hundreds of samples, it is crucial to be able to visualize the provenance graph at different levels of aggregation. Most existing approaches are based on node-link diagrams, which do not scale to the complexity of typical data provenance graphs. In our proposed approach, we reduce the complexity of the graph using hierarchical and motif-based aggregation. Based on user action and graph attributes, a modular degree-of-interest (DoI) function is applied to expand parts of the graph that are relevant to the user. This interest-driven adaptive approach to provenance visualization allows users to review and communicate complex multi-step analyses, which can be based on hundreds of files that are processed by numerous workflows. We have integrated our approach into an analysis platform that captures extensive data provenance information, and demonstrate its effectiveness by means of a biomedical usage scenario. Provenance has been studied extensively in both database and workflow management systems, so far with little convergence of definitions or models. Provenance in databases has generally been defined for relational or complex object data, by propagating fine-grained annotations or algebraic expressions from the input to the output. This kind of provenance has been found useful in other areas of computer science: annotation databases, probabilistic databases, schema and data integration, etc. In contrast, workflow provenance aims to capture a complete description of evaluation - or enactment - of a workflow, and this is crucial to verification in scientific computation. Workflows and their provenance are often presented using graphical notation, making them easy to visualize but complicating the formal semantics that relates their run-time behavior with their provenance records. We bridge this gap by extending a previously-developed dataflow language which supports both database-style querying and workflow-style batch processing steps to produce a workflow-style provenance graph that can be explicitly queried. We define and describe the model through examples, present queries that extract other forms of provenance, and give an executable definition of the graph semantics of dataflow expressions.