Context of processes : achieving thorough documentation in provenance systems through context awareness

Abstract

To fully understand real world processes, having evidence which is as comprehensive as possible is essential. Comprehensive evidence enables the reviewer to have some confidence that they are aware of the nuances of a past scenario and can act appropriately upon them in the future. There are examples of this throughout everyday life the outcome of a court case could be affected by available evidence or an antique could be considered more valuable if certain facts about its history are known. Similarly, in computer systems, evidence of processes allow users to make more informed decisions than if it were not captured. Where computer based experimentation has enabled scientists to perform complicated experiments quickly with ease, understanding the precise circumstances of the process which created a particular set of results is important. Significant recent research has sought to address the problem of understanding the provenance of an data item—the process which led to that data item. Increasingly, these experiments are being performed using systems which are distributed, large scale and open. Comprehensive evidence in these environments is achieved when both documentation of the actions per formed and the circumstances in which they occur are captured. Therefore, in order for a user to achieve confidence in results, we argue the importance of documenting the context of a process. This thesis addresses the problem of how context may be suitably modeled, captured and queried to later answer questions concerning data origin. We begin by defining context as any information describing a scenario which has some bearing on a process's outcome. Based on a number of use cases from a Functional Magnetic Resonance Imaging (fMRI) workflow, we present a model for representation of context. Our model treats each actor in a process as capable of progressing over a number of finite states as they perform actions. We show that each state can be encoded by using a set of monitored variables from an actor's host. Each transition between states therefore is a series of variable changes and this model is shown to be capable of measuring similarity of context when comparing multiple executions of the same process. It also allows us to consider future state changes for actors based on their past execution. We evaluate through the use of our own context capture system which allows common monitoring tools to be used as an indication of state change and recording of context transparently from stake holders. Our experimental findings suggest our approach to both be acceptable in terms of performance (with an overhead of 4–8% against a non context capturing approach) and use case satisfaction.EThOS - Electronic Theses Online ServiceGBUnited Kingdo