Agent-Oriented Data Curation in Bioinformatics

The practitioners of bioinformatics require increasing sophistication from their software tools to take into account the particular characteristics that make their domain complex. For example, there is a great variation of experience of researchers, from novices who would like guidance from experts in the best resources to use to experts that wish to take greater management control of the tools used in their experiments. Also, the range of available, and conflicting, data formats is growing and there is a desire to automate the many trivial manual stages of in-silico experiments. Agent-oriented software development is one approach to tackling the design of complex applications. In this paper, we argue that, in fact, agent-oriented development is a particularly well-suited approach to developing bioinformatics tools that take into account the wider domain characteristics. To illustrate this, we design a data curation tool, which manages the format of experimental data, extend it to better account for the extra requirements placed by the domain characteristics, and show how the characteristics lead to a system well suited to an agent-oriented view.

Agent-Oriented Data Curation in Bioinformatics

The practitioners of bioinformatics require increasing sophistication from their software tools to take into account the particular characteristics that make their domain complex. For example, there is a great variation of experience of researchers, from novices who would like guidance from experts in the best resources to use to experts that wish to take greater management control of the tools used in their experiments. Also, the range of available, and conflicting, data formats is growing and there is a desire to automate the many trivial manual stages of in-silico experiments. Agent-oriented software development is one approach to tackling the design of complex applications. In this paper, we argue that, in fact, agent-oriented development is a particularly well-suited approach to developing bioinformatics tools that take into account the wider domain characteristics. To illustrate this, we design a data curation tool, which manages the format of experimental data, extend it to better account for the extra requirements placed by the domain characteristics, and show how the characteristics lead to a system well suited to an agent-oriented view.

Modelling the Provenance of Data in Autonomous Systems

Determining the provenance of data, i.e. the process that led to that data, is vital in many disciplines. For example, in science, the process that produced a given result must be demonstrably rigorous for the result to be deemed reliable. A provenance system supports applications in recording adequate documentation about process executions to answer queries regarding provenance, and provides functionality to perform those queries. Several provenance systems are being developed, but all focus on systems in which the components are textitreactive, for example Web Services that act on the basis of a request, job submission system, etc. This limitation means that questions regarding the motives of autonomous actors, or textitagents, in such systems remain unanswerable in the general case. Such questions include: who was ultimately responsible for a given effect, what was their reason for initiating the process and does the effect of a process match what was intended to occur by those initiating the process? In this paper, we address this limitation by integrating two solutions: a generic, re-usable framework for representing the provenance of data in service-oriented architectures and a model for describing the goal-oriented delegation and engagement of agents in multi-agent systems. Using these solutions, we present algorithms to answer common questions regarding responsibility and success of a process and evaluate the approach with a simulated healthcare example.

Provenance: The Bridge Between Experiments and Data

Current scientific applications are often structured as workflows and rely on workflow systems to compile abstract experiment designs into enactable workflows that utilise the best available resources. The automation of this step and of the workflow enactment, hides the details of how results have been produced. Knowing how compilation and enactment occurred allows results to be reconnected with the experiment design. We investigate how provenance helps scientists to connect their results with the actual execution that took place, their original experiment and its inputs and parameters.

PrIMe: A Methodology for Developing Provenance-Aware Applications

Provenance refers to the past processes that brought about a given (version of an) object, item or entity. By knowing the provenance of data, users can often better understand, trust, reproduce, and validate it. A provenance-aware application has the functionality to answer questions regarding the provenance of the data it produces, by using documentation of past processes. PrIMe is a software engineering technique for adapting application designs to enable them to interact with a provenance middleware layer, thereby making them provenance-aware. In this article, we specify the steps involved in applying PrIMe, analyse its effectiveness, and illustrate its use with two case studies, in bioinformatics and medicine.