With the availability of powerful computational and communication systems, scientists now readily access large, complicated derived datasets and build on those results to produce, through further processing, yet other derived datasets of interest. The scientific processes used to create such datasets must be clearly documented so that scientists can evaluate their soundness, reproduce the results, and build upon them in responsible and appropriate ways. Here, we present the concept of ananalytic web, which defines the scientific processes employed and details the exact application of those processes in creating derived datasets. The work described here is similar to work often referred to as “scientific workflow,” but emphasizes the need for a semantically rich, rigorously defined process definition language. We illustrate the information that comprises an analytic web for a scientific process that measures and analyzes the flux of water through a forested watershed. This is a complex and demanding scientific process that illustrates the benefits of using a semantically rich, executable language for defining processes and for supporting automatic creation of process provenance metadata.

Process modeling allows for analysis and improvement of processes that coordinate multiple people and tools working together to carry out a task. Process modeling typically focuses on the normative process, that is, how the collaboration transpires when everything goes as desired. Unfortunately, real-world processes rarely proceed that smoothly. A more complete analysis of a process requires that the process model also include details about what to do when exceptional situations arise. We have found that, in many cases, there are abstract patterns that capture the relationship between exception handling tasks and the normative process. Just as object-oriented design patterns facilitate the development, documentation, and maintenance of object-oriented programs, we believe that process patterns can facilitate the development, documentation, and maintenance of process models. In this paper, we focus on the exception handling patterns that we have observed over many years of process modeling. We describe these patterns using three process modeling notations: UML 2.0 Activity Diagrams, BPMN, and Little-JIL. We present both the abstract structure of the pattern as well as examples of the pattern in use. We also provide some preliminary statistical survey data to support the claim that these patterns are found commonly in actual use and discuss the relative merits of the three notations with respect to their ability to represent these patterns.

This paper describes our experiences in exploring the applicability of software engineering approaches to scientific data management problems. Specifically, this paper describes how process definition languages can be used to expedite production of scientific datasets as well as to generate documentation of their provenance. Our approach uses a process definition language that incorporates powerful semantics to encode scientific processes in the form of a Process Definition Graph (PDG). The paper describes how execution of the PDG-defined process can generate Dataset Derivation Graphs (DDGs), metadata that document how the scientific process developed each of its product datasets. The paper uses an example to show that scientific processes may be complex and to illustrate why some of the more powerful semantic features of the process definition language are useful in supporting clarity and conciseness in representing such processes. This work is similar in goals to work generally referred to as Scientific Workflow. The paper demonstrates the contribution that software engineering can make to this domain.

Software is increasingly expected to run in a variety of environments. The environments themselves are often dynamically changing when using mobile computers or embedded systems, for example. Network bandwidth, available power, or other physical conditions may change, necessitating the use of alternative algorithms within the software, and changing resource mixes to support the software. We present Containment Units as a software architecture useful for recognizing environmental changes and dynamically reconfiguring software and resource allocations to adapt to those changes. We present examples of Containment Units used within robotics along with the results of actual executions, and the application of static analysis to obtain assurances that those Containment Units can be expected to demonstrate the robustness for which they were designed.

Little-JIL, a language for programming coordination in processes is an executable, high-level language with a formal (yet graphical) syntax and rigorously defined operational semantics. The central abstraction in Little-JIL is the “step,” which is the focal point for coordination, providing a scoping mechanism for control, data, and exception flow and for agent and resource assignment. Steps are organized into a static hierarchy, but can have a highly dynamic execution structure including the possibility of recursion and concurrency.

Little-JIL, a new language for programming the coordination of agents, is an executable, high-level process programming language with a formal (yet graphical) syntax and rigorously defined operational semantics. Little-JIL is based on two main hypotheses. The first is that the specification of coordination control structures is separable from other process programming language issues. Little-JIL provides a rich set of control structures while relying on separate systems for support in areas such as resource, artifact and agenda management. The second hypothesis is that processes can be executed by agents who know how to perform their tasks but can benefit from coordination support. Accordingly, each step in Little-JIl is assigned to an execution agent (human or automated). These agents are responsible for initiating steps and performing the work associated with them. This approach has so far proven effective in allowing us to clearly and concisely express the agent coordination aspects of a wide variety of software, workflow and other processes.

Although event-based software integration is one of the most prevalent approaches to loose integration, no consistent model for describing it exists. As a result, there is no uniform way to discuss event-based integration, compare approaches and implementations, specify new event-based approaches, or match user requirements with the capabilities of event-based integration systems. We attempt to address these shortcomings by specifying a generic framework for event-based integration , the EBI framework, that provides a flexible, object-oriented model for discussing and comparing event-based integration approaches. The EBI framework can model dynamic and static specification, composition, and decomposition and can be instantiated to describe the features of most common event-based integration approaches. We demonstrate how to use the framework as a reference model by comparing and contrasting three well-known integration systems: FIELD, Polylith, and CORBA.