An Architectural Approach to End User Orchestrations

Abstract

Abstract.Computations are pervasive across many domains, where end users have to compose various heterogeneous computational entities to perform professional activities. Service-Oriented Architecture (SOA) is a widely used mechanism that can support such forms of compositions as it allows heterogeneous systems to be wrapped as services that can then be combined with each other. However, current SOA orchestration languages require writing scripts that are typically too low-level for end users to write, being targeted at professional programmers and business analysts. To address this problem, this paper proposes a composition approach based on an end user specification style called SCORE. SCORE is an architectural style that uses high-level constructs that can be tailored for different domains and automatically translated into executable constructs by tool support. We demonstrate the use of SCORE in two domainsdynamic network analysis and neuroscience, where users are intelligence analysts and neuroscientists respectively, who use the architectural style based vocabulary in SCORE as a basis of their domain-specific compositions that can be formally analyzed.

Resolving Data Mismatches in End-User Compositions

“…The FSL (Sub)Style, shown on the left-hand side of the figure, includes specializations of service components that provide the functionality of some of the tools offered by the FSL neuroscience suite. 10 In previous work we have also demonstrated the refinement of SCORE for the dynamic network analysis domain [8]. Figure 2 shows some of the components in the resulting substyles, i.e., Dynamic Network Analysis and SORASCS.…”

“…Often end users have to compose computational entities that have conflicting assumptions about the data interchanged among them (as shown in Table 1). 8 That is, it is common for their inputs and outputs to be incompatible with those of the other computational entities with which they must be composed. This claim is supported by recent studies that have shown that about 30% of the services in scientific workflows are data conversion services [28].…”

“…These three phases use (i) architectural descriptions for components and compositions to automatically detect different types of data mismatches, (ii) model-generation techniques to support the automatic generation of repair alternatives, and (iii) utility theory to automatically check for satisfaction of multiple QoS constraints in repair alternatives. Note that it is not the end users who create such architectural descriptions; such descriptions already exist and are created by component developers and domain experts through means like SCORE [8] and SCUFL (from Taverna) [11]. We build on our previous work on the SCORE architectural style, which provides a generic modeling vocabulary for the specification of data-flow oriented workflows that comprises the following elements: component types -which represent the primary computational elements, connector types -which represent interactions among components, properties -which represent semantic information about the components and connectors, and constraints -which represent restrictions on the definition and usage of components or connectors, e.g., allowable values of properties and topological restrictions.…”

“…4 End users in these communities often compose computational entities to automate their tasks and in silico 5 experiments. This requires them to work within their domain-specific styles of construction, following the constraints of their domain [8]. They often treat their computations and tools as black boxes, that can be reused across various tasks.…”

Model-Based Assistance for Making Time/Fidelity Trade-Offs in Component Compositions

“…Such architectures form the basis of composition and fidelity analysis. For instance, here we represent end-user compositions in SCORE [16] -a dataflow based style that is customized for the intelligence analysis domain.…”