Scientific workflow has recently become an enabling technology to automate and speed up the scientific discovery process. Although several scientific workflow management systems (SWFMSs) have been developed, a formal scientific workflow composition model in which workflow constructs are fully compositional one with another is still missing. In this paper, we propose a dataflow-based scientific workflow composition framework consisting of (1) a dataflow-based scientific workflow model that separates the declaration of the workflow interface from the definition of its functional body; (2) a set of workflow constructs, including Map, Reduce, Tree, Loop, Conditional, and Curry, which are fully compositional one with another; (3) a dataflow-based exception handling approach to support hierarchical exception propagation and user-defined exception handling. Our workflow composition framework is unique in that workflows are the only operands for composition; in this way, our approach elegantly solves the two-world problem in existing composition frameworks, in which composition needs to deal with both the world of tasks and the world of workflows. The proposed framework is implemented and several case studies are conducted to validate our techniques.

Modern scientific computations are usually data intensive, involving large-scale, heterogeneous and structured scientific datasets. Modeling, organizing, and processing scientific data have become key challenges for scientific workflow management systems (SWFMSs). In contrast to business data, which is usually relational and stored in databases, scientific data is often hierarchically organized and collection oriented. Although several data models have been proposed for SWFMSs, none of them provides a formal data model with a set of well-defined operators. In this paper, we take a first step towards formalizing a collection-oriented data model, called collectional data model, to model hierarchical collection oriented scientific data, and a set of well-defined operators to manipulate and query such data. We then apply the collectional data model to VIEW, a dataflow-based scientific workflow composition framework, whose workflow constructs are extended to support collections. We implement our techniques and validate them by a case study in a biological simulation project.

MapReduce has recently gained a lot of attention as a parallel programming model for scalable data-intensive business and scientific analysis. In order to benefit from this powerful programming model in a scientific workflow environment, we propose a MapReduce-enabled scientific workflow composition framework consisting of: i) a dataflow based scientific workflow model that separates the declaration of the workflow interface from the definition of its functional body; ii) a set of dataflow constructs, including Map, Reduce, Loop, and Conditional, and their composition semantics to enable MapReduce-style scientific workflows; iii) an XML-based scientific workflow specification language, called WSL, in which both Map and Reduce are fully composable with other dataflow constructs in both flat and hierarchical manners. Besides leveraging the power of MapReduce to the workflow level, our workflow composition framework is unique in that workflows are the only operands for composition; in this way, our approach elegantly solves the two-world problem of existing composition frameworks, in which composition needs to deal with both the world of tasks and the world of workflows. The proposed framework is implemented and a case study is conducted to validate our techniques.

Scientific workflows have recently emerged as a new paradigm for scientists to formalize and structure complex and distributed scientific processes to enable and accelerate many scientific discoveries. In contrast to business workflows, which are typically control flow oriented, scientific workflows tend to be dataflow oriented, introducing a new set of requirements for system development. These requirements demand a new architectural design for scientific workflow management systems (SWFMSs). Although several SWFMSs have been developed that provide much experience for future research and development, a study from an architectural perspective is still missing. The main contributions of this paper are: 1) based on a comprehensive survey of the literature and identification of key requirements for SWFMSs, we propose the first reference architecture for SWFMSs; 2) according to the reference architecture, we further propose a service-oriented architecture for View (a VIsual sciEntific Workflow management system); 3) we implemented View to validate the feasibility of the proposed architectures; and 4) we present a View-based scientific workflow application system (SWFAS), called FiberFlow, to showcase the application of our View system.

Provenance management has become increasingly important to support scientific discovery reproducibility, result interpretation, and problem diagnosis in scientific workflow environments. This paper proposes an approach to provenance management that seamlessly integrates the interoperability, extensibility, and reasoning advantages of semantic Web technologies with the storage and querying power of an RDBMS. Specifically, we propose: i) two schema mapping algorithms to map an arbitrary OWL provenance ontology to a relational database schema that is optimized for common provenance queries; ii) two efficient data mapping algorithms to map provenance RDF metadata to relational data according to the generated relational database schema, and iii) a schema-independent SPARQL-to-SQL translation algorithm that is optimized on-the-fly by using the type information of an instance available from the input provenance ontology and the statistics of the sizes of the tables in the database. Experimental results are presented to show that our algorithms are efficient and scalable.