When composing Web services into scientific workflows, users often face the so-called shimming problem when connecting two related but incompatible components. The problem is addressed by inserting a special kind of adaptors, called shims, that perform appropriate data transformations to resolve data type inconsistencies. However, existing shimming techniques provide limited automation and burden users with having to define ontological mappings, generate data transformations, and even manually write shimming code. In addition, these approaches insert many visible shims that clutter workflow design and distract user's attention from functional components of the workflow. To address these issues, we 1) reduce the shimming problem to a runtime coercion problem in the theory of type systems, 2) propose a scientific workflow model and define the notion of well-typed workflows, 3) develop an algorithm to typecheck workflows, 4) design a function that inserts “invisible shims”, or runtime coercions into workflows, thereby solving the shimming problem for any well-typed workflow, 5) implement our automated shimming technique, including all the proposed algorithms, lambda calculus, type system, and translation functions in our VIEW system and present two case studies to validate our approach.

Provenance, which records the history of an in-silico experiment, has been identified as an important requirement for scientific workflows to support scientific discovery reproducibility, result interpretation, and problem diagnosis. Large provenance datasets are composed of many smaller provenance graphs, each of which corresponds to a single workflow execution. In this work, we explore and address the challenge of efficient and scalable storage and querying of large collections of provenance graphs serialized as RDF graphs in an Apache HBase database. Specifically, we propose: (i) novel storage and indexing techniques for RDF data in HBase that are better suited for provenance datasets rather than generic RDF graphs and (ii) novel SPARQL query evaluation algorithms that solely rely on indices to compute expensive join operations, make use of numeric values that represent triple positions rather than actual triples, and eliminate the need for intermediate data transfers over a network. The empirical evaluation of our algorithms using provenance datasets and queries of the University of Texas Provenance Benchmark confirms that our approach is efficient and scalable.

A crucial challenge for scientific workflow management systems is to support the efficient and scalable storage and querying of large provenance datasets that record the history of in silico experiments. As new provenance management systems are being developed, it is important to have benchmarks that can evaluate these systems and provide an unbiased comparison. In this paper, based on the requirements for scientific workflow provenance systems, we design an extensible benchmark that features a collection of techniques and tools for workload generation, query selection, performance measurement, and experimental result interpretation.

Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose volume can potentially grow the scale of the Web. Efficient management of Semantic Web data, expressed using the W3C's Resource Description Framework (RDF), is crucial for supporting new data-intensive, semantics-enabled applications. In this work, we study and compare two approaches to distributed RDF data management based on emerging cloud computing technologies and traditional relational database clustering technologies. In particular, we design distributed RDF data storage and querying schemes for HBase and MySQL Cluster and conduct an empirical comparison of these approaches on a cluster of commodity machines using datasets and queries from the Third Provenance Challenge and Lehigh University Benchmark. Our study reveals interesting patterns in query evaluation, shows that our algorithms are promising, and suggests that cloud computing has a great potential for scalable Semantic Web data management.

Provenance has become increasingly important in scientific workflows and services computing to capture the derivation history of a data product, including the original data sources, intermediate data products, and the steps that were applied to produce the data product. In many cases, both scientific results and the used protocol are sensitive and effective access control mechanisms are essential to protect their confidentiality. In this paper, we propose: 1) a formal scientific workflow provenance model as the basis for querying and access control for workflow provenance; 2) a security model for fine-grained access control for multilevel provenance and an algorithm for the derivation of a full security specification based on inheritance, overriding, and conflict resolution; 3) a formalization of the notion of security views and an algorithm for security view derivation; and 4) a formalization of the notion of secure abstraction views and an algorithm for its computation. A prototype called SecProv has been developed, and experiments show the effectiveness and efficiency of our approach.

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, the metadata that pertains to the derivation history of a data product, has become increasingly important in scientific workflow environments. In many cases, both data products and their provenance can be sensitive and effective access control mechanisms are essential to protect their confidentiality. In this paper, we propose i) a formalization of scientific workflow provenance as the basis for querying and access control; ii) a security specification mechanism for provenance at various granularity levels and the derivation of a full security specification based on inheritance, overriding, and conflict resolution rules; iii) a formalization of security views that are derived from a scientific workflow run provenance for different roles of users; and iv) a framework that integrates abstraction views and security views such that a user can examine provenance at different abstraction levels while respecting the security policy prescribed for her. We have developed the SecProv prototype to validate the effectiveness of our approach.

Relational technology has shown to be very useful for scalable Semantic Web data management. Numerous researchers have proposed to use RDBMSs to store and query voluminous RDF data using SQL and RDF query languages. In this article, we study how RDF queries with the socalled well-designed graph patterns and nested optional patterns can be efficiently evaluated in an RDBMS. We propose to extend relational databases with a novel relational operator, nested optional join (NOJ), that is more efficient than left outer join in processing nested optional patterns of well-designed graph patterns. We design three efficient algorithms to implement the new operator in relational databases: (1) nested-loops NOJ algorithm (NL-NOJ); (2) sortmerge NOJ algorithm (SM-NOJ); and (3) simple hash NOJ algorithm (SH-NOJ). Based on a real-life RDF dataset, we demonstrate the efficiency of our algorithms by comparing them with the corresponding left outer join implementations and explore the effect of join selectivity on the performance of our algorithms.

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.

The development of the Semantic Web, the next-generation Web, greatly relies on the availability of ontologies and powerful annotation tools. However, there is a lack of ontology-based annotation tools for linguistic multimedia data. Existing tools either lack ontology support or provide limited support for multimedia. To fill the gap, we present an ontology-based linguistic multimedia annotation tool, OntoELAN, which features: (1) the support for OWL ontologies; (2) the management of language profiles, which allow the user to choose a subset of ontological terms for annotation; (3) the management of ontological tiers, which can be annotated with language profile terms and, therefore, corresponding ontological terms; and (4) storing OntoELAN annotation documents in XML format based on multimedia and domain ontologies. To our best knowledge, OntoELAN is the first audio/video annotation tool in the linguistic domain that provides support for ontology-based annotation. It is expected that the availability of such a tool will greatly facilitate the creation of linguistic multimedia repositories as islands of the Semantic Web of language engineering.