Semantically Resolving Type Mismatches in Scientific Workflows

Scientists are increasingly utilizing Grids to manage large data sets and execute scientific experiments on distributed resources. Scientific workflows are used as means for modeling and enacting scientific experiments. Windows Workflow Foundation (WF) is a major component of Microsoft’s .NET technology which offers lightweight support for long-running workflows. It provides a comfortable graphical and programmatic environment for the development of extended BPEL-style workflows. WF’s visual features ease the syntactic composition of Web services into scientific workflows but do nothing to assure that information passed between services has consistent semantic types or representations or that deviant flows, errors and compensations are handled meaningfully. In this paper we introduce SAWSDL-compliant annotations for WF and use them with a semantic reasoner to guarantee semantic type correctness in scientific workflows. Examples from bioinformatics are presented

A workflow runtime environment for manycore parallel architectures

We introduce a new Manycore Workflow Runtime Environment (MWRE) to efficiently enact traditional scientific workflows on modern manycore computing architectures. MWRE is compiler-based and translates workflows specified in the XML-based Interoperable Workflow Intermediate Representation (IWIR) into an equivalent C++-based program. This program efficiently enacts the workflow as a stand-alone executable by means of a new callback mechanism that resolves dependencies, transfers data, and handles composite activities. Furthermore, a core feature of MWRE is explicit support for full-ahead scheduling and enactment. Experimental results on a number of real-world workflows demonstrate that MWRE clearly outperforms existing Java-based workflow engines designed for distributed (Grid or Cloud) computing infrastructures in terms of enactment time, is generally better than an existing script-based engine for manycore architectures (Swift), and sometimes gets even close to an artificial baseline implementation of the workflows in the standard OpenMP language for shared memory systems. Experimental results also show that full-ahead scheduling with MWRE using a state-of-the-art heuristic can improve the workflow performance up to 40%.(VLID)2196062Accepted versio

Data-Intensive architecture for scientific knowledge discovery

This paper presents a data-intensive architecture that demonstrates the ability to support applications from a wide range of application domains, and support the different types of users involved in defining, designing and executing data-intensive processing tasks. The prototype architecture is introduced, and the pivotal role of DISPEL as a canonical language is explained. The architecture promotes the exploration and exploitation of distributed and heterogeneous data and spans the complete knowledge discovery process, from data preparation, to analysis, to evaluation and reiteration. The architecture evaluation included large-scale applications from astronomy, cosmology, hydrology, functional genetics, imaging processing and seismology

Combining ontologies and workflows to design formal protocols for biological laboratories

Background Laboratory protocols in life sciences tend to be written in natural language, with negative consequences on repeatability, distribution and automation of scientific experiments. Formalization of knowledge is becoming popular in science. In the case of laboratory protocols two levels of formalization are needed: one for the entities and individuals operations involved in protocols and another one for the procedures, which can be manually or automatically executed. This study aims to combine ontologies and workflows for protocol formalization. Results A laboratory domain specific ontology and the COW (Combining Ontologies with Workflows) software tool were developed to formalize workflows built on ontologies. A method was specifically set up to support the design of structured protocols for biological laboratory experiments. The workflows were enhanced with ontological concepts taken from the developed domain specific ontology. The experimental protocols represented as workflows are saved in two linked files using two standard interchange languages (i.e. XPDL for workflows and OWL for ontologies). A distribution package of COW including installation procedure, ontology and workflow examples, is freely available from http://www.bmr-genomics.it/farm/cow webcite. Conclusions Using COW, a laboratory protocol may be directly defined by wet-lab scientists without writing code, which will keep the resulting protocol's specifications clear and easy to read and maintain

Process modeling using ProSLCSE on web-enabled platform

Process modeling is a relatively complex task that needs to be addressed from a different point of view. The classical approach would be to design the model, to send it for evaluation, then to return feedback to the developing team, and to reevaluate the model with the feedback received from the parties involved. However, it is our understanding that the steps taken during the process modeling could benefit from the advantages that the Internet offers. To demonstrate the usefulness of Internet in process modeling, I have taken an existing tool, ProSLCSE, and implemented it with Java so that it can run on a web-enabled environment. This Web-enabled version of ProSLCSE, also called ProWEB, will not only facilitate the implementation, controlling or standardization of the models, but also accelerate the task of modeling in an efficient and effective way. The developing team of the models would benefit from the tool in a real-time environment. Other parties, like the monitoring agencies, or controlling bodies would add their modification to the application in a sequential form. The implementation of this Web-enabled process modeling will bring a new level of abstraction to the modeling and will minimize the difficulties due to geographical differences for \u27time-depending\u27 projects

Data access and integration in the ISPIDER proteomics grid

Grid computing has great potential for supporting the integration of complex, fast changing biological data repositories to enable distributed data analysis. One scenario where Grid computing has such potential is provided by proteomics resources which are rapidly being developed with the emergence of affordable, reliable methods to study the proteome. The protein identifications arising from these methods derive from multiple repositories which need to be integrated to enable uniform access to them. A number of technologies exist which enable these resources to be accessed in a Grid environment, but the independent development of these resources means that significant data integration challenges, such as heterogeneity and schema evolution, have to be met. This paper presents an architecture which supports the combined use of Grid data access (OGSA-DAI), Grid distributed querying (OGSA-DQP) and data integration (AutoMed) software tools to support distributed data analysis. We discuss the application of this architecture for the integration of several autonomous proteomics data resources

Realizing Adaptive Process-aware Information Systems with ADEPT2

In dynamic environments it must be possible to quickly implement new business processes, to enable ad-hoc deviations from the defined business processes on-demand (e.g., by dynamically adding, deleting or moving process activities), and to support dynamic process evolution (i.e., to propagate process schema changes to already running process instances). These fundamental requirements must be met without affecting process consistency and robustness of the process-aware information system. In this paper we describe how these challenges have been addressed in the ADEPT2 process management system. Our overall vision is to provide a next generation technology for the support of dynamic processes, which enables full process lifecycle management and which can be applied to a variety of application domains