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Loose programming enables analysts to program with concepts instead of procedural code. Data transformations are left underspecified, leaving out procedural details and exploiting knowledge about the applicability of functions to data types. To synthesize workflows of high quality for a geo‐analytical task, the semantic type system needs to reflect knowledge of geographic information systems (GIS) at a level that is deep enough to capture geo‐analytical concepts and intentions, yet shallow enough to generalize over GIS implementations. Recently, core concepts of spatial information and related geo‐analytical concepts were proposed as a way to add the required abstraction level to current geodata models. The core concept data types (CCD) ontology is a semantic type system that can be used to constrain GIS functions for workflow synthesis. However, to date, it is unknown what gain in precision and workflow quality can be expected. In this article we synthesize workflows by annotating GIS tools with these types, specifying a range of common analytical tasks taken from an urban livability scenario. We measure the quality of automatically synthesized workflows against a benchmark generated from common data types. Results show that CCD concepts significantly improve the precision of workflow synthesis.

Loose programming enables analysts to program with concepts instead of procedural code. Data transformations are left underspecified, leaving away procedural details and exploiting knowledge about the applicability of functions to data types. To synthesize workflows of high quality for a geo-analytical task, the semantic type system needs to reflect knowledge of Geographic Information Systems (GIS) on a level that is deep enough to capture geo-analytical concepts and intentions, yet shallow enough to generalize over GIS implementations. Recently, core concepts of spatial information and related geo-analytical concepts were proposed as a way to add the required abstraction level to current geodata models. The core concept data types (CCD) ontology is a semantic type system that can be used to constrain GIS functions for workflow synthesis. However, to date, it is unknown what gain in precision and workflow quality can be expected. In this article, we synthesize workflows by annotating GIS tools with these types, specifying a range of common analytical tasks taken from an urban livability scenario. We measure the quality of automatically synthesized workflows against a benchmark generated from common data types. Results show that CCD concepts significantly improve the precision of workflow synthesis.

Science today is increasingly computational, and many researchers regularly face the need of creating purpose-specific computational pipelines for their specific data analysis problems. The manual composition and implementation of such workflows regularly costs valuable research time. Hence, many scientists wish for a system that would only require an abstract description of their intended data analysis process, and from there automatically compose and implement suitable workflows. In this paper we describe APE (the Automated Pipeline Explorer), a new implementation of a synthesis-based workflow discovery framework that aims to accomplish such automated composition. The framework captures the required technical domain knowledge in the form of tool and type taxonomies and functional tool annotations. Based on this semantic domain model, the framework allows users to specify their intents about workflows at an abstract, conceptual level in the form of natural-language templates. Internally, APE maps them to a temporal logic and translates them into a propositional logic instance of the problem that can be solved by an off-the-shelf SAT solver. From the solutions provided by the solver, APE then constructs executable workflow implementations. First applications of APE on realistic scientific workflow scenarios have shown that it is able to efficiently synthesize meaningful workflows. We use an example from the geospatial application domain as a running example in this paper., Electronic Communications of the EASST, Volume 78: 8th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation - Doctoral Symposium and Industry Day, 2018

In geographic information systems (GIS), analysts answer questions by designing workflows that transform a certain type of data into a certain type of goal. Semantic data types help constrain the application of computational methods to those that are meaningful for such a goal. This prevents pointless computations and helps analysts design effective workflows. Yet, to date it remains unclear which types would be needed in order to ease geo-analytical tasks. The data types and formats used in GIS still allow for huge amounts of syntactically possible but nonsensical method applications. Core concepts of spatial information and related geo-semantic distinctions have been proposed as abstractions to help analysts formulate analytic questions and to compute appropriate answers over geodata of different formats. In essence, core concepts reflect particular interpretations of data which imply that certain transformations are possible. However, core concepts usually remain implicit when operating on geodata, since a concept can be represented in a variety of forms. A central question therefore is: Which semantic types would be needed to capture this variety and its implications for geospatial analysis? In this article, we propose an ontology design pattern of core concept data types that help answer geo-analytical questions. Based on a scenario to compute a liveability atlas for Amsterdam, we show that diverse kinds of geo-analytical questions can be answered by this pattern in terms of valid, automatically constructible GIS workflows using standard sources.

Automated workflow composition is bound to take the work with scientific workflows to the next level. On top of today’s comprehensive eScience infrastructure, it enables the automated generation of possible workflows for a given specification. However, functionality for automated workflow composition tends to be integrated with one of the many available workflow management systems, and is thus difficult or impossible to apply in other environments. Therefore we have developed APE (the Automated Pipeline Explorer) as a command-line tool and API for automated composition of scientific workflows. APE is easily configured to a new application domain by providing it with a domain ontology and semantically annotated tools. It can then be used to synthesize purpose-specific workflows based on a specification of the available workflow inputs, desired outputs and possibly additional constraints. The workflows can further be transformed into executable implementations and/or exported into standard workflow formats. In this paper we describe APE v1.0 and discuss lessons learned from applications in bioinformatics and geosciences.

Motivation Numerous software utilities operating on mass spectrometry (MS) data are described in the literature and provide specific operations as building blocks for the assembly of on-purpose workflows. Working out which tools and combinations are applicable or optimal in practice is often hard. Thus researchers face difficulties in selecting practical and effective data analysis pipelines for a specific experimental design. Results We provide a toolkit to support researchers in identifying, comparing and benchmarking multiple workflows from individual bioinformatics tools. Automated workflow composition is enabled by the tools’ semantic annotation in terms of the EDAM ontology. To demonstrate the practical use of our framework, we created and evaluated a number of logically and semantically equivalent workflows for four use cases representing frequent tasks in MS-based proteomics. Indeed we found that the results computed by the workflows could vary considerably, emphasizing the benefits of a framework that facilitates their systematic exploration. Availability and implementation The project files and workflows are available from https://github.com/bio-tools/biotoolsCompose/tree/master/Automatic-Workflow-Composition. Supplementary information Supplementary data are available at Bioinformatics online.

We apply an IDE for user-level process design and composition to a real-life case study: a complex workflow from an ongoing global cancer-related cachexia research project. Originally buried in a manually operated spreadsheet, the process is now fully automated and integrated into the project database, ensuring the immediate availability, consistency and reproducibility of the outcomes. Our integrated solution enables the scientists to immediately execute the processes and easily customize both processes and data model to continuously changing experimental setups. The data modeling is provided by the Dynamic Web Application framework and the process modeling functionalities by the Java Application Building Center, both following the paradigm of eXtreme Model-Driven Design for model-driven software development.

Increasing the number of females in ICT-related university courses has been a major concern for several years. In 2015, we offered a girls-only computing summer camp for the first time, as a new component in our education and outreach activities to foster studentsâ interest in our discipline. In this paper, we describe the motivation for the camp and how we designed the program, and we report our experiences and survey findings from the first two editions of the camp. They can provide guidance for planning further events targeting females, and help to integrate awareness about underrepresentation of females in other activities. peer-reviewed

Formulating localisation strategies is crucial to ensuring the development of local suppliers, which in turn results in job creation, increased export earnings, and local and national economic growth. The aim of this article is to present a generic decision support system (DSS) to guide localisation decision-making. Multi-criteria decision analysis (MCDA) is used to develop the model, which is then applied to a biometric identity management system (BIMS) case study that places the study within the context of the tool, die and mould-making (TDM) industry of South Africa. Die formulering van lokaliseringstrategieë is van kardinale belang om die ontwikkeling van plaaslike verskaffers te verseker, wat op sy beurt lei tot werkskepping, verhoogde uitvoerverdienste, en plaaslike en nasionale ekonomiese groei. Die doel van die artikel is om 'n generiese besluit ondersteunings stelsel (BOS) vir lokalisering besluitneming aan te bied. Multi-kriteria besluitneming analise (MKBA) word gebruik om die lokalisering besluit ondersteuning stelsel te ontwikkel. Die model is van toepassing op 'n biometriese identiteit bestuur stelsel (BIMS) gevallestudie, wat die studie plaas binne die konteks van die 'tool, die and mould-making' (TDM) bedryf van Suid-Afrika.