Workflow Discovery Through Semantic Constraints: A Geovisualization Case Study

Kasalica, Vedran; Lamprecht, Anna‐Lena

doi:10.1007/978-3-030-24302-9_34

Cited by 4 publications

(27 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The implementation of this approach in APE was inspired by the PROPHETS loose programming framework (Lamprecht et al., 2010; Naujokat, Lamprecht, & Steffen, 2012) and uses the same underlying semantic linear‐time logic (SLTL) synthesis method (Steffen, Margaria, & Freitag, 1993). The approach has already successfully been used in different bioinformatics (Lamprecht, 2013; Palmblad, Lamprecht, Ison, & Schwämmle, 2018) and geoinformatics (Al‐Areqi, Lamprecht, & Margaria, 2016; Kasalica & Lamprecht, 2019; Scheider, Meerlo, et al, 2020) applications. For example, in Kasalica and Lamprecht (2019) we used APE to synthesize cartographic workflows for the automatic creation of maps depicting bird movement patterns in the Netherlands.…”

Section: Workflow Synthesis and Geoservice Compositionmentioning

confidence: 99%

“…The approach has already successfully been used in different bioinformatics (Lamprecht, 2013; Palmblad, Lamprecht, Ison, & Schwämmle, 2018) and geoinformatics (Al‐Areqi, Lamprecht, & Margaria, 2016; Kasalica & Lamprecht, 2019; Scheider, Meerlo, et al, 2020) applications. For example, in Kasalica and Lamprecht (2019) we used APE to synthesize cartographic workflows for the automatic creation of maps depicting bird movement patterns in the Netherlands. We used tools from the GMT (Generic Mapping Tools; Wessel, Smith, Scharroo, Luis, & Wobbe, 2013) collection as the basis for the workflow composition.…”

Section: Workflow Synthesis and Geoservice Compositionmentioning

confidence: 99%

“…The algorithm reasons over concept hierarchies and function signatures to compose adequate workflows. This approach has so far only sporadically been applied to geographic information processing (Kasalica & Lamprecht, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Loose programming of GIS workflows with geo‐analytical concepts

Kruiger

Kasalica

Meerlo

et al. 2020

Transactions in GIS

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Workflow Synthesis and Geoservice Compositionmentioning

confidence: 99%

Section: Workflow Synthesis and Geoservice Compositionmentioning

confidence: 99%

See 1 more Smart Citation

Loose programming of GIS workflows with geo‐analytical concepts

Kruiger

Kasalica

Meerlo

et al. 2020

Transactions in GIS

Self Cite

View full text Add to dashboard Cite

show abstract

“…The default operation for combining and turning lattices into each other is Areal Interpolation (including simple area weighted averages as well as Kriging based approaches and others) [20]. Simple overlay is inappropriate here due to missing self-similarity 40 .…”

Section: Operations On Object Representationsmentioning

confidence: 99%

Ontology of core concept data types for answering geo-analytical questions

Scheider¹,

Meerlo²,

Kasalica³

et al. 2020

JOSIS

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The development of APE was accompanied by three concrete application scenarios for automated workflow composition: 1) The proteomics case study mentioned earlier in this paper [25], 2) a case study on cartographic map generation [16], and 3) geospatial data transformations in the QuAnGIS project [18,26]. The experiences from these applications, in particular the feedback from the involved domain experts, influenced the design decisions that we took during the development of the APE CLI and API.…”

Section: Applications and Lessons Learnedmentioning

confidence: 99%

APE: A Command-Line Tool and API for Automated Workflow Composition

Kasalica

Lamprecht

2020

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Workflow Discovery Through Semantic Constraints: A Geovisualization Case Study

Cited by 4 publications

References 30 publications

Loose programming of GIS workflows with geo‐analytical concepts

Loose programming of GIS workflows with geo‐analytical concepts

Ontology of core concept data types for answering geo-analytical questions

APE: A Command-Line Tool and API for Automated Workflow Composition

Contact Info

Product

Resources

About