Shawn Bowers

• PhD in Computer Science
• Professor (Associate) at Gonzaga University

An advantage of scientific workflow systems is their ability to collect runtime provenance information as an execution trace. Traces include the computation steps invoked as part of the workflow run along with the corresponding data consumed and produced by each workflow step. The information captured by a trace is used to infer “lineage” relations...

An advantage of scientific workflow systems is their ability to collect runtime provenance information as an execution trace. Traces include the computation steps invoked as part of the workflow run along with the corresponding data consumed and produced by each workflow step. The information captured by a trace is used to infer "lineage" relations...

In 2006 the computer science program at Gonzaga University was moved from the College of Arts and Sciences (CAS) to the School of Engineering and Applied Science (SEAS). Before the move, a significant proportion of students majoring in computer science (CS) also majored/minored in another discipline within the CAS. After the move, the proportion dr...

We present a novel, logic-based solution to the challenge of reconciling the meanings of taxonomic names across multiple biological taxonomies. The challenge arises due to limitations inherent in using type-anchored taxonomic names as identifiers of granular semantic similarities and differences being expressed in original and revised taxonomic cla...

A scientific workflow consists of a series of structured activities and computations that arise in scientific problem-solving. Recent work [7] has demonstrated that collection-oriented modelling and design (COMAD) [3] leads to simpler and more robust workflow design. In COMAD, for example, each actor is wrapped with a well defined configuration tha...

Derivations and proofs are a form of provenance in automated deduction that can assist users in understanding how reasoners derive logical consequences from premises. However, system-generated proofs are often overly complex or detailed, and making sense of them is non-trivial. Conversely, without any form of provenance, it is just as hard to know...

We present a location-based approach for executing provenance lineage queries that significantly reduces query execution cost without incurring additional storage costs. The key idea of our approach is to exploit the fact that provenance graphs resemble the workflow graphs that generated them and that many workflow computation models assume workflo...

Classification standards such as the Mammal Species of the World (MSW) aim to unify name usages at the global scale, but may nevertheless experience significant levels of taxonomic change from one edition to the next. This circumstance challenges the biodiversity and phylogenetic data communities to develop more granular identifiers to track taxono...

We explore a novel approach for aligning multiple classifications and phylogenies based on the use of taxonomic concepts, Region Connection Calculus (RCC-5) articulations, and Answer Set Programming (ASP) reasoners. The Perelleschus use case of Franz & Cardona-Duque (2013) includes six related taxonomies (ranging from 1936 to 2013), 54 taxonomic co...

Classifications and phylogenetic inferences of organismal groups change in light of new insights. Over time these changes can result in an imperfect tracking of taxonomic perspectives through the re-/use of Code-compliant or informal names. To mitigate these limitations, we introduce a novel approach for aligning taxonomies through the interaction...

We study model-based diagnosis and propose a new approach of hybrid diagnosis combining black-box and white-box reasoning. We implemented and compared different diagnosis approaches including the standard hitting set algorithm and new approaches using answer set programming engines (DLV, Potassco) in the application of Euler/X toolkit, a logic-base...

The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science sugge...

We introduce Euler/X, a toolkit for logic-based taxonomy integration. Given two taxonomies and a set of alignment constraints between them, Euler/X provides tools for detecting, explaining, and reconciling inconsistencies; finding all possible merges between (consistent) taxonomies; and visualizing merge results. Euler/X employs a number of differe...

This paper presents teaching modules for personal robot programming developed to help make introductory computer science more relevant for first-year engineering majors. The modules provide early exposure to the entrepreneurial mindset through student collaboration; creative thinking for ambiguous problems; learning and persisting through failure;...

We describe recent ontology and annotation editing capabilities to a specialized data management system for observational data. The system supports observations and measurements explicitly, allowing users to upload observational data sets as well as semantically describe and query data sets using formal OWL-DL ontologies. Recent extensions allow us...

Provenance graphs generated from real-world scientific workflows often contain large numbers of nodes and edges denoting various types of provenance information. A standard approach used by workflow systems is to visually present provenance information by displaying an entire (static) provenance graph. This approach makes it difficult for users to...

Fine-grained dependencies within scientific workflow provenance specify lineage relationships between a workflow result and the input data, intermediate data, and computation steps used in the result's derivation. This information is often needed to determine the quality and validity of scientific data, and as such, plays a key role in both provena...

Semantic modeling approaches (e.g., conceptual models, controlled vocabularies, and ontologies) are increasingly being adopted to help address a number of challenges in scientific data management. While semantic information has played a considerable role within bioinformatics, semantic technologies can similarly benefit a wide range of scientific d...

Experimental science can be thought of as the exploration of a large research space, in search of a few valuable results. While it is this "Golden Data" that gets published, the history of the exploration is often as valuable to the scientists as some of its outcomes. We envision an e-research infrastructure that is capable of systematically and au...

A crucial aspect of certain applications such as the ones pertaining to Intelligence domain or Health-care, is to manage and protect sensitive information effectively and efficiently. In this paper, we propose a tagging mechanism to track the flow of ...

Observational data plays a critical role in many scientific disciplines, and scientists are increasingly interested in performing broad-scale analyses by using observational data collected as part of many smaller scientific studies. However, while these data sets often contain similar types of information, they are typically represented using very...

Scientific discoveries are often the result of methodical execution of many interrelated scientific workflows, where workflows and datasets published by one set of users can be used by other users to perform subsequent analyses, leading to implicit or explicit collaboration. In this paper, we describe a data model for “collaborative provenance” tha...

Observational data plays a critical role in many scientific disciplines, and scientists are increasingly interested in performing broad-scale analyses by using data collected as part of many smaller scientific studies. However, while these data sets often contain similar types of information, they are typically represented using very different stru...

Many scientific workflow systems are built on dataflow-based models of computation in which data drives the execution of workflow components. An advantage of using dataflow models is their straightforward semantics (which includes support for branching, merging, and looping) and their ability to concurrently execute workflow steps. However, for man...

Scientific workflow systems are used to integrate existing software components (actors) into larger analysis pipelines to perform in silico experiments. Current approaches for handling data in nested-collection structures, as required in many scientific domains, lead to many record-management actors (shims) that make the workflow structure overly c...

We present Protégé-OWL extensions designed to help scientists define domain-specific ontologies for describing observational data. The extensions pro-vide high-level forms that users can fill out from within Protégé to specify classes used to describe scientific measurements. As a user fills out a form, underlying OWL-DL axioms are automatically as...

This book constitutes the refereed proceedings of the 23rd International Conference on Scientific and Statistical Database Management, SSDBM 2011, held in Portland, OR, USA, in July 2011. The 26 long and 12 short papers presented together with 15 posters were carefully reviewed and selected from 80 submissions. The topics covered are ranked search;...

Scientific collaboration increasingly involves data sharing between separate groups. We consider a scenario where data products of scientific workflows are published and then used by other researchers as inputs to their workflows. For proper interpretation, shared data must be complemented by descriptive metadata. We focus on provenance traces, a p...

Earth and environmental scientists collect and use a wide range of observational data. This data often exhibits high structural and semantic heterogeneity due to the variety of data collected and the ways in which observational datasets are structured in practice. However, to address questions at broad temporal, geographic, and biological scales, r...

Research in the environmental sciences often requires accessing diverse data, collected by numerous data providers over varying spatiotemporal scales, incorporating specialized measurements from a range of instruments. These measurements are typically documented using idiosyncratic, disciplinary specific terms, and stored in management systems rang...

The notion of sharing scientific data has only recently begun to gain ground in science, where data is still considered a private asset. There is growing evidence, however, that the benefits of scientific collaboration through early data sharing during the course of a science project may outgrow the risk of losing exclusive ownership of the data. A...

In prior work it has been shown that the design of scientific workflows can benefit from a collection-oriented modeling paradigm which views scientific workflows as pipelines of XML stream processors. In this paper, we present approaches for exploiting data parallelism in XML processing pipelines through novel compilation strategies to the MapReduc...

The provenance of a data product contains information about how the product was derived, and is crucial for enabling scientists to easily understand, reproduce, and verify scientific results. Currently, most provenance models are designed to capture the provenance related to a single run, and mostly executed by a single user. However, a scientific...

While many scientific workflow systems track and record data provenance, few tools have been developed that provide convenient and effective ways to access and explore this information. Two important ways for provenance information to be accessed and explored is through browsing (i.e., visualizing and navigating data and process dependencies) and q...

This demonstration presents an interactive provenance browser for visualizing and querying data dependency (lineage) graphs produced by scientific workflow runs. The browser allows users to explore different views of provenance as well as to express complex and recursive graph queries through a high-level query language (QLP). Answers to QLP querie...

We consider the task of merging datasets that have been organized using different, but aligned taxonomies. We assume such a merge is intended to create a single dataset that unambiguously describes the information in the source datasets using the alignment. We also assume that the merged result should reflect the observations of the datasets as spe...

Discovery and integration of data is important in many ecological studies, especially those that concern broad-scale ecological questions. Data discovery and integration are often difficult and time consuming tasks for researchers, which is due in part to the use of informal, ambiguous, and sometimes inconsistent terms for describing data content....

Scientific workflows are increasingly used to rapidly integrate existing algorithms to create larger and more complex programs. However, designing workflows using purely dataflow-oriented computation models introduces a number of challenges, including the need to use low-level components to mediate and transform data (so-called shims) and large num...

A key advantage of scientific workflow systems over traditional scripting approaches is their ability to automatically record data and process dependencies introduced during workflow runs. This information is often represented through provenance graphs, which can be used by scientists to better understand, reproduce, and verify scientific results....

In many scientific disciplines, including ecology, hydrology, and earth science, scientific analysis requires access to a broad range of observational data. However, because of the amount and heterogeneity both in the structure and semantics) of observational data, approaches are needed that allow scientists to easily discover and analyze them. To...

We present approaches for exploiting data parallelism in XML pro-cessing pipelines through novel compilation strategies to the Map-Reduce framework. Pipelines in our approach consist of sequences of processing steps that consume XML-structured data and pro-duce, often through calls to "black-box" functions, modified (i.e., updated) XML structures....

We present a method for using aligned ontologies to merge taxonomically organized data sets that have apparently compatible schemas, but potentially different semantics for corresponding domains. We restrict the relationships involved in the alignment to basic set relations and disjunctions of these relations. A merged data set combines the domains...

Business workflow management and business process modeling are mature research areas, whose roots go far back to the early days of office automation systems. Scientific workflow management, on the other hand, is a much more recent phenomenon, triggered by (i) a shift towards data-intensive and computational methods in the natural sciences, and (ii)...

In this paper, we introduce the Open Provenance Model, a model for provenance that is designed to meet the following requirements: (1) To allow provenance information to be exchanged between systems, by means of a compatibility layer based on a shared provenance model. (2) To allow developers to build and share tools that operate on such a provenan...

Taxonomies are widely used to classify information, and multiple (possibly competing) taxonomies often exist for the same domain. Given a set of correspondences between two taxonomies, it is often necessary to "merge" the taxonomies, thereby creating a unied taxonomy (e.g., that can then be used by data integration and discovery applications). We p...

Existing approaches for representing the provenance of scientific workflow runs largely ignore computation models that work over structured data, including XML. Unlike models based on transformation semantics, these computation models often employ update semantics, in which only a portion of an incoming XML stream is modified by each workflow step....

XML process networks are a simple, yet powerful programming paradigm for loosely coupled, coarse-grained dataflow applications such as data-centric scientific workflows. We describe a framework called Delta-XML that is well-suited for applications in which pipelines of data processors modify parts ("deltas") of XML data collections while keeping th...

The amount of ecological data available electronically is increasing at a rapid rate, e.g., over 15,000 data sets are available today in the Knowledge Network for Biocomplexity (KNB) alone. Using the existing search capabilities of these online data repositories, however, scientists struggle to quickly locate data that are relevant to their needs o...

Many scientific workflow systems record provenance information in the form of data and process dependencies as part of workflow execution. Users often wish to explore these dependencies to reproduce, validate, and explain workflow results, e.g., by examining the data and processes that were used to produce particular workflow outputs. A natural int...

Despite an increasing interest in scientific workflow technologies in recent years, workflow design remains a challenging, slow, and often error-prone process, thus limiting the speed of further adoption of scientific workflows. Based on practical experience with data-driven workflows, we identify and illustrate a number of recurring scientific wor...

Scientific workflow systems are increasingly used to automate com- plex data analyses, largely due to their benefits over traditional ap- proaches for workflow design, optimization, and provenance record- ing. Many workflow systems employ a simple dependency model to represent the provenance of data produced by workflow runs. Although commonly adop...

The CleanTax framework relates (aligns) taxonomies (inclu- sion hierarchies) to one another using relations drawn from the RCC-5 algebra. The taxonomies, represented as par- tial orders with additional constraints, can frequently (but not always) be represented with RCC-5 relations as well. Given two aligned taxonomies, CleanTax can infer new rela-...

Recent years have seen a dramatic increase in research and development of scientific workflow systems. These systems promise to make scientists more productive by automating data-driven and compute-intensive analyses. Despite many early achievements, the long-term success of scientific workflow technology critically depends on making these systems...

Observational data (i.e., data that records observations and measurements) plays a key role in many scientific disciplines. Observational data, however, are typically structured and described in ad hoc ways, making its discovery and integration difficult. The wide range of data collected, the variety of ways the data are used, and the needs of exis...

While most scientific workflows systems are based on dataflow, some amount of control-flow modeling is often necessary for engineering fault-tolerant, robust, and adaptive workflows. However, control-flow modeling within dataflow often results in workflow specifications that are hard to comprehend, reuse, and maintain. We describe new modeling cons...

The complexity of scientific workflows for analyzing biological data creates a number of challenges for current workflow and provenance systems. This complexity is due in part to the nature of scientific data (e.g., heterogeneous, nested data collections) and the programming constructs required for automation (e.g., nested workflows, looping, pipel...

We describe a provenance model tailored to scientific workflows based on the collection-oriented modeling and design paradigm. Our implementation within the Kepler scientific workflow system captures the dependencies of data and collection creation events on preexisting data and collections, and embeds these provenance records within the data strea...

SUMMARY Scientific workflows often benefit from or even require advanced modeling constructs, e.g., nesting of subworkflows, cycles for executing loops, data-dependent routing, and pipelined execution. In such settings, an often overlooked aspect of provenance takes center stage: A suitable model of provenance (MoP) for scientific workflows should...

The first Provenance Challenge was set up in order to provide a forum for the community to help understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a Functional Magnetic Resonance Imaging workflow was defined, which participants had to either simulate or run in order t...

Ecology is inherently cross-disciplinary, drawing together many types of information to address questions about the natural world. Finding and integrating relevant data to assist in these analyses is crucial, but is difficult owing to ambiguous terminology and the lack of sufficient information about datasets. Ontologies provide a formal mechanism...

We describe collaborative efforts among a group of Knowledge Representation (KR) experts, domain scientists, and scientific information managers in developing knowledge models for ecological and environmental concepts. The development of formal, structured approaches to KR used by the group (i.e., ontologies) can be informed by evidence marshalled...

Taxonomies are widely used to classify information, and multiple (possibly competing) taxonomies often exist for the same domain. Given a set of correspondences between two taxonomies, it is of- ten necessary to "merge" the taxonomies, thereby creating a uni- fied taxonomy (e.g., that can then be used by data integration and discovery applications)...

Advances in ecology and environmental science increasingly depend on information from multiple disciplines to tackle broader and more complex questions about the natural world. Such advances, however, are hindered by data heterogeneity, which impedes the ability of researchers to discover, interpret, and integrate relevant data that have been colle...

Research in the ecological and environmental sciences increasingly relies on the integration of traditionally small, focused studies to form larger datasets for synthetic analyses. However, a broad range of data types, structures, and semantic subtleties occur in ecological data, making data discovery and integration a difficult and time-consuming...

Research in ecology increasingly relies on the integration of small, focused studies, to produce larger datasets that allow for more powerful, synthetic analyses. The results of these synthetic analyses are critical in guiding decisions about how to sustainably manage our natural environment, so it is important for researchers to effectively discov...

While a number of scientific workflow systems support data prove- nance, they primarily focus on collecting and querying provenance for single workflow runs. Scientific research projects, however, typically involve (1) many interrelated workflows (where data from one or more workflow runs are selected and used as input to subsequent runs) and (2) t...

Changes in biodiversity have been linked to variations in climate and human activities [295]. These changes have implications for a wide range of socially relevant processes, including the spread of infectious disease, invasive species dynamics, and vegetation productivity [27, 70, 203, 291, 294, 376, 426]. Our understanding of biodiversity pattern...

The first Provenance Challenge was set up in order to provide a forum for the community to help understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a Functional Magnetic Resonance Imaging workflow was defined, which participants had to either simulate or run in order t...

We describe a framework called the Uni-Level Description (ULD) for accurately repre- senting information from a broad range of data models. The ULD extends previous meta- data-model approaches by: (a) providing uniform representation and access to data model, schema, and data, and (b) supporting data models with non-traditional schema arrange- ment...

We view scientific workflows as the domain scientist's way to harness cyberinfrastructure for e-Science. Domain scientists are often interested in "end-to-end" frameworks which include data acquisition, transformation, analysis, visualization, and other steps. While there is no lack of technologies and standards to choose from, a simple, unified fr...

Bioinformatics, the application of computational tools to the management and analysis of biological data, has stimulated rapid research advances in genomics through the development of data archives such as GenBank, and similar progress is just beginning within ecology. One reason for the belated adoption of informatics approaches in ecology is the...

Steps in scientific workflows often generate collections of results, causing the data flowing through workflows to become increasingly nested. Be- cause conventional workflow components (or actors) typically operate on simple or application-specific data types, additional actors often are required to man- age these nested data collections. As a res...

Integrated provenance support promises to be a chief advantage of scientific workflow systems over script-based alternatives. While it is often recognized that information gathered during scientific workflow execution can be used automatically to increase fault tolerance (via checkpointing) and to optimize performance (by reusing intermediate data...

Scientific workflows facilitate automation, reuse, and reproducibility of scientific data management and analysis tasks. Scientific workflows are often modeled as dataflow networks, chaining together processing components (called actors) that query, transform, analyse, and visualize scientific datasets. Semantic annotations relate data and actor sc...

Data-centric scientific workflows are often modeled as dataflow process networks. The simplicity of the dataflow framework facilitates workflow design, analysis, and optimization. However, modeling "control-flow intensive" tasks using dataflow constructs often leads to overly complicated workflows that are hard to comprehend, reuse, and maintain. W...

Scientists are confronted with significant data- management problems due to the large volume and high complexity of scientific data. In particular, the latter makes data integration a dicult techni- cal challenge. In this paper, we describe our work on semantic mediation and scientific workflows, and dis- cuss how these technologies address integra...

Scientific workflow systems are problem-solving environments that allow scientists to automate and reproduce data management and analysis tasks. Workflow components include actors (e.g., queries, transformations, analyses, simulations, visualizations), and datasets which are produced and consumed by actors. The increasing number of such components...

Scientific workflows are becoming increasingly important as a unify- ing mechanism for interlinking scientific data management, analysis, simulation, and visualization tasks. Scientific workflow systems are problem-solving envi- ronments, supporting scientists in the creation and execution of scientific work- flows. While current systems permit the...

We describe an approach for pipelining nested data collections in scientific workflows. Our approach logically delimits arbitrarily nested collections of data tokens using special, paired control tokens inserted into token streams, and provides workflow components with high-level operations for managing these collections. Our framework provides new...

The tools used to analyze scientific data are often distinct from those used to archive, retrieve, and query data. A scientific workflow environment, however, allows one to seamlessly combine these functions within the same application. This increase in capability is accompanied by an increase in complexity, especially in workflow tools like Kepler...

The Science Environment for Ecological Knowledge (SEEK) (1) is an information technology project designed to address the many challenges associated with data accessibility and integration of large-scale biocomplexity data in the ecological sciences. The SEEK project is creating cyberinfrastructure encompassing three integrated systems: EcoGrid, a S...

We present an approach to support incremental navigation of structured information, where the structure is introduced by the data model and schema (if present) of a data source. Simple browsing through data values and their connections is an effective way for a user or an automated system to access and explore information. We use our previously def...

Similar to content on the web, scientific data is highly heterogeneous and can benefit from rich semantic descriptions. We are particularly interested in developing an infrastructure for expressing explicit semantic descriptions of ecological data (and life-sciences data in general), and exploiting these descriptions to provide support for automate...

In many data-centric scientific applications it is common to register datasets and computational services with a federation registry (also commonly called a catalog, directory, or repository). For example, the scientific data-handling system under development in the SEEK project must consider various dataset registries, including: MCAT, for access...

Introduction In many data-centric scientific applications it is common to register datasets and computational services with a federation registry (also commonly called a catalog, directory, or repository). For example, the scientific data-handling system under development in the SEEK project must consider various dataset registries, including: MCAT...

Ecologists spend considerable e#ort integrating heterogeneous data for statistical analyses and simulations, for example, to run and test predictive models. Our research is focused on reducing this e#ort by providing data integration and transformation tools, allowing researchers to focus on "real science," that is, discovering new knowledge throug...

Ecologists spend considerable effort integrating heterogene- ous data for statistical analyses and simulations, for example, to run and test predictive models. Our research is focused on reducing this effort by providing data integration and transformation tools, allowing researchers to focus on "real science," that is, discovering new knowledge th...