Workflow Provenance Research Articles

The Systematic Study of Scientific Workflows in Distributed and Cloud Environments: A Review of Privacy Issues in Workflow Provenance, Challenges and Opportunities

The Systematic Study of Scientific Workflows in Distributed and Cloud Environments: A Review of Privacy Issues in Workflow Provenance, Challenges and Opportunities

Cloud infrastructure provenance collection and management to reproduce scientific workflows execution

The emergence of Cloud computing provides a new computing paradigm for scientific workflow execution. It provides dynamic, on-demand and scalable resources that enable the processing of complex workflow-based experiments. With the ever growing size of the experimental data and increasingly complex processing workflows, the need for reproducibility has also become essential. Provenance has been thought of a mechanism to verify a workflow and to provide workflow reproducibility. One of the obstacles in reproducing an experiment execution is the lack of information about the execution infrastructure in the collected provenance. This information becomes critical in the context of Cloud in which resources are provisioned on-demand and by specifying resource configurations. Therefore, a mechanism is required that enables capturing of infrastructure information along with the provenance of workflows executing on the Cloud to facilitate the re-creation of execution environment on the Cloud. This paper presents a framework to Reproduce Scientific Workflow Execution using Cloud-Aware Provenance (ReCAP), along with the proposed mapping approaches that aid in capturing the Cloud-aware provenance information and help in re-provisioning the execution resource on the Cloud with similar configurations. Experimental evaluation has shown the impact of different resource configurations on the workflow execution performance, therefore justifies the need for collecting such provenance information in the context of Cloud. The evaluation has also demonstrated that the proposed mapping approaches can capture Cloud information in various Cloud usage scenarios without causing performance overhead and can also enable the re-provisioning of resources on Cloud. Experiments were conducted using workflows from different scientific domains such as astronomy and neuroscience to demonstrate the applicability of this research for different workflows.

Static analysis of Taverna workflows to predict provenance patterns

Workflows have found adoption in scientific domains particularly due to their automation and provenance features. Using workflows scientists can repeat analyses with different input parameters and later use provenance to access and compare results based on these respective parameters. A common assumption is that by designing an analysis as a workflow we get parameter-to-result traceability for free by using workflow provenance. This assumption holds for cases of coarse-grained traceability where an entire workflow is subjected to repetition and all workflow parameters contribute to all results. However, this assumption is not guaranteed to hold for cases requiring finer grained traceability: where a workflow is configured with collections of parameters and analyses within a workflow are repeated with combinations of parameters from collections. In this paper we identify two dimensions that affect fine-grained traceability: (1) Factorial Design, which is the level of granularity in modelling parameters/data in workflows and in provenance that is supported by a workflow system; and (2) the practice of scientists in successfully encoding Factorial Design into workflows. Taverna is a workflow system that provides extensive features for factorial design. However it also supports a free approach to workflow design which means that scientists may create workflows which could break traceability in provenance when they run. Using a real-world Taverna workflow we show how broken traceability manifests in provenance, rendering it ineffective for accessing workflow outputs derived from particular input parameters. In order to prevent broken traceability from occurring we describe a rule-based static analysis technique which operates over workflow descriptions and anticipates patterns in provenance. Our rules exploit the well-defined execution behaviour in the Taverna system. In order to understand Factorial Design support in workflow systems in general, we provide a comparative survey. We conclude that other workflow systems also provide constructs for Factorial Design, and, similar to Taverna, they too are prone to broken traceability.

Applied CyberGIS in the Age of Complex Spatial Health Data

GIScience 2016 Short Paper Proceedings Applied CyberGIS in the Age of Complex Spatial Health Data M. M. Jankowska 1 , J. Schipperijn 2 , J. Kerr 1 , I. Altintas 1 University of California San Diego, 9500 Gilman Drive La Jolla CA 92093-0811 USA Email: {majankowska; jkerr}@ucsd.edu; altintas@sdsc.edu University of Southern Denmark, Campusvej 55 Odense 5230 Denmark Email: jschipperijn@health.sdu.dk Abstract Advances in data acquisition in a number of fields throughout the health spectrum are resulting in large, complex, and diverse data sets. With proliferation of sensor and spatial data acquisition, the analysis and processing of complex spatial health data analytics will become a pressing problem. CyberGIS can offer solutions in this realm, however no systems have been developed that cater to the specific challenges associated with complex spatial health data such as privacy, real-time analytics, data standardization, data integration, workflow provenance, and a front end interface that is accessible to individuals in the public health realm. In this paper we present SPACES, an in-development CyberGIS to address some of these challenges. We discuss the architecture, and present two test case examples of utilizing SPACES for understanding environmental influences on physical activity. 1. Introduction Public health, health care, and medical advances are increasingly looking to the collection of voluminous data sets. Movements such as Quantified Self, where individuals engage in self- tracking of biological, physical, behavioural, and environmental information (Fawcett 2015), or the Precision Medicine Initiative Cohort announced by President Obama in 2015, which will include extensive data collection and tracking of over one million U.S. participants (Ashley 2015), are driving the need to develop cyberinfrastructures and methodologies for big and complex spatial health data integration, processing, and analysis. Complex spatial health data may include temporally-linked objective measures of behavior and health, molecular data such as genomics, proteomics, or metabolomics, life course data including movement trajectories, self-reported survey and demographic data, environmental data assessing exposures, and finely resolved spatial data. CyberGIS may assist in addressing the challenges of complex spatial health dat. It represents a new-generation GIS based on the synthesis of advanced cyberinfrastructure, geographic information science, and spatial analysis and modeling (Wang 2010). The field of CyberGIS is rapidly developing and the US National Science Foundation funded a 5-year major initiative titled ‘CyberGIS Software Integration for Sustained Geospatial Innovation’ (http://cybergis.org). However, advances in the application of CyberGIS to health specific problems are lacking. Goldberg et al. (2013) presented a comprehensive vision of a Spatial- Health CyberGIS Marketplace, which tackled many of the opportunities as well as challenges of a health focused CyberGIS including confidentiality and privacy protections, real-time analytic methods, data standardization, and a comprehensive end-to-end ecosystem architecture. We would add to this list the need for shareable workflows to promote inter- field collaboration, diverse data type integration, and replicability of analytic processes. Recently, the NSF funded a platform called DELPHI (Data E-platform for personalized population health) (Katsis et al. 2013), one of the first cyberinfrastructures in development specifically catered toward meeting the goals of integrating complex and diverse health data

An agent-based approach for capturing and linking provenance in geoscience workflows

Provenance is becoming increasingly important as web services and computational workflows enable new methods by which work is conducted. Yet, there exist sets of questions that cannot be addressed by current provenance capture systems. We address these challenges by leveraging a service provenance ontology that captures execution details of workflow constituent web services. The ontology is used in conjunction with a multi-agent system to automate provenance aggregation and collation. The use of a multi-agent system eliminates the need to modify service interfaces, as was done in previous research. Simulation experiments are used to evaluate multiple agent topologies and identify an efficient and scalable system that scales to large numbers of workflows.

PTSLGA: A Provenance Tracking System for Linked Data Generating Application

Tracking provenance of RDF resources is an important task in Linked Data generating applications. It takes on a central function in gathering information as well as workflow. Various Linked Data generating applications have evolved for converting legacy data to RDF resources. These data belong to bibliographic, geographic, government, publications, and cross-domains. However, most of them do not support tracking data and workflow provenance for individual RDF resources. In such cases, it is required for those applications to track, store and disseminate provenance information describing their source data and involved operations. In this article, we introduce an approach for tracking provenance of RDF resources. Provenance information is tracked during the conversion process and it is stored into the triple store. Thereafter, this information is disseminated using provenance URIs. The proposed framework has been analyzed using Harvard Library Bibliographic Datasets. The evaluation has been made on datasets through converting legacy data into RDF and Linked Data with provenance. The outcome has been quiet promising in the sense that it enables data publishers to generate relevant provenance information while taking less time and efforts.

The PBase Scientific Workflow Provenance Repository

Scientific workflows and their supporting systems are becoming increasingly popular for compute-intensive and data-intensive scientific experiments. The advantages scientific workflows offer include rapid and easy workflow design, software and data reuse, scalable execution, sharing and collaboration, and other advantages that altogether facilitate “reproducible science”. In this context, provenance – information about the origin, context, derivation, ownership, or history of some artifact – plays a key role, since scientists are interested in examining and auditing the results of scientific experiments. However, in order to perform such analyses on scientific results as part of extended research collaborations, an adequate environment and tools are required. Concretely, the need arises for a repository that will facilitate the sharing of scientific workflows and their associated execution traces in an interoperable manner, also enabling querying and visualization. Furthermore, such functionality should be supported while taking performance and scalability into account. With this purpose in mind, we introduce PBase: a scientific workflow provenance repository implementing the ProvONE proposed standard, which extends the emerging W3C PROV standard for provenance data with workflow specific concepts. PBase is built on the Neo4j graph database, thus offering capabilities such as declarative and efficient querying. Our experiences demonstrate the power gained by supporting various types of queries for provenance data. In addition, PBase is equipped with a user friendly interface tailored for the visualization of scientific workflow provenance data, making the specification of queries and the interpretation of their results easier and more effective.

Automating data-model workflows at a level 12 HUC scale: Watershed modeling in a distributed computing environment

The prototype discussed in this article retrieves Essential Terrestrial Variable (ETV) web services and uses data-model workflows to transform ETV data for hydrological models in a distributed computing environment. The ETV workflow is a service layer to 100's of terabytes of national datasets bundled for fast data access in support of watershed modeling using the United States Geological Survey (USGS) Hydrological Unit Code (HUC) level-12 scale. The ETV data has been proposed as the Essential Terrestrial Data necessary to construct watershed models anywhere in the continental USA (Leonard and Duffy, 2013). Here, we present the hardware and software system designs to support the ETV, data-model, and model workflows using High Performance Computing (HPC) and service-oriented architecture.This infrastructure design is an important contribution to both how and where the workflows operate. We describe details of how these workflow services operate in a distributed manner for modeling CONUS HUC-12 catchments using the Penn State Integrated Hydrological Model (PIHM) as an example. The prototype is evaluated by generating data-model workflows for every CONUS HUC-12 and creating a repository of workflow provenance for every HUC-12 (∼100 km2) for use by researchers as a strategy to begin a new hydrological model study. The concept of provenance for data-model workflows developed here assures reproducibility of model simulations (e.g. reanalysis) from ETV datasets without storing model results which we have shown will require many petabytes of storage.

A Model To Capture Interaction Between Data Provenance And Workflow Provenance

Provenance means origin. There are two types of Provenance namely Data Provenance and Workflow Provenance. Data Provenance refers to the process of recording and tracking the source or origin of data while workflow provenance means history of workflows and their data that were used while performing an operation to get required result. The results which are obtained while performing operations are called datasets. On these datasets, the queries are performed to get required result. While exploring provenance topic in data and workflow, the author have tried to explore how a model can be presented and discussed which can capture interaction between data provenance and workflow provenance. In this paper, the author have discussed further about provenance, its types and its importance and also discussed how to execute query on provenance data. The main focus of this study was on the model which captures interaction between workflow and data provenance including the above mentioned topics.

An Inference-Based Framework to Manage Data Provenance in Geoscience Applications

Data provenance allows scientists to validate their model as well as to investigate the origin of an unexpected value. Furthermore, it can be used as a replication recipe for output data products. However, capturing provenance requires enormous effort by scientists in terms of time and training. First, they need to design the workflow of the scientific model, i.e., workflow provenance, which requires both time and training. However, in practice, scientists may not document any workflow provenance before the model execution due to the lack of time and training. Second, they need to capture provenance while the model is running, i.e., fine-grained data provenance. Explicit documentation of fine-grained provenance is not feasible because of the massive storage consumption by provenance data in the applications, including those from the geoscience domain where data are continuously arriving and are processed. In this paper, we propose an inference-based framework, which provides both workflow and fine-grained data provenance at a minimal cost in terms of time, training, and disk consumption. Our proposed framework is applicable to any given scientific model, and is capable of handling different model dynamics, such as variation in the processing time as well as input data products arrival pattern. Our evaluation of the framework in a real use case with geospatial data shows that the proposed framework is relevant and suitable for scientists in geoscientific domain.

GeoPWProv: Interleaving Map and Faceted Metadata for Provenance Visualization and Navigation

Visualization of geospatial data provenance aims to provide a user-friendly way for easy navigation and increased understanding of the derivation history of geoscientific results. Most existing work focuses on provenance modeling and management. This paper proposes to interleave map and faceted metadata for geospatial data provenance visualization and navigation. It shows how provenance in Web-geoprocessing workflows can be visualized at four levels: feature, dataset, service, and knowledge. A prototypical system, named GeoPWProv, is developed to demonstrate the applicability of the approach.

OPQL: Querying scientific workflow provenance at the graph level

Provenance has become increasingly important in scientific workflows to understand, verify, and reproduce the result of scientific data analysis. Most existing systems store provenance data in provenance stores with proprietary provenance data models and conduct query processing over the physical provenance storages using query languages, such as SQL, SPARQL, and XQuery, which are closely coupled to the underlying storage strategies. Querying provenance at such low level leads to poor usability of the system: a user needs to know the underlying schema to formulate queries; if the schema changes, queries need to be reformulated; and queries formulated for one system will not run in another system. In this paper, we present OPQL, a provenance query language that enables the querying of provenance directly at the graph level. An OPQL query takes a provenance graph as input and produces another provenance graph as output. Therefore, OPQL queries are not tightly coupled to the underlying provenance storage strategies. Our main contributions are: (i) we design OPQL, including six types of graph patterns, a provenance graph algebra, and OPQL syntax and semantics, that supports querying provenance at the graph level; (ii) we implement OPQL using a Web service via our OPMProv system; therefore, users can invoke the Web service to execute OPQL queries in a provenance browser, called OPMProVis. The result of OPQL queries is displayed as a provenance graph in OPMProVis. An experimental study is conducted to evaluate the feasibility and performance of OPMProv on OPQL provenance querying.

Provenance in bioinformatics workflows

In this work, we used the PROV-DM model to manage data provenance in workflows of genome projects. This provenance model allows the storage of details of one workflow execution, e.g., raw and produced data and computational tools, their versions and parameters. Using this model, biologists can access details of one particular execution of a workflow, compare results produced by different executions, and plan new experiments more efficiently. In addition to this, a provenance simulator was created, which facilitates the inclusion of provenance data of one genome project workflow execution. Finally, we discuss one case study, which aims to identify genes involved in specific metabolic pathways of Bacillus cereus, as well as to compare this isolate with other phylogenetic related bacteria from the Bacillus group. B. cereus is an extremophilic bacteria, collected in warm water in the Midwestern Region of Brazil, its DNA samples having been sequenced with an NGS machine.

Provenance and data differencing for workflow reproducibility analysis

SummaryOne of the foundations of science is that researchers must publish the methodology used to achieve their results so that others can attempt to reproduce them. This has the added benefit of allowing methods to be adopted and adapted for other purposes. In the field of e‐Science, services – often choreographed through workflow, process data to generate results. The reproduction of results is often not straightforward as the computational objects may not be made available or may have been updated since the results were generated. For example, services are often updated to fix bugs or improve algorithms. This paper addresses these problems in three ways. Firstly, it introduces a new framework to clarify the range of meanings of ‘reproducibility’. Secondly, it describes a new algorithm, PDIFF, that uses a comparison of workflow provenance traces to determine whether an experiment has been reproduced; the main innovation is that if this is not the case then the specific point(s) of divergence are identified through graph analysis, assisting any researcher wishing to understand those differences. One key feature is support for user‐defined, semantic data comparison operators. Finally, the paper describes an implementation of PDIFF that leverages the power of the e‐Science Central platform that enacts workflows in the cloud. As well as automatically generating a provenance trace for consumption by PDIFF, the platform supports the storage and reuse of old versions of workflows, data and services; the paper shows how this can be powerfully exploited to achieve reproduction and reuse. Copyright © 2013 John Wiley & Sons, Ltd.

Distilling structure in scientific workflows

Distilling structure in scientific workflows

Nonintrusive collection and management of data provenance in scientific workflows

SUMMARYIn this paper, we introduce an efficient mechanism to collect, store, and retrieve data provenance information in workflows of multiphysics simulations. Using notifications, we enable the nonintrusive collection of information about workflow events during workflow execution. Combining these events with workflow structure information, constant for every execution of a workflow, we obtain the data provenance information for the specific run of the workflow. Data provenance information is structured into a graph that represents workflow events on the basis of their causal dependency. We use a graph database to store this graph and utilize the traversal framework provided, to efficiently retrieve data provenance information from the graph by traversing backwards from a data object to every workflow event that is part of its provenance. Finally, we integrate data provenance information with semantics of workflow services to provide complete and meaningful data provenance information. Copyright © 2012 John Wiley & Sons, Ltd.

Putting lipstick on pig

Workflow provenance typically assumes that each module is a "black-box", so that each output depends on all inputs ( coarse-grained dependencies). Furthermore, it does not model the internal state of a module, which can change between repeated executions. In practice, however, an output may depend on only a small subset of the inputs ( fine-grained dependencies) as well as on the internal state of the module. We present a novel provenance framework that marries database-style and workflow-style provenance, by using Pig Latin to expose the functionality of modules, thus capturing internal state and fine-grained dependencies. A critical ingredient in our solution is the use of a novel form of provenance graph that models module invocations and yields a compact representation of fine-grained workflow provenance. It also enables a number of novel graph transformation operations, allowing to choose the desired level of granularity in provenance querying (ZoomIn and ZoomOut), and supporting "what-if" workflow analytic queries. We implemented our approach in the Lipstick system and developed a benchmark in support of a systematic performance evaluation. Our results demonstrate the feasibility of tracking and querying fine-grained workflow provenance.

Enhancing workflow with a semantic description of scientific intent

Scientists are becoming increasingly dependent upon resources available through the Internet including, for example, datasets and computational modelling services, which are changing the way they conduct their research activities. This paper investigates the use of workflow tools enhanced with semantics to facilitate the design, execution, analysis and interpretation of workflow experiments and exploratory studies. Current workflow technologies do not incorporate any representation of experimental constraints and goals, which we refer to in this paper as scientist’s intent. This paper proposes an abstract model of intent based on the Open Provenance Model (OPM) specification. To realise this model a framework based upon a number of Semantic Web technologies has been developed, including the OWL ontology language and the Semantic Web Rule Language (SWRL). Through the use of social simulation case studies the paper illustrates the benefits of using this framework in terms of workflow monitoring, workflow provenance and annotation of experimental results.

The ALPS project release 2.0: open source software for strongly correlated systems

We present release 2.0 of the ALPS (Algorithms and Libraries for Physics Simulations)project, an open source software project to develop libraries and application programs forthe simulation of strongly correlated quantum lattice models such as quantum magnets,lattice bosons, and strongly correlated fermion systems. The code development is centeredon common XML and HDF5 data formats, libraries to simplify and speed up codedevelopment, common evaluation and plotting tools, and simulation programs. Theprograms enable non-experts to start carrying out serial or parallel numerical simulationsby providing basic implementations of the important algorithms for quantum latticemodels: classical and quantum Monte Carlo (QMC) using non-local updates,extended ensemble simulations, exact and full diagonalization (ED), the densitymatrix renormalization group (DMRG) both in a static version and a dynamictime-evolving block decimation (TEBD) code, and quantum Monte Carlo solvers fordynamical mean field theory (DMFT). The ALPS libraries provide a powerfulframework for programmers to develop their own applications, which, for instance,greatly simplify the steps of porting a serial code onto a parallel, distributedmemory machine. Major changes in release 2.0 include the use of HDF5 for binarydata, evaluation tools in Python, support for the Windows operating system,the use of CMake as build system and binary installation packages for Mac OSX and Windows, and integration with the VisTrails workflow provenance tool.The software is available from our web server at http://alps.comp-phys.org/.

Enhancing Workflow with a Semantic Description of Scientific Intent

Scientists are becoming increasingly dependent upon resources available through the Internet including, for example, datasets and computational modelling services, which are changing the way they conduct their research activities. This paper investigates the use of workflow tools enhanced with semantics to facilitate the design, execution, analysis and interpretation of workflow experiments and exploratory studies. Current workflow technologies do not incorporate any representation of experimental constraints and goals, which we refer to in this paper as scientist's intent. This paper proposes an abstract model of intent based on the Open Provenance Model (OPM) specification. To realise this model a framework based upon a number of Semantic Web technologies has been developed, including the OWL ontology language and the Semantic Web Rule Language (SWRL). Through the use of social simulation case studies the paper illustrates the benefits of using this framework in terms of workflow monitoring, workflow provenance and annotation of experimental results.