Geoweaver: Cloud-Oriented Hybrid Workflow Management

Information about the uncertainty associated with Earth science observational data is fundamental to use, re-use, and overall evaluation of the data being used to produce science and support decision making. The associated uncertainty information leads to a quantifiable level of confidence in both the data and the science informing decisions produced using the data. The current breadth and cross-domain depth of understanding and application of uncertainty information, however, are still evolving as the practices associated with quantifying and characterizing uncertainty across various types of Earth observation data are diverse. Since its re-establishment in 2015, the Information Quality Cluster (IQC) of the Earth Science Information Partners (ESIP) has convened numerous sessions within the auspices of ESIP and the American Geophysical Union (AGU) to help collect expert-level information focusing on key aspects of uncertainty of Earth science data and addressed key concerns such as: 1) how uncertainty is quantified (UQ) and characterized (UC), 2) understanding the strengths and limitations of common techniques used in producing and evaluating uncertainty information, 3) implications using uncertainty information as a quality indicator 4) impacts of uncertainty on data fusion/assimilation, 5) various methods for documenting and conveying the uncertainty information to data users, and 6) understanding why certain user communities care about uncertainty and others do not. A key recommendation and action item from the ESIP Summer Meeting 2017 was for the IQC to develop a white paper to establish a clearer understanding of the concept of uncertainty and its communication to data users. The information gathered for this white paper has been provided by Earth science data and informatics experts spanning diverse disciplines and observation systems in the cross-domain Earth sciences. The intention of this white paper is to provide a diversely sampled exposition of both prolific and unique policies and practices, applicable in an international context of diverse policies and working groups, made toward quantifying, characterizing, communicating and making use of uncertainty information throughout the diverse, cross-disciplinary Earth science data landscape.

This thesis describes a data provenance framework and other associated frameworks for utilizing provenance for data quality and reproducibility. We first identify the requirements for the design of a comprehensive provenance framework which can be applicable to various applications, supports a rich set of provenance metadata, and is interoperable with other provenance management systems. We then design and develop a provenance framework, called SimP, addressing such requirements. Next, we present four prominent applications and investigate how provenance data can be beneficial to such applications. The first application is the quality assessment of access control policies. Towards this, we design and implement the ProFact framework which uses provenance techniques for collecting comprehensive data about actions which were either triggered due to a network context or a user (i.e., a human or a device) action. Provenance data are used to determine whether the policies meet the quality requirements. ProFact includes two approaches for policy analysis: structure-based and classification-based. For the structure-based approach, we design tree structures to organize and assess the policy set efficiently. For the classification-based approach, we employ several classification techniques to learn the characteristics of policies and predict their quality. In addition, ProFact supports policy evolution and the assessment of its impact on the policy quality. The second application is workflow reproducibility. Towards this, we implement ProWS which is a provenance-based architecture for retrieving workflows. Specifically, ProWS transforms data provenance into workflows and then organizes data into a set of indexes to support efficient querying mechanisms. ProWS supports composite queries on three types of search criteria: keywords of workflow tasks, patterns of workflow structure, and metadata about workflows (e.g., how often a workflow was used). The third application is the access control policy reproducibility. Towards this, we propose a novel framework, Polisma, which generates attribute-based access control policies from data, namely from logs of historical access requests and their corresponding decisions. Polisma combines data mining, statistical, and machine learning techniques, and capitalizes on potential context information obtained from external sources (e.g., LDAP directories) to enhance the learning process. The fourth application is the policy reproducibility by utilizing knowledge and experience transferability. Towards this, we propose a novel framework, FLAP, which transfer attribute-based access control policies between different parties in a collaborative environment, while considering the challenges of minimal sharing of data and support policy adaptation to address conflict. All frameworks are evaluated with respect to performance and accuracy.

Google Earth Engine (GEE) is an ideal platform for large-scale geospatial agricultural and environmental modeling. However, using GEE to prepare agricultural land use data for geospatial agricultural and environment modeling requires not only the programming skills of GEE APIs but also the knowledge of the data. This paper presents a toolkit AgKit4EE to facilitate the use of the Cropland Data Layer (CDL) products over GEE platform. The toolkit contains a variety of frequently used functions for use of CDL products including crop sequence modeling, crop frequency modeling, confidence layer modeling, and land use change analysis, which can significantly reduce the workload for modelers who perform geospatial agricultural and environmental modeling with CDL data as well as developers who build the GEE-enabled cyberinfrastructure for agricultural land use modeling of the conterminous United States. This presentation was given at the Earth Science Information Partners (ESIP) Summer Meeting held online in July 2020.

This document presents guidelines on how to cite software and services for authors of journal articles. Journal publishers can also provide this as a reference to their authors on how to cite software and services. These guidelines were developed by the Earth Science Information Partners (ESIP) Software and Services Citation Cluster and endorsed by the ESIP Assembly in January 2019.

This report documents the program and the outcomes of Dagstuhl Seminar 12091 ``Principles of Provenance''. The term ``provenance'' refers to information about the origin, context, derivation, ownership or history of some artifact. In both art and science, provenance information is crucial for establishing the value of a real-world artifact, guaranteeing for example that the artifact is an original work produced by an important artist, or that a stated scientific conclusion is reproducible. Since it is much easier to copy or alter digital information than it is to copy or alter real-world artifacts, the need for tracking and management of provenance information to testify the value and correctness of digital information has been firmly established in the last few years. As a result, provenance tracking and management has been studied in many settings, ranging from databases, scientific workflows, business process modeling, and security to social networking and the Semantic Web, but with relatively few interaction between these areas. This Dagstuhl seminar has focused on bringing together researchers from the above and other areas to identify the commonalities and differences of dealing with provenance; improve the mutual understanding of these communities; and identify main areas for further foundational provenance research.

This presentation details how the National Climate Assessment (NCA), by way of compliance with federal mandates for Information Quality (data transparency and accessibility), as a Highly Influential Scientific Assessment (HISA) (reproducible findings), and Foundations for Evidence-Based Policymaking, provides quality metadata for decision and policy-makers, and the general public alike. Details on our tools and processes are explained, along with how the NCA anticipates meeting requirements of open licensing and software documentation in the upcoming 5th National Climate Assessment. This presentation was given in January 2020 at the Earth Science Information Partners (ESIP) Winter Meeting in Bethesda, MD.

A number of challenges are currently impacting the quality of Earth science education in Australia. These include the introduction of a new Australian Curriculum that requires students learn about abstract Earth science concepts; the inadequacy of teachers' professional knowledge to address pedagogically these demands; the limitations of teacher education to alleviate pre-service teachers' perceived pedagogical inadequacy in teaching Earth science; and issues of students' durably held alternative conceptions about Earth science phenomena and perceived disengagement with the subject. These challenges call for research that investigates the efficacy of innovative conceptual change pedagogies that promote students' engagement with Earth science and enhance their conceptual understanding. In response to this need, this study investigated the value of using student-generated stopmotion animation, or 'slowmation', as a conceptual change instructional approach. This study employed a mixed-methods intervention research design, generating both quantitative and qualitative data, in order to investigate three research questions: (1) Does the process of constructing a slowmation have a significant effect on students' conceptual change? (2) How does the process of constructing a slowmation influence students' conceptual change; and (3) Is students' interest, generated by the construction of a slowmation, a significant predictor of conceptual change? Four classes of Year 9 students participated in this study. Two classes were treated as an intervention group and participated in the construction of a slowmation (N=52), while two comparison classes experienced 'teaching as usual' (N=43). All students in the intervention and comparison conditions completed a two-tiered multiple-choice test (i.e., the GeoQuiz), developed and validated by the researcher, which tested students' alternative Earth science conceptions before and after their participation in the study. A Likert-style survey that gauged students' interest in learning science, the Student Interest in Learning Science (SILS) Survey, was also administered to all students before and after the project. Selected students from the intervention condition were audio recorded to capture their discussions during the construction process, and the same students were interviewed about their learning experience upon completion of the project. In answer to the first research question, a significant improvement was found in the GeoQuiz scores of students who constructed a slowmation, which indicates that conceptual change occurred. At the same time, a significant improvement was also found for students in the comparison classes. This suggests that creating a slowmation was no more effective in bringing about conceptual change than teaching as usual. In response to the second research question, analysis of the qualitative data in this study found that the construction process afforded 'teachable moments' as students recursively checked the accuracy of their representations with their teacher. The construction process also stimulated students' enjoyment, which they perceived to enhance their learning. Despite these affordances, however, significant pedagogical considerations arose from the use of slowmation as an instructional strategy in a junior secondary school context. These issues appeared to inhibit opportunities for conceptual change to occur. Finally, in answer to the third research question, it was found that students' interest in learning about science, and geology, was significantly greater if they participated in the construction of a slowmation, compared to teaching as usual. Interest was also found to be a significant predictor of students' conceptual change. The findings from this study have important implications for understanding the value of using slowmation construction as a conceptual change strategy in a junior secondary science context. As such, they informed the development of a pedagogical framework, the Learning with Slowmation framework, for constructing slowmations in a junior secondary science context. This framework, as well as the significance and implications of the broader findings for improving teaching practice in Earth science education, are presented.

The proliferation of service-oriented architectures in the last decade has brought forward an important class of sophisticated distributed applications that are founded on the idea of composing multiple simple services into a complex, coherent whole. Such applications spanning multiple service invocations can be most effectively realized by means of workflows. When it comes to high performance workflow execution, distribution (outscaling) of services is a key concept and also a very straightforward advantage of the workflow paradigm. Concretely, both the constituent services of the workflow and the system that manages their invocations have to be distributed across an environment of computational devices. In a wide spectrum of applications, that entail heterogeneity of the encompassed computational devices, e.g., modern emergency management, invocations of optimal service instances in conjunction to their reliability are fundamental prerequisites of distributed workflow management. At the center of this thesis is a formal model that defines the distributed (i.e., scalable) execution of workflows. To extend this model for reliability in a novel way, which does not affect the scalability of execution, the Safety-Ring system service is presented. The idea behind Safety-Ring is to offer recovery for a wide range of node failures, which host active services of running workflows. To this end, the Safety-Ring provides a scalable, reliable, and consistent data store that is used for the storage of workflow execution state. The novel failure-recovery mechanism features effective reliability such that can be applied on the nodes that host the Safety-Ring service themselves, thus we say the Safety-Ring is self-healing. To apply the reliable (and distributed) execution model, enhanced by Safety-Ring, to heterogeneous node environments, that are predominantly composed of mobile de- vices, this thesis presents the Compass data access protocol. In providing scalable data lookup for its maintained data, the Safety-Ring assumes network runtime characteristics which are rather stable, and thus Safety-Ring implicitly optimizes for the number of queried nodes. Especially in mobile applications, where node network connectivity dynamically changes, data access protocols should aim at reducing the overall data lookup latency, rather than the number of queried nodes. Compass introduces latency optimal paths to each node, which dynamically adapt to changing network characteristics. The scalable data lookup of Safety-Ring is not affected. In case distributed execution of workflows spans services of continuous (stateful) type, their reliability is decoupled from the Safety-Ring. Since such service types are predominantly featured by devices of limited resources, novel approaches to resource conservative recovery of failures for continuous services have to be provided. This thesis builds on proven recovery techniques, such as passive-standby, so as to enhance them for redundancy of the continuous state and thus improve the overall reliability of the system. In doing so the redundancy of state is enforced by means of a lightweight, in terms of network overhead, consistency protocol which allows for its application in resource limited node environments. In order to improve the execution performance of distributed workflows, in terms of throughput, this thesis offers a novel concept to services distribution. At the heart of our approach lie decentralized controllers that autonomously perform dynamic reconfiguration of the execution environment in terms of available services. This primarily affects the Safety-Ring service and all application services. Thereby, the goals of the controllers are to prevent workflow execution bottlenecks and unnecessary service deployments that waste resources. Since the controllers are equipped at any node in the system and can affect any other node of the system we say that the distributed workflow execution model is self-optimizing. Finally, all the presented concepts are implemented within the context of the OSIRIS distributed workflow engine and quantitatively evaluated in a series of experiments. The results of experiments confirm the benefits of our concepts for the distributed workflow execution model.

Radiation therapy has been developed more than 100 years.Recently,with the rapid development of scientific technology,equipment for radiation therapy has been significantly improved from the application of original X-ray equipment for radiotherapy,60Co teletherapy unit to a current application of a system including main therapy equipment such as linear accelerator,brachytherapy machine and including auxiliary apparatus such as simulator,treatment planning system,image-guided apparatus and various body position fixing devices and so on.A staff including doctor,therapist,physicist,engineer and nurse was constructed with work dividing deepen.In recent years,researchers have tried their best to manage various departments collaboratively and efficiently,to ensure quality control and quality assurance,to avoid radiation accident,and to improve therapeutic effect in radiation therapy.In this paper,establishment of an integrated management system or workflow by introducing MOSAIQ(⑧) system and combining with actual situation in hospital was described,and experience for optimization of workflow in radiation therapy management with MOSAIQ⑧ system was summarized.It was shown that MOSAIQ(⑧) system could be a potentially tool to optimize workflow in radiation therapy management,strengthen personnel or information management,enhance quality control and save patients’ treatment time. Key words: Neoplasms; Radiotherapy; Quality control; MOSAIQ(R) system; Computer communication networks

The increasing availability of Earth Observation and geographic data implies great opportunities for those capable to efficiently address the problems arising from the management of this huge amount of data. An efficient management of these data means to respond to the paradigm of the 4 V that usually applies to the problem of the Big Data: Volume - the sheer size of the “data at rest”, Velocity - the speed of new data arriving, Variety - the different manifold, and Veracity - trustworthiness and issues of provenance. The problem of Big Data is not only in the geospatial realm of Earth observation data, but in general in all the location-based data which basically are the main contributors to the deluge of big data. In addition to the need of managing (store, search & find when needed) these data efficiently, the problem arises from the analysis of these data. They need to be quickly processed in order to quickly extract the information content, then they must be analysed in conjunction with other data sources in order to express their real value in the construction of new knowledge. These processes are hastened by the advent of an increasing machine-to-machine communication. The automation of the data analysis requires standardized and linked data so that they can be processed by machines without human intervention. The problem with the standardization of geospatial data is solved by simply observing not only the best practices shared at European level, but mainly the regulatory scenario dictated by the INSPIRE Directive . The publication of spatial data as Linked Open Data may then leverage the reuse of common ontologies and vocabularies that allow the connection of geospatial data with other heterogeneous information. This way new scenarios and business opportunities may arise, as in the case of the real estate market that is mentioned in this article. This contribution aims to identify some business opportunities, related to Linked Open Data and arising from the imminent availability of the Sentinel satellite data, with the European program Copernicus, for companies operating in the so-called downstream services of Earth observation.

The new model of interaction suggested by Cloud Computing has experienced a significant diffusion over the last years thanks to its capability of providing customers with the illusion of an infinite amount of reliable resources. Nevertheless, the challenge of efficiently manage a large collection of virtual computing nodes has just been partially moved from the customer's private datacenter to the larger provider's infrastructure that we generally address as “the cloud”. A lot of effort - in both academic and industrial field - is therefore concentrated on policies for the efficient and autonomous management of virtual infrastructures. The research on this topic is further encouraged by the diffusion of cheap and portable sensors and the availability of almost ubiquitous Internet connectivity that are constantly creating large flows of information about the environment we live in. The need for fast and reliable mechanisms to process these considerable volumes of data has inevitably pushed the evolution from the initial scenario of a single (private or public) cloud towards cloud interoperability, giving birth to several forms of collaboration between clouds. The efficient resource management is further complicated in these heterogeneous environments, making autonomous administration more and more desirable. In this thesis, we initially focus on the challenges of autonomic management in a single-cloud scenario, considering the benefits and shortcomings of centralized and distributed solutions and proposing an original decentralized model. Later in this dissertation, we face the challenge of autonomic management in large interconnected cloud environments, where the movement of virtual resources across the infrastructure nodes is further complicated by the intrinsic heterogeneity of the scenario and difficulties introduced by the higher latency medium between datacenters. According to that, we focus on the cost model for the execution of distributed data-intensive application on multiple clouds and we propose different management policies leveraging cloud interoperability.

The ESIP Agriculture and Climate Cluster (ACC) has, over the past several years, been developing a “pipeline” to transform ongoing or recent agriculture / climate-related research and applications into case studies that become part of the U.S. Climate Resilience Toolkit (CRT; https://toolkit.climate.gov/). These CRT case studies document “how people are building resilience for their businesses and in their communities,” using relevant data and tools. To more explicitly capture the flow of data from initial observations, to derived information, to integration into end user decision-making, and to the values/benefits of decisions, the ACC is involved in an ongoing ESIP Lab-funded project, “Enabling the encoding and visualization of provenance metadata for better discovery and understanding of climate resilience strategies for agriculture-related decision-making.” Capturing this data-to-decisions (D2D) provenance can inform the value of socioeconomic data and information, and help contribute toward better real-world decision making for agriculture and climate resilience strategies. ContributorsNancy J. Hoebelheinrich, Principal, Knowledge Motifs LLCDr. Bill Teng, Principal Scientist, NASA GSFC (ADNET Systems)Dr. Richard Bernknopf, Research Professor, Department of Economics, University of New MexicoDr. Brian Wee, Founder and Managing Director, Massive ConnectionsDr. Arika Virapongse, Principal, Middle Path EcoSolutions For more information, see goo.gl/PHLLNp

This paper presents the specific process used by members of the Earth Science Information Partners (ESIP) Semantic Harmonization Cluster, to harmonize cryospheric terms gathered by the Global Cryosphere Watch (GCW) with two leading semantic resources used in the Earth and Environmental science communities—the Semantic Web for Earth and Environmental Terminology (SWEET) and the Environment Ontology (ENVO). This process led to updates to both ENVO and SWEET as well as the development of an alignment file relating cryospheric terms in ENVO to those in SWEET. In addition, we summarize several leading practices which may be applied to other projects/realms within Earth and Environmental science and perhaps beyond, as well as suggest a generalized process for doing so. This paper describes the history of the effort, the technical and decision-making processes used to resolve differences between semantic resources, and describes several issues encountered, with a focus on those that were addressed during the effort. Lessons learned, examples of the problems encountered and a summary of resulting leading practices growing out of this work is provided.

Presentation at the pre-ESIP FAIR dataset quality information (DQI) workshop held virtually on Monday July 13, 2020, prior to the virtual ESIP 2020 summer meeting (July 14 – 24, 2020). This one-day workshop with two live sessions is co-organized by the Information Quality Cluster (IQC) of the Earth Science Information Partners (ESIP) and the Evaluation and Quality Control (EQC) Team of Barcelona Supercomputing Center (BSC) and sponsored by ESIP.

Introduction to ESIP Information Quality Cluster. This presentation was given at the Earth Science Information Partners (ESIP) Winter Meeting held in Bethesda, MD in January 2020.