We use AI techniques to create a modeling environment that makes sophisticated models accessible to non-experts. Our AI framework for Model INTegration (MINT) assists users to explore scenarios, which MINT can run in a local environment or at scale in a supercomputing facility. We are using MINT with hydrology, agriculture, and drought models for food security.

Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort. We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.

Scientific software is crucial for understanding, reusing and reproducing results in computational sciences. Software is often stored in code repositories, which may contain human readable instructions necessary to use it and set it up. However, a significant amount of time is usually required to understand how to invoke a software component, prepare data in the format it requires, and use it in combination with other software. In this paper we introduce OKG-Soft, an open knowledge graph that describes scientific software in a machine readable manner. OKG-Soft includes: 1) an ontology designed to describe software and the specific data formats it uses; 2) an approach to publish software metadata as an open knowledge graph, linked to other Web of Data objects; and 3) a framework to annotate, query, explore and curate scientific software metadata. OKG-Soft supports the FAIR principles of findability, accessibility, interoperability, and reuse for software. We demonstrate the benefits of OKG-Soft with two applications: a browser for understanding scientific models in the environmental and social sciences, and a portal to combine climate, hydrology, agriculture, and economic software models.

Scientific data is continuously generated throughout the world. However, analyses of these data are typically performed exactly once and on a small fragment of recently generated data. Ideally, data analysis would be a continuous process that uses all the data available at the time, and would be automatically re-run and updated when new data appears. We present a framework for automated discovery from data repositories that tests user-provided hypotheses using expert-grade data analysis strategies, and reassesses hypotheses when more data becomes available. Novel contributions of this approach include a framework to trigger new analyses appropriate for the available data through lines of inquiry that support progressive hypothesis evolution, and a representation of hypothesis revisions with provenance records that can be used to inspect the results. We implemented our approach in the DISK framework, and evaluated it using two scenarios from cancer multi-omics: 1) data for new patients becomes available over time, 2) new types of data for the same patients are released. We show that in all scenarios DISK updates the confidence on the original hypotheses as it automatically analyzes new data.

OntoSoft is a distributed semantic registry for scientific software. This paper describes three major novel contributions of OntoSoft: 1) a software metadata registry designed for scientists, 2) a distributed approach to software registries that targets communities of interest, and 3) metadata crowdsourcing through access control. Software metadata is organized using the OntoSoft ontology along six dimensions that matter to scientists: identify software, understand and assess software, execute software, get support for the software, do research with the software, and update the software. OntoSoft is a distributed registry where each site is owned and maintained by a community of interest, with a distributed semantic query capability that allows users to search across all sites. The registry has metadata crowdsourcing capabilities, supported through access control so that software authors can allow others to expand on specific metadata properties.

Collaboration is ubiquitous in today's science, yet there is limited support for coordinating scientific work. The general-purpose tools that are typically used (e.g., email, shared document editing, social coding sites), have still not replaced in-person meetings, phone calls, and extensive emails needed to coordinate and track collaborative activities. Scientists with diverse knowledge and skills around the globe could collaborate by opening scientific processes that expose all tasks and activities publicly to achieve a shared scientific question. This paper describes the Organic Data Science framework to support scientific collaborations that revolve around complex science questions that require significant coordination, entice contributors to remain engaged for extended periods of time, and enable continuous growth to accommodate new contributors as the work evolves over time. We discuss how the design of this framework incorporates principles followed by successful on-line communities. We present initial results to date of several communities that are collaborating using this framework.

Contributors in hundreds of semantic wiki sites are creating structured information in RDF every day, thus growing the semantic content of the Web in spades. Although wikis have been analyzed extensively, there has been little analysis of the use of semantic wikis. The Provenance Bee Wiki was created to gather and aggregate data from these sites, show how this content is growing over time, and to make all this detailed data readily available to the research community. We also present a high-level analysis of the almost 600 wikis indexed in Provenance Bee Wiki that have less than 5,000 pages.

This paper presents OntoSoft, an ontology to describe metadata for scientific software. The ontology is designed considering how scientists would approach the reuse and sharing of software. This includes supporting a scientist to: 1) identify software, 2) understand and assess software, 3) execute software, 4) get support for the software, 5) do research with the software, and 6) update the software. The ontology is available in OWL and contains more than fifty terms. We are using OntoSoft to structure a software registry for geosciences, and to develop user interfaces to capture its metadata.

Semantic wikis augment wikis with semantic properties that can be used to structure content that can therefore be aggregated and queried through reasoning. Semantic wikis have been adopted by many communities for very diverse purposes, such as organizing genomic knowledge, coding software, learn about hobbies, and tracking environmental data. Although wikis have been analyzed extensively, there has been little analysis of the use of semantic wikis. In this paper, we analyze the formalization of knowledge in 230 semantic wiki communities. We report our findings in terms of the edits of semantic concepts and properties, as well as the communities of editors for these semantic features of the wikis.

Although to-do lists are a ubiquitous form of personal task management, there has been no work on intelligent assistance to automate, elaborate, or coordinate a user’s to-dos. Our research focuses on three aspects of intelligent assistance for to-dos. We investigated the use of intelligent agents to automate to-dos in an office setting. We collected a large corpus from users and developed a paraphrase-based approach to matching agent capabilities with to-dos. We also investigated to-dos for personal tasks and the kinds of assistance that can be offered to users by elaborating on them on the basis of substep knowledge extracted from the Web. Finally, we explored coordination of user tasks with other users through a to-do management application deployed in a popular social networking site. We discuss the emergence of Social Task Networks, which link users‘ tasks to their social network as well as to relevant resources on the Web. We show the benefits of using common sense knowledge to interpret and elaborate to-dos. Conversely, we also show that to-do lists are a valuable way to create repositories of common sense knowledge about tasks.

This paper describes an approach to allow end users to define new procedures through tutorial instruction. Our approach allows users to specify procedures in natural language in the same way that they would instruct another person, while the system handles incompleteness and ambiguity inherent in natural human instruction and formulates follow up questions. We describe the key features of our approach, which include exposing prior knowledge, deductive and heuristic reasoning, shared learning state, and selectively asking questions to the user. We also describe how those key features are realized in our implemented TellMe system, and present preliminary user studies where non-programmers were able to easily specify complex multi-step procedures.

To-Do lists are widely used for personal task management. We propose a novel approach to assist users in managing their To-Dos by matching them to How-To knowledge from the Web. We have implemented a system that, given a To-Do item, provides a number of possibly matching How-Tos, broken down into steps that can be used as new To-Do entries. Our implementation is in the form of a web service that can be easily integrated into existing To-Do applications. This can help users by providing them with an approach to tackle the To-Do by listing smaller, more actionable To-Dos. In this paper we present our implementation, an evaluation of the matching component over two sets of To-Do corpora with very different characteristics, and a discussion of the results.

Computational workflows are a powerful paradigm to represent and manage complex applications, particularly in large-scale distributed scientific data analysis. Workflows represent application components that result in individual computations as well as their interdependences in terms of dataflow. Workflow systems use these representations to manage various aspects of workflow creation and execution for users, such as the automatic assignment of execution resources. This article describes an approach to automating a new aspect of the process: the selection of application components and data sources. We present a novel approach that enables users to specify varying degrees of detail and amount of constraints in a workflow request, including the specification of constraints on input, intermediate or output data in the workflow, abstract workflow component classes rather than specific component implementations, and generic reusable workflow templates that express a pre-defined combination of components. The algorithm elaborates the user request into a set of fully ground workflows with specific choices of data sources and codes to be used so that they can be submitted for mapping and execution. The algorithm searches through the space of possible candidate workflows by creating increasingly more specialized versions of the original template and eliminating candidates that violate constraints cumulated in the candidate workflow as components and data sources are selected. A novel feature of our approach is that it assumes a distributed architecture where data and component catalogues are separate from the workflow system. The algorithm explicitly poses queries to external catalogues, and therefore any reasoning regarding data or component properties is not assumed to occur within the workflow system. We describe our implementation of this approach in the Wings workflow system. This implementation uses the W3C Web Ontology Language and associated reasoners to implement the workflow system as well as the data and component catalogues. This research demonstrates the use of artificial intelligence techniques to support the kinds of automation envisioned by the scientific community for large-scale distributed scientific data analysis.

Workflow systems can manage complex scientific applications with distributed data processing. Although some workflow systems can represent collections of data with very compact abstractions and manage their execution efficiently, there are no approaches to date to manage collections of application components required to express some scientific applications. We present an approach to handle collections of components and data alike in expressive workflow templates whose basic structure is reusable. We also present an algorithm that can elaborate abstract compact workflow templates into execution-ready workflows that enumerate all computations to be carried out. We implemented the proposed approach in the Wings workflow system. Our work is motivated by real-world complex scientific applications that require handling of nested collections of both components and data.

Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multi-dimensional parameter space. While some performance parameters such as grouping of workflow components and their mapping to machines do not a ect the accuracy of the output, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple dimensions of the parameter space. Using two real-world applications in the spatial data analysis domain, we present an experimental evaluation of the proposed framework.

Workflows are becoming an increasingly more common paradigm to manage scientific analyses. As workflow repositories start to emerge, workflow retrieval and discovery becomes a challenge. Studies have shown that scientists wish to discover workflows given properties of workflow data inputs, intermediate data products, and data results. However, workflows typically lack this information when contributed to a repository. Our work addresses this issue by augmenting workflow descriptions with constraints derived from properties about the workflow components used to process data as well as the data itself. An important feature of our approach is that it assumes that component and data properties are obtained from catalogs that are external to the workflow system, consistent with current architectures for computational science.

This paper describes our experience to date employing the systematic mapping and optimization of large- scale scientific application workflows to current and future parallel platforms. The overall goal of the project is to integrate a set of system layers - application program, compiler, run-time environment, knowledge representation, optimization framework, and workflow manager - and through a systematic strategy for workflow mapping, our approach will exploit the vast machine resources available in such parallel platforms to dramatically increase the productivity of application programmers. In this paper, we describe the representation of a biomedical imaging application as a workflow, our early experiences in integrating the set of tools brought together for this project, and implications for future applications.

Assisting users with to-do lists presents new challenges for intelligent user interfaces. This paper presents a detailed analysis of to-do list entries jotted by users of a system that automates tasks for users that we would like to extend to assist users with their to-do entries. We also present four distinct stages of interpretation of to-do entries that can be accomplished and evaluated separately. A system that has good performance in any of these four stages can provide intelligent assistance that is useful to users. Author Keywords User interfaces, to-do lists, automated assistance, natural language interpretation, knowledge acquisition, knowledge collection from web volunteers, office assistants. ACM Classification Keywords H5.m. Information interfaces and presentation (e.g., HCI):

Abstract Our research focuses on creating and executing large‐scale scientific workflows that often involve thousands of computations over distributed, shared resources. We describe an approach to workflow creation and refinement that uses semantic representations to (1) describe complex scientific applications in a data‐independent manner, (2) automatically generate workflows of computations for given data sets, and (3) map the workflows to available computing resources for efficient execution. Our approach is implemented in the Wings/Pegasus workflow system and has been demonstrated in a variety of scientific application domains. This paper illustrates the application‐level provenance information generated Wings during workflow creation and the refinement provenance by the Pegasus mapping system for execution over grid computing environments. We show how this information is used in answering the queries of the First Provenance Challenge. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract The first Provenance Challenge was set up in order to provide a forum for the community to 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 to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarize the participants' contributions. Copyright © 2007 John Wiley & Sons, Ltd.

When designing mixed-initiative systems, full formalization of all potentially relevant knowledge may not be cost-effective or practical. This paper motivates the need for semi-formal representations that combine machine-processable structures with free text statements, and discusses the need to design them in a way that makes the free text more amenable to automated structuring and processing. Our work is done in the context of argumentation systems, and has explored a range of tradeoffs in combining informal free-text statements with formal connectors. The paper compares alternative argument representations which combine structured argument connectors with free text. We discuss merits of the systems based on a variety of analysis structures that we have collected from Web users to date.