Jihie Kim

Wings: Intelligent Workflow-Based Design of Computational Experiments

Describes the Wings intelligent workflow system that assists scientists with designing computational experiments by automatically tracking constraints and ruling out invalid designs, letting scientists focus on their experiments and goals.

An integrated environment for knowledge acquisition

This paper describes an integrated acquisition interface that includes several techniques previously developed to support users in various ways as they add new knowledge to an intelligent system. As a result of this integration, the individual techniques can take better advantage of the context in which they are invoked and provide stronger guidance to users. We describe the current implementation using examples from a travel planning domain, and demonstrate how users can add complex knowledge to the system.

An intelligent assistant for interactive workflow composition

Complex applications in many areas, including scientific computations and business-related web services, are created from collections of components to form computational workflows. In many cases end users have requirements and preferences that depend on how the workflow unfolds, and that cannot be specified beforehand. Workflow editors enable users to formulate workflows, but the editors need to be augmented with intelligent assistance in order to help users in several key aspects of the task, namely: 1) keeping track of detailed constraints across selected components and their connections; 2) specifying the workflow flexibly, e.g., top-down, bottom-up, from requirements, or from available data; and 3) taking partial or incomplete descriptions of workflows and understanding the steps needed for their completion. We present an approach that combines knowledge bases (that have rich representations of components) together with planning techniques (that can track the relations and constraints among individual steps). We illustrate the approach with an implemented system called CAT (Composition Analysis Tool) that analyzes workflows and generates error messages and suggestions in order to help users compose complete and consistent workflows.

Simplifying construction of complex workflows for non-expert users of the Southern California Earthquake Center Community Modeling Environment

Workflow systems often present the user with rich interfaces that express all the capabilities and complexities of the application programs and the computing environments that they support. However, non-expert users are better served with simple interfaces that abstract away system complexities and still enable them to construct and execute complex workflows. To explore this idea, we have created a set of tools and interfaces that simplify the construction of workflows. Implemented as part of the Community Modeling Environment developed by the Southern California Earthquake Center, these tools, are integrated into a comprehensive workflow system that supports both domain experts as well as non expert users.

Learning to Detect Conversation Focus of Threaded Discussions

In this paper we present a novel feature-enriched approach that learns to detect the conversation focus of threaded discussions by combining NLP analysis and IR techniques. Using the graph-based algorithm HITS, we integrate different features such as lexical similarity, poster trustworthiness, and speech act analysis of human conversations with feature-oriented link generation functions. It is the first quantitative study to analyze human conversation focus in the context of online discussions that takes into account heterogeneous sources of evidence. Experimental results using a threaded discussion corpus from an undergraduate class show that it achieves significant performance improvements compared with the baseline system.

A semantic framework for automatic generation of computational workflows using distributed data and component catalogues

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.

User studies of knowledge acquisition tools: methodology and lessons learned

Knowledge acquisition research concerned with the development of knowledge acquisition tools is in need of a methodological approach to evaluation. This paper describes experimental methodology to conduct studies and experiments of users modifying knowledge bases with knowledge acquisition tools. The paper also reports on the lessons learned from several experiments that have been performed using this methodology. The hope is that it will help others design user evaluations of knowledge acquisition tools. Ideas are discussed for improving the current methodology and some open issues that remain.

An integrated framework for performance-based optimization of scientific workflows

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 affect 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.

Supporting plan authoring and analysis

Interactive tools to help users author plans or processes are essential in a variety of domains. KANAL helps users author sound plans by simulating them, checking for a variety of errors and presenting the results in an accessible format that allows the user to see an overview of the plan steps or timelines of objects in the plan. From our experience in two domains, users tend to interleave plan authoring and plan checking while extending background knowledge of actions. This has led us to refine KANAL to provide a high-level overview of plans and integrate a tool for refining the background knowledge about actions used to check plans. We report on these lessons learned and new directions in KANAL.

Can Online Discussion Participation Predict Group Project Performance? Investigating the Roles of Linguistic Features and Participation Patterns.

Although many college courses adopt online tools such as Q&A online discussion boards, there is no easy way to measure or evaluate their effect on learning. As a part of supporting instructional assessment of online discussions, we investigate a predictive relation between characteristics of discussion contributions and student performance. Inspired by existing work on dialogue acts, project-based learning, and instructional analysis of student-generated text in generating predictive models, we make use of dialogue roles, linguistic features, and work patterns. In particular, we model the Q&A dialog roles that participants play, emotional features covered by LIWC (Linguistic Inquiry and Word Count), cohesiveness of the dialogue, the coherence captured by Coh-Metrix, and temporal patterns of participation. We use a discussion corpus from eight semesters of a computer science course, covering conversations of 173 student groups (370 students). We first remove various noises in student discussion data and normalize the discussion data. We then apply machine learning techniques and text analysis tools for classifying dialogue features efficiently. The extracted dialogue and participation features are used as predictive variables for project grades. The correlation and regression analyses indicate that the number of answers provided to others, the number of positive emotion expressions, and how early students communicate their problems before the deadline correlate with project grades. This finding confirms the argument that in assessing student online activities, we need to capture how they interact, not just how often they participate.