Donna L. Gresh

An architecture for a scientific visualization system

The architecture of the Data Explorer, a scientific visualization system, is described. Data Explorer supports the visualization of a wide variety of data by means of a flexible set of visualization modules. A single powerful data model common to all modules allows a wide range of data types to be imported and passed between modules. There is integral support for parallelism, affecting the data model and the execution model. The visualization modules are highly interoperable, due in part to the common data model, and exemplified by the renderer. An execution model facilitates parallelization of modules and incorporates optimizations such as caching. The two-process client-server system structure consists of a user interface that communicates with an executive via a dataflow language.<<ETX>>

WEAVE: a system for visually linking 3-D and statistical visualizations, applied to cardiac simulation and measurement data

WEAVE (Workbench Environment for Analysis and Visual Exploration) is an environment for creating interactive visualization applications. WEAVE differs from previous systems in that it provides transparent linking between custom 3D visualizations and multidimensional statistical representations, and provides interactive color brushing between all visualizations. The authors demonstrate how WEAVE can be used to rapidly prototype a biomedical application, weaving together simulation data, measurement data, and 3D anatomical data concerning the propagation of excitation in the heart. These linked statistical and custom three-dimensional visualizations of the heart can allow scientists to more effectively study the correspondence of structure and behavior.

Workforce optimization: identification and assignment of professional workers using constraint programming

Matching highly skilled people to available positions is a high-stakes task that requires careful consideration by experienced resource managers. A wrong decision may result in significant loss of value due to understaffing, underqualification or overqualification of assigned personnel, and high turnover of poorly matched workers. While the importance of quality matching is clear, dealing with pools of hundreds of jobs and resources in a dynamic market generates a significant amount of pressure to make decisions rapidly. We present a novel solution designed to bridge the gap between the need for high-quality matches and the need for timeliness. By applying constraint programming, a subfield of artificial intelligence, we are able to deal successfully with the complex constraints encountered in the field and reach near-optimal assignments that take into account all resources and positions in the pool. The considerations include constraints on job role, skill level, geographical location, language, potential retraining, and many more. Constraints are applied at both the individual and team levels. This paper introduces the technology and then describes its use by IBM Global Services, where large numbers of service and consulting employees are considered when forming teams assigned to customer projects.

Applying supply chain optimization techniques to workforce planning problems

The IBM Research Division has developed the Resource Capacity Planning (RCP) Optimizer to support the Workforce Management Initiative (WMI) of IBM. RCP applies supply chain management techniques to the problem of planning the needs of IBM for skilled labor in order to satisfy service engagements, such as consulting, application development, or customer support. This paper describes two RCP models and presents two approaches to solving each of them. We also describe the motivation for using one approach over another. The models are built using the Watson Implosion Technology toolkit, which consists of a supply chain model, solvers for analysis and optimization, and an Application Programming Interface (API) for developing a solution. The models that we built solve two core resource planning problems, gap/glut analysis and resource action planning. The gap/glut analysis is similar to material requirements planning (MRP), in which shortages (gaps) and excesses (gluts) of resources are determined on the basis of expected demand. The goal of the resource action planning problem is to determine what resource actions to take in order to fill the gaps and reduce the gluts. The gap/glut analysis engine is currently deployed within the IBM service organization to report gaps and gluts in personnel.

Mapping, modeling, and visual exploration of structure-function relationships in the heart

It is becoming clear that the emergent, integrative behaviors of biological systems result from complex interactions between all system components, and that knowledge of each component is not sufficient to understand such behaviors. In this paper, we describe our approach to the integrative modeling of cardiac function. This approach spans multiple levels of biological analysis, ranging from subcellular to tissue. We have applied diverse analytical methods, including imaging techniques for measurement of anatomic structure and biophysical and biochemical responses of cells and tissue, parallel computing techniques for the numerical solution of large systems of model equations, and interactive visual exploration of model dynamic behavior.

Optimatch: Applying Constraint Programming to Workforce Management of Highly-skilled Employees

Today many companies face the challenge of matching highly-skilled professionals to high-end positions in large organizations and human deployment agencies. Unlike traditional Workforce Management problems such as shift scheduling, highly-skilled employees are professionally distinguishable from each other and hence non-interchangeable. Our work specifically focuses on the services industry, where much of the revenue comes from the assignment of highly professional workers. Here, non-accurate matches may result in significant monetary losses and other negative effects. We deal with very large pools of both positions and employees, where optimal decisions should be made rapidly in a dynamic environment. Since traditional Operations Research (OR) methods fail to answer this problem, we employ Constraint Programming (CP), a subfield of Artificial Intelligence with strong algorithmic foundations. Our CP model builds on new constraint propagators designed for this problem (but applicable elsewhere), as well as on information retrieval methods used for analyzing the complex text describing high-end professionals and positions. Optimatch, which is based on this technology and is being used by IBM services organizations, provides strong experimental results.

PRIMA: A case study of using information visualization techniques for patient record analysis

We have created an application, called PRIMA (Patient Record intelligent Monitoring and Analysis), which can be used to visualize and understand patient record data. It was developed to better understand a large collection of patient records of bone marrow transplants at Hadassah Hospital in Jerusalem, Israel. It is based on an information visualization toolkit, Opal, which has been developed at the IBM T.J. Watson Research Center. Opal allows intelligent, interactive visualization of a wide variety of different types of data. The PRIMA application is generally applicable to a wide range of patient record data, as the underlying toolkit is flexible with regard to the form of the input data. This application is a good example of the usefulness of information visualization techniques in the bioinformatics domain, as these techniques have been developed specifically to deal with diverse sets of often unfamiliar data. We illustrate several unanticipated findings which resulted from the use of a flexible and interactive information visualization environment.

An interactive framework for visualizing foreign currency exchange options

Analyzing options is a complex, multi-variate process. Option behavior depends on a variety of market conditions which vary over the time course of the option. The goal of this project is to provide an interactive visual environment which allows the analyst to explore these complex interactions, and to select and construct specific views for communicating information to non-analysts (e.g., marketing managers and customers). In this paper we describe an environment for exploring 2- and 3-dimensional representations of options data, dynamically varying parameters, examining how multi-variate relationships develop over time, and exploring the likelihood of the development of different outcomes over the life of the option. We also demonstrate how this tool has been used by analysts to communicate to non-analysts how particular options no longer deliver the behavior they were originally intended to provide.

Visualization, optimization, business strategy: a case study

We describe a visualization application intended for operational use in formulating business strategy in the customer service arena. The visualization capability provided in this application implicitly allows the user to better formulate the objective function for large optimization runs which act to minimize costs based on certain input parameters. Visualization is necessary because many of the inputs to the optimization runs are themselves strategic business decisions which are not pre-ordained. Both information visualization presentations and three-dimensional visualizations are included to help users better understand the cost/benefit tradeoffs of these strategic business decisions. Here, visualization explicitly provides value not possible algorithmically, as the perceived benefit of different combinations of service level does not have an a priori mathematical formulation. Thus, we take advantage of the fundamental power of visualization, bringing the users intuition and pattern recognition skills into the solution, while simultaneously taking advantage of the strength of algorithmic approaches to quickly and accurately find an optimal solution to a well-defined problem.

Case study: an environment for understanding protein simulations using game graphics

We describe a visualization system designed for interactive study of proteins in the field of computational biology. Our system incorporates multiple, custom, three-dimensional and two-dimensional linked views of the proteins. We take advantage of modem commodity graphics cards, which are typically designed for games rather than scientific visualization applications, to provide instantaneous linking between views and three-dimensional interactivity on standard personal computers. Furthermore, we anticipate the usefulness of game techniques such as bump maps and skinning for scientific applications.