This work reports the findings from two studies which evaluate how the presentation of information about medicine side effects influences the understanding of the associated risks. We analyze the performance in terms of understanding and preferences of an initial set of five visual supports to communicate the likelihood of side effects, that were presented via an online survey. After evaluation of results, a follow-up survey, based on a modified set of visualizations, provided additional insights into the most promising visual support. In both studies, numeracy of respondents was collected and subsequently analyzed. We obtained 75 (initial study) and 259 (follow-up study) valid responses. The initial study ruled out spider and pie charts which confused the participants and pointed seemed to indicate bar charts as most promising support. The second study indicated that the grouped icon bar chart may be the most consensual variation among bar charts.

We describe the issues surrounding the visualization and communication of risk. Colloquially, “risk” has many meanings; we define it as a probability distribution (discrete or continuous), the entirety of which is important to be understood. While the display of uncertainty has had some measure of attention, though primarily in one dimension, the communication of risk is complicated by the difficulty for many people to accurately understand and contemplate the idea of a probability distribution to begin with, in part due to well-documented psychological biases. We review the scientific and experimental background of this subject and propose guidelines for the effective presentation of risk information, in the specific application area of transportation in a metropolitan area.

Today many companies face the challenge of matching highly-skilled professionals to high-end positions in large organizations and human deployment agencies. Non-accurate matches in these businesses can result in significant monetary losses and other negative effects. Unlike traditional Workforce Management (WM) problems such as shift scheduling, highly-skilled employees are professionally distinguishable from each other and hence non-interchangeable. Therefore, the techniques used for shift-scheduling can't be applied to the highly-skilled WM domain. Our work focuses on providing a Constraint Programming solution for supporting the assignment of highly-skilled professionals. Our experience shows that CP is well adapted to this problem. CP supports very well the underlying constraints. In addition, the rich expressive language supported by CP allows us to provide a convenient mechanism for changing and adding new matching and preference constraints. Based on this technology, we have built a tool that is currently being used by IBM service organizations and provides strong business results.

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.

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.

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.

A Sense-and-Respond enterprise is one that is constantly evolving to keep up with the rapidly a changing business environment. While these enterprises are "data rich," they are often at the same time "information poor." There is a critical need to: • Display large amounts of data – to see the both the "needle" and the "hay." • Make complex patterns stand out – to focus on relationships that are relevant. • Interact with the data – to isolate any interesting subset as the analysis proceeds. • Correlate across many variables – to identify complex interrelationships. • Be easy to understand – to make learning quick, to make the information usable by a wide audience and to facilitate communication among decision-makers. Monitoring the vast amounts of data is more than just providing real time alerts. It about saying “There is something going on here and here is all the information that will help you find the needle in the haystack and make a well informed timely decision”. In this paper we introduce data visualization techniques that are well-suited to finding the needle in the haystack because it can easily present large volumes of data, provide interactivity to explore the data, make visual patterns easy to see and make multivariate analysis simple and easy to comprehend.

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 user's 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.

Information Visualization (InfoVis) encompasses techniques of visualization that deal primarily with abstract data, that is, data for which the user has no preconceived mental model. This is in contrast to, for example, volume or flow data. For this reason, interaction is particularly important in InfoVis: for exploration, analysis, and presentation of data. Interaction allows the user to implicitly form mental models of the correlations and relationships in the data, through recognition of patterns, marking or focusing in on those patterns, forming mental hypotheses and testing them, and so on. Some interaction techniques are very specific to InfoVis (even though they can be and are applied to other areas as well), such as Focus+Context and Linking+Brushing. This paper surveys InfoVis techniques with an orientation toward interaction aspects, rather than data model or display dimension. It also tries to put the work into perspective by including aspects such as user studies for the evaluation of methods.

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.

Bioinformatics poses a challenging domain for computer generated visualization techniques. For starters, is this a domain of scientific or information visualization? Typical scientific data sets have inherent spatial metaphors such as fluid flow in the human heart or chemical bonding of molecules. In contrast, exploration of cyberspace or searching of document repositories are traditional information visualization problems requiring the creation of a visual metaphor. How do we handle data mining of genomic information where the visual metaphor might not be obvious but the results can push forward scientific discovery?

In December 1999, IBM announced the start of a five-year effort to build a massively parallel computer, to be applied to the study of biomolecular phenomena such as protein folding. The project has two main goals: to advance our understanding of the mechanisms behind protein folding via large-scale simulation, and to explore novel ideas in massively parallel machine architecture and software. This project should enable biomolecular simulations that are orders of magnitude larger than current technology permits. Major areas of investigation include: how to most effectively utilize this novel platform to meet our scientific goals, how to make such massively parallel machines more usable, and how to achieve performance targets, with reasonable cost, through novel machine architectures. This paper provides an overview of the Blue Gene project at IBM Research. It includes some of the plans that have been made, the intended goals, and the anticipated challenges regarding the scientific work, the software applic ation, and the hardware design.

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.