We describe and analyze the idea of data-enhanced predictive modeling (DEM). The term “enhanced” here refers to the case that the data used for modeling is sampled not from the true target population, but from an alternative (closely related) population, from which much larger samples are available. This leads to a “bias-variance” tradeoff, which implies that in some cases, DEM can improve pred...

In this paper, we explore the surface tessellation problem, in particular, the problem of meshing a surface with the added consideration of incorporating constructible building components. When a surface is tessellated into discrete counterparts, there are certain conditions that usually occur at the boundary of the surface, especially, when the surface is being trimmed, for example, irregularl...

The goal of predictive monitoring is to help the business achieve their goals, help them take the right business path, predict outcomes, estimate delivery time, and make business processes risk aware. In this thesis, we have carefully collected and reviewed in detail all literature which falls in this process mining category. The objective of the thesis is to design a Predictive Monitoring Fram...

This paper highlights the interrelationship of research completed by a team of investigators and presented in the several individual papers comprising this Special Section on the Office of Naval Research Sponsored Sea Ice Electromagnetic Accelerated Research Initiative. The objectives of the initiative were to: understand the mechanisms and processes that link the morphological and physical pro...

Distributed heterogeneous resources coordinated by fast networks provide a new platform for resource-intensive parallel applications. However, careful scheduling of such applications is required to achieve their performance potential. Since resource load varies dynamically, the scheduling mechanism may consider redistribution of application tasks to improve performance. In this paper, we focus ...

A basic problem in empirical economics involves using data from one domain to make out-of-sample predictions for a different, but related environment. When the choice data are binary, a canonical method for making these types of predictions is the logistic choice model. We investigate if it is possible to improve out-of-sample predictions by changing two aspects of the canonical approach: 1) Us...

Segal and Spaeth’s The Supreme Court and the Attitudinal Model Revisisted argues that judges are policymakers who decide cases primarily (and sometimes exclusively) on the basis of their personal policy preferences. This is particularly true of Supreme Court justices, for the American political system leaves them unconstrained when issuing decisions on the merits. Segal and Spaeth label this th...