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Domain Engineering: An Empirical Study
No full textThis paper presents a summary and analysis of data gathered from thirteen domain engineering projects, participant surveys, and demographic information. Taking a failure modes approach, project data is compared to an ideal model of the DARE methodology, revealing valuable insights into points of failure in the domain engineering process. This study suggests that success is a function of the domain analyst’s command of a specific set of domain engineering concepts and skills, the time invested in the process, and persistence in difficult areas. We conclude by presenting strategies to avoid points of failure in future domain engineering projects
Deterministic Parallel Global Parameter Estimation for a Model of the Budding Yeast Cell Cycle
Get PDFTwo parallel deterministic direct search algorithms are used to find improved
parameters for a system of differential equations designed to simulate the cell cycle of budding
yeast. Comparing the model simulation results to experimental data is difficult because most of
the experimental data is qualitative rather than quantitative. An algorithm to convert simulation
results to mutant phenotypes is presented. Vectors of parameters defining the differential equation
model are rated by a discontinuous objective function. Parallel results on a 2200 processor
supercomputer are presented for a global optimization algorithm, DIRECT, a local optimization
algorithm, MADS, and a hybrid of the two
Computer-Supported Collaborative Production
Get PDFThis paper proposes the concept of collaborative production as a focus of concern within the general area of collaborative work. We position the concept with respect to McGrath's framework for small group dynamics and the more familiar collaboration processes of awareness, coordination, and communication (McGrath 1991). After reviewing research issues and computer-based support for these interacting aspects of collaboration, we turn to a discussion of implications for how to design improved support for collaborative production. We illustrate both the challenges of collaborative production and our design implications with a collaborative map-updating scenario drawn from the work domain of geographical information systems
Ensemble-based chemical data assimilation I: An idealized setting
Get PDFData assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air quality forecasting, similar to the role it has in numerical weather prediction. Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper we assess the performance of the ensemble Kalman filter (EnKF). Results in an idealized setting show that EnKF is promising for chemical data assimilation
Scientists in the MIST: Simplifying Interface Design for End Users
Get PDFWe are building a Malleable Interactive Software Toolkit (MIST), a tool set and infrastructure to simplify the design and construction of dynamically-reconfigurable (malleable) interactive software. Malleable software offers the end-user powerful tools to reshape their interactive environment on the fly. We aim to make the construction of such software straightforward, and to make reconfiguration of the resulting systems approachable and manageable to an educated, but non-specialist, user. To do so, we draw on a diverse body of existing research on alternative approaches to user interface (UI) and interactive software construction, including declarative UI languages, constraint-based programming and UI management, reflection and data-driven programming, and visual programming techniques
Long-Haul TCP vs. Cascaded TCP
Get PDFIn this work, we investigate the bandwidth and transfer time of long-haul TCP versus
cascaded TCP [5].
First, we discuss the models for TCP throughput. For TCP flows in support of
bulk data transfer (i.e., long-lived TCP flows), the TCP throughput models have been
derived [2, 3]. These models rely on the congestion-avoidance algorithm of TCP.
Though these models cannot be applied with short-lived TCP connections, our interest
relative to logistical networking is in longer-lived TCP flows anyway, specifically TCP
flows that spend significantly more time in the steady-state congestion-avoidance phase
rather than the transient slow-start phase. However, in the case where short-lived TCP
connections must be modeled, several TCP latency models have been proposed [1, 4]
and based on these latency models, the throughput and transfer time of short-lived TCP
connections are obtainable.
Using the above models, the transfer times for a data file of size S packets can be
computed for both long-haul TCP and cascaded TCP. The performance of both systems
is compared via their transfer times. One system is said to be preferred if its tranfer time
is lower than that of the other. Based on these performance comparisons, we develop a
decision model that decides whether to use the cascaded TCP or long-haul TCP
Ensemble-based chemical data assimilation II: Real observations
Get PDFData assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air quality forecasting, similar to the role it has in numerical weather prediction. Considerable progress has been made recently in the
development of variational tools for chemical data assimilation. In this paper we assess the performance of the ensemble Kalman filter (EnKF) and compare it with a state of the art 4D-Var approach. We analyze different aspects that affect the assimilation process, investigate several ways to avoid filter divergence, and investigate the assimilation of emissions. Results with a real model and real observations show that EnKF is a
promising approach for chemical data assimilation. The results also point to several issues on which further research is necessary
Entangled Design Knowledge: Relationships as an Approach to Claims Reuse
Get PDFAs a discipline, human-computer interaction produces creative and innovative designs that could provide a reusable collection of design knowledge on which future efforts could build. It is unfortunate that so much of this knowledge is not fully reused by designers today. To encourage the use of previously identified HCI knowledge, we propose a model of reuse building on Carroll?s notion of claims, design knowledge components that capture the positive and negative psychological effects of design features. We address four challenges associated with reuse in a library of claims, adopted from software engineering?a discipline in which the notion of reuse has been prevalent for quite some time. Building on Krueger?s definition of reuse and his conceptualization of four key aspects?abstraction, selection, specification, and integration?we propose a reuse approach based on incorporating these four aspects into the design process. To abstract, select, specify and integrate claims, we identify claim relationships, descriptions of connections between claims. We portray how claim relationships can be used to aid in identifying claim types, searching for claims, creating new claims, and aggregating claims. By integrating relationships into a claims library, we demonstrate how they can be applied to assist claims reuse and present studies related to each application of the relationships
Ensemble-based Chemical Data Assimilation III: Filter Localization
Get PDFData assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air quality forecasting, similar to the role it has in numerical weather prediction. Considerable progress has been made recently in the
development of variational tools for chemical data assimilation. In this paper we implement and assess the performance of a localized ``perturbed observations'' ensemble Kalman filter (LEnKF). We analyze different settings of the ensemble localization, and investigate the joint assimilation of the state, emissions and boundary conditions. Results with a real model and real observations show that LEnKF is a promising approach
for chemical data assimilation. The results also point to several issues on which future research is necessary
Autoregressive Models of Background Errors for Chemical Data Assimilation
Get PDFThe task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems that efficiently integrate the observational data and the models. Data assimilation (DA) is the process of adjusting the states or parameters of a model in such a way that its outcome (prediction) is close, in some distance metric, to observed (real) states. It is widely accepted that a key ingredient of successful data assimilation is a realistic estimation of the background error distribution. This paper introduces a new method for estimating the background errors which are modeled using autoregressive processes. The proposed approach is computationally inexpensive and captures the error correlations along the flow lines