A Framework to Improve Reuse in Weather-Based DSS Based on Coupling Weather Conditions
12 Dec 2017-pp 40-58
TL;DR: A framework to exploit reuse of the suggestions which have been prepared for the past combinations of observed and forecasted values over the years to develop automatic weather-based DSS in various domains with minimal human intervention is proposed.
Abstract: In weather-based decision support system (DSS), the domain experts provide suggestions to carry out appropriate measures to improve the efficiency of the respective domain by analyzing both the forecasted and observed weather values. In this paper, to provide suggestions for a given combination of forecasted and observed values, we have proposed a framework to exploit reuse of the suggestions which have been prepared for the past combinations of observed and forecasted values over the years. We define the notion of coupled weather condition (CWC) which represents the weather conditions of two consecutive durations for a given combination of weather variables. By employing the domain-specific categories, the proposed framework exploits the reuse of CWCs for the given domain. We have applied the proposed framework by considering the case study of agromet advisory service of India Meteorological Department (IMD). The extent of reuse has been computed by considering 30 years of weather data from Rajendranagar, Hyderabad, Telangana State, based on the weather categories data provided by IMD. The reuse over 30 years is computed by considering the period of year and crop seasons of a year. Period is defined as portion of time of the year(s) that is considered to analyze the similarity. The results are very positive. The results show that the percentage of reuse of CWCs with three weather variables for the period of year is about 77% after five years. The results provide the scope to develop automatic weather-based DSS in various domains with minimal human intervention and improve the utilization of the generated content.
TL;DR: In this paper, the potential economic value of climate forecasts for farm scale management decisions in one location in the Southeast USA (Tifton, GA; 3123 0 N; 8331 0 W) for comparison with previously derived results for the Pampas region of Argentina.
Abstract: Climate variability leads to economic and food security risks throughout the world because of its major influences on agriculture. Accurate forecasts of climate 3‐6 months ahead of time can potentially allow farmers and others in agriculture to make decisions to reduce unwanted impacts or take advantage of expected favorable climate. However, potential benefits of climate forecasts vary considerably because of many physical, biological, economic, social, and political factors. The purpose of this study was to estimate the potential economic value of climate forecasts for farm scale management decisions in one location in the Southeast USA (Tifton, GA; 3123 0 N; 8331 0 W) for comparison with previously-derived results for the Pampas region of Argentina. The same crops are grown in both regions but at different times of the year. First, the expected value of tailoring crop mix to El Nino-Southern Oscillation (ENSO) phases for a typical farm in Tifton was estimated using crop models and historical daily weather data. Secondly, the potential values for adjusting management of maize (Zea maize L.) to different types of climate forecasts (perfect knowledge of (a) ENSO phase, (b) growing season rainfall categories, and (c) daily weather) were estimated for Tifton and Pergamino, Argentina (3355 0 S; 6033 0 W). Predicted benefits to the farm of adjusting crop mix to ENSO phase averaged from US$ 3 to 6 ha 1 over all years, depending on the farmer’s initial wealth and aversion to risk. Values calculated for Argentina were US$ 9‐15 for Pergamino and up to US$ 35 for other locations in the Pampas. Varying maize management by ENSO phase resulted in predicted forecast values of US$ 13 and 15 for Tifton and Pergamino, respectively. The potential value of perfect seasonal forecasts of rainfall tercile on maize profit was higher than for ENSO-based forecasts in both regions (by 28% in Tifton and 70% in Pergamino). Perfect knowledge of daily weather over the next season provided an upper limit on expected value of about US$ 190 ha 1 for both regions. Considering the large areas of field crop production in these regions, the estimated economic potential is very high. However, there are a number of challenges to realize these benefits. These challenges are generally related to the uncertainty of climate forecasts and to the complexities of agricultural systems. © 2000 Elsevier Science B.V. All rights reserved.
TL;DR: This study examines the repositories for 25 software systems from a NASA software development environment that actively reuses software to identify two categories of factors that characterize successful reuse-based software development of large-scale systems: module design factors and module implementation factors.
Abstract: Software reuse enables developers to leverage past accomplishments and facilitates significant improvements in software productivity and quality. Software reuse catalyzes improvements in productivity by avoiding redevelopment and improvements in quality by incorporating components whose reliability has already been established. This study addresses a pivotal research issue that underlies software reuse - what factors characterize successful software reuse in large-scale systems. The research approach is to investigate, analyze, and evaluate software reuse empirically by mining software repositories from a NASA software development environment that actively reuses software. This software environment successfully follows principles of reuse-based software development in order to achieve an average reuse of 32 percent per project, which is the average amount of software either reused or modified from previous systems. We examine the repositories for 25 software systems ranging from 3,000 to 112,000 source lines from this software environment. We analyze four classes of software modules: modules reused without revision, modules reused with slight revision (<25 percent revision), modules reused with major revision (/spl ges/25 percent revision), and newly developed modules. We apply nonparametric statistical models to compare numerous development variables across the 2,954 software modules in the systems. We identify two categories of factors that characterize successful reuse-based software development of large-scale systems: module design factors and module implementation factors. We also evaluate the fault rates of the reused, modified, and newly developed modules. The module design factors that characterize module reuse without revision were (after normalization by size in source lines): few calls to other system modules, many calls to utility functions, few input-output parameters, few reads and writes, and many comments. The module implementation factors that characterize module reuse without revision were small size in source lines and (after normalization by size in source lines): low development effort and many assignment statements. The modules reused without revision had the fewest faults, fewest faults per source line, and lowest fault correction effort. The modules reused with major revision had the highest fault correction effort and highest fault isolation effort as wed as the most changes, most changes per source line, and highest change correction effort. In conclusion, we outline future research directions that build on these software reuse ideas and strategies.
TL;DR: Several advanced solutions such as mixed knowledge systems, that combine numerical methods with AI-based tools, and the prospects of using Ambient Intelligence concepts in DSS construction are described.
Abstract: The technical and social systems of the present day are ever more complex and complicated objects. Their models are characterized by large numbers of state and control variables, time delays, and different time constants. Also they show constraints in their information infrastructure and risk sensitivity aspects. Such systems are called large –scale complex systems (LSS). Hierarchical approach has been for several decades one of the most utilized methodologies for controlling large-scale systems. When human intervention is necessary decision support systems (DSS) can represent a solution. A DSS is an adaptive and evolving information system meant to implement several of the functions of a human support team that would otherwise be needed to help the decision-maker to overcome his/her limits and constraints he/she may face when approaching decision problems that count in the organization. This paper aims at reviewing several aspects concerning LSS control and the utilization and technology of DSS. Particular emphasis is put on real-time DSS and multiparticipant (group) DSS. Several advanced solutions such as mixed knowledge systems, that combine numerical methods with AI-based tools, and the prospects of using Ambient Intelligence concepts in DSS construction are described.
01 Jan 2014
TL;DR: This paper presents multiple innovations associated with an electronic health record system developed to support evidence-based medicine practice, and highlights a new construct, based on the technology acceptance model, to explain end users' acceptance of this technology through a lens of continuous behavioral adaptation and change.
Abstract: This paper presents multiple innovations associated with an electronic health record system developed to support evidence-based medicine practice, and highlights a new construct, based on the technology acceptance model, to explain end users' acceptance of this technology through a lens of continuous behavioral adaptation and change. We show that this new conceptualization of technology acceptance reveals a richer level of detail of the developmental course whereby individuals adjust their behavior gradually to assimilate technology use. We also show that traditional models such as technology acceptance model (TAM) are not capable of delineating this longitudinal behavioral development process. Our TAM-derived analysis provides a lens through which we summarize the significance of this project to research and practice. We show that our application is an excellent exemplar of the ''end-to-end'' IS design realization process; it has drawn upon multiple disciplines to formulate and solve challenges in medical knowledge engineering, just-in-time provisioning of computerized decision-support advice, diffusion of innovation and individual users' technology acceptance, usability of human-machine interfaces in healthcare, and sociotechnical issues associated with integrating IT applications into a patient care delivery environment.