Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences

We present MOWCATL, an efficient method for mining frequent sequential association rules from multiple sequential data sets with a time lag between the occurrence of an antecedent sequence and the corresponding consequent sequence. This approach finds patterns in one or more sequences that precede the occurrence of patterns in other sequences, with respect to user-specified constraints. In addition to the traditional frequency and support constraints in sequential data mining, this approach uses separate antecedent and consequent inclusion constraints

Drought Monitoring Using Data Mining Techniques: A Case Study for Nebraska, USA

Drought has an impact on many aspects of society. To help decision makers reduce the impacts of drought, it is important to improve our understanding of the characteristics and relationships of atmospheric and oceanic parameters that cause drought. In this study, the use of data mining techniques is introduced to find associations between drought and several oceanic and climatic indices that could help users in making knowledgeable decisions about drought responses before the drought actually occurs. Data mining techniques enable users to search for hidden patterns and find association rules for target data sets such as drought episodes. These techniques have been used for commercial applications, medical research, and telecommunications but not for drought. In this study, two time-series data mining algorithms are used in Nebraska to illustrate the identification of the relationships between oceanic parameters and drought indices. The algorithms provide flexibility in time-series analyses and identify drought episodes separate from normal and wet conditions, and find relationships between drought and oceanic indices in a manner different from the traditional statistical associations. The drought episodes were determined based on the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI). Associations were observed between drought episodes and oceanic and atmospheric indices that include the Southern Oscillation Index (SOI), the Multivariate ENSO Index (MEI), the Pacific/North American (PNA) index, the North Atlantic Oscillation (NAO) Index, and the Pacific Decadal Oscillation (PDO) Index. The experimental results showed that among these indices, the SOI, MEI, and PDO have relatively stronger relation-ships with drought episodes over selected stations in Nebraska. Moreover, the study suggests that data mining techniques can help us to monitor drought using oceanic indices as a precursor of drought

Geospatial Decision Support for Drought Risk Management

Drought affects virtually all regions of the world and results in significant economic, social, and environmental impacts. The Federal Emergency Management Agency estimates annual drought-related losses in the U.S. at $6–$8 billion, which is more than any other natural hazard. Congress enacted the Agricultural Risk Protection Act of 2000 to encourage the U.S. Department of Agriculture (USDA) Risk Management Agency (RMA) and farmers to be more proactive in managing drought risk. Through the NSF’s Digital Government Program, the USDA RMA is working with the University of Nebraska–Lincoln Computer Science and Engineering Department, National Drought Mitigation Center (NDMC), and High Plains Regional Climate Center (HPRCC) to develop new geospatial decision-support tools to address agricultural drought hazards and identify regions of vulnerability in the management of drought risk. The goal of the National Agricultural Decision Support System (NADSS) research project is to develop a support system of geospatial analyses to enhance drought risk assessment and exposure analysis. Users can monitor progress and interact with the system by visiting nadss.unl.edu

Contributing Factors and Trends in the Usage of IT Outsourcing in Manufacturing and Service Sectors

The study projects long-term trends in the IT outsourcing in the manufacturing and service sectors of the United States. The findings suggest that the respondents perceive that there is approximately an equal usage of IT outsourcing in each of manufacturing and service sectors. Furthermore, the Desired Characteristics of Outsourcing – enabler of organisational flexibility, dynamics, and adaptability, redirection of resources, and increased control of operating costs – are positively related to the change in the requirement of IT outsourcing in the case of manufacturing. Meanwhile for the service sector, the Administrative Motivation for Outsourcing – lack of expertise, promising service offerings to subscribe, shrinkage in system life cycle – are positively associated with growth/no-growth in IT outsourcing

Current and Future Contributing Factors and Trends in the Usage of IT Cloud Computing in Manufacturing and Service Sectors

Cloud computing provides organisations with the ability to quickly change infrastructure, provider and service levels. The study looks at factors affecting the usage of cloud computing by organisations in the manufacturing and service sectors in the United States. Extrinsic motivation was found to be a significant factor for change in cloud computing budget allocation in manufacturing, whereas intrinsic motivation was a significant factor in the service sector. The study also shows that organisations’ future budgets include internal and external sources to fulfil their IT needs

Topic Areas: Government and social policy; Drought Risk Management; Data integration and statistics

We are developing an advanced Geospatial Decision Support System (GDSS) to improve the quality and accessibility of drought related data for drought risk management. This is part of a Digital Government project aimed at developing and integrating new information technologies for improved government services in the USDA Risk Management Agency (RMA) and the Natural Resources Conservation Service (NRCS). Our overall goal is to substantially improve RMA's delivery of risk management services in the near-term and provide a foundation and directions for the future. We integrate spatio-temporal knowledge discovery techniques into our GDSS using a combination of data mining techniques applied to rich, geospatial, time-series data. Our data mining objectives are to: 1) find relationships between user-specified target episodes and other climatic events and 2) predict the target episodes. Understanding relationships between changes in soil moisture regimes and global climatic events such as El Niño could provide a reasonable drought mitigation strategy for farmers to adjust planting dates, hybrid selection, plant populations, tillage practices or crop rotations. This work highlights the innovative data mining approaches integral to our project's success and provides preliminary results that indicate our system’s potential to substantially improve RMA's delivery of drought risk management services

A Geospatial Decision Support System for Drought Risk Management

Drought affects virtually all regions of the world and results in significant economic, social, and environmental impacts. The Federal Emergency Management Agency estimates annual drought-related losses in the United States at $6-8 billion, which is more than any other natural hazard. Congress enacted the Agricultural Risk Protection Act of 2000 to encourage the United States Department of Agriculture (USDA) Risk Management Agency (RMA) and farmers to be more proactive in managing drought risk. Through the National Science Foundation (NSF) Digital Government program, the USDA RMA is working with the University of Nebraska−Lincoln Computer Science and Engineering (CSE) Department, National Drought Mitigation Center (NDMC), and High Plains Regional Climate Center (HPRCC) to develop new geospatial decision support tools to address agricultural drought hazards and identify regions of vulnerability in the management of drought risk. The goal of this research project is to develop a support system of geospatial analyses that will enhance drought risk assessment and exposure analysis. The tools and technologies developed have been integrated into the National Agricultural Decision Support System (NADSS), http://nadss.unl.edu/