Grey relational analysis is useful for the multi-input, discrete data and uncertain experimental study. Developed in this paper is the application of the grey relational analysis for optimizing the electropolishing of 316L stainless steel with multiple performance characteristics. The processing parameters (temperature, current density, and electrolyte composition) are optimized with considerat...

There is growing interest in run-time detection as parallel and distributed systems grow larger and more complex. The work targets run-time analysis of complex, interactive scientific applications for purposes of attaining scalability improvements with respect to the amount and complexity of the data transmitted, transformed, and shared among different application components. Such improvements ...

• Mining Uncertain Heterogenous Information Networks Scalable and intelligent methods to handle the challenges of uncertainty, heterogeneity, and large volumes and/or low bandwidth, focused on three major data sources in military. network + spatial-temporal data + streams. • Domain Integration for Decision Support Integrate knowledge learned from networks, spatial-temporal data and streams all ...

To facilitate uncertain internet opinion emergency decision involved with multiple fuzzy index values, we design a new prewarning decision system mechanism for uncertain internet opinion emergency based on case reasoning and grey relational analysis. Through the invented software in recent patents in data mining areas we first collect the internet opinion and detect the emergency data. Then by ...

Resolving domain incompatibility among independently developed databases often involves uncertain information. DeMichiel 5] showed that uncertain information can be generated by the mapping of connicting attributes to a common domain, based on some domain knowledge. In this paper, we show that uncertain information can also arise when the database integration process requires information not di...

We postulate that the popularity and efficiency of SQL for querying relational databases makes the language a viable solution to retrieving data from data streams. In response, we have developed a system, dQUOB, that uses SQL queries to extract data from streaming data in real time. The high performance needs of applications such as scientific visualization motivates our search for optimization...

We postulate that the popularity and efficiency of SQL for querying relational databases makes the language a viable solution to retrieving data from data streams. In response, we have developed a system, dQUOB, that uses SQL queries to extract data from streaming data in real time. The high performance needs of applications such as scientific visualization motivates our search for optimization...

This chapter deals with data mining in uncertain XML data models, this uncertainty typically coming from imprecise automatic processes. We first review the literature on modeling uncertain data, starting with well-studied relational models and moving then to their semistructured counterparts. We focus on a specific probabilistic XML model, that allows representing arbitrary finite distributions...