JBPM (Java Business Process Management) is a completely-defined, rich-functional workflow engine. However, JBPM is born with some defects. It does not support returning tasks, counter signatures or repeated tasks. Besides, it does not provide a complete application framework, so that it can’t be used directly. This paper analyzes the problems JBPM encounters and puts forward a more applicable application framework design based on JBPM workflow engine. The framework provides business logic handling framework, data saving, data validating, and so on. Meanwhile the framework provides a basic solution for returning tasks, random jumps and counter signatures. The framework is well applied in the background project as its main architecture.
The outlier detection is to select uncommon data from a data set, which can significantly improve the quality of results for the data mining algorithms. A typical feature of the outliers is that they are always far away from a majority of data in the data set. In this paper, we present a graph-based outlier detection algorithm named INOD, which makes use of this feature of the outlier. The DistMean-neighborhood is used to calculate the cumulative in-degree for each data. The data, whose cumulative in-degree is smaller than a threshold, is judged as an outlier candidate. A KNN-based selection algorithm is used to determine the final outlier. Experimental results show that the INOD algorithm can improve the precision 80% higher and decrease the error rate 75% lower than the classical LOF algorithm.
On the basis of this article in the use of automation and control theory of factor analysis and cluster analysis to analyze the problems of university graduates reflected, to resolve these problems, focus on one of the root causes of these problems is not college students on the course of self-management science, the author use the greedy algorithm dialysis college students how scientific management of social work, and the corresponding countermeasures.
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