A Query Language for Workflow Logs

A business process (workflow) is an assembly of tasks to accomplish a business goal. Real-world workflow models often demanded to change due to new laws and policies, changes in the environment, and so on. To understand the inner workings of a business process to facilitate changes, workflow logs have the potential to enable inspecting, monitoring, diagnosing, analyzing, and improving the design of a complex workflow. Querying workflow logs, however, is still mostly an ad hoc practice by workflow managers. In this article, we focus on the problem of querying workflow log concerning both control flow and dataflow properties. We develop a query language based on “incident patterns” to allow the user to directly query workflow logs instead of having to transform such queries into database operations. We provide the formal semantics and a query evaluation algorithm of our language. By deriving an accurate cost model, we develop an optimization mechanism to accelerate query evaluation. Our experiment results demonstrate the effectiveness of the optimization and achieves up to 50× speedup over an adaption of existing evaluation method.

Evaluation Algorithm for Team Strength Based on the Collected Healthcare Data through IoT and Smart Devices

In the past, the fans used to evaluate the strength of the team according to the victory and defeat ranking or according to their own intuition and preferences, however, the strength of the team is difficult to measure in analytical figures. The team’s winning rate is not the only factor to be considered to determine the strength of the team. There are many factors to be considered for determining the strength of the team. According to the variation coefficient of basketball scoring frequency, the paper designs the principal model of basketball players’ pitching target system. The data is captured by IoT devices and smart devices. The algorithm sets the number of the frequency of Gabor filter transformation features, controls the error accumulation, extracts the cascade features of basketball score video, constructs the video conversion discrimination rules, detects the basketball target, and obtains the tracking target contour to frame information. Finally, it realizes the target tracking detection of the team based on the team strength using an evaluation algorithm. The aim of this research work is to determine the strength of the team based on the healthcare data, team cohesiveness, and variance coefficient of basketball score frequency. The study on the coefficient of variation for basketball score frequency in teams can provide a theoretical research direction for team strength evaluation and meet the real-time needs of the coefficient of variation of basketball score frequency in teams. The empirical results show that the designed algorithm has the optimal execution time, more successful evaluation targets, high efficiency, and more reliability in evaluating the strength of the team.

Evaluation Algorithm of Skilled Talents’ Quality Based on Deep Belief Network Model

In order to solve the problem that the evaluation algorithm is easy to fall into local extremum, which leads to slow convergence speed, a skilled talent quality evaluation algorithm based on a deep belief network model was designed. Establish an evaluation set with 4 first level indicators and 14 second level indicators, and calculate the corresponding weights to complete the construction of the evaluation index system. A DBN structure composed of several RBMs and a BP network is constructed. Based on the DBN, a quality evaluation algorithm is designed. The algorithm training is used to evaluate the test data and output the evaluation level. The experimental results show that the convergence speed of DBN based evaluation algorithm is significantly better than that of BP neural network and SVM based evaluation algorithm under the same number of iterations, which is suitable for the accurate evaluation of talent quality.

Evaluation Algorithm of Labor Legal Effectiveness for Affirmative Action against Gender Discrimination

Aiming at the problems of large evaluation error and low accuracy of determining the key degree of evaluation indicators in the existing evaluation of labor legal effectiveness, this paper designs a labor legal effectiveness evaluation algorithm for affirmative action against gender discrimination. Firstly, using hits degree, the degree of gender discrimination, and social influence, enterprise practice and government supervision and management are determined as the evaluation indexes of labor legal effectiveness in this paper, and on this basis, the labor legal effectiveness evaluation system against gender discrimination is designed. Then, the judgment matrix of the evaluation index of labor legal effectiveness against gender discrimination is constructed. After normalization, the weight of the evaluation index is calculated by entropy method, which lays a foundation for subsequent research. Finally, the tree enhanced Bayesian network is used to classify the labor legal effectiveness evaluation indicators, and the correlation between the indicators is determined through the Spearman rank correlation coefficient. Finally, the labor legal effectiveness evaluation model against gender discrimination is designed through the clustering algorithm, and the labor legal effectiveness evaluation indicators against gender discrimination are input to complete the effective evaluation. The experimental results show that the error of the evaluation algorithm is small, and the accuracy of determining the key degree of the evaluation index is high.

Estimation and Analysis of Sports Energy Consumption Based on Gait Tactile Parameters

The morbidity of obesity and related metabolic syndrome is on the rise, which may be related to the decrease of physical activity. Therefore, keeping energy balance is the basic premise to prevent multiple metabolic syndromes, and the research on the composition and application of energy consumption has become a hot spot. The combination of expectation-maximization algorithm and MapReduce computing model realizes the migration of traditional algorithm to “cloud computing” platform. The physical fitness evaluation algorithm based on collaborative filtering is constructed, and a gait tactile recognition algorithm is proposed by feature selection based on the MEMS sensor. Finally, the algorithm is tested, and a conclusion is drawn. This algorithm is effective in monitoring and recognizing human gait. With the increase of weightlessness characteristics, the sensitivity of detection remains unchanged, and the specificity will increase. In a word, it is scientific and effective. Thus, the reference for establishing the index system of tactile biomechanical parameters of adolescent gait and studying the low-cost and portable energy measurement method of multiparameter indexes is provided.

Evaluation Algorithm of Ecological Energy-Saving Effect of Green Buildings Based on Gray Correlation Degree

The environmental protection attribute and energy-saving level of green buildings cannot be described by the traditional evaluation model. In order to solve the above problems, a new ecological energy-saving effect evaluation algorithm of green buildings based on gray correlation degree is designed. Based on the framework of building energy-saving index system, the environmental protection evaluation standards are divided and the results are used to screen the energy-saving indexes, so as to complete the establishment of green building ecological energy-saving index system and standards. Then, the evaluation set is established, and the evaluation scale of each layer of indicators is accurately located according to the weight value of each index. On this basis, the membership matrix is constructed. By calculating the index weight and determining the fuzzy synthesis operator, the rating process of the algorithm is improved and the analysis of the evaluation algorithm of environmental protection and energy conservation indicators of green building materials based on gray correlation degree is realized. The experimental results show that the designed algorithm has good stability of the fitting curve, can save energy, and has low cost.

Research on Video Quality Evaluation of Sparring Motion Based on BPNN Perception

The quality of boxing video is affected by many factors. For example, it needs to be compressed and encoded before transmission. In the process of transmission, it will encounter network conditions such as packet loss and jitter, which will affect the video quality. Combined with the proposed nine characteristic parameters affecting video quality, this paper proposes an architecture of video quality evaluation system. Aiming at the compression damage and transmission damage of leisure sports video, a video quality evaluation algorithm based on BP neural network (BPNN) is proposed. A specific Wushu video quality evaluation algorithm system is implemented. The system takes the result of feature engineering of 9 feature parameters of boxing video as the input and the subjective quality score of video as the training output. The mapping relationship is established by BPNN algorithm, and the objective evaluation quality of boxing video is finally obtained. The results show that using the neural network analysis model, the characteristic parameters of compression damage and transmission damage used in this paper can get better evaluation results. Compared with the comparison algorithm, the accuracy of the video quality evaluation method proposed in this paper has been greatly improved. The subjective characteristics of users are evaluated quantitatively and added to the objective video quality evaluation model in this paper, so as to make the video evaluation more accurate and closer to users.