Cluster Computing

Qi, Lixia

doi: 10.1007/s10586-017-0786-7pmid: N/A

Through the analysis of workflow management system based on Web, the work, following WFMC workflow model, proposed a universal workflow management system with the combination of specific application requirements of enterprises. Workflow management system based on Web was described from system function, software structure, system structure, workflow engine and security. The actual case of the design of aeronautical structure showed that workflow system performance can be greatly improved by using Web technology.

Li, Zhichao; Gan, Siyun; Jia, Ru; Fang, Jun

doi: 10.1007/s10586-017-0867-7pmid: N/A

Capture-removal methods were often used to estimate the unknown population size and variance, which are applied in Biology, Ecology and Sociology. In this study, the improved capture removal model was adapted to explore the propagation scale as well as the involved population size of network information dissemination, and then, empirical analysis was carried out using the dissemination of public opinion on ‘8 $$\cdot $$ · 12’ Tianjin port explosion as an example. Our results indicate that the proposed method can effectively estimate the range of the spread of the hot spots in social networks. This conclusion might be that social network has gradually become an important path and mode of communication in public discourse, and provide evidence for sampling estimation in big data analysis.

Gao, Wei; Farahani, Mohammad; Aslam, Adnan; Hosamani, Sunilkumar

doi: 10.1007/s10586-017-0887-3pmid: N/A

Recent years, a large amount of ontology learning algorithms have been applied in different disciplines and engineering. The ontology model is presented as a graph and the key of ontology algorithms is similarity measuring between concepts. In the learning frameworks, the information of each ontology vertex is expressed as a vector, thus the similarity measuring can be determined via the distance of the corresponding vector. In this paper, we study how to get an optimal distance function in the ontology setting. The tricks we presented are divided into two parts: first, the ontology distance learning technology in the setting that the ontology data have no labels; then, the distance learning approaches in the setting that the given ontology data are carrying real numbers as their labels. The result data of the four simulation experiments reveal that our new ontology trick has high efficiency and accuracy in ontology similarity measure and ontology mapping in special engineering applications.

Qiu, Lirong; Yu, Jia; Pu, Qiumei; Xiang, Chuncheng

doi: 10.1007/s10586-017-0844-1pmid: N/A

We study the task of ontology matching that is used mainly for solving the semantic heterogeneity problems, which concentrates on finding semantically related entities between different ontologies. Many previous works exploit the character-level or token-level information of the descriptions of an entity in ontology directly when applying the string-based matcher or token based matcher to find the corresponding entities. They ignored the higher level correlations between different descriptions of an entity. To address this problem, we propose a representation learning method based on deep neural networks which aim at learning the high level abstract representations of the input entity. Particularly, the representations of the entities are learned in an unsupervised way firstly, and then fine-tuned in a supervised manner with the training data. The experiment results show that our approaches can learn useful representations for entities from its descriptive information to better measure the similarity between entities.

Wu, Yanbin; Zhou, Yapeng; Guo, Yiqiang; Wang, Li

doi: 10.1007/s10586-017-0875-7pmid: N/A

In order to investigate the effects of carbon emissions in land consolidation projects, carbon emissions generated during the five construction processes of land consolidation project, i.e., land levelling, irrigation & drainage, road construction, cropland protection & eco-environment maintenance, and village renovation, as well as the data of carbon emissions caused by regional land-use change, are analyzed from the perspectives of energy consumption and land-use change using the Intergovernmental Panel on Climate Change inventories based on the land consolidation project implemented in the Township of Jiangwucheng, Ci County, Hebei, China. The role of carbon source/sink and the associated calculating method are determined, based on which the corresponding suggestions regarding the reduction of carbon emissions in land consolidation projects are proposed with the aim of providing a general scientific reference for research on carbon emissions in land consolidation and practical low-carbon land consolidation projects. Results obtained from this work demonstrate that (1) the amount of CO $$_{2}$$ 2 emissions caused by energy consumption is 12481.549 t, and the amounts of CO $$_{2}$$ 2 emissions caused by irrigation & drainage and county road construction are 5932.980 and 4494.005 t, corresponding to CO $$_{2}$$ 2 emissions per unit investment of 1.605 t/10,000-yuan and 2.029 t/10,000-yuan, respectively, substantially higher than the values of any other sub-project; (2) land-use change caused by the implementation of this land consolidation project arouses a 1704.346 t carbon content increase of soil and vegetation in this region with the maximum carbon variations detected in cropland and grass land, exhibiting the effect of carbon sink; (3) according to the cost-benefit analysis of environmental economics, implementing this land consolidation project generates a carbon content rise equivalent to 9222.684 t carbon emission, where the carbon emissions per unit consolidation area and the carbon emissions per unit investment are 2.89 t/hm $$^{2}$$ 2 and 1.028 t/10,000-yuan, respectively.

Wu, Yanbin; Guo, Yiqiang; Liu, Lin; Huang, Ni; Wang, Li

doi: 10.1007/s10586-017-0854-zpmid: N/A

Changes in land use affect the terrestrial carbon stock through changes in the land cover. Research on land use and analysis of variations in carbon stock have practical applications in the optimization of land use and the mitigation of climate change effects. This study was conducted in Baixiang and Julu counties in the Taihang Piedmont by employing the trend analysis method to characterize the variation in county land use and carbon stock. The findings show that in both counties, agricultural and unused land areas decreased while built-up land area increased, and the reduction in cropland was the main reason behind the agricultural land reduction. An inflection point appeared on the cropland curves of Julu, because the cropland area decreased by 1576.97 hm $$^{2}$$ 2 from 2004 to 2006. Cropland area in Baixiang decreased from 1996 to 1998 by a total of 129.89 hm $$^{2}$$ 2 and then remained relatively stable after 1998. The total carbon storage and variation in land use in the two counties displayed similar trends. Total carbon reserves in Julu increased by 2.76 $$\times $$ × 10 $$^{4}$$ 4 tC (carbon equivalent), while those in Baixiang decreased by 0.63 $$\times $$ × 10 $$^{4}$$ 4 tC. Carbon stock of built-up land in Julu and Baixiang increased by 2.44 $$\times $$ × 10 $$^{4}$$ 4 and 1.22 $$\times $$ × 10 $$^{4}$$ 4 tC, respectively.

Xuesong, Yan; Jie, Sun; Chengyu, Hu

doi: 10.1007/s10586-017-0787-6pmid: N/A

Urban water supply network is easily affected by intentional or occasional chemical and biological pollution, which threatens the health of consumers. In recent years, drinking water contamination happens occasionally, which seriously harms social stabilization and safety. Placing sensors in water supply pipes can monitor water quality in real time, which may prevent contamination accidents. However, how to reversely locate pollution sources through the detecting information from water quality sensors is a challengeable issue. Its difficulties lie in that limited sensors, massive pipe network nodes and dynamic water demand of users lead to the uncertainty, large-scale and dynamism of this optimization problem. This paper mainly studies the uncertainty problem in contaminant sources identification (CSI). The previous study of CSI supposes that hydraulic output (e.g., water demand) is known. Whereas, the inherent variability of urban water consumption brings an uncertain problem that water demand presents volatility. In this paper, the water demand of water supply network nodes simulated by Gaussian model is stochastic, and then being used to solve the problem of CSI, simulation–optimization method finds the minimum target of CSI and concentration which meet the simulation value and detected value of sensors. This paper proposes an improved genetic algorithm to solve the CSI problem under uncertainty water demand and comparative experiments are placed on two water distribution networks of different sizes.

Han, Chun-yan

doi: 10.1007/s10586-017-0792-9pmid: N/A

Dividing the image into superpixels contributes to further processing of the image. Simple linear iterative clustering (SLIC) algorithm achieves good segmentation result by clustering color and distance characteristics of pixels. However, finite superpixels easily cause under-segmentation. Therefore, the work corrects segmentation result of SLIC by k-means clustering method calculating similarity based on weighted Euclidean distance. After that, the under-segmentation superpixel blocks are conducted with k-means clustering based on binary classification. Result shows that the corrected SLIC segmentation has better visual effect and index.

Fei, Nina; Yang, Youlong

doi: 10.1007/s10586-017-0824-5pmid: N/A

Learning causality from data is known as the causal discovery problem, and it is an important and relatively new field. In many applications, there often exist latent variables, if such latent variables are completely ignored, which can lead to the estimation results seriously biased. In this paper, a method of combining exploratory factor analysis and path analysis (EFA-PA) is proposed to infer the causality in the presence of latent variables. Our method expands latent variables as well as their linear causal relationships with observed variables, which enhances the accuracy of causal models. Such model can be thought of as the simplest possible causal models for continuous data. The EFA-PA is very similar to that of structural equation model, but the theoretical model established by the structural equation model needs to be modified in the process of data fitting until the ideal model is established.The model gained by EFA-PA not only avoids subjectivity but also reduces estimation complexity. It is found that the EFA-PA estimation model is superior to the other models. EFA-PA can provides a basis for the correct estimation of the causal relationship between the observed variables in the presence of latent variables. The experiment shows that EFA-PA is better than the structural equation model.

Tao, Yuan; Zhang, Yu-xiang; Ma, Si-yuan; Fan, Ke; Li, Mo-yan; Guo, Feng-ming; Xu, Zheng

doi: 10.1007/s10586-017-0813-8pmid: N/A

In order to effectively deal with the APT and 0 day attacks, a new classified protection model of information system is proposed by combining the big data analysis and the threat intelligence technologies. And immune factors network algorithm is proposed based on the classified model. So that the useful information can be actively accessed and extracted from a large number of security information. The consequences of the threat information and the effective measures can be timely analysis, and the threat intelligence of classified protection can be timely shared. So the emergency response, bulletins and early warning can be timely done.