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1.
Business processes are dynamic and change due to diverse factors. While existing approaches aim to detect drifts in the process structure, Tesseract looks for temporal drifts in activity interim times. This orthogonal view on the process extends the traditional data cube of events – case id, activities and timestamps – by a fourth dimension and improves the operational support by a visualization of temporal drifts in real-time.Insights about temporal deviations lead to an augmented awareness of imminent failures or improved service times. The detection of related structural concept drifts can be improved by early warning, as operation times of critical parts often increase before they catastrophically fail. 相似文献
2.
Urban water sources are susceptible to various contamination events as a result of natural, accidental, and human-induced occurrences. An early warning monitoring system provides timely information on changes in urban water quality. In this study, an analysis was made with CANARY event detection software (EDS) to monitor water quality parameters in river water and to identify the onset of anomalous water quality periods. Water quality signals including pH, conductivity, and turbidity from the Milwaukee River over specified periods during the summer season of 2018–2020 were employed as inputs to event detection algorithms in CANARY. The data analysis results show that CANARY can be useful as an early warning system for monitoring contamination in urban water sources and help to identify abnormal conditions quickly. The sensibility of the model relies on optimizing the configuration parameters, which involves selecting the ideal set of parameters for the event detection algorithm and adjusting the BED parameters to increase or decrease the probability of generating an alarm. The number of events reported between the Linear Prediction Correction Filter (LPCF) and Multivariate Nearest Neighbor (MVNN) algorithms varied as a result of different residual calculation mechanisms. Climate factors that contributed to the abnormal water quality events in the river were examined. The analysis of rainfall on water quality was carried out using a statistical method by determining whether there is a significant difference (p-value) between the seasonal mean water quality data and the mean value of water parameters during the sampling duration. Regression analysis was also performed to estimate the best model that describes the relationship between each of the water quality parameters and temperature. 相似文献
3.
事件时序关系的研究在问答系统、信息抽取和文本自动摘要等自然语言处理领域起着重要的作用。首先借鉴英文的标注方法,将中文事件的时序关系分为4类,并给出了具体的标注方法。然后给出了一个中文时序关系的分类方法。最后,在标注语料库上测试了中文事件时序关系分类的性能,结果表明该方法优于规则方法。 相似文献
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在事件信息的抽取中,事件要素的提取是一个难点。现有的事件要素抽取主要是基于机器学习的方法,这类方法容易受到语料稀疏性的影响。该文提出一种基于事件本体的事件要素提取方法,该方法将事件要素推理分为两步: 一、通过事件要素词和事件指示词的位置关系来初步填充要素值,并将得出的置信度较高的事件作为种子事件;二、利用第一步得出的种子事件,查询事件本体中的事件类约束和基于事件非分类关系的推理规则,并对要素进行推理,进一步对事件要素进行填充和修正。实验结果表明,该方法能较好地提升事件要素提取的准确度。 相似文献
7.
构建基于Watir的物联网Web事件处理系统框架。首先搭建Zigbee无线传感器网络,把终端节点的属性描述到网页并进行服务器部署,其中用微数据来描述每一个传感器的静态属性,用Js文件描述动态属性,然后采用Web自动化测试框架Watir,通过Css Selector定位技术实现对动态页面数据的实时采集,将实时采集的数据通过预处理后添加事件处理,提取关键和有效的事件数据并保存。同时利用Watir对多个事件条件下不同平台的物联网网页进行测试,结果表明基于Watir的物联网Web事件处理方法能准确和高效地获取事件数据。 相似文献
8.
地域性的空间认同以及人情味的
社区建设,一直是老城更新的追求理念之
一,在现阶段大部分城市向着存量规划以及
精细化设计迈进时,这一目标尤为突出。城市
更新中的事件空间理论,以活动为线索,串联
空间组织,可以有效增加更新片区的空间认同
感,但在我国城市更新工作中尚处于起步阶
段。现以重庆白象街旧城更新为例,以理论
和案例结合的分析路径探讨问题,包括城市
更新过程中事件空间理论运用的适应性,如
何挖掘与组织城市多样性的事件活动,以及
如何运用规划手段,激发市民对更新后片区
空间产生认同感,为城市更新和社区发展带
来新的生命力。 相似文献
9.
《Digital Communications & Networks》2021,7(4):559-569
Online social media networks are gaining attention worldwide, with an increasing number of people relying on them to connect, communicate and share their daily pertinent event-related information. Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People. In this paper, a novel Event Detection model based on Scoring and Word Embedding (ED-SWE) is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets. The proposed ED-SWE model can distill high-quality tweets, reduce the negative impact of the advent of spam, and identify latent events in the data streams automatically. Moreover, a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data. In order to further improve the performance of the Expectation-Maximization (EM) iteration algorithm, a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely. Finally, a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event. Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models. 相似文献
10.
Event classification is inherently sequential and multimodal. Therefore, deep neural models need to dynamically focus on the most relevant time window and/or modality of a video. In this study, we propose the Multimodal Attentive Fusion Network (MAFnet), an architecture that can dynamically fuse visual and audio information for event recognition. Inspired by prior studies in neuroscience, we couple both modalities at different levels of visual and audio paths. Furthermore, the network dynamically highlights a modality at a given time window relevant to classify events. Experimental results in AVE (Audio-Visual Event), UCF51, and Kinetics-Sounds datasets show that the approach can effectively improve the accuracy in audio-visual event classification. Code is available at: https://github.com/numediart/MAFnet 相似文献