2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)最新文献_第3页

Automatic service composition using POMDP and provenance data 使用POMDP和来源数据自动组合服务

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597243

Mahsa Naseri, S. Ludwig

{"title":"Automatic service composition using POMDP and provenance data","authors":"Mahsa Naseri, S. Ludwig","doi":"10.1109/CIDM.2013.6597243","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597243","url":null,"abstract":"Service composition is the process of combining services in a specific order to achieve a specific goal, whereby the initial and goal states are determined in advance. The service composition problem is very similar to standard planning problems since the idea is to discover a path between the initial and goal states. In service composition, the composition of services identifies this path. In this paper, we exploit provenance information along with Partially Observable Markov Decision Processes (POMDP) to compose the services automatically. The POMDP method has been used in literature for the purpose of robot planning and navigation. In this research, we argue that due to partial observability of service and system states, the POMDP approach provides better solutions for the QoS-aware service composition in dynamic workflow environments. For the purpose of solving the POMDP, service details and the POMDP distributions are learnt from the provenance store. Provenance data contains information regarding workflows, services, their specifications and execution details. This information facilitates the service composition process to be performed more intelligently and efficiently.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114494415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Semantic annotation tools survey 语义注释工具综述

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597251

P. Oliveira, João Rocha

引用次数: 15

Interpretable magnification factors for topographic maps of high dimensional and structured data 高维和结构化数据地形图的可解释放大系数

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597242

Nikolaos Gianniotis

{"title":"Interpretable magnification factors for topographic maps of high dimensional and structured data","authors":"Nikolaos Gianniotis","doi":"10.1109/CIDM.2013.6597242","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597242","url":null,"abstract":"Visualisation of high-dimensional data is typically formulated as a non-linear mapping between a high-dimensional space and a two-dimensional latent space. The goal is that similar data items should be projected to similar coordinates in the latent space. Nevertheless, due to the non-linearity data items that are distant in the high-dimensional space may be projected close to each other in the latent space. Therefore, magnification factors must be analysed in order to detect stretches and contractions in the embedded latent space. However, magnification factors may not be straightforward to communicate to practitioners who are not aware of such manifestations in the embedded latent space, and only care about an accurate depiction that shows which data items are similar. The goal of this work is to devise a more convenient visualisation that corrects for magnifications and thus depicts the true distances between data items more faithfully. We present an approach based on a multidimensional scaling technique that corrects the obtained visualisations by distorting them according to local magnification factors. The approach is general and we demonstrate it on different visualisation algorithms, such as GTM extensions, the autoencoder, the GP-LVM, and on different types of data.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117250986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Discovering signature patterns from event logs 从事件日志中发现签名模式

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597225

R. Bose, Wil M.P. van der Aalst

{"title":"Discovering signature patterns from event logs","authors":"R. Bose, Wil M.P. van der Aalst","doi":"10.1109/CIDM.2013.6597225","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597225","url":null,"abstract":"More and more information about processes is recorded in the form of so-called “event logs”. High-tech systems such as X-ray machines and high-end copiers provide their manufacturers and services organizations with detailed event data. Larger organizations record relevant business events for process improvement, auditing, and fraud detection. Traces in such event logs can be classified as desirable or undesirable (e.g., faulty or fraudulent behavior). In this paper, we present a comprehensive framework for discovering signatures that can be used to explain or predict the class of seen or unseen traces. These signatures are characteristic patterns that can be used to discriminate between desirable and undesirable behavior. As shown, these patterns can, for example, be used to predict remotely whether a particular component in an X-ray machine is broken or not. Moreover, the signatures also help to improve systems and organizational processes. Our framework for signature discovery is fully implemented in ProM and supports class labeling, feature extraction and selection, pattern discovery, pattern evaluation and cross-validation, reporting, and visualization. A real-life case study is used to demonstrate the applicability and scalability of the approach.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126802624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 49

Predicting YouTube content popularity via Facebook data: A network spread model for optimizing multimedia delivery 通过Facebook数据预测YouTube内容的受欢迎程度:优化多媒体交付的网络传播模型

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597239

D. Soysa, D. G. Chen, O. Au, A. Bermak

引用次数: 15

Wanna improve process mining results? 想要改进过程挖掘结果吗?

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597227

Jagadeesh Chandra J. C. Bose, R. Mans, Wil M.P. van der Aalst

{"title":"Wanna improve process mining results?","authors":"Jagadeesh Chandra J. C. Bose, R. Mans, Wil M.P. van der Aalst","doi":"10.1109/CIDM.2013.6597227","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597227","url":null,"abstract":"The growing interest in process mining is fueled by the increasing availability of event data. Process mining techniques use event logs to automatically discover process models, check conformance, identify bottlenecks and deviations, suggest improvements, and predict processing times. Lion's share of process mining research has been devoted to analysis techniques. However, the proper handling of problems and challenges arising in analyzing event logs used as input is critical for the success of any process mining effort. In this paper, we identify four categories of process characteristics issues that may manifest in an event log (e.g. process problems related to event granularity and case heterogeneity) and 27 classes of event log quality issues (e.g., problems related to timestamps in event logs, imprecise activity names, and missing events). The systematic identification and analysis of these issues calls for a consolidated effort from the process mining community. Five real-life event logs are analyzed to illustrate the omnipresence of process and event log issues. We hope that these findings will encourage systematic logging approaches (to prevent event log issues), repair techniques (to alleviate event log issues) and analysis techniques (to deal with the manifestation of process characteristics in event logs).","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114486984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 136

Assessment of voting ensemble for estimating software development effort 评估投票集合以评估软件开发工作

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597253

Mahmoud O. Elish

引用次数: 22

SAE: Social Adaptive Ensemble classifier for data streams SAE:数据流的社会自适应集成分类器

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597237

Heitor Murilo Gomes, F. Enembreck

{"title":"SAE: Social Adaptive Ensemble classifier for data streams","authors":"Heitor Murilo Gomes, F. Enembreck","doi":"10.1109/CIDM.2013.6597237","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597237","url":null,"abstract":"This work encompasses the development of a new ensemble classifier that uses a Social Network abstraction for Data Stream Classification, namely the Social Adaptive Ensemble (SAE). In the context of data stream classification, concept drift is considered one of the most difficult and important issues to be addressed. Ensemble classifiers can be successfully applied to data streams as long as the ensemble efficiently adapts itself in the occurrence of a concept drift. SAE algorithm inherits strategies from other ensemble methods, such as Online Bagging [4] and DWM [2], and merge these with the notion of connectivity between similar classifiers w.r.t. their individual predictions. The relational data obtained through measuring similarities between classifiers is used to arrange ensemble members in a social network structure that allows us to identify subgroups (subnetworks) of highly similar classifiers. Being able to identify similar classifiers allows us to implement a combination strategy that first combines predictions within similar classifiers and later combines these into the final prediction. Moreover, this combination strategy assigns more weight to recently added classifiers predictions during concept drifts, since these are dissimilar to all other existing classifiers. The similarity between classifiers is also used to identify and remove redundant classifiers. This effectively saves systems resources and sometimes improves accuracy. We present empirical experiments with synthetic data streams containing abrupt, gradual and no drift showing that SAE is a valid option for stream classification, especially when data stream characteristics (e.g. presence of abrupt drifts) are previously unknown and system resources, such as CPU time and memory space, are a concern.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131774865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

A principled approach to mining from noisy logs using Heuristics Miner 使用启发式Miner从噪声日志中挖掘的原则性方法

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597226

Phil Weber, B. Bordbar, P. Tiňo

引用次数: 12

Revised online learning with kernels for classification and regression 修正了在线学习的分类和回归核

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597247

Guoqi Li, N. Ning, K. Ramanathan, Luping Shi

引用次数: 0