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Mining workflow processes from distributed workflow enactment event logs  [cached]
Kwanghoon Pio Kim
Knowledge Management & E-Learning : an International Journal , 2012,
Abstract: Workflow management systems help to execute, monitor and manage work process flow and execution. These systems, as they are executing, keep a record of who does what and when (e.g. log of events). The activity of using computer software to examine these records, and deriving various structural data results is called workflow mining. The workflow mining activity, in general, needs to encompass behavioral (process/control-flow), social, informational (data-flow), and organizational perspectives; as well as other perspectives, because workflow systems are "people systems" that must be designed, deployed, and understood within their social and organizational contexts. This paper particularly focuses on mining the behavioral aspect of workflows from XML-based workflow enactment event logs, which are vertically (semantic-driven distribution) or horizontally (syntactic-driven distribution) distributed over the networked workflow enactment components. That is, this paper proposes distributed workflow mining approaches that are able to rediscover ICN-based structured workflow process models through incrementally amalgamating a series of vertically or horizontally fragmented temporal workcases. And each of the approaches consists of a temporal fragment discovery algorithm, which is able to discover a set of temporal fragment models from the fragmented workflow enactment event logs, and a workflow process mining algorithm which rediscovers a structured workflow process model from the discovered temporal fragment models. Where, the temporal fragment model represents the concrete model of the XML-based distributed workflow fragment events log.
A Workflow Process Mining Algorithm Based on Synchro-Net
Xing-Qi Huang,Li-Fu Wang,Wen Zhao,Shi-Kun Zhang,Chong-Yi Yuan,
Xing-Qi Huang
,Li-Fu Wang,Wen Zhao,Shi-Kun Zhang,and Chong-Yi Yuan

计算机科学技术学报 , 2006,
Abstract: Sometimes historic information about workflow execution is needed to analyze business processes. Process mining aims at extracting information from event logs for capturing a business process in execution. In this paper a process mining algorithm is proposed based on Synchro-Net which is a synchronization-based model of workflow logic and workflow semantics. With this mining algorithm based on the model, problems such as invisible tasks and short-loops can be dealt with at ease. A process mining example is presented to illustrate the algorithm, and the evaluation is also given.
Mining Event Logs to Support Workflow Resource Allocation  [PDF]
Tingyu Liu,Yalong Cheng,Zhonghua Ni
Computer Science , 2012, DOI: 10.1016/j.knosys.2012.05.010
Abstract: Workflow technology is widely used to facilitate the business process in enterprise information systems (EIS), and it has the potential to reduce design time, enhance product quality and decrease product cost. However, significant limitations still exist: as an important task in the context of workflow, many present resource allocation operations are still performed manually, which are time-consuming. This paper presents a data mining approach to address the resource allocation problem (RAP) and improve the productivity of workflow resource management. Specifically, an Apriori-like algorithm is used to find the frequent patterns from the event log, and association rules are generated according to predefined resource allocation constraints. Subsequently, a correlation measure named lift is utilized to annotate the negatively correlated resource allocation rules for resource reservation. Finally, the rules are ranked using the confidence measures as resource allocation rules. Comparative experiments are performed using C4.5, SVM, ID3, Na\"ive Bayes and the presented approach, and the results show that the presented approach is effective in both accuracy and candidate resource recommendations.
Research on organizational structure mining based on workflow logs
基于工作流日志的组织结构挖掘研究

GAO Ang,YANG Yang,WANG Yue-wei,HE Guang-jun,
高昂
,杨扬,王玥薇,何广军

计算机应用研究 , 2009,
Abstract: In order to mining the organizational setting and interactions among coworkers from workflow logs, defined metrics to establish the relationships among originators by analyzing the information in the workflow logs. This paper presented three methods for mining organizational structure from workflow logs, default mining, mining based on the similarity of activities and mining based on the similarity of cases. By applying these methods, it could construct the corresponding organizational network which reflected the organizational entities involved in the workflow process and corrected the presented organizational structure. This paper gave an example to explain it.
Research of workflow schema mining from workflow logs
工作流日志中的工作流模式图挖掘技术研究*

WEN Yi-ping,ZHAO Yi-jiang,
文一凭
,赵肄江

计算机应用研究 , 2008,
Abstract: Workflow mining can construct process models from logs of past executions of system. Most of approaches for it assume a graphical representation of the model, namely control flow graph. Workflow schema mining extends workflow mining in nature. This paper discussed the techniques of schema mining, analyzed the challenging problems involved in it and introduced an algorithm of schema mining.
New Algorithm Research for Mining Workflow Frequent Pattern
一种新的工作流频繁模式挖掘算法研究

GAO Ang,YANG Yang,WANG Yue-wei,
高昂
,杨扬,王玥薇

计算机科学 , 2009,
Abstract: To improve mining accuracy of workflow models,a new algorithm for mining workflow frequent pattern was proposed.Firstly,the Workflow Model depend Matrix(WM) was defined,and set up WM by using workflow logs.Se-condly,using the depend relation of activities as frequent itemsets,an alogrithm was designed to automatically generate frequent itemsets based on WM.Finally,got the workflow frequent pattern by disposing frequent itemsets.The algorithm has advantage in disposing the interleaving relations between acti...
Text Mining Perspectives in Microarray Data Mining  [PDF]
Jeyakumar Natarajan
ISRN Computational Biology , 2013, DOI: 10.1155/2013/159135
Abstract: Current microarray data mining methods such as clustering, classification, and association analysis heavily rely on statistical and machine learning algorithms for analysis of large sets of gene expression data. In recent years, there has been a growing interest in methods that attempt to discover patterns based on multiple but related data sources. Gene expression data and the corresponding literature data are one such example. This paper suggests a new approach to microarray data mining as a combination of text mining (TM) and information extraction (IE). TM is concerned with identifying patterns in natural language text and IE is concerned with locating specific entities, relations, and facts in text. The present paper surveys the state of the art of data mining methods for microarray data analysis. We show the limitations of current microarray data mining methods and outline how text mining could address these limitations. 1. Introduction DNA microarrays facilitate the simultaneous measurement of the expression levels of thousands of genes [1, 2]. As a result, this high-throughput technology has led to increased amount of gene expression data. Microarrays have been used for a variety of studies, including gene coregulation studies, gene function identification studies, identification of pathway and gene regulatory networks, predictive toxicology, clinical diagnosis, and sequence variance studies. For a complete description about microarrays and its analytical tasks, refer to the books [3–5]. Current microarray data mining methods such as clustering, classification, and association analysis are based on statistical and machine learning algorithms. Most of these techniques are purely data driven and do not incorporate significant amounts of biological knowledge. Considering the statistically ill-defined nature of microarray data (many more variables than observations) and the massive body of existing biological knowledge, it is imperative that we exploit that knowledge for analysis and interpretation of microarray data. Text mining techniques constitute a promising technology for automating the incorporation of scientific knowledge in the microarray data mining process. Applying domain knowledge is fundamental in any scientific discovery process. In biology, domain knowledge is available in vast collections of the literature in natural language form such as abstracts [6] and full-text journal articles [7, 8] and also as textual annotations in databases such as SwissProt [9] and GenBank [10] For example, the biological abstract database PubMed
An iterative workflow for mining the human intestinal metaproteome
Koos Rooijers, Carolin Kolmeder, Catherine Juste, Jo?l Doré, Mark de Been, Sjef Boeren, Pilar Galan, Christian Beauvallet, Willem M de Vos, Peter J Schaap
BMC Genomics , 2011, DOI: 10.1186/1471-2164-12-6
Abstract: Two human fecal samples for which metagenomic data had been collected, were analyzed for their metaproteome using liquid chromatography-mass spectrometry and used to benchmark the developed iterative workflow to other methods. The results show that the developed method is able to detect over 3,000 peptides per fecal sample from the spectral data by circumventing the lack of a defined proteome without naive translation of matched metagenomes and cross-species peptide identification.The developed iterative workflow achieved an approximate two-fold increase in the amount of identified spectra at a false discovery rate of 1% and can be applied in metaproteomic studies of the human intestinal tract or other complex ecosystems.The human intestinal tract is colonized since birth by a large number of microbes, together making a complex ecosystem, even considered an organ by itself [1]. Many studies indicate a pivotal role for the intestinal microbes in carbohydrate metabolism, production of vitamins, inflammatory response regulation, fat metabolism and other biological processes of the human host [2,3]. In adults, the community consists of around 1014 cells [4-6], with a complexity that is predicted to include over 5000 microbial species [3]. While recent progress has been made in characterizing the genomes of around 200 intestinal species in the Human Microbiome Project (HMP) [7], the vast majority has not yet been cultured. Hence, these are known as phylotypes as their presence can be deduced from molecular markers such as 16S rRNA and other nucleotide sequences. This approach has shown that most of the intestinal phylotypes belong to a limited set of phyla, including the Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria and Verrucomicrobia [5]. In healthy adults the intestinal microbiota fluctuates around a stable individual core of phylotypes that are affected by host genetics, environmental and stochastic factors [3]. High throughput metagenomics studies, such
ANALYSIS METHODS OF WORKFLOW EXECUTION FOR DATA BASED ON ROUGH SET THEORY IN DATA MINING
Anoop Shrivastava
International Journal of Advanced Technology & Engineering Research , 2012,
Abstract: Exploring the trivial workflow data needs high performance data processing technology. In this research work we put forward analysis method of workflow execution data based on data mining. The main idea of it is to retrieve the workflow data to a data warehouse and adopt OLAP technology and data mining method to support customers to select different measures and view the corresponding data in different dimensions and different abstract levels, which is important for them to make decision. This research work presents the use of a relatively new method, the Rough Set (RS) theory for knowledge acquisition in time sequence condition monitoring. An additional attraction of the RS theory is that it allows automated generation of knowledge models, offering clear explanations to the inferences performed in diagnosis.
Workflow Mining Optimization Based on Hybrid Adaptive Genetic Algorithm
基于混合自适应遗传算法的工作流挖掘优化

GU Chun-qin,TAO Qian,WU Jia-pei,CHANG Hui-you,YAO Qing-d,YI Yang,
顾春琴
,陶乾,吴家培,常会友,姚卿达,衣杨

计算机科学 , 2010,
Abstract: Current workflow mining algorithm using local strategy couldn't ensure that a globally optimal process model was mined. The algorithm was also sensitive to noise. To solve the problems, a hybrid adaptive genetic algorithm (HAGA) was proposed. Firstly, Elementary Workflow net (EW-net) was defined. The enabling and firing rules of EW-net were given, and the process model was described. Secondly, a converting algorithm proposed was used to convert the process model to EW-net, and an evaluating function of the individual fitness was presented in order to measure the compliance between event log and mined process model. Lastly, hybrid adaptive crossover and mutation rates were designed according to evolution stage and parents' similarity. The simulation testing results demonstrate that the new algorithm has noise immunity and is more robust than a algorithm, and it can find better solution and converge faster than the simple genetic algorithm (SGA) employing general genetic strategy.
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