Timestamps play a key role in process mining because it determines the chronology of which events occurred and subsequently how they are ordered in process modelling.The timestamp in process mining gives an insight on...Timestamps play a key role in process mining because it determines the chronology of which events occurred and subsequently how they are ordered in process modelling.The timestamp in process mining gives an insight on process performance,conformance,and modelling.This therefore means problems with the timestamp will result in misrepresentations of the mined process.A few articles have been published on the quantification of data quality problems but just one of the articles at the time of this paper is based on the quantification of timestamp quality problems.This article evaluates the quality of timestamps in event log across two axes using eleven quality dimensions and four levels of potential data quality problems.The eleven data quality dimensions were obtained by doing a thorough literature review of more than fifty process mining articles which focus on quality dimensions.This evaluation resulted in twelve data quality quantification metrics and the metrics were applied to the MIMIC-ll dataset as an illustration.The outcome of the timestamp quality quantification using the proposed typology enabled the user to appreciate the quality of the event log and thus makes it possible to evaluate the risk of carrying out specific data cleaning measures to improve the process mining outcome.展开更多
With the development of large scale text processing, the dimension of text feature space has become larger and larger, which has added a lot of difficulties to natural language processing. How to reduce the dimension...With the development of large scale text processing, the dimension of text feature space has become larger and larger, which has added a lot of difficulties to natural language processing. How to reduce the dimension has become a practical problem in the field. Here we present two clustering methods, i.e. concept association and concept abstract, to achieve the goal. The first refers to the keyword clustering based on the co occurrence of展开更多
Data with large dimensions will bring various problems to the application of data envelopment analysis(DEA).In this study,we focus on a“big data”problem related to the considerably large dimensions of the input-outp...Data with large dimensions will bring various problems to the application of data envelopment analysis(DEA).In this study,we focus on a“big data”problem related to the considerably large dimensions of the input-output data.The four most widely used approaches to guide dimension reduction in DEA are compared via Monte Carlo simulation,including principal component analysis(PCA-DEA),which is based on the idea of aggregating input and output,efficiency contribution measurement(ECM),average efficiency measure(AEC),and regression-based detection(RB),which is based on the idea of variable selection.We compare the performance of these methods under different scenarios and a brand-new comparison benchmark for the simulation test.In addition,we discuss the effect of initial variable selection in RB for the first time.Based on the results,we offer guidelines that are more reliable on how to choose an appropriate method.展开更多
文摘Timestamps play a key role in process mining because it determines the chronology of which events occurred and subsequently how they are ordered in process modelling.The timestamp in process mining gives an insight on process performance,conformance,and modelling.This therefore means problems with the timestamp will result in misrepresentations of the mined process.A few articles have been published on the quantification of data quality problems but just one of the articles at the time of this paper is based on the quantification of timestamp quality problems.This article evaluates the quality of timestamps in event log across two axes using eleven quality dimensions and four levels of potential data quality problems.The eleven data quality dimensions were obtained by doing a thorough literature review of more than fifty process mining articles which focus on quality dimensions.This evaluation resulted in twelve data quality quantification metrics and the metrics were applied to the MIMIC-ll dataset as an illustration.The outcome of the timestamp quality quantification using the proposed typology enabled the user to appreciate the quality of the event log and thus makes it possible to evaluate the risk of carrying out specific data cleaning measures to improve the process mining outcome.
文摘With the development of large scale text processing, the dimension of text feature space has become larger and larger, which has added a lot of difficulties to natural language processing. How to reduce the dimension has become a practical problem in the field. Here we present two clustering methods, i.e. concept association and concept abstract, to achieve the goal. The first refers to the keyword clustering based on the co occurrence of
文摘Data with large dimensions will bring various problems to the application of data envelopment analysis(DEA).In this study,we focus on a“big data”problem related to the considerably large dimensions of the input-output data.The four most widely used approaches to guide dimension reduction in DEA are compared via Monte Carlo simulation,including principal component analysis(PCA-DEA),which is based on the idea of aggregating input and output,efficiency contribution measurement(ECM),average efficiency measure(AEC),and regression-based detection(RB),which is based on the idea of variable selection.We compare the performance of these methods under different scenarios and a brand-new comparison benchmark for the simulation test.In addition,we discuss the effect of initial variable selection in RB for the first time.Based on the results,we offer guidelines that are more reliable on how to choose an appropriate method.
