Survival of the Fastest: Using Sequential Pattern Analysis to Measure Efficiency of Complex Organizational Processes Measuring the efficiency of complex and dynamic organizational processes is a problem that remains unresolved in the management science literature. We propose a novel sequence transverse velocity measurement of the sequential decision processes from the itemset transition speed of frequent subsequences. Specifically, sequential decision chains of different source sequences under comparison are modeled as timestamped itemset sequences and their frequent subsequences are extracted using sequential pattern discovery algorithm. The subsequence transverse velocity is represented as the inverse ranking of the traversing time duration of a common subsequence among the source sequences that share this subsequence. The proposed velocity measurement is then combined into a velocity timeline accompanying each source sequence by weighting the velocity of the constituent subsequences according to the statistical and contextual significance of the subsequences. To demonstrate the usage of this velocity measurement and its related computational tools, the paper also includes two empirical studies: one based on realworld business expansion chains and the other based on healthcare delivery systems.

Approximate Approach to Finding Generic Utility of Sequential Patterns Majority of utility pattern mining (UPM) literature assumes that the utility of particular patterns is known beforehand. At the same time, in frequent pattern mining (FPM), all patterns assume the same value. A problem with many datasets is that information about the utility of patterns is not easily or at all available and the utility is not directly proportional to the frequency of the specific pattern. One approach to finding the utility could be formulated in terms of how a particular pattern contributes to effectiveness of a specific machine learning (ML) algorithm. In this paper, we focus in particular on sequential patterns, e.g. time series. We propose an approximate approach to finding the generic utility of sequential patterns based on intergroup separation calculated using subsequence Dynamic Time Warping (DTW) similarity. Such utility could then be used in supervised algorithms and with some limitations in unsupervised algorithms. It can be applied both for regular and streaming application. We provide a proof of effectiveness of our approach using PAMAP2 Physical Activity Monitoring Data Set, an open dataset from the UCI Machine Learning Repository. For dissimilar activities (e.g. lying and ascending the stairs) we identify subsequences with normalized utility exceeding 0.5, where 1 would be a difference between total lack of heart rate and maximal heart rate all the time. For very similar activities (e.g. Nordic walking and ascending stairs) we find subsequences with utility exceeding 0.12.