A Petri-Net Based Approach to Measure the Learnability of Interactive Systems

We propose an approach to measure the learnability of an interactive system. Our approach relies on recording in a user log all the user actions that take place during a run of the system and on replaying them over one or more interaction models of the system. Each interaction model describes the expected way of executing a relevant task provided by the system. The proposed approach is able to identify deviations between the interaction models and the user log and to assess the weight of such deviations through a fitness value, which estimates how much a log adheres to the models. Our thesis is that by measuring the rate of such a fitness value for subsequent executions of the system we can not only understand if the system is learnable with respect to its relevant tasks, but also to identify potential learning issues. © 2016 Copyright held by the owner/author(s)

Pemanfaatan Process Mining pada E-commerce

Organisasi menyimpan rekaman aktivitas proses bisnis yang terjadi pada proses di lapangan dalam log data dengan berbagai format. Rekaman aktivitas ini disimpan dalam rangka menghasilkan sebuah model proses bisnis berdasarkan aktivitas pengguna pada proses nyata di lapangan. Dari proses model yang dihasilkan dapat dilakukan analisis tentang kesesuaian antara proses bisnis yang terjadi pada proses nyata di lapangan dengan proses bisnis yang diharapakan oleh organisasi. Analisis ini disebut sebagai conformance checking yang bertujuan untuk mendeteksi deviasi yang terjadi antara proses bisnis yang diharapkan dengan proses bisnis dari proses nyata di lapangan dan sebaliknya. Suatu proses bisnis dikatakan sudah sesuai dengan regulasi (compliant) apabila tidak ada deviasi/nonconformance dalam eksekusinya dari proses bisnis yang telah didefinisikan mengikuti standar

Business Process Evaluation of Outpatient Services Using Process Mining

A business needs an evaluation to increase its services and adaptability to the environment changes. Business process evaluation is one of the several ways for business development. This paper reports an assessment of outpatient service process at RSUD Sukoharjo for BPJS Health insurance’s patient using process mining to get an objective process model. We implement the Inductive Miner infrequent approach and analyze the process model with conformance checking and performance analysis. Stakeholders can utilize the results of the evaluation to understand the real service condition and plan an action to improve their services. We can conclude that there is a bottleneck in the waiting time of the registration process with an average of 1.5-2 hours, a polyclinic treatment process with an average of 1-2 hours and pharmacy process with an average of 0.5-1 hours

Mine your own business : using process mining to turn big data into real value

Like most IT-related phenomena, also the growth of event data complies with Moore’s Law. Similar to the number of transistors on chips, the capacity of hard disks, and the computing power of computers, the digital universe is growing exponentially and roughly doubling every 2 years. Although this is not a new phenomenon, suddenly many organizations realize that increasing amounts of “Big Data” (in the broadest sense of the word) need to be used intelligently in order to compete with other organizations in terms of efficiency, speed and service. However, the goal is not to collect as much data as possible. The real challenge is to turn event data into valuable insights. Only process mining techniques directly relate event data to end-to-end business processes. Existing business process modeling approaches generating piles of process models are typically disconnected from the real processes and information systems. Data-oriented analysis techniques (e.g., data mining and machines learning) typically focus on simple classification, clustering, regression, or rule-learning problems. This keynote paper provides pointers to recent developments in process mining thereby clearly showing that process mining provides a natural link between processes and data on the one hand and performance and compliance on the other hand

"Mine your own business" : using process mining to turn big data into real value

Like most IT-related phenomena, also the growth of event data complies with Moore’s Law. Similar to the number of transistors on chips, the capacity of hard disks, and the computing power of computers, the digital universe is growing exponentially and roughly doubling every 2 years. Although this is not a new phenomenon, suddenly many organizations realize that increasing amounts of "Big Data" (in the broadest sense of the word) need to be used intelligently in order to compete with other organizations in terms of efficiency, speed and service. However, the goal is not to collect as much data as possible. The real challenge is to turn event data into valuable insights. Only process mining techniques directly relate event data to end-to-end business processes. Existing business process modeling approaches generating piles of process models are typically disconnected from the real processes and information systems. Data-oriented analysis techniques (e.g., data mining and machines learning) typically focus on simple classification, clustering, regression, or rule-learning problems. This keynote paper provides pointers to recent developments in process mining thereby clearly showing that process mining provides a natural link between processes and data on the one hand and performance and compliance on the other hand. Keywords: Process Mining, Process Discovery, Conformance Checking, Business Process Management