Generation of Process Sequence Based on Implicit Temporal Overlap Function

Activities within processes occur in sequence, and the discovering of these sequences is an essential step and of great significance to process mining. This paper is aimed at intelligently discovering process sequences that lie within the helpdesk unit event log, which was primarily obtained from the 4TU repository. Explicit approaches have mostly being applied to mining rules and little attention given to sequences that can be generated via implicit approach. Hence, an implicit approach to association rule discovery was adopted using the modified temporal overlap scoring module (TOSM). The module was implemented using Java programming language. The experimental results showed that the temporal overlap module discovered sequences in an intelligent manner by factoring in the overlap property and identifying hidden dependencies. The resulting association rule generated for each sequence, as represented in the lift value, was recorded as significant to the entire log as compared to that of the explicit approach. Keywords: Hierarchical Temporal Memory, Overlap, Process Aware Information System, Event Logs, Process Sequence. DOI: 10.7176/CEIS/11-4-04 Publication date:June 30th 202

A Role of Heuristics Miner Algorithm in the Business Process System

The main idea of process mining is to extract knowledge or information from event logs recorded by an information system. Till now, the information in these event logs was rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, organizational, social, and performance information from event logs. Process mining has become a bright research area. In this paper we discuss the challenging process mining domain and demonstrate a heuristics driven process mining algorithm; the so called Heuristics Miner in detail. Heuristics Miner is a practical applicable mining algorithm that can deal with noise, and can be used to express the main behavior that is not all details and exceptions, registered in an event log. The business process system has a complex process system, which deals about many cases or audit trail entries and various event logs. This paper deals about the role of Heuristics Miner algorithm in the field of Business process system, also the Heuristics Miner algorithm is compared with the other mining algorithm such as α-algorithm. Hence, we analyzed that, is it possible to develop a control flow process mining algorithm such as Heuristics Miner algorithm can discover all the common control flow structures and is robust to noisy logs at once? This paper attempts to provide an answer to this question

Toasters, Seat Belts, and Inferring Program Properties

Abstract. Today's software does not come with meaningful guarantees. This position paper explores why this is the case, suggests societal and technical impediments to more dependable software, and considers what realistic, meaningful guarantees for software would be like and how to achieve them. If you want a guarantee, buy a toaster. Clint Eastwood (The Rookie, 1990

Discovering Petri Net Models of Discrete-Event Processes by Computing T-Invariants

International audienceThis paper addresses the problem of discovering a Petri Net (PN) from a long event sequence representing the behavior of discrete-event processes. A method for building a 1-bounded PN able to execute the events sequence S is presented; it is based on determining causality and concurrence relations between events and computing the t-invariants. This novel method determines the structure and the initial marking of an ordinary PN, which reproduces the behavior in S. The algorithms derived from the method are efficient and have been implemented and tested on numerous examples of diverse complexity. Note to Practitioners—Model discovery is useful to perform reverse engineering of ill-known systems. The algorithms proposed in this paper build 1-bounded PN models, which are enough powerful to describe many discrete-event processes from industry. The efficiency of the method allows processing very large sequences. Thus, an automated modeling tool can be developed for dealing with data issued from real systems

A Framework for Discovery and Diagnosis of Behavioral Transitions in Event-streams

Date stream mining techniques can be used in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment. When the quality of some data mined models varies significantly from nearby models—as defined by quality metrics—then the user’s behavior is automatically flagged as a potentially significant behavioral change. Decision tree, sequence pattern and Hidden Markov modeling being used in this study. These three types of modeling can expose different aspect of user’s behavior. In case of decision tree modeling, the specific changes in user behavior can automatically characterized by differencing the data-mined decision-tree models. The sequence pattern modeling can shed light on how the user changes his sequence of actions and Hidden Markov modeling can identifies the learning transition points. This research describes how model-quality monitoring and these three types of modeling as a generic framework can aid recognition and diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The date stream mining techniques mentioned are used to monitor patient goals as part of a clinical plan to aid cognitive rehabilitation. In this context, real time data mining aids clinicians in tracking user behaviors as they attempt to achieve their goals. This generic framework can be widely applicable to other real-time data-intensive analysis problems. In order to illustrate this fact, the similar Hidden Markov modeling is being used for analyzing the transactional behavior of a telecommunication company for fraud detection. Fraud similarly can be considered as a potentially significant transaction behavioral change

Process Mining Concepts for Discovering User Behavioral Patterns in Instrumented Software

Process Mining is a technique for discovering “in-use” processes from traces emitted to event logs. Researchers have recently explored applying this technique to documenting processes discovered in software applications. However, the requirements for emitting events to support Process Mining against software applications have not been well documented. Furthermore, the linking of end-user intentional behavior to software quality as demonstrated in the discovered processes has not been well articulated. After evaluating the literature, this thesis suggested focusing on user goals and actual, in-use processes as an input to an Agile software development life cycle in order to improve software quality. It also provided suggestions for instrumenting software applications to support Process Mining techniques