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

Mining and Improving Composite Web Services Recovery Mechanisms

International audienceEnsuring composite services reliability is a challenging problem. Indeed, due to the inherent autonomy and heterogeneity of Web services it is difficult to predict and reason about the behavior of the overall composite service. Generally, previous approaches develop, using their modeling formalisms, a set of techniques to analyze the composition model and check “correctness” properties. Although powerful, these approaches may fail, in some cases, to ensure CS reliable executions even if they formally validate its composition model. This is because properties specified in the studied composition model remains assumptions that may not coincide with the reality (i.e. effective CS executions). Sharing the same issue, we present a reengineering approach that starts from CS executions log to improve its recovery mechanisms. Basically, we propose a set of mining techniques to discover CS transactional behavior from an event based log. Then, based on this mining step, we use a set of rules in order to improve its reliability

Implementing a Decision-Aware System for Loan Contracting Decision Process

The paper introduces our work related to the design and implementation of a decision-aware system focused on the loan contracting decision process. A decision-aware system is a software that enables the user to make a decision in a simulated environment and logs all the actions of the decision maker while interacting with the software. By using a mining algorithm on the logs, it creates a model of the decision process and presents it to the user. The main design issue introduced in the paper is the possibility to log the mental actions of the user. The main implementation issues are: user activity logging programming and technologies used. The first section of the paper introduces the state-of-the-art research in process mining and the framework of our research; the second section argues the design of the system; the third section introduces the actual implementation and the fourth section shows a running example.Decision-Aware Systems, Decision Activity Logs, Decision Mining, Codeigniter, JSON

Robotic Process Mining: Vision and Challenges

Robotic process automation (RPA) is an emerging technology that allows organizations automating repetitive clerical tasks by executing scripts that encode sequences of fine-grained interactions with Web and desktop applications. Examples of clerical tasks include opening a file, selecting a field in a Web form or a cell in a spreadsheet, and copy-pasting data across fields or cells. Given that RPA can automate a wide range of routines, this raises the question of which routines should be automated in the first place. This paper presents a vision towards a family of techniques, termed robotic process mining (RPM), aimed at filling this gap. The core idea of RPM is that repetitive routines amenable for automation can be discovered from logs of interactions between workers and Web and desktop applications, also known as user interactions (UI) logs. The paper defines a set of basic concepts underpinning RPM and presents a pipeline of processing steps that would allow an RPM tool to generate RPA scripts from UI logs. The paper also discusses research challenges to realize the envisioned pipeline