126 research outputs found
Fuzzy mining - adaptive process simplification based on multi-perspective metrics
Process Mining is a technique for extracting process models from execution logs. This is particularly useful in situations where people have an idealized view of reality. Real-life processes turn out to be less structured than people tend to believe. Unfortunately, traditional process mining approaches have problems dealing with unstructured processes. The discovered models are often "spaghetti-like", showing all details without distinguishing what is important and what is not. This paper proposes a new process mining approach to overcome this problem. The approach is configurable and allows for different faithfully simplified views of a particular process. To do this, the concept of a roadmap is used as a metaphor. Just like different roadmaps provide suitable abstractions of reality, process models should provide meaningful abstractions of operational processes encountered in domains ranging from healthcare and logistics to web services and public administration
Visual analysis of sensor logs in smart spaces: Activities vs. situations
Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. Our research is focused on developing a visual analysis pipeline (service) that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The basic assumption is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed pipeline is employed to automatically extract models to be reused for ambient intelligence. In this paper, we present an user evaluation aimed at demonstrating the effectiveness of the approach, by comparing it wrt. a relevant state-of-the-art visual tool, namely SITUVIS
Revealing Daily Human Activity Pattern using Process Mining Approach
In the last few years, with the emergence of ambient assisted living, the study of human behavioral pattern took a wide interest from research communities around the world. In many literatures, pattern recognition was widely adopted approach to implements in human behavior study from computing perspective. Pattern recognition brings a promising results in terms of accuracy for modeling human behavior. But the problem with this approach is the complexity of knowledge representation which formulated in mathematical model. In turns, a correction by the experts is hardly conducted. In another hand, gathering a graphical insight is not a trivial task. This paper investigate the use of process mining technology to gives an alternative to such problems. Process mining is data-driven approach to infer a graphical representation of any kind of process. In terms of human behavior, process can be defined as sequences of activities performed by human on daily basis. From the conducted experiments process mining was shown a potential use to infer a human daily activity pattern in a graphical representation
Relational Algebra for In-Database Process Mining
The execution logs that are used for process mining in practice are often
obtained by querying an operational database and storing the result in a flat
file. Consequently, the data processing power of the database system cannot be
used anymore for this information, leading to constrained flexibility in the
definition of mining patterns and limited execution performance in mining large
logs. Enabling process mining directly on a database - instead of via
intermediate storage in a flat file - therefore provides additional flexibility
and efficiency. To help facilitate this ideal of in-database process mining,
this paper formally defines a database operator that extracts the 'directly
follows' relation from an operational database. This operator can both be used
to do in-database process mining and to flexibly evaluate process mining
related queries, such as: "which employee most frequently changes the 'amount'
attribute of a case from one task to the next". We define the operator using
the well-known relational algebra that forms the formal underpinning of
relational databases. We formally prove equivalence properties of the operator
that are useful for query optimization and present time-complexity properties
of the operator. By doing so this paper formally defines the necessary
relational algebraic elements of a 'directly follows' operator, which are
required for implementation of such an operator in a DBMS
Mining Disease Courses across Organizations: A Methodology Based on Process Mining of Diagnosis Events Datasets
BerlĂn (Alemania) (23-27 julio 2019)This work was supported in part by grants TRA2015-63708-R and TRA2016-78886-C3-1-R (Spanish Government) and Topus (Madrid Regional Government)
A Mining Algorithm for Extracting Decision Process Data Models
The paper introduces an algorithm that mines logs of user interaction with simulation software. It outputs a model that explicitly shows the data perspective of the decision process, namely the Decision Data Model (DDM). In the first part of the paper we focus on how the DDM is extracted by our mining algorithm. We introduce it as pseudo-code and, then, provide explanations and examples of how it actually works. In the second part of the paper, we use a series of small case studies to prove the robustness of the mining algorithm and how it deals with the most common patterns we found in real logs.Decision Process Data Model, Decision Process Mining, Decision Mining Algorithm
Process mining online assessment data
Traditional data mining techniques have been extensively applied to find interesting patterns, build descriptive and predictive models from large volumes of data accumulated through the use of different information systems. The results of data mining can be used for getting a better understanding of the underlying educational processes, for generating recommendations and advice to students, for improving management of learning objects, etc. However, most of the traditional data mining techniques focus on data dependencies or simple patterns and do not provide a visual representation of the complete educational (assessment) process ready to be analyzed. To allow for these types of analysis (in which the process plays the central role), a new line of data-mining research, called process mining, has been initiated. Process mining focuses on the development of a set of intelligent tools and techniques aimed at extracting process-related knowledge from event logs recorded by an information system. In this paper we demonstrate the applicability of process mining, and the ProM framework in particular, to educational data mining context. We analyze assessment data from recently organized online multiple choice tests and demonstrate the use of process discovery, conformance checking and performance analysis techniques
Application of Process Mining and Sequence Clustering in Recognizing an Industrial Issue
Process mining has become one of the best programs that can outline the event
logs of production processes in visualized detail. We have addressed the
important problem that easily occurs in the industrial process called
Bottleneck. The analysis process was focused on extracting the bottlenecks in
the production line to improve the flow of production. Given enough stored
history logs, the field of process mining can provide a suitable answer to
optimize production flow by mitigating bottlenecks in the production stream.
Process mining diagnoses the productivity processes by mining event logs, this
can help to expose the opportunities to optimize critical production processes.
We found that there is a considerable bottleneck in the process because of the
weaving activities. Through discussions with specialists, it was agreed that
the main problem in the weaving processes, especially machines that were
exhausted in overloading processes. The improvement in the system has measured
by teamwork; the cycle time for process has improved to 91%, the worker's
performance has improved to 96%,product quality has improved by 85%, and lead
time has optimized from days and weeks to hours
Using Process Mining and Model-driven Engineering to Enhance Security of Web Information Systems
Due to the development of Smart Cities and Internet of Things, there has been an increasing interest in the use of Web information systems in different areas and domains. Besides, the number of attacks received by this kind of systems is increasing continuously. Therefore, there is a need to strengthen their protection and security. In this paper, we propose a method based on Process Mining and Model- Driven Engineering to improve the security of Web information systems. Besides, this method has been applied to the SID Digital Library case study and some preliminary results to improve the security of this system are described
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