1,721,101 research outputs found
Conformance checking based on multi-perspective declarative process models
No full textProcess mining is a family of techniques that aim at analyzing business process execution data recorded in event logs. Conformance checking is a branch of this discipline embracing approaches for verifying whether the behavior of a process, as recorded in a log, is in line with some expected behavior provided in the form of a process model. Recently, techniques for conformance checking based on declarative specifications have been developed. Such specifications are suitable to describe processes characterized by high variability. However, an open challenge in the context of conformance checking with declarative models is the capability of supporting multi-perspective specifications. This means that declarative models used for conformance checking should not only describe the process behavior from the control flow point of view, but also from other perspectives like data or time. In this paper, we close this gap by presenting an approach for conformance checking based on MP-Declare, a multi-perspective version of the declarative process modeling language Declare. The approach has been implemented in the process mining tool ProM and has been experimented using artificial and real-life event logs
Business Models Enhancement through Discovery of Roles
No full textControl flow discovery algorithms are able to reconstruct the workflow of a business process from a log of performed activities. These algorithms, however, do not pay attention to the reconstruction of roles, i.e. they do not group activities according to the skills required to perform them. Information about roles in business processes is commonly considered important and explicitly integrated into the process representation, e.g. as swimlanes in BPMN diagrams. This work proposes an approach to enhance a business process model with information on roles. Specifically, the identification of roles is based on the detection of handover of roles. On the basis of candidates for roles handover, the set of activities is first partitioned and then subsets of activities which are performed by the same originators are merged, so to obtain roles. All significant partitions of activities are automatically generated. Experimental results on several logs show that the set of generated roles is not too large and it always contains the correct definition of roles. We also propose an entropy based measure to rank the candidate roles which returns promising experimental results
Process Mining meets Statistical Model Checking: Towards a Novel Approach to Model Validation and Enhancement
No full textWe propose a novel research line integrating Statistical Model Checking (SMC), a family of simulation-based analysis techniques from quantitative formal methods, with Process Mining (PM), a collection of data-driven process-oriented techniques. SMC and PM are complementary. SMC focuses on performing the right number of simulations to obtain statistically-reliable estimations (e.g., the probability of success of an attack). PM focuses on reconstructing a model of a system using logs of its traces. Nevertheless, both approaches aim at providing evidence of issues/guarantees of the system, and at proposing enhancements. We aim at enriching SMC by explaining why it produced specific estimates. This might help, e.g., identifying issues in the model (validation) or suggesting improvements (enhancement). Given that SMC uses statistics to decide what is the correct number of simulations (or traces), we avoid by-construction the complex issue of under-representation of system behavior in the logs crucial to many PM exercises. This work-in-progress paper demonstrates the proposed methodology and its usefulness using a simple example from the security threat modeling domain. We show how PM helps highlighting both mistakes in the model, and possibilities for improvement.<br/
Online Discovery of Declarative Process Models from Event Streams
No full textToday’s business processes are often controlled and supported by information systems. These systems record real-time information about business processes during their executions. This enables the analysis at runtime of the process behavior. However, many modern systems produce “big data”, i.e., collections of data sets so large and complex that it becomes impossible to store and process all of them. Moreover, few processes are in steady-state but, due to changing circumstances, they evolve and systems need to adapt continuously. In this paper, we present a novel framework for the discovery of LTL-based declarative process models from streaming event data in settings where it is impossible to store all events over an extended period of time or where processes evolve while being analyzed. The framework continuously updates a set of valid business constraints based on the events occurred in the event stream. In addition, our approach is able to provide meaningful information about the most significant concept drifts, i.e., changes occurring in a process during its execution. We report about experimental results obtained using synthetic logs and a real-life event log pertaining to the treatment of patients diagnosed with cancer in a large Dutch academic hospital
PURPLE: a PURPose-guided Log GEnerator (Extended Abstract)
No full textProcess mining collects a variety of techniques. To test and compare these techniques, we need event logs tailored to their specific mining purposes, e.g., process discovery and conformance checking. To this aim, we propose the PURPLE tool, a generator of event logs supporting different mining purposes. PURPLE performs guided simulations of a business model, shaping the resulting event log by the selected mining purpose
Online Process Discovery to Detect Concept Drifts in LTL-Based Declarative Process Models
No full textToday's business processes are often controlled and supported by information systems. These systems record real-time information about business processes during their executions. This enables the analysis at runtime of the process behavior. However, many modern systems produce "big data", i.e., collections of data sets so large and complex that it becomes impossible to store and process all of them. Moreover, few processes are in steady-state and due to changing circumstances processes evolve and systems need to adapt continuously. In this paper, we present a novel framework for the discovery of LTL-based declarative process models from streaming event data in settings where it is impossible to store all events over an extended period or where processes evolve while being analyzed. The framework continuously updates a set of valid business constraints based on the events occurred in the event stream. In addition, our approach is able to provide meaningful information about the most significant concept drifts, i.e., changes occurring in a process during its execution. We report about experimental results obtained using logs pertaining the health insurance claims handling in a travel agenc
SocialSpy: Browsing (Supposedly) Hidden Information in Online Social Networks
No full textOnline Social Networks are becoming the most important “places” where people share information about their lives. With the increasing concern that users have about privacy, most social networks offer ways to control the privacy of the user. Unfortunately, we believe that current privacy settings are not as effective as users might think. In this paper, we highlight this problem focusing on one of the most popular social networks, Facebook. In particular, we show how easy it is to retrieve information that a user might have set as (and hence thought as) “private”. As a case study, we focus on retrieving the list of friends for users that did set this information as “hidden” (to non-friends). We propose four different strategies to achieve this goal, and we evaluate them. The results of our thorough experiments show the feasibility of our strategies as well as their effectiveness: our approach is able to retrieve a significant percentage of the names of the “hidden” friends: i.e., some 25% on average, and more than 70% for some users. © Springer International Publishing Switzerland 2015
A Business Process Metric Based on the Alpha Algorithm Relations
No full textWe present a metric for the comparison of business process models. This new metric is based on a representation of a given model as two sets of local relations between pairs of activities in the model. In order to build this two sets, the same relations defined for the Alpha Algorithm [2] are considered. The proposed metric is then applied to hierarchical clustering of business process models and the whole procedure is implemented and made publicly available
Enhancing Threat Model Validation: A White-Box Approach based on Statistical Model Checking and Process Mining
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