processmining
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| The Security group mainly focus on extending and developing techniques that exploit process-related data for auditing user behavior. Our main contributions include: | The Security group mainly focus on extending and developing techniques that exploit process-related data for auditing user behavior. Our main contributions include: | ||
| - | **History-based conformance checking:** There may exist a number of explanations why a process execution is not conforming. In these works, we discuss how probable explanations for non-conformity between a process execution and a process model can be constructed and how they can be ranked with respect to their criticality. | ||
| + | {{ http:// | ||
| + | **History-based conformance checking:** There may exist a number of explanations why a process execution is not conforming. | ||
| + | Alignment-based conformance checking techniques pinpoint the deviations causing nonconformity based on a cost | ||
| + | function. However, such a cost function is often manually defined on the basis of human judgment and thus error-prone, | ||
| + | We have proposed an approach to automatically define the cost function | ||
| + | based on information extracted from the past process executions. | ||
| + | In particular, we have investigated discuss how probable explanations for non-conformity between a process execution and a process model can be constructed and how they can be ranked with respect to their criticality. \\ | ||
| + | \\ | ||
| - | {{http:// | ||
| - | **Discovering frequent anomalous patterns:** Classic conformance checking techniques derive low-level deviations occurred in every single process execution. However, an analysts may have more interest in knowing diagnostics at a higher-level of granularity. In this work, we focus on providing the analyst with a “deviations dashboard”, | ||
| + | {{ http:// | ||
| + | **Discovering frequent anomalous patterns:** Classic conformance checking techniques derive low-level deviations occurred in every single process execution. However, an analysts may have more interest in knowing diagnostics at a higher-level of granularity. In this work, we focus on providing an analyst with a “deviations dashboard”, | ||
| + | \\ | ||
| - | + | {{ http:// | |
| - | **Linking data and process perspectives for deviation analysis:** Analyzing user behavior from process or data perspectives independently, may not be sufficient to expose | + | **Linking data and process perspectives for deviation analysis: |
| + | The problem lies in that fact that organizations often lack proper mechanisms to control | ||
| + | and monitor users’ activities and their data usage. Although several auditing approaches | ||
| + | have been proposed to assess the compliance of actual executed behavior, existing | ||
| + | approaches focus on either checking data accesses against security policies (data | ||
| + | perspective) or checking user activities against the activities needed to conduct business | ||
| + | processes (process perspective). | ||
| + | independently may not be sufficient to expose | ||
| == References: == | == References: == | ||
| - | - Mahdi Alizadeh, Massimiliano de Leoni, Nicola Zannone: Constructing Probable Explanations of Nonconformity: | + | - Mahdi Alizadeh, Massimiliano de Leoni, Nicola Zannone: Constructing Probable Explanations of Nonconformity: |
| - | - Mahdi Alizadeh, Massimiliano de Leoni, Nicola Zannone: History-Based Construction of Alignments for Conformance Checking: Formalization and Implementation. SIMPDA (Revised Selected Papers) 2014: 58-78 | + | - Mahdi Alizadeh, Massimiliano de Leoni, Nicola Zannone: History-Based Construction of Alignments for Conformance Checking: Formalization and Implementation. SIMPDA (Revised Selected Papers) 2014: 58-78 [[http:// |
| - | - Mahdi Alizadeh and Nicola Zannone. Risk-based analysis of business process executions. In Proceedings of ACM Conference on Data and Application Security and Privacy, pages 130–132. ACM, 2016. | + | - Mahdi Alizadeh and Nicola Zannone. Risk-based analysis of business process executions. In Proceedings of ACM Conference on Data and Application Security and Privacy, pages 130–132. ACM, 2016. [[http:// |
| - | - Laura Genga, Domenico Potena, Orazio Martino, Mahdi Alizadeh, Claudia Diamantini, Nicola Zannone: Subgraph Mining for Anomalous Pattern Discovery in Event Logs. NFMCP@PKDD/ | + | - Laura Genga, Domenico Potena, Orazio Martino, Mahdi Alizadeh, Claudia Diamantini, Nicola Zannone: Subgraph Mining for Anomalous Pattern Discovery in Event Logs. NFMCP@PKDD/ |
| - | | + | - Laura Genga, Mahdi Alizadeh, Domenico Potena, Claudia Diamantini, Nicola Zannone: APD tool: Mining Anomalous Patterns from Event Logs. BPM (Demos) 2017 [[http:// |
| - | - Laura Genga, Mahdi Alizadeh, Domenico Potena, Claudia Diamantini, Nicola Zannone: APD tool: Mining Anomalous Patterns from Event Logs. BPM (Demos) 2017 | + | - Arya Adriansyah, Boudewijn F. van Dongen, Nicola Zannone: Privacy Analysis of User Behavior Using Alignments. it - Information Technology 55(6): 255-260 (2013) |
| - | | + | - Arya Adriansyah, Boudewijn F. van Dongen, Nicola Zannone: Controlling Break-the-Glass through Alignment. SocialCom 2013: 606-611 |
| - | - Arya Adriansyah, Boudewijn F. van Dongen, Nicola Zannone: Privacy Analysis of User Behavior Using Alignments. it - Information Technology 55(6): 255-260 (2013) | + | - Sebastian Banescu, Milan Petkovic, Nicola Zannone: Measuring Privacy Compliance Using Fitness Metrics. BPM 2012: 114-119 |
| - | - Arya Adriansyah, Boudewijn F. van Dongen, Nicola Zannone: Controlling Break-the-Glass through Alignment. SocialCom 2013: 606-611 | + | |
| - | - Sebastian Banescu, Milan Petkovic, Nicola Zannone: Measuring Privacy Compliance Using Fitness Metrics. BPM 2012: 114-119 | + | |
processmining.1508429005.txt.gz · Last modified: 2021/01/10 20:59 (external edit)