Process mining-enabled jurimetrics

Unger, Adriana Jacoto; Neto, José Francisco dos Santos; Fantinato, Marcelo; Peres, Sarajane Marques; Trecenti, Julio; Hirota, Renata

doi:10.1145/3462757.3466137

Cited by 12 publications

(9 citation statements)

References 14 publications

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“…The second branch of works investigates the duration of a case in machine learning [16,22,23], and a mathematical [24][25][26][27] perspective. The last category encompasses works that analyze the lawsuits from a process mining perspective [28][29][30].…”

Section: Related Workmentioning

confidence: 99%

“…A third investigated branch includes works that incorporate process mining techniques in the analysis of legal proceedings. Unger et al [28] identified bottlenecks and the most frequent procedural movements in cases from the Court of Justice of the State of São Paulo, Brazil. They also identified ad hoc behavior of the analysed legal proceedings.…”

Section: Process Mining Applied On Judicial Casesmentioning

confidence: 99%

“…In this work, we consider the findings from previous process mining approaches and measure the complexity of the judicial cases. As in [28,29], we recognized complex behavior and therefore used clustering to leverage the analysis as in [30]. However, we drive the analysis from a time perspective, incorporating the found clusters into the pool of features that will serve as input for the regression models.…”

Section: Process Mining Applied On Judicial Casesmentioning

confidence: 99%

See 2 more Smart Citations

Investigation of Lawsuit Process Duration: A Machine Learning and Process Mining Approach

Vercosa,

Silva,

Cruz

et al. 2024

Preprint

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Delays in legal proceedings can significantly harm both corporate finances and individual lives. While many jurisdictions rely on subjective criteria such as reasonable process duration to manage case processing times, machine learning offers an alternative approach by estimating lawsuit durations and assessing the impact of multiple variables on these timelines. This study explores the use of machine learning to estimate lawsuit duration, considering multiple variables, including control-flow characteristics. We observed that the sequences of procedural movements exhibit complex patterns, making them suitable for analysis using clustering techniques employed in process mining. We framed the problem as a supervised learning regression task, incorporating clustering results as additional features. Employing linear regression, support vector regression, and gradient boosting methods, we analyzed over 60,000 cases from Brazilian labor courts. The gradient boosting model achieved an R2-score of 0.87, and feature importance analysis revealed that clustering techniques features emerged among the most significant in time estimation.

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Section: Related Workmentioning

confidence: 99%

Section: Process Mining Applied On Judicial Casesmentioning

confidence: 99%

Section: Process Mining Applied On Judicial Casesmentioning

confidence: 99%

See 1 more Smart Citation

Investigation of Lawsuit Process Duration: A Machine Learning and Process Mining Approach

Vercosa,

Silva,

Cruz

et al. 2024

Preprint

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“…, 2015; Spurr, 2000). In Unger (Unger et al. , 2021), the authors analyse with process mining techniques the durations and phases of trials in Brazil, focussing on the differences of digital and on-paper processes, and on detecting the phases of the processes that can introduce inefficiency, analysing process logs.…”

Section: Related Literaturementioning

confidence: 99%

“…In these works, the response variable is quantitative, and the independent variable is binary or quantitative (Zhou, 2008;Walsh et al, 2015;Spurr, 2000). In Unger (Unger et al, 2021), the authors analyse with process mining techniques the durations and phases of trials in Brazil, focussing on the differences of digital and on-paper processes, and on detecting the phases of the processes that can introduce inefficiency, analysing process logs. More recently, the ability to predict the duration in days of court cases using artificial intelligence has been investigated, and while effective in normal processes, the need of better understanding the impact of features in more critical cases is stated.…”

Section: Hypothesesmentioning

confidence: 99%

Improving the analysis of the judiciary performance - the use of data mining techniques to assess the timeliness of civil trials

Pernici,

Bono,

Piro

et al. 2023

IJPSM

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PurposeThe purpose of this paper is to show how data mining techniques can improve the performance management of the judiciary, helping judges in steering position with specific and timely measures. It explores different approaches to analyse the length of trials, based on the case of an Italian judicial office.Design/methodology/approachThe paper presents a temporal analysis to compare the timeliness of trials, using data and process mining approaches with the support of a specific software to represent graphically the results. Data were gathered directly from the office data base, improving precision and the opportunity to monitor specific phases of the trials.FindingsThe results highlight the progress that can be reached using data mining approaches to develop performance analyses helping courts to correct inefficiencies and to manage the personnel distribution, overcoming the critical comments arisen against traditional KPI (Raine, 2000). The work proposes a methodology to analyse cases deriving from different juridical matters useful to set up a performance monitoring system that could be diffused to different courts.Research limitations/implicationsThe limitations of the research regard the analysis of a selected, limited number of cases in terms of judicial matters.Practical implicationsData mining techniques can improve the performance management processes in providing more accurate feedback to the judicial offices leaders and increasing the organisational learning.Social implicationsThe performance of the judiciary is one of the relevant issues that emerged in the recent decade in the field of public sector reforms. Several reasons explain this interest, which has gone beyond the specific legal disciplines to involve public policy, management, economics and ICT studies.Originality/valueConsidering the literature on the judiciary (Visser et al., 2019; Di Martino et al., 2021; Troisi and Alfano, 2023) the contribution differs as both the methodological approach and the predictive analysis considers the intrinsic differences that define cases belonging to different juridical matters performing a cross-sectional analysis, with a specific focus of process variants.

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A Method for Bottleneck Detection, Prediction, and Recommendation Using Process Mining Techniques

Piest

Bemthuis

Cutinha

et al. 2023

Communications in Computer and Information Science

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Bottlenecks arise in many processes, often negatively impacting performance. Process mining can facilitate bottleneck analysis, but research has primarily focused on bottleneck detection and resolution, with limited attention given to the prediction of bottlenecks and recommendations for improving process performance. As a result, operational support for bottleneck resolution is often partially or not realized. The aim of this paper is to propose a method for Bottleneck Detection, Prediction, and Recommendation (BDPR) using process mining techniques to achieve operational support. A design science research methodology is adopted to design, develop, and demonstrate the BDPR method. A systematic literature review and a developed classification model provide theoretical support for the BDPR method and offer scholarly in the field of process mining a starting point for research. The BDPR method extends the utility of the classification model and aims to provide guidance to scholars and practitioners for assessing, selecting, evaluating, and implementing process mining techniques to realize operational support. A case study at a logistics service provider demonstrates the use of the proposed BDPR method.

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Process mining-enabled jurimetrics

Cited by 12 publications

References 14 publications

Investigation of Lawsuit Process Duration: A Machine Learning and Process Mining Approach

Investigation of Lawsuit Process Duration: A Machine Learning and Process Mining Approach

Improving the analysis of the judiciary performance - the use of data mining techniques to assess the timeliness of civil trials

A Method for Bottleneck Detection, Prediction, and Recommendation Using Process Mining Techniques

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