Enabling routine discovery from high noise UI logs: Related Work
Creators: name: Anonymus
Publisher: name: Zenodo
Year: 2024

description: This resource extends the details related to the related work section of the publication "Enabling Routine Discovery from High Noise UI Logs: A Time Series Data Mining Approach", published in the journal Computers in Industry.

This file comprises the results of two systematic queries performed in the Scopus digital library. The purpose of these queries was to gather relevant literature for a study on the discovery of routines from UI logs characterized by high noise and uncertain behavioral patterns.

Contents:

Query 1: This query focuses on searching for literature related to Robotic Process Automation (RPA) and Robotic Process Mining (RPM) in the context of User Interface (UI) logs. It includes terms related to the discovery of motifs, patterns, or routines, and segmentation, recognition, identification, detection, or extraction. The results are limited to the Computer Science subject area and include conference papers and articles.

Query 2: This query targets literature involving motifs or time series in the context of UI logs, with a focus on discovery and segmentation. Similar to the first query, it is limited to the Computer Science subject area and includes conference papers and articles.

For both queries, the results are filtered based on inclusion/exclusion criteria:



Field: Only papers from the Computer Science field are considered.

Type of Publication: Only conference and article papers are included.


File Structure:



Sheet 1 - Query 1 Results: Focused on routine discovery using UI logs

Sheet 2 - Query 2 Results: Focused on motif discovery and time series approaches using logs


Comparison Classification Scheme:

Both tables include the same columns to classify all studies:



Title: The title of the research paper or article.

Excluded: Indicates if the primary study is excluded because it does not align with the approach topic.

Exclusion Reason: Provides the reason for exclusion.

Type of Log: Indicates whether the approach considers UI logs or event logs.

Routine Discovery: States whether the paper proposes something related to routine discovery/segmentation. Options include: (1) Frequent-Pattern Identification (identifying repetitive sequences of interactions), (2) Hierarchical Clustering (grouping similar interactions into clusters), and (3) Motif Discovery (identifying recurring patterns or structures).

Preprocessing: Indicates whether the approach considers preprocessing methods for the log, and what type of preprocessing, such as removing events based on criteria like type.

Encoding: Defines how the approach encodes/transforms the events to apply the discovery of the routines. Options include: (1) Ordinal (one number per different event type), (2) Convert sequence of events to a time series, and (3) A special encoding of Aghabaghery et al.'s multi-layer network.

End-to-end Discovery: Indicates whether the approach enables end-to-end routine discovery, such as in human-in-the-loop examples.

Applied: States whether the approach has an RPA or PM application.

Validation: Indicates whether the approach includes any validation and if it is based on synthetic, real data, or both.

Noise: States whether the approach considers logs that include noise.


Usage:

This file is intended for researchers and practitioners to better understand the original publication and to provide a comprehensive list of relevant studies that can be used to understand the current state-of-the-art and identify gaps in existing research.