Information Systems最新文献_第6页

Using AI explainable models and handwriting/drawing tasks for psychological well-being 利用人工智能可解释模型和手写/绘画任务促进心理健康

IF 3 2区计算机科学

Information Systems Pub Date : 2024-09-28 DOI: 10.1016/j.is.2024.102465

Francesco Prinzi , Pietro Barbiero , Claudia Greco , Terry Amorese , Gennaro Cordasco , Pietro Liò , Salvatore Vitabile , Anna Esposito

{"title":"Using AI explainable models and handwriting/drawing tasks for psychological well-being","authors":"Francesco Prinzi , Pietro Barbiero , Claudia Greco , Terry Amorese , Gennaro Cordasco , Pietro Liò , Salvatore Vitabile , Anna Esposito","doi":"10.1016/j.is.2024.102465","DOIUrl":"10.1016/j.is.2024.102465","url":null,"abstract":"<div><div>This study addresses the increasing threat to Psychological Well-Being (PWB) posed by Depression, Anxiety, and Stress conditions. Machine learning methods have shown promising results for several psychological conditions. However, the lack of transparency in existing models impedes practical application. The study aims to develop explainable machine learning models for depression, anxiety and stress prediction, focusing on features extracted from tasks involving handwriting and drawing.</div><div>Two hundred patients completed the Depression, Anxiety, and Stress Scale (DASS-21) and performed seven tasks related to handwriting and drawing. Extracted features, encompassing pressure, stroke pattern, time, space, and pen inclination, were used to train the explainable-by-design Entropy-based Logic Explained Network (e-LEN) model, employing first-order logic rules for explanation. Performance comparison was performed with XGBoost, enhanced by the SHAP explanation method.</div><div>The trained models achieved notable accuracy in predicting depression (0.749 ±0.089), anxiety (0.721 ±0.088), and stress (0.761 ±0.086) through 10-fold cross-validation (repeated 20 times). The e-LEN model’s logic rules facilitated clinical validation, uncovering correlations with existing clinical literature. While performance remained consistent for depression and anxiety on an independent test dataset, a slight degradation was observed for stress prediction in the test task.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"127 ","pages":"Article 102465"},"PeriodicalIF":3.0,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective data exploration through clustering of local attributive explanations 通过对局部归因解释的聚类进行有效的数据探索

IF 3 2区计算机科学

Information Systems Pub Date : 2024-09-28 DOI: 10.1016/j.is.2024.102464

Elodie Escriva , Tom Lefrere , Manon Martin , Julien Aligon , Alexandre Chanson , Jean-Baptiste Excoffier , Nicolas Labroche , Chantal Soulé-Dupuy , Paul Monsarrat

{"title":"Effective data exploration through clustering of local attributive explanations","authors":"Elodie Escriva , Tom Lefrere , Manon Martin , Julien Aligon , Alexandre Chanson , Jean-Baptiste Excoffier , Nicolas Labroche , Chantal Soulé-Dupuy , Paul Monsarrat","doi":"10.1016/j.is.2024.102464","DOIUrl":"10.1016/j.is.2024.102464","url":null,"abstract":"<div><div>Machine Learning (ML) has become an essential tool for modeling complex phenomena, offering robust predictions and comprehensive data analysis. Nevertheless, the lack of interpretability in these predictions often results in a closed-box effect, which the field of eXplainable Machine Learning (XML) aims to address. Local attributive XML methods, in particular, provide explanations by quantifying the contribution of each attribute to individual predictions, referred to as influences. This type of explanation is the most acute as it focuses on each instance of the dataset and allows the detection of individual differences. Additionally, aggregating local explanations allows for a deeper analysis of the underlying data. In this context, influences can be considered as a new data space to reveal and understand complex data patterns. We hypothesize that these influences, derived from ML explanations, are more informative than the original raw data, especially for identifying homogeneous groups within the data. To identify such groups effectively, we utilize a clustering approach. We compare clusters formed using raw data against those formed using influences computed by various local attributive XML methods. Our findings reveal that clusters based on influences consistently outperform those based on raw data, even when using models with low accuracy.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"127 ","pages":"Article 102464"},"PeriodicalIF":3.0,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Lakehouse: A survey and experimental study 数据湖：调查与实验研究

IF 3 2区计算机科学

Information Systems Pub Date : 2024-09-26 DOI: 10.1016/j.is.2024.102460

Ahmed A. Harby , Farhana Zulkernine

{"title":"Data Lakehouse: A survey and experimental study","authors":"Ahmed A. Harby , Farhana Zulkernine","doi":"10.1016/j.is.2024.102460","DOIUrl":"10.1016/j.is.2024.102460","url":null,"abstract":"<div><div>Efficient big data management is a dire necessity to manage the exponential growth in data generated by digital information systems to produce usable knowledge. Structured databases, data lakes, and warehouses have each provided a solution with varying degrees of success. However, a new and superior solution, the data Lakehouse, has emerged to extract actionable insights from unstructured data ingested from distributed sources. By combining the strengths of data warehouses and data lakes, the data Lakehouse can process and merge data quickly while ingesting and storing high-speed unstructured data with post-storage transformation and analytics capabilities. The Lakehouse architecture offers the necessary features for optimal functionality and has gained significant attention in the big data management research community. In this paper, we compare data lake, warehouse, and lakehouse systems, highlight their strengths and shortcomings, identify the desired features to handle the evolving challenges in big data management and analysis and propose an advanced data Lakehouse architecture. We also demonstrate the performance of three state-of-the-art data management systems namely HDFS data lake, Hive data warehouse, and Delta lakehouse in managing data for analytical query responses through an experimental study.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"127 ","pages":"Article 102460"},"PeriodicalIF":3.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proactive conformance checking: An approach for predicting deviations in business processes 主动一致性检查：预测业务流程偏差的方法

IF 3 2区计算机科学

Information Systems Pub Date : 2024-09-23 DOI: 10.1016/j.is.2024.102461

Michael Grohs , Peter Pfeiffer , Jana-Rebecca Rehse

{"title":"Proactive conformance checking: An approach for predicting deviations in business processes","authors":"Michael Grohs , Peter Pfeiffer , Jana-Rebecca Rehse","doi":"10.1016/j.is.2024.102461","DOIUrl":"10.1016/j.is.2024.102461","url":null,"abstract":"<div><div>Modern business processes are subject to an increasing number of external and internal regulations. Compliance with these regulations is crucial for the success of organizations. To ensure this compliance, process managers can identify and mitigate deviations between the predefined process behavior and the executed process instances by means of conformance checking techniques. However, these techniques are inherently reactive, meaning that they can only detect deviations after they have occurred. It would be desirable to detect and mitigate deviations before they occur, enabling managers to proactively ensure compliance of running process instances. In this paper, we propose Business Process Deviation Prediction (BPDP), a novel predictive approach that relies on a supervised machine learning model to predict which deviations can be expected in the future of running process instances. BPDP is able to predict individual deviations as well as deviation patterns. Further, it provides the user with a list of potential reasons for predicted deviations. Our evaluation shows that BPDP outperforms existing methods for deviation prediction. Following the idea of action-oriented process mining, BPDP thus enables process managers to prevent deviations in early stages of running process instances.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"127 ","pages":"Article 102461"},"PeriodicalIF":3.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporal graph processing in modern memory hierarchies 现代存储器分层中的时序图处理

IF 3 2区计算机科学

Information Systems Pub Date : 2024-09-21 DOI: 10.1016/j.is.2024.102462

Alexander Baumstark, Muhammad Attahir Jibril, Kai-Uwe Sattler

引用次数: 0

Bridging reading and mapping: The role of reading annotations in facilitating feedback while concept mapping 连接阅读和绘图：在绘制概念图时，阅读注释在促进反馈中的作用

IF 3 2区计算机科学

Information Systems Pub Date : 2024-09-06 DOI: 10.1016/j.is.2024.102458

Oscar Díaz, Xabier Garmendia

{"title":"Bridging reading and mapping: The role of reading annotations in facilitating feedback while concept mapping","authors":"Oscar Díaz, Xabier Garmendia","doi":"10.1016/j.is.2024.102458","DOIUrl":"10.1016/j.is.2024.102458","url":null,"abstract":"<div><p>Concept maps are visual tools for organizing knowledge, commonly used in education and design. The process often involves reading and developing conceptual models, where feedback is crucial. Learners (e.g., students, designers) often refer to reading materials, and receive feedback from instructors (e.g., teachers, stakeholders) based on the maps they create. However, annotations made by learners, like highlights, are usually not visible to instructors, limiting tailored feedback. We propose incorporating annotation practices into concept mapping. Learners could highlight text and link these highlights to existing or newly created concepts in their concept map. This way, instructors can access both the concept map and the relevant readings for better feedback. This vision is realized through <em>Concept&Go</em>, a plug-in for the editor <em>CmapCloud</em>. This extension aims at the interplay between mapping, reading, and feedback during concept mapping. The effectiveness of this approach is demonstrated through a focus group (n=5) and a UTAUT evaluation (n=12). <em>Concept&Go</em> is publicly available.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"127 ","pages":"Article 102458"},"PeriodicalIF":3.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0306437924001169/pdfft?md5=f1df1b7c90dae26d25484ea7d7b77c25&pid=1-s2.0-S0306437924001169-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A universal approach for simplified redundancy-aware cross-model querying 简化冗余感知跨模型查询的通用方法

IF 3 2区计算机科学

Information Systems Pub Date : 2024-09-04 DOI: 10.1016/j.is.2024.102456

Pavel Koupil, Daniel Crha, Irena Holubová

{"title":"A universal approach for simplified redundancy-aware cross-model querying","authors":"Pavel Koupil, Daniel Crha, Irena Holubová","doi":"10.1016/j.is.2024.102456","DOIUrl":"10.1016/j.is.2024.102456","url":null,"abstract":"<div><p>Numerous challenges and open problems have appeared with the dawn of multi-model data. In most cases, single-model solutions cannot be straightforwardly extended, and new, efficient approaches must be found. In addition, since there are no standards related to combining and managing multiple models, the situation is even more complicated and confusing for users.</p><p>This paper deals with the most important aspect of data management — querying. To enable the user to grasp all the popular models, we base our solution on the abstract categorical representation of multi-model data, which can be viewed as a graph. To unify the querying of multi-model data, we enable the user to query the categorical graph using a SPARQL-based model-agnostic query language called MMQL. The query is then decomposed and translated into languages of the underlying systems. The intermediate results are then combined into the final categorical result that can be expressed in any selected format. The support for cross-model redundancy enables one to create distinct query plans and choose the optimal one. We also introduce a proof-of-concept implementation of our solution called <em>MM-quecat</em>.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"127 ","pages":"Article 102456"},"PeriodicalIF":3.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tri-AL: An open source platform for visualization and analysis of clinical trials Tri-AL：用于临床试验可视化和分析的开源平台

IF 3 2区计算机科学

Information Systems Pub Date : 2024-09-04 DOI: 10.1016/j.is.2024.102459

Pouyan Nahed , Mina Esmail Zadeh Nojoo Kambar , Kazem Taghva , Lukasz Golab

{"title":"Tri-AL: An open source platform for visualization and analysis of clinical trials","authors":"Pouyan Nahed , Mina Esmail Zadeh Nojoo Kambar , Kazem Taghva , Lukasz Golab","doi":"10.1016/j.is.2024.102459","DOIUrl":"10.1016/j.is.2024.102459","url":null,"abstract":"<div><p>ClinicalTrials.gov hosts an online database with over 440,000 medical studies (as of 2023) evaluating drugs, supplements, medical devices, and behavioral treatments. Target users include scientists, medical researchers, pharmaceutical companies, and other public and private institutions. Although ClinicalTrials has some filtering ability, it does not provide visualization tools, reporting tools or historical data; only the most recent state of each trial is visible to users. To fill these functionality gaps, we present <em>Tri-AL</em>: an open-source data platform for clinical trial visualization, information extraction, historical analysis, and reporting. This paper describes the design and functionality of <em>Tri-AL</em>, including a programmable module to incorporate machine learning models and extract disease-specific data from unstructured trial reports, which we demonstrate using Alzheimer’s disease reporting as a case study. We also highlight the use of <em>Tri-AL</em> for trial participation analysis in terms of sex, gender, race and ethnicity. The source code is publicly available at <span><span>https://github.com/pouyan9675/Tri-AL</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"127 ","pages":"Article 102459"},"PeriodicalIF":3.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Electricity behaviors anomaly detection based on multi-feature fusion and contrastive learning 基于多特征融合和对比学习的用电行为异常检测

IF 3 2区计算机科学

Information Systems Pub Date : 2024-09-02 DOI: 10.1016/j.is.2024.102457

Yongming Guan , Yuliang Shi , Gang Wang , Jian Zhang , Xinjun Wang , Zhiyong Chen , Hui Li

{"title":"Electricity behaviors anomaly detection based on multi-feature fusion and contrastive learning","authors":"Yongming Guan , Yuliang Shi , Gang Wang , Jian Zhang , Xinjun Wang , Zhiyong Chen , Hui Li","doi":"10.1016/j.is.2024.102457","DOIUrl":"10.1016/j.is.2024.102457","url":null,"abstract":"<div><p>Abnormal electricity usage detection is the process of discovering and diagnosing abnormal electricity usage behavior by monitoring and analyzing the electricity usage in the power system. How to improve the accuracy of anomaly detection is a popular research topic. Most studies use neural networks for anomaly detection, but ignore the effect of missing electricity data on anomaly detection performance. Missing value completion is an important method to improve the quality of electricity data and to optimize the anomaly detection performance. Moreover, most studies have ignored the potential correlation relationship between spatial features by modeling the temporal features of electricity data. Therefore, this paper proposes an electricity anomaly detection model based on multi-feature fusion and contrastive learning. The model integrates the temporal and spatial features to jointly accomplish electricity anomaly detection. In terms of temporal feature representation learning, an improved bi-directional LSTM is designed to achieve the missing value completion of electricity data, and combined with CNN to capture the electricity consumption behavior patterns in the temporal data. In terms of spatial feature representation learning, GCN and Transformer are used to fully explore the complex correlation relationships among data. In addition, in order to improve the performance of anomaly detection, this paper also designs a gated fusion module and combines the idea of contrastive learning to strengthen the representation ability of electricity data. Finally, we demonstrate through experiments that the method proposed in this paper can effectively improve the performance of electricity behavior anomaly detection.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"127 ","pages":"Article 102457"},"PeriodicalIF":3.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A framework for measuring the quality of business process simulation models 衡量业务流程模拟模型质量的框架

IF 3 2区计算机科学

Information Systems Pub Date : 2024-08-22 DOI: 10.1016/j.is.2024.102447

David Chapela-Campa , Ismail Benchekroun , Opher Baron , Marlon Dumas , Dmitry Krass , Arik Senderovich

{"title":"A framework for measuring the quality of business process simulation models","authors":"David Chapela-Campa , Ismail Benchekroun , Opher Baron , Marlon Dumas , Dmitry Krass , Arik Senderovich","doi":"10.1016/j.is.2024.102447","DOIUrl":"10.1016/j.is.2024.102447","url":null,"abstract":"<div><p>Business Process Simulation (BPS) is an approach to analyze the performance of business processes under different scenarios. For example, BPS allows us to estimate the impact of adding one or more resources on the cycle time of a process. The starting point of BPS is a process model annotated with simulation parameters (a BPS model). BPS models may be manually designed, based on information collected from stakeholders and from empirical observations, or automatically discovered from historical execution data. Regardless of its provenance, a key question when using a BPS model is how to assess its quality. In particular, in a setting where we are able to produce multiple alternative BPS models of the same process, this question becomes: How to determine which model is better, to what extent, and in what respect? In this context, this article studies the question of how to measure the quality of a BPS model with respect to its ability to accurately replicate the observed behavior of a process. Rather than pursuing a one-size-fits-all approach, the article recognizes that a process covers multiple perspectives. Accordingly, the article outlines a framework that can be instantiated in different ways to yield quality measures that tackle different process perspectives. The article defines a number of concrete quality measures and evaluates these measures with respect to their ability to discern the impact of controlled perturbations on a BPS model, and their ability to uncover the relative strengths and weaknesses of two approaches for automated discovery of BPS models. The evaluation shows that the proposed measures not only capture how close a BPS model is to the observed behavior, but they also help us to identify the sources of discrepancies.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"127 ","pages":"Article 102447"},"PeriodicalIF":3.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0306437924001054/pdfft?md5=7958dc6fdab5faf4469760f9d839425a&pid=1-s2.0-S0306437924001054-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142089258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0