Information Systems最新文献

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

{"title":"Effective data exploration through clustering of local attributive explanations","authors":"","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":null,"pages":null},"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

{"title":"Data Lakehouse: A survey and experimental study","authors":"","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":null,"pages":null},"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

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

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

{"title":"Bridging reading and mapping: The role of reading annotations in facilitating feedback while concept mapping","authors":"","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":null,"pages":null},"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

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

{"title":"Tri-AL: An open source platform for visualization and analysis of clinical trials","authors":"","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":null,"pages":null},"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

{"title":"Electricity behaviors anomaly detection based on multi-feature fusion and contrastive learning","authors":"","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":null,"pages":null},"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

{"title":"A framework for measuring the quality of business process simulation models","authors":"","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":null,"pages":null},"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

PathEL: A novel collective entity linking method based on relationship paths in heterogeneous information networks PathEL：基于异构信息网络关系路径的新型集体实体链接方法

IF 3 2区计算机科学

Information Systems Pub Date : 2024-08-13 DOI: 10.1016/j.is.2024.102433

{"title":"PathEL: A novel collective entity linking method based on relationship paths in heterogeneous information networks","authors":"","doi":"10.1016/j.is.2024.102433","DOIUrl":"10.1016/j.is.2024.102433","url":null,"abstract":"<div><p>Collective entity linking always outperforms independent entity linking because it considers the interdependencies among entities. However, the existing collective entity linking methods often have high time complexity, do not fully utilize the relationship information in heterogeneous information networks (HIN) and most of them are largely dependent on the special features associated with Wikipedia. Based on the above problems, this paper proposes a novel collective entity linking method based on relationship path in heterogeneous information networks (PathEL). The PathEL classifies complex relationships in HIN into 1-hop paths and 3 types of 2-hop paths, and measures entity correlation by the path information among entities, ultimately combining textual semantic information to realize collective entity linking. In addition, facing the high complexity of collective entity linking, this paper proposes to solve the problem by combining the variable sliding window data processing method and the two-step pruning strategy. The variable sliding window data processing method limits the number of entity mentions in each window and the pruning strategy reduces the number of candidate entities. Finally, the experimental results of three benchmark datasets verify that the model proposed in this paper performs better in entity linking than the baseline models. On the AIDA CoNLL dataset, compared to the second-ranked model, our model has improved P, R, and F1 scores by 1.61%, 1.54%, and 1.57%, respectively.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142007080","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

An incremental algorithm for repairing denial constraint violations 修复拒绝约束违规行为的增量算法

IF 3 2区计算机科学

Information Systems Pub Date : 2024-08-05 DOI: 10.1016/j.is.2024.102435

{"title":"An incremental algorithm for repairing denial constraint violations","authors":"","doi":"10.1016/j.is.2024.102435","DOIUrl":"10.1016/j.is.2024.102435","url":null,"abstract":"<div><p>Data repairing algorithms are extensively studied for improving data quality. Denial constraints (DCs) are commonly employed to state quality specifications that data should satisfy and hence facilitate data repairing since DCs are general enough to subsume many other dependencies. Data in practice are usually frequently updated, which motivates the quest for efficient incremental repairing techniques in response to data updates. In this paper, we present the first incremental algorithm for repairing DC violations. Specifically, given a relational instance <span><math><mi>I</mi></math></span> consistent with a set <span><math><mi>Σ</mi></math></span> of DCs, and a set <span><math><mo>△</mo></math></span> <span><math><mi>I</mi></math></span> of tuple insertions to <span><math><mi>I</mi></math></span>, our aim is to find a set <span><math><mo>△</mo></math></span> <span><math><msup><mrow><mi>I</mi></mrow><mrow><mo>′</mo></mrow></msup></math></span> of tuple insertions such that <span><math><mi>Σ</mi></math></span> is satisfied on <span><math><mrow><mi>I</mi><mo>+</mo><mo>△</mo></mrow></math></span> <span><math><msup><mrow><mi>I</mi></mrow><mrow><mo>′</mo></mrow></msup></math></span>. We first formalize and prove the complexity of the problem of incremental data repairing with DCs. We then present techniques that combine auxiliary indexing structures to efficiently identify DC violations incurred by <span><math><mo>△</mo></math></span> <span><math><mi>I</mi></math></span> <em>w.r.t.</em> <span><math><mi>Σ</mi></math></span>, and further develop an efficient repairing algorithm to compute <span><math><mo>△</mo></math></span> <span><math><msup><mrow><mi>I</mi></mrow><mrow><mo>′</mo></mrow></msup></math></span> by resolving DC violations. Finally, using both real-life and synthetic datasets, we conduct extensive experiments to demonstrate the effectiveness and efficiency of our approach.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141963870","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