Data & Knowledge Engineering最新文献_第8页

State-transition-aware anomaly detection under concept drifts 概念漂移下的状态转换感知异常检测

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-09-28 DOI: 10.1016/j.datak.2024.102365

Bin Li, Shubham Gupta, Emmanuel Müller

{"title":"State-transition-aware anomaly detection under concept drifts","authors":"Bin Li, Shubham Gupta, Emmanuel Müller","doi":"10.1016/j.datak.2024.102365","DOIUrl":"10.1016/j.datak.2024.102365","url":null,"abstract":"<div><div>Detecting temporal abnormal patterns over streaming data is challenging due to volatile data properties and the lack of real-time labels. The abnormal patterns are usually hidden in the temporal context, which cannot be detected by evaluating single points. Furthermore, the normal state evolves over time due to concept drifts. A single model does not fit all data over time. Autoencoders have recently been applied for unsupervised anomaly detection. However, they are trained on a single normal state and usually become invalid after distributional drifts in the data stream. This paper uses an Autoencoder-based approach STAD for anomaly detection under concept drifts. In particular, we propose a state-transition-aware model to map different data distributions in each period of the data stream into states, thereby addressing the model adaptation problem in an interpretable way. In addition, we analyzed statistical tests to detect the drift by examining the sensitivity and powers. Furthermore, we present considerable ways to estimate the probability density function for comparing the distributional similarity for state transitions. Our experiments evaluate the proposed method on synthetic and real-world datasets. While delivering comparable anomaly detection performance as the state-of-the-art approaches, STAD works more efficiently and provides extra interpretability. We also provide insightful analysis of optimal hyperparameters for efficient model training and adaptation.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"154 ","pages":"Article 102365"},"PeriodicalIF":2.7,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Reasoning on responsibilities for optimal process alignment computation 最佳流程对齐计算的责任推理

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-09-19 DOI: 10.1016/j.datak.2024.102353

Matteo Baldoni, Cristina Baroglio, Elisa Marengo, Roberto Micalizio

{"title":"Reasoning on responsibilities for optimal process alignment computation","authors":"Matteo Baldoni, Cristina Baroglio, Elisa Marengo, Roberto Micalizio","doi":"10.1016/j.datak.2024.102353","DOIUrl":"10.1016/j.datak.2024.102353","url":null,"abstract":"<div><p>Process alignment aims at establishing a matching between a process model run and a log trace. To improve such a matching, process alignment techniques often exploit contextual conditions to enable computations that are more informed than the simple edit distance between model runs and log traces. The paper introduces a novel approach to process alignment which relies on contextual information expressed as <em>responsibilities</em>. The notion of responsibility is fundamental in business and organization models, but it is often overlooked. We show the computation of optimal alignments can take advantage of responsibilities. We leverage on them in two ways. First, responsibilities may sometimes justify deviations. In these cases, we consider them as correct behaviors rather than errors. Second, responsibilities can either be met or neglected in the execution of a trace. Thus, we prefer alignments where neglected responsibilities are minimized.</p><p>The paper proposes a formal framework for responsibilities in a process model, including the definition of cost functions for computing optimal alignments. We also propose a branch-and-bound algorithm for optimal alignment computation and exemplify its usage by way of two event logs from real executions.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"154 ","pages":"Article 102353"},"PeriodicalIF":2.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169023X24000776/pdfft?md5=df35ebc627d0abaf942b9666c2d2c159&pid=1-s2.0-S0169023X24000776-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Big data classification using SpinalNet-Fuzzy-ResNeXt based on spark architecture with data mining approach 使用基于数据挖掘方法的星火架构 SpinalNet-Fuzzy-ResNeXt 进行大数据分类

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-09-17 DOI: 10.1016/j.datak.2024.102364

M. Robinson Joel , K. Rajakumari , S. Anu Priya , M. Navaneethakrishnan

{"title":"Big data classification using SpinalNet-Fuzzy-ResNeXt based on spark architecture with data mining approach","authors":"M. Robinson Joel , K. Rajakumari , S. Anu Priya , M. Navaneethakrishnan","doi":"10.1016/j.datak.2024.102364","DOIUrl":"10.1016/j.datak.2024.102364","url":null,"abstract":"<div><div>In the modern networking topology, big data is highly essential for several domains like e-commerce, healthcare, and finance. Big data classification has offered effectual performance in several applications. Still, big data classification is highly difficult and the recognized classification approaches require a longer duration and numerous resources for executing the accessible data. For resolving such issues, the spark-based classification approach is required. In this work, the hybrid SpinalNet-Fuzzy-ResNeXt model called SFResNeXt is implemented to classify the big data. Here, the SpinalNet and ResNeXt are merged, where the layers are fused with the fuzzy concept. The initial process is the outlier detection. The Holoentrophy method is used to detect the outlier data, and it is removed. Moreover, duplicate detection is performed by fingerprinting approach to detect the repeated data. The, Association Rule Mining (ARM) method is employed for feature selection. The big data is classified by the SFResNeXt. Furthermore, the SFResNeXt-based big data classification offered the accuracy, sensitivity, and specificity of 0.905, 0.914, and 0.922 using the heart disease dataset.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"154 ","pages":"Article 102364"},"PeriodicalIF":2.7,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

SRank: Guiding schema selection in NoSQL document stores SRank：指导 NoSQL 文档存储中的模式选择

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-09-14 DOI: 10.1016/j.datak.2024.102360

Shelly Sachdeva , Neha Bansal , Hardik Bansal

{"title":"SRank: Guiding schema selection in NoSQL document stores","authors":"Shelly Sachdeva , Neha Bansal , Hardik Bansal","doi":"10.1016/j.datak.2024.102360","DOIUrl":"10.1016/j.datak.2024.102360","url":null,"abstract":"<div><div>The rise of big data has led to a greater need for applications to change their schema frequently. NoSQL databases provide flexibility in organizing data and offer multiple choices for structuring and storing similar information. While schema flexibility speeds up initial development, choosing schemas wisely is crucial, as they significantly impact performance, affecting data redundancy, navigation cost, data access cost, and maintainability. This paper emphasizes the importance of schema design in NoSQL document stores. It proposes a model to analyze and evaluate different schema alternatives and suggest the best schema out of various schema alternatives. The model is divided into four phases. The model inputs the Entity-Relationship (ER) model and workload queries. In the Transformation Phase, the schema alternatives are initially developed for each ER model, and subsequently, a schema graph is generated for each alternative. Concurrently, workload queries undergo conversion into query graphs. In the Schema Evaluation phase, the Schema Rank (SRank) is calculated for each schema alternative using query metrics derived from the query graphs and path coverage generated from the schema graphs. Finally, in the Output phase, the schema with the highest SRank is recommended as the most suitable choice for the application. The paper includes a case study of a Hotel Reservation System (HRS) to demonstrate the application of the proposed model. It comprehensively evaluates various schema alternatives based on query response time, storage efficiency, scalability, throughput, and latency. The paper validates the SRank computation for schema selection in NoSQL databases through an extensive experimental study. The alignment of SRank values with each schema's performance metrics underscores this ranking system's effectiveness. The SRank simplifies the schema selection process, assisting users in making informed decisions by reducing the time, cost, and effort of identifying the optimal schema for NoSQL document stores.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"154 ","pages":"Article 102360"},"PeriodicalIF":2.7,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Relating behaviour of data-aware process models 数据感知流程模型的相关行为

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-09-12 DOI: 10.1016/j.datak.2024.102363

Marco Montali, Sarah Winkler

{"title":"Relating behaviour of data-aware process models","authors":"Marco Montali, Sarah Winkler","doi":"10.1016/j.datak.2024.102363","DOIUrl":"10.1016/j.datak.2024.102363","url":null,"abstract":"<div><p>Data Petri nets (DPNs) have gained traction as a model for data-aware processes, thanks to their ability to balance simplicity with expressiveness, and because they can be automatically discovered from event logs. While model checking techniques for DPNs have been studied, more complex analysis tasks that are highly relevant for BPM are beyond methods known in the literature. We focus here on equivalence and inclusion of process behaviour with respect to language and configuration spaces, optionally taking data into account. Such comparisons are important in the context of key process mining tasks, namely process repair and discovery, and related to conformance checking. To solve these tasks, we propose approaches for bounded DPNs based on <em>constraint graphs</em>, which are faithful abstractions of the reachable state space. Though the considered verification tasks are undecidable in general, we show that our method is a decision procedure DPNs that admit a <em>finite history set</em>. This property guarantees that constraint graphs are finite and computable, and was shown to hold for large classes of DPNs that are mined automatically, and DPNs presented in the literature. The new techniques are implemented in the tool <span>ada</span>, and an evaluation proving feasibility is provided.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"154 ","pages":"Article 102363"},"PeriodicalIF":2.7,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169023X24000879/pdfft?md5=ee932b18bac18fd1e3c1e769269d7d67&pid=1-s2.0-S0169023X24000879-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 framework for understanding event abstraction problem solving: Current states of event abstraction studies 理解事件抽象解决问题的框架：事件抽象研究的现状

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-09-06 DOI: 10.1016/j.datak.2024.102352

Jungeun Lim, Minseok Song

引用次数: 0

A conceptual framework for the government big data ecosystem (‘datagov.eco’) 政府大数据生态系统（"datagov.eco"）概念框架

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-09-05 DOI: 10.1016/j.datak.2024.102348

Syed Iftikhar Hussain Shah , Vassilios Peristeras , Ioannis Magnisalis

{"title":"A conceptual framework for the government big data ecosystem (‘datagov.eco’)","authors":"Syed Iftikhar Hussain Shah , Vassilios Peristeras , Ioannis Magnisalis","doi":"10.1016/j.datak.2024.102348","DOIUrl":"10.1016/j.datak.2024.102348","url":null,"abstract":"<div><p>The public sector, private firms, and civil society constantly create data of high volume, velocity, and veracity from diverse sources. This kind of data is known as big data. As in other industries, public administrations consider big data as the “new oil\" and employ data-centric policies to transform data into knowledge, stimulate good governance, innovative digital services, transparency, and citizens' engagement in public policy. More and more public organizations understand the value created by exploiting internal and external data sources, delivering new capabilities, and fostering collaboration inside and outside of public administrations. Despite the broad interest in this ecosystem, we still lack a detailed and systematic view of it. In this paper, we attempt to describe the emerging Government Big Data Ecosystem as a <em>socio-technical network</em> of people, organizations, processes, technology, infrastructure, standards & policies, procedures, and resources. This ecosystem supports <em>data functions</em> such as data collection, integration, analysis, storage, sharing, use, protection, and archiving. Through these functions, <em>value is created</em> by promoting evidence-based policymaking, modern public services delivery, data-driven administration and open government, and boosting the data economy. Through a Design Science Research methodology, we propose a conceptual framework, which we call ‘datagov.eco’. We believe our ‘datagov.eco’ framework will provide insights and support to different stakeholders’ profiles, including administrators, consultants, data engineers, and data scientists.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"154 ","pages":"Article 102348"},"PeriodicalIF":2.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Unraveling the foundations and the evolution of conceptual modeling—Intellectual structure, current themes, and trajectories 解读概念模型的基础和演变--知识结构、当前主题和发展轨迹

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-09-04 DOI: 10.1016/j.datak.2024.102351

Jacky Akoka , Isabelle Comyn-Wattiau , Nicolas Prat , Veda C. Storey

{"title":"Unraveling the foundations and the evolution of conceptual modeling—Intellectual structure, current themes, and trajectories","authors":"Jacky Akoka , Isabelle Comyn-Wattiau , Nicolas Prat , Veda C. Storey","doi":"10.1016/j.datak.2024.102351","DOIUrl":"10.1016/j.datak.2024.102351","url":null,"abstract":"<div><div>The field of conceptual modeling has now been in existence for over five decades. To understand how this field has evolved and should continue to evolve, it is useful to examine the contributions made over time and the themes that have emerged. In this research, we apply bibliometric analysis to a corpus of over 4700 research papers spanning from 1976 to 2023. We successively apply co-citation, bibliographic coupling, and main path analysis. Co-citation and citation networks are produced that surface the intellectual structure of the field, the main themes, and the relationships among major and influential research papers over time. We identify four areas in the intellectual structure of the field: conceptual modeling and databases; grammars and guidelines for conceptual modeling; requirements engineering and information systems design methodologies; and ontology constructs for conceptual modeling. Between 2017 and 2023, we distinguish nine research themes, including domain-specific conceptual modeling and applications, ontologies and applications, genomics, and datastores and multi-model data. The main path analysis identifies several trajectories among the major and most influential papers. This leads to insights into the lineage of key, influential papers in conceptual modeling research. The primordial nature of the main paths identified encompasses two important aspects. The first revolves around refining and complementing the entity-relationship model. The second identifies the contribution of ontologies for conceptual modeling to make the models more robust. Based on the findings from this bibliometric analysis, we propose several directions for future conceptual modeling research.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"154 ","pages":"Article 102351"},"PeriodicalIF":2.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Data engineering and modeling for artificial intelligence 人工智能的数据工程和建模

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-09-01 DOI: 10.1016/j.datak.2024.102346

Carlos Ordonez, Wojciech Macyna, Ladjel Bellatreche

引用次数: 0

Capturing and Analysing Employee Behaviour: An Honest Day’s Work Record 捕捉和分析员工行为：诚实的日常工作记录

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-08-31 DOI: 10.1016/j.datak.2024.102350

Iris Beerepoot, Tea Šinik, Hajo A. Reijers

{"title":"Capturing and Analysing Employee Behaviour: An Honest Day’s Work Record","authors":"Iris Beerepoot, Tea Šinik, Hajo A. Reijers","doi":"10.1016/j.datak.2024.102350","DOIUrl":"10.1016/j.datak.2024.102350","url":null,"abstract":"<div><p>For a range of reasons, organisations collect data on the work behaviour of their employees. However, each data collection technique displays its own unique mix of intrusiveness, information richness, and risks. For the sake of understanding the differences between data collection techniques, we conducted a multiple-case study in a multinational professional services organisation, tracking six participants throughout a workday using non-participant observation, screen recording, and timesheet techniques. This led to 136 hours of data. Our findings show that relying on one data collection technique alone cannot provide a comprehensive and accurate account of activities that are screen-based, offline, or overtime. The collected data also provided an opportunity to investigate the use of <em>process mining</em> for analysing employee behaviour, specifically with respect to the completeness of the collected data. Our study underlines the importance of judiciously selecting data collection techniques, as well as using a sufficiently broad data set to generate reliable insights into employee behaviour.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"154 ","pages":"Article 102350"},"PeriodicalIF":2.7,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169023X24000740/pdfft?md5=0803a6136e27919fd8c8a868fa63e889&pid=1-s2.0-S0169023X24000740-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142149252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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