ACM Journal of Data and Information Quality最新文献_第4页

Incentive Mechanism Design for Responsible Data Governance: A Large-scale Field Experiment 负责任数据治理的激励机制设计:一项大规模现场实验

IF 2.1

ACM Journal of Data and Information Quality Pub Date : 2023-04-19 DOI: 10.1145/3592617

Christina Timko, Malte Niederstadt, Naman Goel, Boi Faltings

{"title":"Incentive Mechanism Design for Responsible Data Governance: A Large-scale Field Experiment","authors":"Christina Timko, Malte Niederstadt, Naman Goel, Boi Faltings","doi":"10.1145/3592617","DOIUrl":"https://doi.org/10.1145/3592617","url":null,"abstract":"A crucial building block of responsible artificial intelligence is responsible data governance, including data collection. Its importance is also underlined in the latest EU regulations. The data should be of high quality, foremost correct and representative, and individuals providing the data should have autonomy over what data is collected. In this article, we consider the setting of collecting personally measured fitness data (physical activity measurements), in which some individuals may not have an incentive to measure and report accurate data. This can significantly degrade the quality of the collected data. On the other hand, high-quality collective data of this nature could be used for reliable scientific insights or to build trustworthy artificial intelligence applications. We conduct a framed field experiment (N = 691) to examine the effect of offering fixed and quality-dependent monetary incentives on the quality of the collected data. We use a peer-based incentive-compatible mechanism for the quality-dependent incentives without spot-checking or surveilling individuals. We find that the incentive-compatible mechanism can elicit good-quality data while providing a good user experience and compensating fairly, although, in the specific study context, the data quality does not necessarily differ under the two incentive schemes. We contribute new design insights from the experiment and discuss directions that future field experiments and applications on explainable and transparent data collection may focus on.","PeriodicalId":44355,"journal":{"name":"ACM Journal of Data and Information Quality","volume":"23 1","pages":"1 - 18"},"PeriodicalIF":2.1,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83296585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Theory and Practice of Relational-to-RDF Temporal Data Exchange and Query Answering 关系型与rdf时态数据交换与查询应答的理论与实践

IF 2.1

ACM Journal of Data and Information Quality Pub Date : 2023-04-08 DOI: 10.1145/3591359

J. Ao, Zehui Cheng, Rada Y. Chirkova, Phokion G. Kolaitis

{"title":"Theory and Practice of Relational-to-RDF Temporal Data Exchange and Query Answering","authors":"J. Ao, Zehui Cheng, Rada Y. Chirkova, Phokion G. Kolaitis","doi":"10.1145/3591359","DOIUrl":"https://doi.org/10.1145/3591359","url":null,"abstract":"We consider the problem of answering temporal queries on RDF stores, in presence of atemporal RDFS domain ontologies, of relational data sources that include temporal information, and of rules that map the domain information in the source schemas into the target ontology. Our proposed practice-oriented solution consists of two rule-based domain-independent algorithms. The first algorithm materializes target RDF data via a version of data exchange that enriches both the data and the ontology with temporal information from the relational sources. The second algorithm accepts as inputs temporal queries expressed in terms of the domain ontology using a lightweight temporal extension of SPARQL, and ensures successful evaluation of the queries on the materialized temporally-enriched RDF data. To study the quality of the information generated by the algorithms, we develop a general framework that formalizes the relational-to-RDF temporal data-exchange problem. The framework includes a chase formalism and a formal solution for the problem of answering temporal queries in the context of relational-to-RDF temporal data exchange. In this article, we present the algorithms and the formal framework that proves correctness of the information output by the algorithms, and also report on the algorithm implementation and experimental results for two application domains.","PeriodicalId":44355,"journal":{"name":"ACM Journal of Data and Information Quality","volume":"25 1","pages":"1 - 27"},"PeriodicalIF":2.1,"publicationDate":"2023-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72468768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

To Link or Synthesize? An Approach to Data Quality Comparison 链接还是合成?一种数据质量比较方法

IF 2.1

ACM Journal of Data and Information Quality Pub Date : 2023-02-21 DOI: 10.1145/3580487

Duncan Smith, M. Elliot, J. Sakshaug

引用次数: 1

Introduction to the Special Issue on Truth and Trust Online 《真相与信任在线》特刊简介

IF 2.1

ACM Journal of Data and Information Quality Pub Date : 2023-02-03 DOI: 10.1145/3578242

Dustin Wright, Paolo Papotti, Isabelle Augenstein

引用次数: 0

Experimental Evaluation of Covariates Effects on Periocular Biometrics: A Robust Security Assessment Framework 眼周生物特征协变量效应的实验评估:一个稳健的安全性评估框架

IF 2.1

ACM Journal of Data and Information Quality Pub Date : 2023-01-30 DOI: 10.1145/3579029

Gautam Kumar, Sambit Bakshi, A. K. Sangaiah, Pankaj Kumar Sa

{"title":"Experimental Evaluation of Covariates Effects on Periocular Biometrics: A Robust Security Assessment Framework","authors":"Gautam Kumar, Sambit Bakshi, A. K. Sangaiah, Pankaj Kumar Sa","doi":"10.1145/3579029","DOIUrl":"https://doi.org/10.1145/3579029","url":null,"abstract":"The growing integration of technology into our lives has resulted in unprecedented amounts of data that are being exchanged among devices in an Internet of Things (IoT) environment. Authentication, identification, and device heterogeneities are major security and privacy concerns in IoT. One of the most effective solutions to avoid unauthorized access to sensitive information is biometrics. Deep learning-based biometric systems have been proven to outperform traditional image processing and machine learning techniques. However, the image quality covariates associated with blur, resolution, illumination, and noise predominantly affect recognition performance. Therefore, assessing the robustness of the developed solution is another important concern that still needs to be investigated. This article proposes a periocular region-based biometric system and explores the effect of image quality covariates (artifacts) on the performance of periocular recognition. To simulate the real-time scenarios and understand the consequences of blur, resolution, and bit-depth of images on the recognition accuracy of periocular biometrics, we modeled out-of-focus blur, camera shake blur, low-resolution, and low bit-depth image acquisition using Gaussian function, linear motion, interpolation, and bit plan slicing, respectively. All the images of the UBIRIS.v1 database are degraded by varying strength of image quality covariates to obtain degraded versions of the database. Afterward, deep models are trained with each degraded version of the database. The performance of the model is evaluated by measuring statistical parameters calculated from a confusion matrix. Experimental results show that among all types of covariates, camera shake blur has less effect on the recognition performance, while out-of-focus blur significantly impacts it. Irrespective of image quality, the convolutional neural network produces excellent results, which proves the robustness of the developed model.","PeriodicalId":44355,"journal":{"name":"ACM Journal of Data and Information Quality","volume":"74 1","pages":"1 - 25"},"PeriodicalIF":2.1,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89379695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey on Soft Computing Techniques for Federated Learning- Applications, Challenges and Future Directions 面向联邦学习的软计算技术综述——应用、挑战和未来方向

IF 2.1

ACM Journal of Data and Information Quality Pub Date : 2023-01-30 DOI: 10.1145/3575810

Y. Supriya, T. Gadekallu

引用次数: 6

A Survey on Edge Intelligence and Lightweight Machine Learning Support for Future Applications and Services 面向未来应用和服务的边缘智能和轻量级机器学习支持调查

IF 2.1

ACM Journal of Data and Information Quality Pub Date : 2023-01-25 DOI: 10.1145/3581759

Kyle Hoffpauir, Jacob Simmons, Nikolas Schmidt, Rachitha Pittala, Isaac Briggs, Shanmukha Makani, Y. Jararweh

{"title":"A Survey on Edge Intelligence and Lightweight Machine Learning Support for Future Applications and Services","authors":"Kyle Hoffpauir, Jacob Simmons, Nikolas Schmidt, Rachitha Pittala, Isaac Briggs, Shanmukha Makani, Y. Jararweh","doi":"10.1145/3581759","DOIUrl":"https://doi.org/10.1145/3581759","url":null,"abstract":"As the number of devices connected to the Internet has grown larger, so too has the intensity of the tasks that these devices need to perform. Modern networks are more frequently working to perform computationally intensive tasks on low-power devices and low-end hardware. Current architectures and platforms tend towards centralized and resource-rich cloud computing approaches to address these deficits. However, edge computing presents a much more viable and flexible alternative. Edge computing refers to a distributed and decentralized network architecture in which demanding tasks such as image recognition, smart city services, and high-intensity data processing tasks can be distributed over a number of integrated network devices. In this article, we provide a comprehensive survey for emerging edge intelligence applications, lightweight machine learning algorithms, and their support for future applications and services. We start by analyzing the rise of cloud computing, discuss its weak points, and identify situations in which edge computing provides advantages over traditional cloud computing architectures. We then divulge details of the survey: the first section identifies opportunities and domains for edge computing growth, the second identifies algorithms and approaches that can be used to enhance edge intelligence implementations, and the third specifically analyzes situations in which edge intelligence can be enhanced using any of the aforementioned algorithms or approaches. In this third section, lightweight machine learning approaches are detailed. A more in-depth analysis and discussion of future developments follows. The primary discourse of this article is in service of an effort to ensure that appropriate approaches are applied adequately to artificial intelligence implementations in edge systems, mainly, the lightweight machine learning approaches.","PeriodicalId":44355,"journal":{"name":"ACM Journal of Data and Information Quality","volume":"67 1","pages":"1 - 30"},"PeriodicalIF":2.1,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79103940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Choice of Textual Knowledge Base in Automated Claim Checking 自动索赔检查中文本知识库的选择

IF 2.1

ACM Journal of Data and Information Quality Pub Date : 2023-01-25 DOI: 10.1145/3561389

Dominik Stammbach, Boya Zhang, Elliott Ash

引用次数: 1

A Multifactor Ring Signature based Authentication Scheme for Quality Assessment of IoMT Environment in COVID-19 Scenario 基于多因素环签名的IoMT环境质量评估认证方案

IF 2.1

ACM Journal of Data and Information Quality Pub Date : 2023-01-20 DOI: 10.1145/3575811

Kakali Chatterjee, Ashutosh Kumar Singh, Neha, K. Yu

引用次数: 1

Uniqueness Constraints for Object Stores 对象存储的唯一性约束

IF 2.1

ACM Journal of Data and Information Quality Pub Date : 2023-01-19 DOI: 10.1145/3581758

Philipp Skavantzos, Uwe Leck, Kaiqi Zhao, S. Link

{"title":"Uniqueness Constraints for Object Stores","authors":"Philipp Skavantzos, Uwe Leck, Kaiqi Zhao, S. Link","doi":"10.1145/3581758","DOIUrl":"https://doi.org/10.1145/3581758","url":null,"abstract":"Object stores offer an increasingly popular choice for data management and analytics. As with every data model, managing the integrity of objects is fundamental for data quality but also important for the efficiency of update and query operations. In response to shortcomings of unique and existence constraints in object stores, we propose a new principled class of constraints that separates uniqueness from existence dimensions of data quality, and fully supports multiple labels and composite properties. We illustrate benefits of the constraints on real-world examples of property graphs where node integrity is enforced for better update and query performance. The benefits are quantified experimentally in terms of perfectly scaling the access to data through indices that result from the constraints. We establish axiomatic and algorithmic characterizations for the underlying implication problem. In addition, we fully characterize which non-redundant families of constraints attain maximum cardinality for any given finite sets of labels and properties. We exemplify further use cases of the constraints: elicitation of business rules, identification of data quality problems, and design for data quality. Finally, we propose extensions to managing the integrity of objects in object stores such as graph databases.","PeriodicalId":44355,"journal":{"name":"ACM Journal of Data and Information Quality","volume":"15 1","pages":"1 - 29"},"PeriodicalIF":2.1,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73458508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1