ACM Transactions on Management Information Systems最新文献_第6页

Application Massive Data Processing Platform for Smart Manufacturing Based on Optimization of Data Storage 基于数据存储优化的智能制造海量数据处理平台应用

IF 2.5

ACM Transactions on Management Information Systems Pub Date : 2022-04-13 DOI: 10.1145/3508395

Bin Ren, Yu-Qiang Chen, Fu-Cheng Wang

{"title":"Application Massive Data Processing Platform for Smart Manufacturing Based on Optimization of Data Storage","authors":"Bin Ren, Yu-Qiang Chen, Fu-Cheng Wang","doi":"10.1145/3508395","DOIUrl":"https://doi.org/10.1145/3508395","url":null,"abstract":"The aim of smart manufacturing is to reduce manpower requirements of the production line by applying technology of huge amounts of data to the manufacturing industry. Smart manufacturing is also called Industry 4.0, and the platform for processing huge amounts of data has an indispensable role. The massive data processing platform is like the brain of the entire factory, receiving all data from production line sensors via edge computing, processing, and analyzing, and finally making feedback decisions. With the innovation of production technology, the data that the platform needs to process has become diverse and complex, and the amount has become increasingly large. As well, many precision manufacturing industries have begun to enter the field of Industry 4.0. In addition to the accuracy and availability of data processing, there is emphasis on the real-time nature of data processing. After the sensor receives the data, the platform must provide feedback within a short period of time. This article proposes a massive data processing platform based on the Lambda architecture, which has the coexistence of stream processing and batch processing to meet real-time feedback needs of high-precision manufacturing. To verify the effectiveness of the optimization, it is based on real data from the manufacturing industry. To generate a large amount of test data to confirm the optimization of the storage of pictures. The results show that it optimizes the storage and optimization of the image data generated by the Automated Optical Inspection technology used in manufacturing today and optimizes the query for data storage. It also reduces the consumption of a large amount of memory as expected, and the query for Hive reduced the time spent.","PeriodicalId":45274,"journal":{"name":"ACM Transactions on Management Information Systems","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45767267","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

AI-augmented Business Process Management Systems: A Research Manifesto 人工智能增强的业务流程管理系统：研究宣言

IF 2.5

ACM Transactions on Management Information Systems Pub Date : 2022-01-30 DOI: 10.1145/3576047

M. Dumas, Fabiana Fournier, Lior Limonad, Andrea Marrella, M. Montali, Jana-Rebecca Rehse, R. Accorsi, D. Calvanese, Giuseppe De Giacomo, Dirk Fahland, A. Gal, M. Rosa, Hagen Volzer, I. Weber

引用次数: 24

Time Series Prediction Using Deep Learning Methods in Healthcare 医疗保健中使用深度学习方法的时间序列预测

IF 2.5

ACM Transactions on Management Information Systems Pub Date : 2021-08-30 DOI: 10.1145/3531326

M. Morid, O. R. Sheng, Josef A. Dunbar

{"title":"Time Series Prediction Using Deep Learning Methods in Healthcare","authors":"M. Morid, O. R. Sheng, Josef A. Dunbar","doi":"10.1145/3531326","DOIUrl":"https://doi.org/10.1145/3531326","url":null,"abstract":"Traditional machine learning methods face unique challenges when applied to healthcare predictive analytics. The high-dimensional nature of healthcare data necessitates labor-intensive and time-consuming processes when selecting an appropriate set of features for each new task. Furthermore, machine learning methods depend heavily on feature engineering to capture the sequential nature of patient data, oftentimes failing to adequately leverage the temporal patterns of medical events and their dependencies. In contrast, recent deep learning (DL) methods have shown promising performance for various healthcare prediction tasks by specifically addressing the high-dimensional and temporal challenges of medical data. DL techniques excel at learning useful representations of medical concepts and patient clinical data as well as their nonlinear interactions from high-dimensional raw or minimally processed healthcare data. In this article, we systematically reviewed research works that focused on advancing deep neural networks to leverage patient structured time series data for healthcare prediction tasks. To identify relevant studies, we searched MEDLINE, IEEE, Scopus, and ACM Digital Library for relevant publications through November 4, 2021. Overall, we found that researchers have contributed to deep time series prediction literature in 10 identifiable research streams: DL models, missing value handling, addressing temporal irregularity, patient representation, static data inclusion, attention mechanisms, interpretation, incorporation of medical ontologies, learning strategies, and scalability. This study summarizes research insights from these literature streams, identifies several critical research gaps, and suggests future research opportunities for DL applications using patient time series data.","PeriodicalId":45274,"journal":{"name":"ACM Transactions on Management Information Systems","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47144606","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: 10

Optimal Employee Recruitment in Organizations under Attribute-Based Access Control. 基于属性的访问控制下组织中的最佳员工招聘。

IF 2.5

ACM Transactions on Management Information Systems Pub Date : 2021-01-01 DOI: 10.1145/3403950

Arindam Roy, Shamik Sural, Arun Kumar Majumdar, Jaideep Vaidya, Vijayalakshmi Atluri

{"title":"Optimal Employee Recruitment in Organizations under Attribute-Based Access Control.","authors":"Arindam Roy, Shamik Sural, Arun Kumar Majumdar, Jaideep Vaidya, Vijayalakshmi Atluri","doi":"10.1145/3403950","DOIUrl":"10.1145/3403950","url":null,"abstract":"For any successful business endeavor, recruitment of required number of appropriately qualified employees in proper positions is a key requirement. For effective utilization of human resources, reorganization of such workforce assignment is also a task of utmost importance. This includes situations when the under-performing employees have to be substituted with fresh applicants. Generally, the number of candidates applying for a position is large and hence, the task of identifying an optimal subset becomes critical. Moreover, a human resource manager would also like to make use of the opportunity of retirement of employees to improve manpower utilization. However, the constraints enforced by the security policies prohibit any arbitrary assignment of tasks to employees. Further, the new employees should have the capabilities required to handle the assigned tasks. In this article, we formalize this problem as the Optimal Recruitment Problem (ORP), wherein the goal is to select the minimum number of fresh employees from a set of candidates to fill the vacant positions created by the outgoing employees, while ensuring satisfiability of the specified security conditions. The model used for specification of authorization policies and constraints is Attribute Based Access Control (ABAC), since it is considered to be the de facto next generation framework for handling organizational security policies. We show that the ORP problem is NP-hard and propose a greedy heuristic for solving it. Extensive experimental evaluation shows both the effectiveness as well as efficiency of the proposed solution.","PeriodicalId":45274,"journal":{"name":"ACM Transactions on Management Information Systems","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8078840/pdf/nihms-1647775.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38933564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Algorithms and Applications to Weighted Rank-one Binary Matrix Factorization. 加权秩一二元矩阵分解的算法及应用。

IF 2.5

ACM Transactions on Management Information Systems Pub Date : 2020-05-01 DOI: 10.1145/3386599

Haibing Lu, X I Chen, Junmin Shi, Jaideep Vaidya, Vijayalakshmi Atluri, Yuan Hong, Wei Huang

{"title":"Algorithms and Applications to Weighted Rank-one Binary Matrix Factorization.","authors":"Haibing Lu, X I Chen, Junmin Shi, Jaideep Vaidya, Vijayalakshmi Atluri, Yuan Hong, Wei Huang","doi":"10.1145/3386599","DOIUrl":"https://doi.org/10.1145/3386599","url":null,"abstract":"Many applications use data that are better represented in the binary matrix form, such as click-stream data, market basket data, document-term data, user-permission data in access control, and others. Matrix factorization methods have been widely used tools for the analysis of high-dimensional data, as they automatically extract sparse and meaningful features from data vectors. However, existing matrix factorization methods do not work well for the binary data. One crucial limitation is interpretability, as many matrix factorization methods decompose an input matrix into matrices with fractional or even negative components, which are hard to interpret in many real settings. Some matrix factorization methods, like binary matrix factorization, do limit decomposed matrices to binary values. However, these models are not flexible to accommodate some data analysis tasks, like trading off summary size with quality and discriminating different types of approximation errors. To address those issues, this article presents weighted rank-one binary matrix factorization, which is to approximate a binary matrix by the product of two binary vectors, with parameters controlling different types of approximation errors. By systematically running weighted rank-one binary matrix factorization, one can effectively perform various binary data analysis tasks, like compression, clustering, and pattern discovery. Theoretical properties on weighted rank-one binary matrix factorization are investigated and its connection to problems in other research domains are examined. As weighted rank-one binary matrix factorization in general is NP-hard, efficient and effective algorithms are presented. Extensive studies on applications of weighted rank-one binary matrix factorization are also conducted.","PeriodicalId":45274,"journal":{"name":"ACM Transactions on Management Information Systems","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3386599","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38655899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

An Interactive, Web-based High Performance Modeling Environment for Computational Epidemiology. 计算流行病学的交互式、基于网络的高性能建模环境。

IF 2.5

ACM Transactions on Management Information Systems Pub Date : 2014-07-01 DOI: 10.1145/2629692

Suruchi Deodhar, Keith R Bisset, Jiangzhuo Chen, Yifei Ma, Madhav V Marathe

{"title":"An Interactive, Web-based High Performance Modeling Environment for Computational Epidemiology.","authors":"Suruchi Deodhar, Keith R Bisset, Jiangzhuo Chen, Yifei Ma, Madhav V Marathe","doi":"10.1145/2629692","DOIUrl":"https://doi.org/10.1145/2629692","url":null,"abstract":"We present an integrated interactive modeling environment to support public health epidemiology. The environment combines a high resolution individual-based model with a user-friendly web-based interface that allows analysts to access the models and the analytics back-end remotely from a desktop or a mobile device. The environment is based on a loosely-coupled service-oriented-architecture that allows analysts to explore various counter factual scenarios. As the modeling tools for public health epidemiology are getting more sophisticated, it is becoming increasingly hard for non-computational scientists to effectively use the systems that incorporate such models. Thus an important design consideration for an integrated modeling environment is to improve ease of use such that experimental simulations can be driven by the users. This is achieved by designing intuitive and user-friendly interfaces that allow users to design and analyze a computational experiment and steer the experiment based on the state of the system. A key feature of a system that supports this design goal is the ability to start, stop, pause and roll-back the disease propagation and intervention application process interactively. An analyst can access the state of the system at any point in time and formulate dynamic interventions based on additional information obtained through state assessment. In addition, the environment provides automated services for experiment set-up and management, thus reducing the overall time for conducting end-to-end experimental studies. We illustrate the applicability of the system by describing computational experiments based on realistic pandemic planning scenarios. The experiments are designed to demonstrate the system's capability and enhanced user productivity.","PeriodicalId":45274,"journal":{"name":"ACM Transactions on Management Information Systems","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2629692","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32925307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15