2022 IEEE 24th Conference on Business Informatics (CBI)最新文献_第5页

Another Approach of DeFi: P2P Smart Contracts DeFi的另一种方法:P2P智能合约

2022 IEEE 24th Conference on Business Informatics (CBI) Pub Date : 2022-06-01 DOI: 10.1109/CBI54897.2022.10054

Ansel Shaidullin, Mikhail M. Komarov

引用次数: 0

SecRiskAI: a Machine Learning-Based Approach for Cybersecurity Risk Prediction in Businesses SecRiskAI:基于机器学习的企业网络安全风险预测方法

2022 IEEE 24th Conference on Business Informatics (CBI) Pub Date : 2022-06-01 DOI: 10.1109/CBI54897.2022.00008

M. Franco, Erion Sula, Alberto Huertas Celdrán, E. Scheid, L. Granville, B. Stiller

{"title":"SecRiskAI: a Machine Learning-Based Approach for Cybersecurity Risk Prediction in Businesses","authors":"M. Franco, Erion Sula, Alberto Huertas Celdrán, E. Scheid, L. Granville, B. Stiller","doi":"10.1109/CBI54897.2022.00008","DOIUrl":"https://doi.org/10.1109/CBI54897.2022.00008","url":null,"abstract":"Cyberattacks have increased in number and severity, negatively impacting businesses and their services. As such, cybersecurity can no longer be seen just as a technological issue, but it must also be recognized as critical to the economy and society. Current solutions struggle to find indicators of unpredictable risks, limiting their ability to perform accurate risk assessments. This work thus introduces SecRiskAI, an approach that employs Machine Learning (ML) to assess and predict how exposed a business is to cybersecurity risks. For this purpose, four ML algorithms were implemented, trained, and evaluated using synthetic datasets representing characteristics of different sizes of businesses (e.g., number of employees, business sector, and known vulnerabilities). Moreover, a Web-based user interface is provided to simplify the risk prediction workflow. The quantitative evaluation performed on SecRiskAI shows a minimal performance overhead and the high accuracy of the ML models, while a case study assesses the feasibility of the overall process for decision-makers.","PeriodicalId":447040,"journal":{"name":"2022 IEEE 24th Conference on Business Informatics (CBI)","volume":"304 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122775371","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: 4

Developing a Generic Predictive Computational Model using Semantic data Pre-Processing with Machine Learning Techniques and its application for Stock Market Prediction Purposes 基于语义数据预处理和机器学习技术的通用预测计算模型及其在股票市场预测中的应用

2022 IEEE 24th Conference on Business Informatics (CBI) Pub Date : 2022-06-01 DOI: 10.1109/CBI54897.2022.00013

Natalia Yerashenia, D. C. Y. Fee, A. Bolotov

{"title":"Developing a Generic Predictive Computational Model using Semantic data Pre-Processing with Machine Learning Techniques and its application for Stock Market Prediction Purposes","authors":"Natalia Yerashenia, D. C. Y. Fee, A. Bolotov","doi":"10.1109/CBI54897.2022.00013","DOIUrl":"https://doi.org/10.1109/CBI54897.2022.00013","url":null,"abstract":"In this paper, we present a Generic Predictive Computational Model (GPCM) and apply it by building a Use Case for the FTSE 100 index forecasting. This involves the mining of heterogeneous data based on semantic methods (ontology), graph-based methods (knowledge graphs, graph databases) and advanced Machine Learning methods. The main focus of our research is data pre-processing aimed at a more efficient selection of input features. The GPCM model pipeline's cycles involve the propagation of the (initially raw) data to the Graph Database structured by an ontology and regular updates of the features' weights in the Graph Database by the feedback loop from the Machine Learning Engine. The Graph Database queries output the most valuable features that, in turn, serve as the input for the Machine Learning-based prediction. The end-product of this process is fed back to the Graph Database to update the weights. We report on practical experiments evaluating the effectiveness of the GPCM application in forecasting the FTSE 100 index. The underlying dataset contains multiple parameters related to predicting time-series data, where Long Short-Term Memory (LSTM) is known to be one of the most efficient machine learning methods. The most challenging task here has been to overcome the known restrictions of LSTM, which is capable of analysing one input parameter only. We solved this problem by combining several parallel LSTMs, a Concatenation unit, which merges the LSTMs' outputs (into a time-series matrix), and a Linear Regression Unit. which produces the final result.","PeriodicalId":447040,"journal":{"name":"2022 IEEE 24th Conference on Business Informatics (CBI)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124286820","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

An Adaptable Framework for Entity Matching Model Selection in Business Enterprises 企业实体匹配模型选择的适应性框架

2022 IEEE 24th Conference on Business Informatics (CBI) Pub Date : 2022-06-01 DOI: 10.1109/CBI54897.2022.00017

Alex Boyko, Siamak Farshidi, Zhiming Zhao

{"title":"An Adaptable Framework for Entity Matching Model Selection in Business Enterprises","authors":"Alex Boyko, Siamak Farshidi, Zhiming Zhao","doi":"10.1109/CBI54897.2022.00017","DOIUrl":"https://doi.org/10.1109/CBI54897.2022.00017","url":null,"abstract":"Entity matching is the process of identifying data in different data sources that refer to the same real-world entity. A significant number of entity matching approaches have been introduced in the literature, which complicates the selection process. In this study, we propose a framework to support researchers in finding the best fitting entity matching model (s) based on the characteristics of their datasets. The proposed framework can be extended by adding more models, features, and use cases. To evaluate the framework, we have conducted a case study in the context of a business enterprise to support them with finding the right entity matching model out of five preselected models by the case study experts. The case study participants confirmed the framework's usefulness in assisting them in finding the best-fitting entity matching models. Having the knowledge regarding entity matching models readily available supports decision-makers at business enterprises in making more efficient and effective decisions that meet their requirements and priorities. Furthermore, such reusable knowledge can be employed by other researchers to develop new concepts and solutions for future challenges.","PeriodicalId":447040,"journal":{"name":"2022 IEEE 24th Conference on Business Informatics (CBI)","volume":"12 30","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114046809","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

Resource Requirements in Business Process Modelling from an Operations Management Perspective 从运营管理角度看业务流程建模中的资源需求

2022 IEEE 24th Conference on Business Informatics (CBI) Pub Date : 2021-06-18 DOI: 10.1109/CBI54897.2022.10047

A. Goel, Min-Bin Lin

引用次数: 4