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A real-time Decision Support System for big data analytic: A case of Dynamic Vehicle Routing Problems

Procedia Computer Science 2020年 176卷 938-947页

作者： Ines Sbai Saoussen Krichen Université de Tunis Institut Supérieur de Gestion de Tunis LARODEC Laboratory Tunisia

Recently, the explosion of large amounts of traffic data has guided data scientists to create models with big data for a better decision-making. big data applications process and analyze this huge amounts of data (collected from a variety of heterogeneous data sources) that cannot be processed with traditional technologies. In this paper, big data frameworks are used for solving an optimization problem known as Dynamic Vehicle Routing Problem (DVRP). Hence, due to the NP-Hardness of the problem and to deal with a large size of data, we develop a parallel Spark Genetic Algorithm named (S-GA). This parallelism aims to take the advantage of Spark’s in-memory computing ability (as a master-slave distribution computing) and GA’s iterations operations. Parallel operations were used for fitness evaluation and genetic operations. Based on the parallel S-GA a decision support system is developed for the DVRP in order to generate the best routes. The experiments show that our proposed architecture is improved due to its capacity when coping with big data optimization problems by interconnecting components and deploying on different nodes of a cluster.

关键词： big data analytic Decision Support System DVRP S-GA Spark

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Classifying big data analytic Approaches: A Generic Architecture 12th

Classifying Big Data Analytic Approaches: A Generic Architec...

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12th International Joint Conference on Software Technologies (ICSOFT)

作者： Cardinale, Yudith Guehis, Sonia Rukoz, Marta Univ Simon Bolivar Dept Comp & TI Caracas 1086A Venezuela Univ Paris Nanterre F-92001 Nanterre France Univ Paris 09 PSL Res Univ CNRS UMR 7243LAMSADE F-75016 Paris France

ISBN: (纸本)9783319936413;9783319936406

The explosion of the huge amount of generated data to be analyzed by several applications, imposes the trend of the moment, the big data boom, which in turn causes the existence of a vast landscape of architectural solutions. Non expert users who have to decide which analytical solutions are the most appropriates for their particular constraints and specific requirements in a big data context, are today lost, faced with a panoply of disparate and diverse solutions. To support users in this hard selection task, in a previous work, we proposed a generic architecture to classify big data analytical Approaches and a set of criteria of comparison/evaluation. In this paper, we extend our classification architecture to consider more types of big data analytic tools and approaches and improve the list of criteria to evaluate them. We classify different existing big data analytics solutions according to our proposed generic architecture and qualitatively evaluate them in terms of the criteria of comparison. Additionally, we propose a preliminary design of a decision support system, intended to generate suggestions to users based on such classification and on a qualitative evaluation in terms of previous users experiences, users requirements, nature of the analysis they need, and the set of evaluation criteria.

关键词： big data analytic analytic models for big data analytical data management applications

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big data analytic Approaches Classification 12

Big Data Analytic Approaches Classification

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12th International Joint Conference on Software Technologies (ICSOFT)

作者： Cardinale, Yudith Guehis, Sonia Rukoz, Marta Univ Simon Bolivar Dept Computat Caracas Venezuela Univ Paris Nanterre F-92001 Nanterre France PSL Res Univ Univ Paris Dauphine UMR 7243 CNRSLAMSADE F-75016 Paris France

ISBN: (纸本)9789897582622

analytical data management applications, affected by the explosion of the amount of generated data in the context of big data, are shifting away their analytical databases towards a vast landscape of architectural solutions combining storage techniques, programming models, languages, and tools. To support users in the hard task of deciding which big data solution is the most appropriate according to their specific requirements, we propose a generic architecture to classify analytical approaches. We also establish a classification of the existing query languages, based on the facilities provided to access the big data architectures. Moreover, to evaluate different solutions, we propose a set of criteria of comparison, such as OLAP support, scalability, and fault tolerance support. We classify different existing big data analytics solutions according to our proposed generic architecture and qualitatively evaluate them in terms of the criteria of comparison. We illustrate how our proposed generic architecture can be used to decide which big data analytic approach is suitable in the context of several use cases.

关键词： big data analytic analytic Models for big data analytical data Management Applications

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Real Time big data analytic: Security Concern and Challenges with Machine Learning Algorithm

Real Time BIG Data Analytic: Security Concern and Challenges...

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Conference on IT in Business, Industry and Government (CSIbig)

作者： Singh, Jainendra Maharaja Surajmal Inst Dept Comp Sci New Delhi India

ISBN: (纸本)9781479930630;9781479930647

with great power of data comes great responsibility! A big data initiative should not only focus on the volume, velocity or variety of the data, but also on the best way to protect it. Security is usually an afterthought, but Elemental provides the right technology framework to get you the deep visibility and multilayer security any big data project requires. Multilevel protection of your data processing nodes means implementing security controls at the application, operating system and network level while keeping a bird's eye on the entire system using actionable intelligence to deter any malicious activity, emerging threats and vulnerabilities. Advances in Machine Learning (ML) provide new challenges and solutions to the security problems encountered in applications, technologies and theories. Machine Learning (ML) techniques have found widespread applications and implementations in security issues. Many ML techniques, approaches, algorithms, methods and tools are extensively used by security experts and researchers to achieve better results and to design robust systems.

关键词： big data analytic Machine Learning Algorithm Security Challenges Mahout Hadoop

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Assessing the representativeness of large medical data using population stability index

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BMC MEDICAL RESEARCH METHODOLOGY 2025年第1期25卷 1-8页

作者： Lu, Sheng-Chieh Song, Wenye Pfob, Andre Gibbons, Chris Univ Texas MD Anderson Canc Ctr Dept Symptom Res 6565 MD Anderson Blvd Houston TX 77030 USA Heidelberg Univ Hosp Dept Obstet & Gynecol Neuenheimer Feld 672 D-69120 Heidelberg Germany Natl Ctr Tumor Dis NCT Neuenheimer Feld 280 D-69120 Heidelberg Germany German Canc Res Ctr Neuenheimer Feld 280 D-69120 Heidelberg Germany

BackgroundUnderstanding sample representativeness is key to interpreting findings from epidemiological research and applying these findings to broader populations. Though techniques for assessing sample representativeness are available, they rely on access to raw data detailing the population of interest which are often not readily available and may not be suitable for comparing large datasets. In reality, population-based data are often only available in an aggregated format. In this study, we aimed to examine the capability of population stability index (PSI), a popular metric to assess data drift for artificial intelligence studies, in detecting sample differences using population-based *** obtained United States cancer statistics from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database. We queried the SEER 17-registry research database to obtain cancer count data by age, sex, and cancer site groups from the rate sessions of the SEER*State incidence database for 2000 and 2015 - 2020. We then calculated PSI scores to estimate yearly data distribution shift from 2015 to 2020 for each variable. We compared the PSI results to the Chi-Square and Cram & eacute;r's V tests for the same *** for PSI comparing age, sex, and cancer site distribution between years ranged widely from 2.96 to less than 0.01. In line with our expectations, we found moderate to substantial differences in cancer population characteristics between 2000 and all other included years using PSI. Despite small effect sizes (Cram & eacute;r's V 0.01 - 0.09), Chi-Square tests were significant for most comparisons, indicating likely type-I error caused by our large *** stability index can be used to examine sample differences in healthcare studies where only binned data are available or where large datasets may reduce the reliability of other metrics. Inclusion of PSI in epidemiological research will give grea

关键词： Population stability index Sample representativeness big data analytic

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Sequential mediator and moderator model of intention for the implementation of chatbots in Malaysian government agencies

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JOURNAL OF DECISION SYSTEMS 2025年第1期34卷

作者： Jais, Ramizatunnisah Ngah, Abdul Hafaz Univ Malaysia Terengganu Fac Business Econ & Social Dev Kuala Terengganu Terengganu Malaysia Univ Teknol MARA Cawangan Terengganu Fac Business & Management Kampus Dungun Dungun Terengganu Malaysia

Although chatbots can effectively curtail customer complaints and handle large volume of calls, their adoption among Malaysian government agencies (MGAs) remains limited. This study explored the variables affecting chatbot technology adoption within MGAs by selecting their top management personnel as respondents. The study identified factors influencing the successful integration of chatbots using the TOE framework. Empirical data were gathered using purposive and snowball sampling, then distributed via email to MGAs. A strong response rate was obtained from 348 targeted respondents, with 262 actively participating. The analysis was conducted using SmartPLS. The hypotheses on the relationships between big data analytics (BDA), organisational learning capability (OLC), government support (GS), organisational readiness (OR), and citizen demand (CD) were validated, verifying their importance in boosting the adoption motivation of chatbots. The study observed the mediated effects of OLC and GS as sequential mediators between BDA and chatbot adoption intention among MGAs. However, while OR showed a positive relationship, its hypothesized moderating effect was not supported. The model explained approximately 48.8% of the variance in the intention to adopt chatbots among MGAs (R 2 = 0.488). Consequently, these insights were valuable for policymakers and department heads aiming to advance public service delivery by integrating chatbot technologies.

关键词： big data analytic organisational learning capability government policy organisational readiness chatbot government sector sequential mediator Technology-organisational-environment framework (TOE)

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Real-Time Intelligent Automatic Transportation Safety Based on big data Management

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第7期23卷 9702-9711页

作者： Liu, Yishu Zhang, Qi Lv, Zhihan Fuzhou Univ Int Studies & Trade Sch Econ & Management Fuzhou 350000 Fujian Peoples R China Fuzhou Univ Int Studies & Trade Ctr Fac Dev Fuzhou 350000 Fujian Peoples R China Qingdao Univ Sch Data Sci & Software Engn Qingdao 266071 Peoples R China

The purposes are to analyze the safety and real-time performance of intelligent transportation under big data and solve urban transportation safety problems. The existing problems of complex traffic data collection and single factor analysis are analyzed. Guangzhou target road section and its historical traffic flow are taken as the data source, the big data analytics (BDA) platform is constructed after preprocessing, and then the deep belief network (DBN) is introduced to build the intelligent transportation model based on BDA. Finally, the model is simulated to analyze its performance. The results reveal that the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the proposed algorithm are 4.42%, 6.21%, and 8.03%, respectively compared to Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Neuro fuzzy c-means model (FCM), Deep Neural Network (DNN), and neural network (NN). The mean absolute error (MAE) and mean absolute percentage error (MAPE) are 4.42%, 6.21% and 8.03% respectively. The prediction accuracy is significantly better than that of other algorithms. The analysis of Accuracy, Precision, Recall and F1 suggests that the safety prediction accuracy of the proposed algorithm can reach up to 88.57%, which is at least 3.19% higher than that of other algorithms. Meantime, the proposed algorithm can effectively inhibit the spread of congestion, and achieve the effect of timely evacuation for traffic congestion. Regarding the data transmission performance, the delay is less than 40ms and the packet loss rate is less than 0.1, which is significantly better than other model algorithms in real-time performance. Hence, the constructed intelligent transportation algorithm based on BDA has high precision and excellent congestion evacuation performance, which can provide an experimental basis for constructing intelligent and safe transportation in the future.

关键词： big data analytic intelligent transportation transportation safety DBN

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A Reinforcement Learning-Based Framework for Crowdsourcing in Massive Health Care Internet of Things

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big data 2022年第2期10卷 161-170页

作者： Almagrabi, Alaa Omran Ali, Rashid Alghazzawi, Daniyal AlBarakati, Abdullah Khurshaid, Tahir King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah Saudi Arabia Sejong Univ Sch Intelligent Mechatron Engn Dept Smart Device Engn Seoul South Korea Yeungnam Univ Dept Elect Engn Gyongsan 38541 South Korea

Rapid advancements in the internet of things (IoT) are driving massive transformations of health care, which is one of the largest and critical global industries. Recent pandemics, such as coronavirus 2019 (COVID-19), include increasing demands for ubiquitous, preventive, and personalized health care to be provided to the public at reduced risks and costs with rapid care. Mobile crowdsourcing could potentially meet the future massive health care IoT (mH-IoT) demands by enabling anytime, anywhere sense and analyses of health-related data to tackle such a pandemic situation. However, data reliability and availability are among the many challenges for the realization of next-generation mH-IoT, especially in COVID-19 epidemics. Therefore, more intelligent and robust health care frameworks are required to tackle such pandemics. Recently, reinforcement learning (RL) has proven its strengths to provide intelligent data reliability and availability. The action-state learning procedure of RL-based frameworks enables the learning system to enhance the optimal use of the information as the time passes and data increases. In this article, we propose an RL-based crowd-to-machine (RLC2M) framework for mH-IoT, which leverages crowdsourcing and an RL model (Q-learning) to address the health care information processing challenges. The simulation results show that the proposed framework rapidly converges with accumulated rewards to reveal the sensing environment situation.

关键词： health care IoT crowdsouring reinforcement learning massive data big data analytic internet of medical things

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Mobile cloud platform for big data analytic

Mobile cloud platform for big data analytic

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2014 International Conference on Network Security and Communication Engineering(NSCE 2014)

作者： N.W.Win T.Thein University of Computer Studies

big data technologies are to extract value from very large data volumes of a variety of data by enabling *** the fast deployment of cloud computing to mobile devices,big data analysis is shifting from personal computer to mobile *** this stream of change of technology,a new mobile cloud platform which can offer seamless connectivity and high query processing performance is *** paper presents a mobile cloud platform for accessing big data in the cloud through mobile *** web service technology is applied to meet seamless connectivity between mobile device and cloud *** Model is applied to provide high query processing *** query processing performance of proposed platform is evaluated and compared with the performance of other traditional high level query languages,such as Pig,Hive,and *** results show that our platform can provide better query processing performance.

关键词： big data analytic HadoopMapReduce Mobile cloud RESTfulweb service

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Integrating big data and blockchain to manage energy smart grids—TOTEM framework

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Blockchain(Research and Applications) 2022年第3期3卷 36-45页

作者： Dhanya Therese Jose Jørgen Holme Antorweep Chakravorty Chunming Rong Department of Computer Science Electrical Engineering University of Stavanger4036StavangerNorway

The demand for electricity is increasing exponentially day by day,especially with the arrival of electric *** the smart community neighborhood project,electricity should be produced at the household or community level and sold or bought according to the *** the actors can produce,sell,and buy according to the demands,thus the name *** solutions can contribute to this in several ways,such as machine learning for analyzing the household data for customer demand and peak hours for the usage of electricity,blockchain as a trustworthy platform for selling or buying,data hub,and ensuring data security and privacy of ***:Token for controlled computation is a framework that allows users to analyze the data without moving the data from the data owner's *** also ensures the data security and privacy of the ***,in this article,we will show the importance of the TOTEM architecture in the EnergiX project and how the extended version of TOTEM can be efficiently merged with the demands of the current and similar projects.

关键词： Blockchain big data analytic Hyperledger fabric Hadoop Map Reduce Docker PIVT

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