Big data analytics最新文献_第4页

Upgraded Thoth: Software for Data Visualization and Statistics 升级Thoth:数据可视化和统计软件

Big data analytics Pub Date : 2023-03-16 DOI: 10.3390/analytics2010015

R. Laher, F. Masci, L. Rebull, S. Schurr, Wendy Burt, A. Laity, M. Swain, D. Shupe, S. Groom, B. Rusholme, M. Kong, J. Good, V. Gorjian, R. Akeson, B. Fulton, D. Ciardi, S. Carey

{"title":"Upgraded Thoth: Software for Data Visualization and Statistics","authors":"R. Laher, F. Masci, L. Rebull, S. Schurr, Wendy Burt, A. Laity, M. Swain, D. Shupe, S. Groom, B. Rusholme, M. Kong, J. Good, V. Gorjian, R. Akeson, B. Fulton, D. Ciardi, S. Carey","doi":"10.3390/analytics2010015","DOIUrl":"https://doi.org/10.3390/analytics2010015","url":null,"abstract":"Thoth is a free desktop/laptop software application with a friendly graphical user interface that facilitates routine data-visualization and statistical-calculation tasks for astronomy and astrophysical research (and other fields where numbers are visualized). This software has been upgraded with many significant improvements and new capabilities. The major upgrades consist of: (1) six new graph types, including 3D stacked-bar charts and 3D surface plots, made by the Orson 3D Charts library; (2) new saving and loading of graph settings; (3) a new batch-mode or command-line operation; (4) new graph-data annotation functions; (5) new options for data-file importation; and (6) a new built-in FITS-image viewer. There is now the requirement that Thoth be run under Java 1.8 or higher. Many other miscellaneous minor upgrades and bug fixes have also been made to Thoth. The newly implemented plotting options generally make possible graph construction and reuse with relative ease, without resorting to writing computer code. The illustrative astronomy case study of this paper demonstrates one of the many ways the software can be utilized. These new software features and refinements help make astronomers more efficient in their work of elucidating data.","PeriodicalId":93078,"journal":{"name":"Big data analytics","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81525652","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

Application of Mixture Models for Doubly Inflated Count Data 混合模型在双重膨胀计数数据中的应用

Big data analytics Pub Date : 2023-03-11 DOI: 10.3390/analytics2010014

Monika Arora, N. Chaganty

引用次数: 0

A Voronoi-Based Semantically Balanced Dummy Generation Framework for Location Privacy 基于voronoi的位置隐私语义平衡虚拟生成框架

Big data analytics Pub Date : 2023-03-03 DOI: 10.3390/analytics2010013

Aditya Tadakaluru, Xiao Qin

{"title":"A Voronoi-Based Semantically Balanced Dummy Generation Framework for Location Privacy","authors":"Aditya Tadakaluru, Xiao Qin","doi":"10.3390/analytics2010013","DOIUrl":"https://doi.org/10.3390/analytics2010013","url":null,"abstract":"Location-based services (LBS) require users to provide their current location for service delivery and customization. Location privacy protection addresses concerns associated with the potential mishandling of location information submitted to the LBS provider. Location accuracy has a direct impact on the quality of service (QoS), where higher location accuracy results in better QoS. In general, the main goal of any location privacy technique is to achieve maximum QoS while providing minimum or no location information if possible, and using dummy locations is one such location privacy technique. In this paper, we introduced a temporal constraint attack whereby an adversary can exploit the temporal constraints associated with the semantic category of locations to eliminate dummy locations and identify the true location. We demonstrated how an adversary can devise a temporal constraint attack to breach the location privacy of a residential location. We addressed this major limitation of the current dummy approaches with a novel Voronoi-based semantically balanced framework (VSBDG) capable of generating dummy locations that can withstand a temporal constraint attack. Built based on real-world geospatial datasets, the VSBDG framework leverages spatial relationships and operations. Our results show a high physical dispersion cosine similarity of 0.988 between the semantic categories even with larger location set sizes. This indicates a strong and scalable semantic balance for each semantic category within the VSBDG’s output location set. The VSBDG algorithm is capable of producing location sets with high average minimum dispersion distance values of 5861.894 m for residential locations and 6258.046 m for POI locations. The findings demonstrate that the locations within each semantic category are scattered farther apart, entailing optimized location privacy.","PeriodicalId":93078,"journal":{"name":"Big data analytics","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83719603","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

Survey of Distances between the Most Popular Distributions 最流行分布之间的距离调查

Big data analytics Pub Date : 2023-03-01 DOI: 10.3390/analytics2010012

M. Kelbert

引用次数: 1

Smart Multimedia Information Retrieval 智能多媒体信息检索

Big data analytics Pub Date : 2023-02-20 DOI: 10.3390/analytics2010011

Stefan Wagenpfeil

引用次数: 1

The SP Theory of Intelligence, and Its Realisation in the SP Computer Model, as a Foundation for the Development of Artificial General Intelligence 智能的SP理论及其在SP计算机模型中的实现，作为通用人工智能发展的基础

Big data analytics Pub Date : 2023-02-17 DOI: 10.3390/analytics2010010

J. Wolff

引用次数: 0

Dynamic Skyline Computation with LSD Trees 动态天际线计算与LSD树

Big data analytics Pub Date : 2023-02-09 DOI: 10.3390/analytics2010009

D. Köppl

引用次数: 0

Untangling Energy Consumption Dynamics with Renewable Energy Using Recurrent Neural Network 基于递归神经网络的可再生能源能源消费动态分析

Big data analytics Pub Date : 2023-02-01 DOI: 10.3390/analytics2010008

Munshi Md. Shafwat Yazdan, Shah Saki, Raaghul Kumar

{"title":"Untangling Energy Consumption Dynamics with Renewable Energy Using Recurrent Neural Network","authors":"Munshi Md. Shafwat Yazdan, Shah Saki, Raaghul Kumar","doi":"10.3390/analytics2010008","DOIUrl":"https://doi.org/10.3390/analytics2010008","url":null,"abstract":"The environmental issues we are currently facing require long-term prospective efforts for sustainable growth. Renewable energy sources seem to be one of the most practical and efficient alternatives in this regard. Understanding a nation’s pattern of energy use and renewable energy production is crucial for developing strategic plans. No previous study has been performed to explore the dynamics of power consumption with the change in renewable energy production on a country-wide scale. In contrast, a number of deep learning algorithms have demonstrated acceptable performance while handling sequential data in the era of data-driven predictions. In this study, we developed a scheme to investigate and predict total power consumption and renewable energy production time series for eleven years of data using a recurrent neural network (RNN). The dynamics of the interaction between the total annual power consumption and renewable energy production were investigated through extensive exploratory data analysis (EDA) and a feature engineering framework. The performance of the model was found to be satisfactory through the comparison of the predicted data with the observed data, the visualization of the distribution of the errors and root mean squared error (RMSE), and the R2 values of 0.084 and 0.82. Higher performance was achieved by increasing the number of epochs and hyperparameter tuning. The proposed framework has the potential to be used and transferred to investigate the trend of renewable energy production and power consumption and predict future scenarios for different communities. The incorporation of a cloud-based platform into the proposed pipeline to perform predictive studies from data acquisition to outcome generation may lead to real-time forecasting.","PeriodicalId":93078,"journal":{"name":"Big data analytics","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84623505","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-Guided Analytics Process: Using Theories to Underpin an Analytics Process for New Banking Product Development Using Segmentation-Based Marketing Analytics Leveraging on Marketing Intelligence 理论指导的分析过程:利用基于细分市场的营销分析，利用营销智能，利用理论来支持新银行产品开发的分析过程

Big data analytics Pub Date : 2023-02-01 DOI: 10.3390/analytics2010007

Tristan Lim, Tao Pan, C. Ong, Shuaiwei Chen, Jie Jun Jeremy Chia

{"title":"Theory-Guided Analytics Process: Using Theories to Underpin an Analytics Process for New Banking Product Development Using Segmentation-Based Marketing Analytics Leveraging on Marketing Intelligence","authors":"Tristan Lim, Tao Pan, C. Ong, Shuaiwei Chen, Jie Jun Jeremy Chia","doi":"10.3390/analytics2010007","DOIUrl":"https://doi.org/10.3390/analytics2010007","url":null,"abstract":"Retail banking is undergoing considerable product competitiveness and disruptions. New product development is necessary to tackle such challenges and reinvigorate product lines. This study presents an instrumental real-life banking case study, where marketing analytics was utilized to drive a product differentiation strategy. In particular, the study applied unsupervised machine learning techniques of link analysis, latent class analysis, and association analysis to undertake behavioral-based market segmentation, in view of attaining a profitable competitive advantage. To underpin the product development process with well grounded theoretical framing, this study asked the research question: “How may we establish a theory-driven approach for an analytics-driven process?” Findings of this study include a theoretical conceptual framework that underpinned the end-to-end segmentation-driven new product development process, backed by the empirical literature. The study hopes to provide: (i) for managerial practitioners, the use of case-based reasoning for practice-oriented new product development design, planning, and diagnosis efforts, and (ii) for researchers, the potentiality to test of the validity and robustness of an analytical-driven NPD process. The study also hopes to drive a wider research interest that studies the theory-driven approach for analytics-driven processes.","PeriodicalId":93078,"journal":{"name":"Big data analytics","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90448763","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

MAFFN_YOLOv5: Multi-Scale Attention Feature Fusion Network on the YOLOv5 Model for the Health Detection of Coral-Reefs Using a Built-In Benchmark Dataset MAFFN_YOLOv5:基于内置基准数据集的YOLOv5模型的多尺度关注特征融合网络珊瑚礁健康检测

Big data analytics Pub Date : 2023-01-19 DOI: 10.3390/analytics2010006

Sivamani Kalyana Sundara Rajan, Nedumaran Damodaran

引用次数: 2