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21st International Conference on Human-Computer Interaction (HCII)

作者： Lunterova, Anna Spetko, Ondrej Palamas, George Aalborg Univ AC Meyers Vaenge 15 DK-2450 Copenhagen Denmark

ISBN: (纸本)9783030300333;9783030300326

The following research proposes a web based, interactive data visualization of a large food dataset, with an aim to raise awareness of their nutritional value. The system consists of an automatic dimensionality reduction scheme, based on t-SNE algorithm, and an exploratory user interface based on dynamic views of the data. Our approach allows users to effectively compare and relate visual representations of nutritional value in both micro and macro levels. The communicativeness of the application was evaluated regarding user experience and the storytelling functionality. Results show that the participants experienced a shift in perception from one mainly focused on taste to one aware of nutritional aspects. Additionally, the results suggest that a narrative experience can be generated, along with an effective long-term engagement.

关键词： big data visualization Dimensionality reduction Exploratory data visualization Interactive data storytelling

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Improved visualization of large temporal series for the evaluation of good environmental status

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APPLIED ACOUSTICS 2019年 148卷 55-61页

作者： Miralles, Ramon Lara, Guillermo Gosalbez, Jorge Bosch, Ignacio Leon, Antonio Univ Politecn Valencia Inst Telecommun & Multimedia Applicat iTEAM Valencia Spain Univ Politecn Valencia Dept Comunicac Valencia Spain

Passive acoustic monitoring of underwater sounds is an emerging discipline that can be used to guarantee that anthropogenic noise meets acceptable limits, to detect the presence of cetacean species, and to ensure sustainable exploitation of our oceans and seas. In this scenario, graphical representation techniques play a key role in helping to reveal seasonal structures of human made noises. Nevertheless, for very long temporal series, it might be challenging to find a graphic visualization technique that allows representing a time range that is long enough to capture these seasonal events, while at the same time preserving short isolated events. We propose a framework for the creation of such visualization techniques and analyze the different stages involved: data reduction, color encoding, and signal processing on graphs. All of this is applied to data from deployments in two marine protected areas in order to provide an acoustic panorama and identify seasonal events. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： big data visualization Acoustic images Ambient noise characterization Submarine environment Surveillance data reduction Hough transform

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visualization of big data security： a case study on the KDD99 cup data set

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Digital Communications and Networks 2017年第4期3卷 250-259页

作者： Zichan Ruan Yuantian Miao Lei Pan Nicholas Patterson Jun Zhang School of Information Technology Deakin University Geelong VIC 3220 Australia

Cyber security has been thrust into the limelight in the modern technological era because of an array of attacks often bypassing tmtrained intrusion detection systems （IDSs）. Therefore, greater attention has been directed on being able deciphering better methods for identifying attack types to train IDSs more effectively. Keycyber-attack insights exist in big data; however, an efficient approach is required to determine strong attack types to train IDSs to become more effective in key areas. Despite the rising growth in IDS research, there is a lack of studies involving big data visualization, which is key. The KDD99 data set has served as a strong benchmark since 1999; therefore, we utilized this data set in our experiment. In this study, we utilized hash algorithm, a weight table, and sampling method to deal with the inherent problems caused by analyzing big data; volume, variety, and velocity. By utilizing a visualization algorithm, we were able to gain insights into the KDD99 data set with a clear iden- tification of ＂normal＂ clusters and described distinct clusters of effective attacks.

关键词： big data visualization Sampling method MDS PCA

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A survey towards an integration of big data analytics to big insights for value-creation

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INFORMATION PROCESSING & MANAGEMENT 2018年第5期54卷 758-790页

作者： Saggi, Mandeep Kaur Jain, Sushma Thapar Univ Dept Comp Sci Patiala Punjab India

big data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision making. The developments in big data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of big data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of big data Analytics;(ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation;and (iii) the current challenges of big data Analytics as well as possible future directions.

关键词： big data data analytics Machine learning big data visualization Decision-making Smart agriculture Smart city application Value-creation Value-discover Value-realization

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Using LSTM Neural Network for Time Series Predictions in Financial Markets 18

Using LSTM Neural Network for Time Series Predictions in Fin...

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18th IEEE International Symposium on Network Computing and Applications (IEEE NCA)

作者： Zhelev, Svetoslav Avresky, Dimiter R. Tech Univ Sofia Sofia Bulgaria IRIANC Munich Germany

ISBN: (纸本)9781728125220

The article will test if Long Short Term Memory (LSTM) neural networks are suitable for high frequency foreign exchange (forex) trading. Major world currencies often correlate and affect each other. We will try to take advantage and we will feed time series data of several currency pairs and do correlation analysis on them. When traders do technical analysis they inspect different time periods in order to get better understanding on currency trends. We will also test if feeding the neural network with different time periods will lead to better predictions.

关键词： Neural Networks RNN LSTM Foreign Exchange Deep Learning big data big data visualization

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Heatflip: Temporal-Spatial Sampling for Progressive Heat Maps on Social Media data

Heatflip: Temporal-Spatial Sampling for Progressive Heat Map...

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IEEE International Conference on big data (big data)

作者： Stoehr, Niklas Meyer, Johannes Markl, Volker Bai, Qiushi Kim, Taewoo Chen, De-Yu Li, Chen Tech Univ Berlin Database Syst & Informat Management Grp Berlin Germany Univ Calif Irvine Dept Comp Sci Irvine CA USA

ISBN: (纸本)9781538650356

Keyword-based heat maps are a natural way to explore and analyze the spatial properties of social media data. Dealing with large datasets, there may be many different keywords, making offline pre-computations very hard. Interactive frameworks that exploit database sampling can address this challenge. We present a novel middleware technique called Heatflip, which issues diametrically opposed samples into the temporal and spatial dimensions of the data stored in an external database. Spatial samples provide insights into the temporal distribution and vice versa. The progressive exploration approach benefits from adaptive indexing and combines the retrieval and visualization of the data in a middleware layer. Without any a priori knowledge of the underlying data, the middleware can generate accurate heat maps in 85% shorter processing times than conventional systems. In this paper, we discuss the analytical background of Heatflip, showcase its scalability, and validate its performance when visualizing large amounts of social media data.

关键词： data exploration Social media analytics big data visualization database sampling Progressive computing

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Analysis and Research of data Mining Technology in the Horizon of Cloud Computing

Analysis and Research of Data Mining Technology in the Horiz...

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2019 7th International Conference on Machinery,Materials and Computing Technology(ICMMCT 2019)

作者： Juan Peng Xi'an Eurasia University

With the continuous development and progress of the economy and society,science and technology information technology is constantly improving in order to meet the needs of social *** an emerging technology in the development of the Internet,Cloud Computing has gradually become an indispensable part of people's lives,and is widely used in the military,medical and financial *** on the rapid development of Internet information technology,the number of users using the Internet is increasing day by day,generating a large amount of information *** proliferation of information data has pushed society from the age of information scarcity to the era of information overload,so people began to explore a new technology to save and analyze the data,and then extract the hidden value behind the *** order to achieve this,it is necessary to solve the storage problem,processing problem and mining problem of massive data information,and the birth of Cloud Computing provides a direction and way for solving this *** paper analyzes and explores the data Mining technology in the Cloud Computing environment,and analyzes its scalability and versatility from the architecture of the application platform,which provides a reference for researchers engaged in data Mining applications.

关键词： Cloud Computing technology big data visualization data Mining Analytical research

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visualization of RFID-enabled shopfloor logistics big data in Cloud Manufacturing

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第1-4期84卷 5-16页

作者： Zhong, Ray Y. Lan, Shulin Xu, Chen Dai, Qingyun Huang, George Q. Univ Hong Kong Dept Ind & Mfg Syst Engn HKU ZIRI Lab Phys Internet Hong Kong Hong Kong Peoples R China Shenzhen Univ Coll Informat Engn Shenzhen Peoples R China Shenzhen Univ Inst Intelligent Comp Sci Shenzhen Peoples R China Guangdong Polytech Normal Univ Guangzhou Guangdong Peoples R China

Cloud Manufacturing twining with Internet of Things (IoT) has been waked up to achieve final intelligent manufacturing. With the IoT technologies such as radio frequency identification (RFID) implemented in manufacturing sites, enormous data will be generated. Such data are so complex, abstract, and variable so that it is difficult to make full use of the data which carry great myriad of useful information and knowledge. This paper presents a visualization approach for the RFID-enabled shopfloor logistics big data from Cloud Manufacturing. An innovative RFID-Cuboid model is used for reconstructing the RFID raw data given the production logic and time series. Several contributions are highlighted. Firstly, a possible approach to integrate IoT and Cloud Manufacturing is introduced to upgrade and transform the traditional industry for an intelligent future. Secondly, an RFID-Cuboid model is proposed by using the production logic and time stamps to chain the RFID data so that the data could be interpreted. Thirdly, a real-life case is reported to show the feasibility and practicality of the proposed visualization approach to help different end-users to ease their daily operations. Lessons and insights from this case are meaningful for the implementation of IoT-enabled Cloud Manufacturing and big data analytics in industry field.

关键词： big data visualization RFID Cloud Manufacturing Shopfloor Logistics

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Cancer-disease associations: A visualization and animation through medical big data

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016年 127卷 44-51页

作者： Iqbal, Usman Hsu, Chun-Kung Phung Anh (Alex) Nguyen Clinciu, Daniel Livius Lu, Richard Syed-Abdul, Shabbir Yang, Hsuan-Chia Wang, Yao-Chin Huang, Chu-Ya Huang, Chih-Wei Chang, Yo-Cheng Hsu, Min-Huei Jian, Wen-Shan Li, Yu-Chuan (Jack) Taipei Med Univ Coll Med Sci & Technol Grad Biomed Informat Taipei Taiwan Taipei Med Univ ICHIT Taipei Taiwan Natl Yang Ming Univ Inst Biomed Informat Taipei 112 Taiwan Minist Hlth & Welf Bur Int Cooperat Taipei Taiwan Taipei Med Univ Sch Hlth Care Adm Taipei Taiwan Macau Univ Sci & Technol Fac Hlth Sci Macau Peoples R China Taipei Med Univ Wan Fang Hosp Dept Dermatol Taipei Taiwan

Objective: Cancer is the primary disease responsible for death and disability worldwide. Currently, prevention and early detection represents the best hope for cure. Knowing the expected diseases that occur with a particular cancer in advance could lead to physicians being able to better tailor their treatment for cancer. The aim of this study was to build an animated visualization tool called as Cancer Associations Map Animation (CAMA), to chart the association of cancers with other disease over time. Methods: The study population was collected from the Taiwan National Health Insurance database during the period January 2000 to December 2002, 782 million outpatient visits were used to compute the associations of nine major cancers with other diseases. A motion chart was used to quantify and visualize the associations between diseases and cancers. Results: The CAMA motion chart that was built successfully facilitated the observation of cancer-disease associations across ages and genders. The CAMA system can be accessed online at http://203.71.86.98/web/***1. Conclusion: The CAMA animation system is an animated medical data visualization tool which provides a dynamic, time-lapse, animated view of cancer-disease associations across different age groups and gender. Derived from a large, nationwide healthcare dataset, this exploratory data analysis tool can detect cancer comorbidities earlier than is possible by manual inspection. Taking into account the trajectory of cancer-specific comorbidity development may facilitate clinicians and healthcare researchers to more efficiently explore early stage hypotheses, develop new cancer treatment approaches, and identify potential effect modifiers or new risk factors associated with specific cancers. (C) 2016 Published by Elsevier Ireland Ltd.

关键词： Visual analytics Disease visualization big data visualization Cancer disease visualization Cancer comorbidities visualization

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Geometrical and topological approaches to big data

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2017年 67卷 286-296页

作者： Snasel, Vaclav Nowakova, Jana Xhafa, Fatos Barolli, Leonard VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Comp Sci 17 Listopadu 15-2172 Ostrava 70833 Czech Republic Tech Univ Catalonia Dept Comp Sci Omega BlvdC Jordi Girona 1-3 Barcelona 08034 Spain FIT Fac Informat Engn Dept Informat & Commun Engn Higashi Ku 3-30-1 Wajiro Higashi Fukuoka 8110295 Japan

Modern data science uses topological methods to find the structural features of data sets before further supervised or unsupervised analysis. Geometry and topology are very natural tools for analysing massive amounts of data since geometry can be regarded as the study of distance functions. Mathematical formalism, which has been developed for incorporating geometric and topological techniques, deals with point cloud data sets, i.e. finite sets of points. It then adapts tools from the various branches of geometry and topology for the study of point cloud data sets. The point clouds are finite samples taken from a geometric object, perhaps with noise. Topology provides a formal language for qualitative mathematics, whereas geometry is mainly quantitative. Thus, in topology, we study the relationships of proximity or nearness, without using distances. A map between topological spaces is called continuous if it preserves the nearness structures. Geometrical and topological methods are tools allowing us to analyse highly complex data. These methods create a summary or compressed representation of all of the data features to help to rapidly uncover particular patterns and relationships in data. The idea of constructing summaries of entire domains of attributes involves understanding the relationship between topological and geometric objects constructed from data using various features. A common thread in various approaches for noise removal, model reduction, feasibility reconstruction, and blind source separation, is to replace the original data with a lower dimensional approximate representation obtained via a matrix or multi-directional array factorization or decomposition. Besides those transformations, a significant challenge of feature summarization or subset selection methods for big data will be considered by focusing on scalable feature selection. Lower dimensional approximate representation is used for big data visualization. The cross-field between topology and

关键词： big data Industry 4.0 Topological data analysis Persistent homology Dimensionality reduction big data visualization

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