Showing 1-25 of 31,493 resultsfor "Index Terms":real-world data
"Index Terms":real-world data
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Effective Tooling for Linked Data Publishing in Scientific Research
Sumit Purohit;William Smith;Alan Chappell;Patrick West;Benno Lee;Eric Stephan;Peter Fox
2016 IEEE Tenth International Conference on Semantic Computing (ICSC)
Year: 2016 | Conference Paper |
Cited by: Papers (6)
Challenges that make it difficult to find, share, and combine published data, such as data heterogeneity and resource discovery, have led to increased adoption of semantic data standards and data publishing technologies. To make data more accessible, interconnected and discoverable, some domains are being encouraged to publish their data as Linked Data. Consequently, this trend greatly increases t...Show More
Construction of Empirical Care Pathways Process Models From Multiple Real-World Datasets
Juan González-García;Carlos Tellería-Orriols;Francisco Estupiñán-Romero;Enrique Bernal-Delgado
Care pathways (CPWs) are “multidisciplinary care plans that detail essential care steps for patients with specific clinical problems.” While CPWs impact on health or cost outcomes is vastly studied, an in-depth analysis of the real-world implementation of the CPWs is an area that still remains underexplored. The present work describes how to apply an existing process mining methodology to construc...Show More
Semi-Supervised Image Deraining Using Gaussian Processes
Recent CNN-based methods for image deraining have achieved excellent performance in terms of reconstruction error as well as visual quality. However, these methods are limited in the sense that they can be trained only on fully labeled data. Due to various challenges in obtaining real world fully-labeled image deraining datasets, existing methods are trained only on synthetically generated data an...Show More
A Big Data Platform for the Management of Local Energy Communities Data
2023 IEEE International Conference on Big Data (BigData)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
In this paper we present a big data platform designed to collect and analyze energy data of Local Energy Communities, with the goal to improve the conscious use of energy by the users. The platform, originally commissioned by ENEA, is designed to acquire and manage different kinds of data (e.g., energy consumption and production, weather data, etc.) coming from multiple sources in many formats. In...Show More
Leveraging real-world data while protecting privacy Framework using personal agent system for distributed data
Tomohiro Inoue;Taichi Kawabata;Hiroyuki Maeomichi;Mika Ishizuka;Koichi Takasugi;Akihiro Tsutsui;Ikuo Yoda
2013 IEEE 10th Consumer Communications and Networking Conference (CCNC)
Year: 2013 | Conference Paper |
We propose a real-world data exchange framework based on the concept of data ownership. The most important issues with these data are the protection of user privacy and preserve the value of the information derived from the data to consumers. This framework enables the protection of privacy and enables maximum leverage of real-world data belonging to each person at the same time. In our framework,...Show More
Location-Based Social Network Data Generation Based on Patterns of Life
Joon-Seok Kim;Hyunjee Jin;Hamdi Kavak;Ovi Chris Rouly;Andrew Crooks;Dieter Pfoser;Carola Wenk;Andreas Züfle
2020 21st IEEE International Conference on Mobile Data Management (MDM)
Year: 2020 | Conference Paper |
Cited by: Papers (16)
Location-based social networks (LBSNs) have been studied extensively in recent years. However, utilizing real-world LBSN data sets yields several weaknesses: sparse and small data sets, privacy concerns, and a lack of authoritative ground-truth. To overcome these weaknesses, we leverage a large-scale LBSN simulation to create a framework to simulate human behavior and to create synthetic but reali...Show More
Research and Implementation of Real World Traditional Chinese Medicine Clinical Scientific Research Information Electronic Medical Record Sharing System
Yu Tian;Qi Xie;Runshun Zhang;Xuezhong Zhou;Tiancai Wen;Lei Zhang;Xingping Zhang;Bin Wang;Xu Miao;Jie Yang;Baoyan Liu
2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
The standardization degree of traditional Chinese medicine clinical data in the real world is low and heterogeneous data aggregation among institutions is difficult, which leads to the difficulty of sharing clinical data and scientific research data of traditional Chinese medicine. This paper designs and implements a real world traditional Chinese medicine clinical scientific research information ...Show More
Reputation-Aware Data Fusion and Malicious Participant Detection in Mobile Crowdsensing
Yujian Tang;Samia Tasnim;Niki Pissinou;S.S. Iyengar;Abdur Shahid
2018 IEEE International Conference on Big Data (Big Data)
Year: 2018 | Conference Paper |
Cited by: Papers (22)
Mobile crowdsensing, an emerging sensing paradigm, promotes scalability and reduction in the deployment of specialized sensing devices for large-scale data collection in a decentralized fashion. However, its open structure allows malicious entities to interrupt a system by reporting fabricated or erroneous data, making trust evaluation a highly important issue in mobile crowdsensing applications. ...Show More
Investigating Data Repair steps for EHR Big Data
2022 3rd International Conference on Next Generation Computing Applications (NextComp)
Year: 2022 | Conference Paper |
This paper builds on previous research with the aim of optimizing data quality methodologies for Big Data systems, with a focus on Electronic Health Records. This optimization is performed for organisations aiming to follow a data-centric data quality strategy. One of the most important stages of a data quality lifecycle is involved with correcting dirty data detected. There is a lack of knowledge...Show More
Twin Neurons for Efficient Real-World Data Distribution in Networks of Neural Cliques: Applications in Power Management in Electronic Circuits
Bartosz Boguslawski;Vincent Gripon;Fabrice Seguin;Frédéric Heitzmann
Associative memories are data structures that allow retrieval of previously stored messages given part of their content. They, thus, behave similarly to the human brain's memory that is capable, for instance, of retrieving the end of a song, given its beginning. Among different families of associative memories, sparse ones are known to provide the best efficiency (ratio of the number of bits store...Show More
Estimation of Battery Capacity Fade using Real-World Vehicle Data for Diagnosis of Abnormal Capacity Loss
Zirun Jia;Zekun Zhang;Zhenyu Sun;Peng Liu;Zhenpo Wang;Zhaosheng Zhang
2023 IEEE Energy Conversion Congress and Exposition (ECCE)
Year: 2023 | Conference Paper |
Accurate estimation of battery capacity and diagnosis of its degradation state are essential for safe battery management. This paper presents an advanced method for accurate capacity estimation and abnormal capacity degradation diagnosis of electric vehicle battery systems. Base on the real-world electric vehicles (EVs) data, the reference capacity of the battery system can be calculated by integr...Show More
On the Usability of Big (Social) Data
Sunil Choenni;Niels Netten;Mortaza Shoae-Bargh;Rochelle Choenni
2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom)
Year: 2018 | Conference Paper |
Cited by: Papers (4)
Due to the growing availability of huge amounts of data of different types and the growing capabilities to analyze these data, the expectations of big data applications are high. In this paper, we argue that the usability of big data in the social domain is far from trivial. If the outcomes of big data are wrongly interpreted, this may shape the development of our society in a wrong direction. The...Show More
Application of Big Data Analytics and Machine Learning to Large-Scale Synchrophasor Datasets: Evaluation of Dataset ‘Machine Learning-Readiness’
Philip Hart;Lijun He;Tianyi Wang;Vijay S. Kumar;Kareem Aggour;Arun Subramanian;Weizhong Yan
This manuscript presents a data quality analysis and holistic ‘machine learning-readiness’ evaluation of a representative set of large-scale, real-world phasor measurement unit (PMU) datasets provided under the United States Department of Energy-funded FOA 1861 research program. A major focus of this study is to understand the present-day suitability of large-scale, real-world synchrophasor datase...Show More
Uncovering the Risks and Drawbacks Associated With the Use of Synthetic Data for Grammatical Error Correction
Seonmin Koo;Chanjun Park;Seolhwa Lee;Jaehyung Seo;Sugyeong Eo;Hyeonseok Moon;Heuiseok Lim
In a Data-Centric AI paradigm, the model performance is enhanced without altering the model architecture, as evidenced by real-world and benchmark dataset demonstrations. With the advancements of large language models (LLM), it has become increasingly feasible to generate high-quality synthetic data, while considering the need to construct fully synthetic datasets for real-world data containing nu...Show More
Parallel Sets: interactive exploration and visual analysis of categorical data
Categorical data dimensions appear in many real-world data sets, but few visualization methods exist that properly deal with them. Parallel Sets are a new method for the visualization and interactive exploration of categorical data that shows data frequencies instead of the individual data points. The method is based on the axis layout of parallel coordinates, with boxes representing the categorie...Show More
A Scalable and Robust Framework for Data Stream Ingestion
2018 IEEE International Conference on Big Data (Big Data)
Year: 2018 | Conference Paper |
Cited by: Papers (22)
An essential part of building a data-driven organization is the ability to handle and process continuous streams of data to discover actionable insights. The explosive growth of interconnected devices and the social Web has led to a large volume of data being generated on a continuous basis. Streaming data sources such as stock quotes, credit card transactions, trending news, traffic conditions, t...Show More
Real-World Multi-Domain Data Applications for Generalizations to Clinical Settings
Nooshin Mojab;Vahid Noroozi;Darvin Yi;Manoj P. Nallabothula;Abdullah Aleem;Philip S. Yu;Joelle A. Hallak
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)
Year: 2020 | Conference Paper |
Cited by: Papers (4)
With promising results of machine learning based models in computer vision, applications on medical imaging data have been increasing exponentially. Deep learning models perform well when trained on standardized datasets from artificial settings. However, generalization and translation to real-world clinical settings are challenging. The complexity of real-world applications in healthcare emanates...Show More
The researchers have shown broad concern about detection and recognition of fraudsters since telecommunication operators and the individual user are both suffering significant losses from fraud activities. Researchers have proposed various solutions to counter fraudulent activity. However, those methods may lose effectiveness in fraud detection because fraudsters always tend to cover their tracks ...Show More
Synthetic Vs Real: A Comparative Study on AI Generated Art for Improved Learning in AI
Ruchi;Lovish Kumar;Sneha Aggarwal;Tanisha Verma;Harshita;Vikas Wasson
This research offers an in-depth comparison of emotion detection models developed using real-world and synthetic datasets in the field of artificial intelligence and machine learning. The research rigorously analyses model performance, generalization capacities, and robustness in various circumstances to evaluate the use of synthetic data for machine learning applications. We provide thorough empi...Show More
Capitalize Your Data: Optimal Selling Mechanisms for IoT Data Exchange
Qinya Li;Zun Li;Zhenzhe Zheng;Fan Wu;Shaojie Tang;Zhao Zhang;Guihai Chen
More and more IoT data is being traded online in cloud-based data marketplaces due to the fast-growing market demand. Within the current data selling mechanisms, data consumers have difficulties in making purchasing decisions due to uncertain IoT data quality and inflexible pricing interface. To resolve these issues, potential solutions could be to launch data demonstrations and release free sampl...Show More
A Provenance Meta Learning Framework for Missing Data Handling Methods Selection
2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
Year: 2020 | Conference Paper |
Cited by: Papers (4)
Missing data is a big problem in many real-world data sets and applications, which can lead to wrong or misleading results of analyses and lower quality and confidence in the results. A large number of missing data handling methods have been proposed in the research community but there exists no universally single best method which can handle all the missing data problems. To select the right meth...Show More
Plotly-Resampler: Effective Visual Analytics for Large Time Series
Jonas Van Der Donckt;Jeroen Van der Donckt;Emiel Deprost;Sofie Van Hoecke
2022 IEEE Visualization and Visual Analytics (VIS)
Year: 2022 | Conference Paper |
Cited by: Papers (12)
Visual analytics is arguably the most important step in getting acquainted with your data. This is especially the case for time series, as this data type is hard to describe and cannot be fully understood when using for example summary statistics. To realize effective time series visualization, four requirements have to be met; a tool should be (1) interactive, (2) scalable to millions of data poi...Show More
A Review on Integration of Big Data and IoT
2018 4th International Conference on Computing Sciences (ICCS)
Year: 2018 | Conference Paper |
Cited by: Papers (8)
Communication plays a vital role whether we talk about formal or informal but when it comes to device communication or objects communication. IoT plays very important role to understand real world objects after transforming into virtual objects and generate huge amount of data which is in structured as well as unstructured form. In this paper author is focusing on various attributes which may prov...Show More
Personal data have been increasingly used in data-driven applications to improve quality of life. However, privacy preservation of personal data while sharing it with analysts/ researchers has become an essential requirement to be met by data owners (hospitals, banks, insurance companies, etc.). The existing literature on privacy preservation does not precisely quantify the vulnerability of each i...Show More
As smart speakers continue to proliferate, question answering (QA) by smart devices is being woven into our daily lives. This study assumes question answering related to daily life events detected by context recognition systems, such as activity recognition and indoor positioning systems, e.g., answering questions like “Did my grandma eat dinner?” and “How many times did my grandpa go to the toile...Show More
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