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Chat2Query: A Zero-Shot Automatic Exploratory data Analysis System with Large Language models 40

Chat2Query: A Zero-Shot Automatic Exploratory Data Analysis ...

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40th IEEE international conference on data Engineering, ICDE 2024

作者： Zhu, Jun-Peng Cai, Peng Niu, Boyan Ni, Zheming Xu, Kai Huang, Jiajun Wan, Jianwei Ma, Shengbo Wang, Bing Zhang, Donghui Tang, Liu Liu, Qi East China Normal University China Ping CAP

ISBN: (纸本)9798350317152

data analysts often encounter two primary challenges while conducting exploratory data analysis by SQL: (1) the need to skillfully craft SQL queries, and (2) the requirement to generate suitable visualizations that enhance the interpretation of query results. The emergence of large language models (LLMs) has inaugurated a paradigm shift in text-to-SQL and data-to-chart. This paper presents Chat2Query, an LLM -empowered zero-shot automatic exploration data analysis system. Firstly, Chat2Query provides a user-friendly interface that allows users to employ natural languages to interact with the database directly. Secondly, Chat2Query offers an LLM -empowered text-to-SQL generator, SQL rewriter, SQL formatter, and data-to-chart generator. Thirdly, Chat2Query is uniquely distinguished by its underlying incorporation of the TiDB Serverless, fostering superior elasticity and scalability. This strategic integration empowers Chat2Query with the capability to seamlessly adapt to change workloads, aligning with the evolving demands of the user. We have implemented and deployed Chat2Query in the production environment, and demonstrate its usability and efficiency in three representative real-world scenarios. © 2024 IEEE.

关键词： Query processing

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3rd international conference on Smart Applications and data Analysis for Smart Cyber-Physical Systems, SADASC 2020

3rd International Conference on Smart Applications and Data ...

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3rd international conference on Smart Applications and data Analysis for Smart Cyber-Physical Systems, SADASC 2020

ISBN: (纸本)9783030451820

The proceedings contain 27 papers. The special focus in this conference is on Smart Applications and data Analysis for Smart Cyber-Physical Systems. The topics include: Unsupervised Deep Collaborative Filtering Recommender System for E-Learning Platforms;decision Tree Model Based Gene Selection and Classification for Breast Cancer Risk Prediction;dynamic Partitioning of Transportation Network Using Evolutionary Spectral Clustering;towards a Feature Selection for Multi-label Text Classification in Big data;Hardware Implementation of Roadway Classification System in FPGA Platform;Optimization of LSTM Algorithm Through Outliers – Application to Financial Time Series Forecasting;An Artificial Neural Network Combined to Object Oriented Method for Land Cover Classification of High Resolution RGB Remote Sensing Images;Implementing an ANN Model to Predict the Notch Band Frequency of an UWB Antenna;optimization of Truck-Shovel Allocation Problem in Open-Pit Mines;moving database Cost models from Darkness to Light;multi-objective Optimization of Heterogeneous Vehicles Routing in the Case of Medical Waste Using Genetic Algorithm;Safe and Optimal Path Planning for Autonomous UAV Using a Decomposition-Coordination Method;an Execution Time Comparison of Parallel Computing algorithms for Solving Heat Equation;automatic Microservices Identification from a Set of Business Processes;numerical Study of Damaged, Failure and Cracking of Concrete Beam Reinforced by Honeycomb Sandwich Panel Structures;Convolutional Neural Networks for Multimodal Brain MRI Images Segmentation: A Comparative Study;Towards a New ICT Competency Framework Using Open Linked data;an Overview of Gradient Descent Algorithm Optimization in Machine Learning: Application in the Ophthalmology Field;Building Valid Career Ontologies with B-CPNs.

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Deep Learning models to Analyze Sentiments of People Regarding New Vaccines

Deep Learning Models to Analyze Sentiments of People Regardi...

引用

Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Asmaa Khoudi Yasmine Draoui Nadjet Aoutou Computer Science Department LIMOSE Laboratory Faculty of Sciences University M'Hamed Bougara Boumerdes Algeria Computer Science Department Faculty of Sciences University M'Hamed Bougara Boumerdes Algeria

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

In recent years, sentiment analysis has been one of the most active research areas in natural language processing. The wide range of its applications is the main reason for its popularity. The purpose of this work is to analyze the sentiments of Twitter users towards new vaccines using deep learning algorithms. The goal of this studey is to develop an effective model for classifying sentiments expressed in tweets. We use three deep learning algorithms that are CNN, LSTM and Bi-LSTM. To train and create a classifier from textual data, text should be transformed into numerical data. We use GloVe to transform text and create the word embedding for the DL models. We conduct comparison on the three developed algorithms. Our experiments demonstrate that the best model for the classification is the Bi-LSTM since it achieved the highest accuracy value of 0.82 on the test data.

关键词： Deep learning Sentiment analysis Social networking (online) Computational modeling Blogs data models Vaccines Numerical models Classification algorithms Long short term memory

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GOOGLE EARTH ENGINE FOR REMOTE SENSING BIG data: A COMPREHENSIVE REVIEW

GOOGLE EARTH ENGINE FOR REMOTE SENSING BIG DATA: A COMPREHEN...

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2024 international conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Garima Acharjya, Kalyan Karthikeyan, M.P. Department of Computer Science & Engineering Vivekananda Global University Jaipur India Maharishi School of Engineeing & Technology Maharishi University of Information Technology Uttar Pradesh India Bangalore India

ISBN: (纸本)9798350355338

In recent years, the amount of data collected by Remote Sensing (RS) devices has increased significantly. This has created a difficulty for practical administration and analysis of these datasets utilising desktop computer resources and traditional software packages. In response, Google unveiled the Earth Engine from Google (GEE), a platform for cloud computing designed to successfully handle the difficulties associated with huge data processing. Since its launch in the year 2010 GEE has shown its ability to handle large geospatial information across large regions and track changes in the environment over an extended period of time. It was created 10 years ago, but only lately has its full potential of RS applications come to light. This research aims to thoroughly examine a number of aspects of the GEE structure, including datasets, features, benefits, drawbacks, and a variety of applications. A comprehensive analysis of 450 scientific papers published in 150 publications between January 2010 and May 2020 demonstrated the extensive use of Sentinel and Landsat datasets in the GEE paradigm. Interestingly, supervised machine learning algorithms—like Random Forest—have become more and more popular for image categorization applications. Numerous domains, such as hydrology, development, natural disaster observing, climate analysis, land cover/use categorization, and image processing, have found use for GEE. This investigation reveals the immense potential of GEE with RS technologies and offers insightful information on its capabilities and range of uses throughout the studied time. © 2024 IEEE.

关键词： Cloud platforms

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Multi-Scale Deep Ensemble Learning for Melanoma Skin Cancer Detection 23

Multi-Scale Deep Ensemble Learning for Melanoma Skin Cancer ...

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23rd IEEE international conference on Information Reuse and Integration for data Science (IEEE IRI)

作者： Guergueb, Takfarines Akhloufi, Moulay A. Univ Moncton Dept Comp Sci Percept Robot & Intelligent Machines Res Grp PRIM Moncton NB Canada

ISBN: (纸本)9781665466035

Skin cancer is the most prevalent type of cancer in the world. Melanoma, specifically is among the deadliest variants of cancer, But if detected early, most melanomas can be cured with minor surgery. In this study we propose an innovative melanoma detection pipeline which utilizes ensemble learning to combine the predictive power of several deep convolutional neural network models. All experiments are performed using image data acquired from the Society for Imaging Informatics in Medicine and the international Skin Imaging Collaboration SIIM-ISIC 2020. The proposed approach achieved a high performance with an Area Under Curve (AUC) of 99.02% outperforming many state-of-the-art algorithms.

关键词： Pipelines Imaging Training data Surgery Melanoma Predictive models Prediction algorithms

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Bitcoin price prediction based on financial data, technical indicators, and news headlines sentiment analysis using CNN and GRU deep learning algorithms 3

Bitcoin price prediction based on financial data, technical ...

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3rd international conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Arjmand, Masoud Kazeminia, Saman Sajedi, Hedieh University of Tehran Kish International Campus Department of Computer Engineering Kish Iran NHL Stenden University of Applied Science Department of Computer Vision and Data Science Leeuwarden Netherlands University of Tehran Department of Mathematics Statistics and Computer Science Tehran Iran

ISBN: (纸本)9798350381580

Bitcoin is the leading cryptocurrency with the highest market value among digital currencies. Therefore, predicting the value of Bitcoin can help to understand the entire cryptocurrency market. However, Bitcoin has had a lot of price fluctuations since its inception. In this paper, we are going to forecast the price of Bitcoin using news headline analysis, technical analysis indicators, and historical financial data. The news headlines used in this article are scraped from the Cointelegraph news website, which contains 3988 news headlines related to Bitcoin between 2/7/2020 and 3/8/2021. A transformer pre-trained model on cryptocurrency-related texts called CryptoBERT, which is a BERT-based sentiment analysis model, has been used to analyze the textual data. Also, a novel hybrid 2DCNN-GRU deep learning model has been used to predict the price. To adjust the parameters of this model, a parameter tuning method based on orthogonal arrays called the Taguchi method has been employed. Finally, to examine the proposed model's efficiency, the obtained results have been compared with other deep learning models from the literature review that used text data to predict bitcoin prices. The results show that this model outperformed other models in terms of MAE criterion, while in the other three criteria, namely MSE, RMSE and MAPE, it still demonstrated good results. © 2024 IEEE.

关键词： Forecasting

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6th international conference on Information Management and Big data, SIMBig 2019

6th International Conference on Information Management and B...

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6th international conference on Information Management and Big data, SIMBig 2019

ISBN: (纸本)9783030461393

The proceedings contain 32 papers. The special focus in this conference is on Information Management and Big data. The topics include: Sparse Non-negative Matrix Factorization for Retrieving Genomes Across Metagenomes;collect Ethically: Reduce Bias in Twitter datasets;big data Recommender System for Encouraging Purchases in New Places Taking into Account Demographics;privacy Preservation and Inference with Minimal Mobility Information;development of a Hand Gesture Based Control Interface Using Deep Learning;a Progressive Formalization of Tacit Knowledge to Improve Semantic Expressiveness of Biodiversity data;peruvian Sign Language Recognition Using a Hybrid Deep Neural Network;chronic Pain Estimation Through Deep Facial Descriptors Analysis;detection of Non-small Cell Lung Cancer Adenocarcinoma Using Supervised Learning algorithms Applied to Metabolomic Profiles;SCUT Sampling and Classification algorithms to Identify Levels of Child Malnutrition;characterization of Salinity Impact on Synthetic Floc Strength via Nonlinear Component Analysis;spanish Sentiment Analysis Using Universal Language Model Fine-Tuning: A Detailed Case of Study;comparing Predictive Machine Learning algorithms in Fit for Work Occupational Health Assessments;recognition of the Image of a Person, Based on Viola-Jones;a Place to Go: Locating Damaged Regions After Natural Disasters Through Mobile Phone data;come with Me Now: New Potential Consumers Identification from Competitors;global Brand Perception Based on Social Prestige, Credibility and Social Responsibility: A Clustering Approach;using Embeddings to Predict Changes in Large Semantic Graphs;super Resolution Approach Using Generative Adversarial Network models for Improving Satellite Image Resolution;computer-Assisted Learning for Chinese Based on Character Families;recurrence Plot Representation for Multivariate Time-Series Analysis.

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The Stair Sketch: Bringing more Clarity to Memorize Recent Events 38

The Stair Sketch: Bringing more Clarity to Memorize Recent E...

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38th IEEE international conference on data Engineering (ICDE)

作者： Zhao, Yikai Zhang, Yubo Yi, Pu Yang, Tong Cui, Bin Uhlig, Steve Peking Univ Sch Comp Sci Beijing Peoples R China Peking Univ Natl Engn Lab Big Data Anal Technol & Applicat Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London England

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

data stream processing has become fundamental in computer science, with a wide range of applications, such as in databases, data mining, and security. Memorizing when an item appears in the data stream is one important task in stream processing. Because the older data is, the less value it has, memorizing recent events with higher accuracy is desirable. To achieve this, we propose a novel data stream processing structure named the Stair sketch. Our key idea is to organize the memory used by different time periods in the shape of stairs. We deploy the Stair sketch on Bloom filters, CM sketches, and CU sketches as case studies. Experiment results show that our approach outperforms state-of-the-art algorithms by more than 5x in accuracy while providing comparable efficiency. The source code of the Stair sketch is available at GitHub.

关键词： Shape databases Stairs Filtering algorithms data engineering Frequency estimation Security

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Indoor intelligent driving scene-based traffic signal data set

Indoor intelligent driving scene-based traffic signal data s...

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2020 international conference on data processing algorithms and models, icdpam 2020

作者： Fu, En Pan, Weiguo Pan, Feng Yang, Qing Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing100101 China College of Robotics Beijing Union University Beijing100101 China

In recent years, with the rapid development of intelligent driving technology, vision based traffic signal target detection is one of the urgent problems to be solved;especially with the extensive application of deep learning technology, the traffic signal data set is more and more important. Due to the variety of traffic signals, there is no data set that can cover all categories at home and abroad. This paper presents a traffic signal data set based on indoor traffic scene. The currently disclosed data category includes 22 categories, including traffic signs and traffic lights. Simultaneously, this paper also uses a two-stage deep learning method to conduct experiments. In order to facilitate other researchers to conduct further research on the disclosed data set in this paper. © Published under licence by IOP Publishing Ltd.

关键词： Traffic signals

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Sentiment Analysis on Educational Tweets: A Case of National Education Policy 2020 1

Sentiment Analysis on Educational Tweets: A Case of National...

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1st IEEE international conference on Contemporary Computing and Communications, InC4 2023

作者： Siddique, Mohammad Mustafa Kumar, Sandeep Department of Computer Science and Engineering Kengeri Bengaluru India

ISBN: (纸本)9798350335774

Due to COVID-19 pandemic lockdowns, the transition from traditional class-room-based approaches, there has been rise in online education. There is a growing need to adopt the best global academic and innovative practices and implement the National Education Policy-2020 (NEP) in Indian education. This study uses a dataset, NEPEduset, created by gathering tweets about education. An attempt has been made in this study to examine the tweets by preprocessing, generating labels or sentiments using standard tools and libraries in Python language, applying and comparing various machine learning (ML) algorithms. ML approaches are powerful and used in various applications ranging from sentiment analysis, text analysis, natural language processing (NLP), image processing, object detection. ML methods are widely used in sentiment analysis tasks and text annotations. This work uses Text-Blob, Valence Aware Dictionary for Sentiment Reasoning (VADER), and a Customized method, SentiNEP to analyze the sentiment score of tweets' text. SentiNEP method is shown is produce better results for various experiments conducted for the dataset, NEPEduset. Various supervised ML models have been applied for text classification of user sentiment. Word2Vec feature extraction technique has been applied to build and evaluate the models. Performance metrics such as precision, accuracy, F1 score and recall have been used to evaluate the ML models. The results reveal that the support vector machine and random forest classifiers achieve higher accuracy with Word2Vec. The performance results have been compared with VADER, TextBlob and SentiNEP. It has been found that the SentiNEP method produces better results. © 2023 IEEE.

关键词： Sentiment analysis

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