Big Data Research最新文献_第9页

A Parallel Fusion Graph Convolutional Network for Aspect-Level Sentiment Analysis 面向层面情感分析的并行融合图卷积网络

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2023-05-28 DOI: 10.1016/j.bdr.2023.100378

Yuxin Wu, Guofeng Deng

{"title":"A Parallel Fusion Graph Convolutional Network for Aspect-Level Sentiment Analysis","authors":"Yuxin Wu, Guofeng Deng","doi":"10.1016/j.bdr.2023.100378","DOIUrl":"https://doi.org/10.1016/j.bdr.2023.100378","url":null,"abstract":"<div><p>Sentiment analysis<span> has always been an important basic task in the NLP<span> field. Recently, graph convolutional networks (GCNs) have been widely used in aspect-level sentiment analysis. Because GCNs have good aggregation effects, every node can contain neighboring node information. However, in previous studies, most models used only a single GCN to learn contextual information. The GCN relies on the construction method of the graph, and a single GCN will cause the model to focus on a certain relationship of nodes that depends on the construction method and ignore other information. In addition, when the GCN aggregates node information, it cannot determine whether the aggregated information is useful, so it will inevitably introduce noise. We propose a model that fuses two parallel GCNs to learn different relational features between sentences at the same time, and we add a gate mechanism to the GCN to filter the noise introduced by the GCN when aggregating information. Finally, we validate our model on public datasets, and the experiments show that compared to state-of-the-art models, our model performs better.</span></span></p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"32 ","pages":"Article 100378"},"PeriodicalIF":3.3,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49714247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MLPQ: A Dataset for Path Question Answering over Multilingual Knowledge Graphs MLPQ:一个多语言知识图路径问答数据集

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2023-05-28 DOI: 10.1016/j.bdr.2023.100381

Yiming Tan , Yongrui Chen , Guilin Qi , Weizhuo Li , Meng Wang

{"title":"MLPQ: A Dataset for Path Question Answering over Multilingual Knowledge Graphs","authors":"Yiming Tan , Yongrui Chen , Guilin Qi , Weizhuo Li , Meng Wang","doi":"10.1016/j.bdr.2023.100381","DOIUrl":"https://doi.org/10.1016/j.bdr.2023.100381","url":null,"abstract":"<div><p>Knowledge Graph-based Multilingual Question Answering (KG-MLQA), as one of the essential subtasks in Knowledge Graph-based Question Answering (KGQA), emphasizes that questions on the KGQA task can be expressed in different languages to solve the lexical gap between questions and knowledge graph(s). However, the existing KG-MLQA works mainly focus on the semantic parsing<span> of multilingual questions but ignore the questions that require integrating information from cross-lingual knowledge graphs (CLKG). This paper extends KG-MLQA to Cross-lingual KG-based multilingual Question Answering (CLKGQA) and constructs the first CLKGQA dataset over multilingual DBpedia named MLPQ, which contains 300K questions in English, Chinese, and French. We further propose a novel KG sampling algorithm<span> for KG construction, making the MLPQ support the research of different types of methods. To evaluate the dataset, we put forward a general question answering workflow whose core idea is to transform CLKGQA into KG-MLQA. We first use the Entity Alignment (EA) model to merge CLKG into a single KG and get the answer to the question by the Multi-hop QA model combined with the Multilingual pre-training model. By instantiating the above QA workflow, we establish two baseline models for MLPQ, one of which uses Google translation to obtain alignment entities, and the other adopts the recent EA model. Experiments show that the baseline models are insufficient to obtain the ideal performances on CLKGQA. Moreover, the availability of our benchmark contributes to the community of question answering and entity alignment.</span></span></p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"32 ","pages":"Article 100381"},"PeriodicalIF":3.3,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49729716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Spatiotemporal Prediction Based on Feature Classification for Multivariate Floating-Point Time Series Lossy Compression 基于特征分类的多变量浮点时间序列有损压缩时空预测

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2023-05-28 DOI: 10.1016/j.bdr.2023.100377

Huimin Feng , Ruizhe Ma , Li Yan , Zongmin Ma

{"title":"Spatiotemporal Prediction Based on Feature Classification for Multivariate Floating-Point Time Series Lossy Compression","authors":"Huimin Feng , Ruizhe Ma , Li Yan , Zongmin Ma","doi":"10.1016/j.bdr.2023.100377","DOIUrl":"https://doi.org/10.1016/j.bdr.2023.100377","url":null,"abstract":"<div><p><span>A large amount of time series is produced because of the frequent use of IoT<span> devices and sensors. Time series compression is widely adopted to reduce storage overhead<span> and transport costs. At present, most state-of-the-art approaches focus on univariate time series. Therefore, the task of compressing multivariate time series (MTS) is still an important but challenging problem. Traditional MTS compression methods treat each variable individually, ignoring the correlations across variables. This paper proposes a novel MTS prediction method, which can be applied to compress MTS to achieve a higher compression ratio. The method can extract the spatial and temporal correlation across multiple variables, achieving a more accurate prediction and improving the lossy </span></span></span>compression performance<span> of MTS based on the prediction-quantization-entropy framework. We use a convolutional neural network<span> (CNN) to extract the temporal features of all variables within the window length. Then the features generated by CNN are transformed, and the image classification algorithm extracts the spatial features of the transformed data. Predictions are made according to spatiotemporal characteristics. To enhance the robustness of our model, we integrate the AR autoregressive linear model in parallel with the proposed network. Experimental results demonstrate that our work can improve the prediction accuracy of MTS and the MTS compression performance in most cases.</span></span></p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"32 ","pages":"Article 100377"},"PeriodicalIF":3.3,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49713957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Meta-Learning Based Dynamic Adaptive Relation Learning for Few-Shot Knowledge Graph Completion 基于元学习的知识图补全动态自适应关系学习

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2023-05-01 DOI: 10.1016/j.bdr.2023.100394

Linqin Cai, Ling-Yong Wang, Rongdi Yuan, Tingjie Lai

引用次数: 1

Botnet DGA Domain Name Classification Using Transformer Network with Hybrid Embedding 基于混合嵌入变压器网络的僵尸网络DGA域名分类

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2023-05-01 DOI: 10.1016/j.bdr.2023.100395

Ling Ding, Peng Du, Hai-wei Hou, Jian Zhang, Di Jin, Shifei Ding

引用次数: 0

A Multi-View Filter for Relation-Free Knowledge Graph Completion 无关系知识图补全的多视图过滤器

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2023-05-01 DOI: 10.1016/j.bdr.2023.100397

Juan Li, Wen Zhang, Hongtao Yu

引用次数: 1

Task-Oriented Collaborative Graph Embedding Using Explicit High-Order Proximity for Recommendation 基于显式高阶接近度推荐的面向任务的协同图嵌入

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2023-04-01 DOI: 10.1016/j.bdr.2023.100382

Mintae Kim, Wooju Kim

引用次数: 0

What Is a Multi-Modal Knowledge Graph: A Survey 什么是多模态知识图谱:综述

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2023-03-01 DOI: 10.2139/ssrn.4229435

Jing-hui Peng, Xinyu Hu, Wenbo Huang, Jian Yang

引用次数: 2

Predicting Household Electric Power Consumption Using Multi-step Time Series with Convolutional LSTM 基于卷积LSTM的多步时间序列预测家庭用电量

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2023-02-28 DOI: 10.1016/j.bdr.2022.100360

Lucia Cascone , Saima Sadiq , Saleem Ullah , Seyedali Mirjalili , Hafeez Ur Rehman Siddiqui , Muhammad Umer

{"title":"Predicting Household Electric Power Consumption Using Multi-step Time Series with Convolutional LSTM","authors":"Lucia Cascone , Saima Sadiq , Saleem Ullah , Seyedali Mirjalili , Hafeez Ur Rehman Siddiqui , Muhammad Umer","doi":"10.1016/j.bdr.2022.100360","DOIUrl":"https://doi.org/10.1016/j.bdr.2022.100360","url":null,"abstract":"<div><p>Energy consumption prediction has become an integral part of a smart and sustainable environment. With future demand forecasts, energy production and distribution can be optimized to meet the needs of the growing population. However, forecasting the demand of individual households is a challenging task due to the diversity of energy consumption patterns. Recently, it has become popular with artificial intelligence-based smart energy-saving designs, smart grid planning and social Internet of Things (IoT) based smart homes. Despite existing approaches for energy demand forecast, predominantly, such systems are based on one-step forecasting and have a short forecasting period. For resolving this issue and obtain high prediction accuracy, this study follows the prediction of household appliances' power in two phases. In the first phase, a long short-term memory (LSTM) based model is used to predict total generative active power for the coming 500 hours. The second phase employs a hybrid deep learning model that combines convolutional characteristics of neural network with LSTM for household electrical energy consumption forecasting of the week ahead utilizing Social IoT-based smart meter readings. Experimental results reveal that the proposed convolutional LSTM (ConvLSTM) architecture outperforms other models with the lowest root mean square error value of 367 kilowatts for weekly household power consumption.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"31 ","pages":"Article 100360"},"PeriodicalIF":3.3,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49711519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

GeoYCSB: A Benchmark Framework for the Performance and Scalability Evaluation of Geospatial NoSQL Databases GeoYCSB:地理空间NoSQL数据库性能和可扩展性评估的基准框架

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2023-02-28 DOI: 10.1016/j.bdr.2023.100368

Suneuy Kim, Yvonne Hoang, Tsz Ting Yu, Yuvraj Singh Kanwar

{"title":"GeoYCSB: A Benchmark Framework for the Performance and Scalability Evaluation of Geospatial NoSQL Databases","authors":"Suneuy Kim, Yvonne Hoang, Tsz Ting Yu, Yuvraj Singh Kanwar","doi":"10.1016/j.bdr.2023.100368","DOIUrl":"https://doi.org/10.1016/j.bdr.2023.100368","url":null,"abstract":"<div><p>The proliferation of geospatial applications has tremendously increased the variety, velocity, and volume of spatial data that data stores have to manage. Traditional relational databases reveal limitations in handling such big geospatial data, mainly due to their rigid schema requirements and limited scalability. Numerous NoSQL databases have emerged and actively serve as alternative data stores for big spatial data.</p><p>This study presents a framework, called GeoYCSB, developed for benchmarking NoSQL databases with geospatial workloads. To develop GeoYCSB, we extend YCSB, a de facto benchmark framework for NoSQL systems, by integrating into its design architecture the new components necessary to support geospatial workloads. GeoYCSB supports both microbenchmarks and macrobenchmarks and facilitates the use of real datasets in both. It is extensible to evaluate any NoSQL database, provided they support spatial queries, using geospatial workloads performed on datasets of any geometric complexity. We use GeoYCSB to benchmark two leading document stores, MongoDB and Couchbase, and present the experimental results and analysis. Finally, we demonstrate the extensibility of GeoYCSB by including a new dataset consisting of complex geometries and using it to benchmark a system with a wide variety of geospatial queries: Apache Accumulo, a wide-column store, with the GeoMesa framework applied on top.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"31 ","pages":"Article 100368"},"PeriodicalIF":3.3,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49733847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2