Big Data Research最新文献_第10页

Efficiently Mining Colocation Patterns for Range Query 有效地挖掘范围查询的托管模式

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2023-02-28 DOI: 10.1016/j.bdr.2023.100369

Srikanth Baride , Anuj S. Saxena , Vikram Goyal

{"title":"Efficiently Mining Colocation Patterns for Range Query","authors":"Srikanth Baride , Anuj S. Saxena , Vikram Goyal","doi":"10.1016/j.bdr.2023.100369","DOIUrl":"https://doi.org/10.1016/j.bdr.2023.100369","url":null,"abstract":"<div><p>Colocation pattern mining finds a set of features whose instances frequently appear nearby in the same geographical space. Most of the existing algorithms for colocation patterns find nearby objects by a user-provided single-distance threshold. The value of the distance threshold is data specific and choosing a suitable distance for a user is not easy. In most real-world scenarios, it is rather meant to define spatial proximity by a distance range. It also provides flexibility to observe the change in the colocation patterns with distance and interprets the result better. Algorithms for mining colocations with a single distance threshold cannot be applied directly to the range of distances due to the computational overhead. We identify several structural properties of the collocation patterns and use them to propose an efficient single-pass colocation mining algorithm for distance range query, namely <span><math><mi>R</mi><mi>a</mi><mi>n</mi><mi>g</mi><mi>e</mi><mo>−</mo><mi>C</mi><mi>o</mi><mi>M</mi><mi>i</mi><mi>n</mi><mi>e</mi></math></span>. We compare the performance of the <span><math><mi>R</mi><mi>a</mi><mi>n</mi><mi>g</mi><mi>e</mi><mo>−</mo><mi>C</mi><mi>o</mi><mi>M</mi><mi>i</mi><mi>n</mi><mi>e</mi></math></span> with adapted versions of the famous Join-less colocation mining approach using both real-world and synthetic data sets and show that <span><math><mi>R</mi><mi>a</mi><mi>n</mi><mi>g</mi><mi>e</mi><mo>−</mo><mi>C</mi><mi>o</mi><mi>M</mi><mi>i</mi><mi>n</mi><mi>e</mi></math></span> outperforms the other algorithms.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"31 ","pages":"Article 100369"},"PeriodicalIF":3.3,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49733848","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

Accelerating Columnar Storage Based on Asynchronous Skipping Strategy 基于异步跳过策略的列式存储加速

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2023-02-28 DOI: 10.1016/j.bdr.2022.100352

Wenhai Li , Zheng Yang , Lingfeng Deng , Zhiling Cheng , Weidong Wen , Yanxiang He

{"title":"Accelerating Columnar Storage Based on Asynchronous Skipping Strategy","authors":"Wenhai Li , Zheng Yang , Lingfeng Deng , Zhiling Cheng , Weidong Wen , Yanxiang He","doi":"10.1016/j.bdr.2022.100352","DOIUrl":"https://doi.org/10.1016/j.bdr.2022.100352","url":null,"abstract":"<div><p>Many database applications, such as OnLine Analytical Processing (OLAP), web-based information extraction or scientific computation, need to select a subset of fields based on several user-defined filters. Developers of these applications require effective assembly methods for on-demand filtering and aggregation, which raises new challenges in deploying parallel computing components on top of columnar storage.</p><p>To efficiently generate qualified records, an asynchronous skipping strategy is presented to speed up filtering and decoding in the column-based storage. Concentrating on filtering-pushdown in parallel analytical workloads, we offer in-depth analysis on record assembly. We highlight the bottleneck of traditional record-wise assembling methods in the cases of evaluating analytical tasks on a nested schema. With a concurrent queue structure, an asynchronous skipping strategy is presented to evaluate column scan separately by a software pipeline involving an optionally different set of threads. We show how to intensively read the sequential blocks of each column, and how to effectively eliminate invalid payloads by integrating filtering-pushdown in an asynchronous I/O stack.</p><p>We implement a columnar storage supporting filtering-pushdown in nested schema. Our experiments are conducted on a de-facto standard benchmark using both variant-selectivity scans and ad-hoc queries. The results revealed that in parallel I/O-intensive workloads, our implementation improved the I/O performance of the state-of-the arts by 1.3X∼2.7X. Coupling the asynchronous strategy with filtering-pushdown, our implementation remarkably outperforms its competitors with heavyweight coding workloads on both HDD and SSD.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"31 ","pages":"Article 100352"},"PeriodicalIF":3.3,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49733846","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

Properties and Performance of the ABCDe Random Graph Model with Community Structure 具有群落结构的ABCDe随机图模型的性质与性能

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2022-11-28 DOI: 10.1016/j.bdr.2022.100348

Bogumił Kamiński , Tomasz Olczak , Bartosz Pankratz , Paweł Prałat , François Théberge

{"title":"Properties and Performance of the ABCDe Random Graph Model with Community Structure","authors":"Bogumił Kamiński , Tomasz Olczak , Bartosz Pankratz , Paweł Prałat , François Théberge","doi":"10.1016/j.bdr.2022.100348","DOIUrl":"https://doi.org/10.1016/j.bdr.2022.100348","url":null,"abstract":"<div><p>In this paper, we investigate properties and performance of synthetic random graph models with a built-in community structure. Such models are important for evaluating and tuning community detection algorithms that are unsupervised by nature. We propose <strong>ABCDe</strong>—a multi-threaded implementation of the <strong>ABCD</strong> (Artificial Benchmark for Community Detection) graph generator. We discuss the implementation details of the algorithm and compare it with both the previously available sequential version of the <strong>ABCD</strong> model and with the parallel implementation of the standard and extensively used <strong>LFR</strong> (Lancichinetti–Fortunato–Radicchi) generator. We show that <strong>ABCDe</strong> is more than ten times faster and scales better than the parallel implementation of <strong>LFR</strong> provided in <span>NetworKit</span>. Moreover, the algorithm is not only faster but random graphs generated by <strong>ABCD</strong> have similar properties to the ones generated by the original <strong>LFR</strong> algorithm, while the parallelized <span>NetworKit</span> implementation of <strong>LFR</strong> produces graphs that have noticeably different characteristics.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"30 ","pages":"Article 100348"},"PeriodicalIF":3.3,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214579622000429/pdfft?md5=5b249e2f347f9c9eeb348b655a88cf99&pid=1-s2.0-S2214579622000429-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91599233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Twig-Based Algorithm for Top-k Subgraph Matching in Large-Scale Graph Data 基于小枝的大规模图数据Top-k子图匹配算法

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2022-11-28 DOI: 10.1016/j.bdr.2022.100350

Haiwei Zhang , Qijie Bai , Yining Lian , Yanlong Wen

{"title":"A Twig-Based Algorithm for Top-k Subgraph Matching in Large-Scale Graph Data","authors":"Haiwei Zhang , Qijie Bai , Yining Lian , Yanlong Wen","doi":"10.1016/j.bdr.2022.100350","DOIUrl":"https://doi.org/10.1016/j.bdr.2022.100350","url":null,"abstract":"<div><p><span><span><span>Subgraph matching aims to find similar substructures in a single graph according to a given query graph and is known as a basic query for graph data management. There exist many categories of subgraph matching solutions. Subgraph isomorphism, which is thought of an NP-complete problem, is an initial solution for the subgraph matching task. To speed up the procedure, graph simulation has been presented to match subgraphs with a </span>polynomial complexity of time. Unfortunately, graph simulation usually loses topologies of matched subgraphs because of its loose restrictions. In this paper, we propose an </span>approximation approach named kSGM (top-</span><strong>k S</strong>ubraph <strong>G</strong>raph <strong>M</strong>atching) for subgraph matching based on twig patterns. First, we transform query graphs into twig patterns and match candidate substructures in graph data. Second, we present an optimized join strategy along with top-k mechanism, including join order selection based on cost evaluation and optimized pruning based on maximum/minimum possible score. Finally, we design experiments on real-life and synthetic graph data to evaluate the performance of our work. The results show that our proposed kSGM obviously reduces the time complexity and guarantee the correctness for answering the queries of subgraph matching compared to existing algorithms.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"30 ","pages":"Article 100350"},"PeriodicalIF":3.3,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136939468","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

Correlation Expert Tuning System for Performance Acceleration 性能加速相关专家调谐系统

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2022-11-28 DOI: 10.1016/j.bdr.2022.100345

Yanfeng Chai , Jiake Ge , Qiang Zhang , Yunpeng Chai , Xin Wang , Qingpeng Zhang

{"title":"Correlation Expert Tuning System for Performance Acceleration","authors":"Yanfeng Chai , Jiake Ge , Qiang Zhang , Yunpeng Chai , Xin Wang , Qingpeng Zhang","doi":"10.1016/j.bdr.2022.100345","DOIUrl":"10.1016/j.bdr.2022.100345","url":null,"abstract":"<div><p>One configuration can not fit all workloads and diverse resources limitations in modern databases. Auto-tuning methods based on reinforcement learning (RL) normally depend on the exhaustive offline training process with a huge amount of performance measurements, which includes large inefficient knobs combinations under a trial-and-error method. The most time-consuming part of the process is not the RL network training but the performance measurements for acquiring the reward values of target goals like higher throughput or lower latency. In other words, the whole process nearly could be considered as a zero-knowledge method without any experience or rules to constrain it. So we propose a correlation expert tuning system (CXTuning) for acceleration, which contains a correlation knowledge model to remove unnecessary training costs and a multi-instance mechanism (MIM) to support fine-grained tuning for diverse workloads. The models define the importance and correlations among these configuration knobs for the user's specified target. But knobs-based optimization should not be the final destination for auto-tuning. Furthermore, we import an abstracted architectural optimization method into CXTuning as a part of the progressive expert knowledge tuning (PEKT) algorithm. Experiments show that CXTuning can effectively reduce the training time and achieve extra performance promotion compared with the state-of-the-art auto-tuning method.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"30 ","pages":"Article 100345"},"PeriodicalIF":3.3,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214579622000399/pdfft?md5=959f53ff5a4e8dcd1c236afdbde633e4&pid=1-s2.0-S2214579622000399-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86236930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Intelligent Government Complaint Prediction Approach 一种智能政府投诉预测方法

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2022-11-28 DOI: 10.1016/j.bdr.2022.100336

Siqi Chen , Yanling Zhang , Bin Song , Xiaojiang Du , Mohsen Guizani

{"title":"An Intelligent Government Complaint Prediction Approach","authors":"Siqi Chen , Yanling Zhang , Bin Song , Xiaojiang Du , Mohsen Guizani","doi":"10.1016/j.bdr.2022.100336","DOIUrl":"10.1016/j.bdr.2022.100336","url":null,"abstract":"<div><p><span>Recent advances in machine learning<span> (ML) bring more opportunities for greater implementation of smart government construction. However, there are many challenges in terms of government data application due to the previous nonstandard records and man-made errors. In this paper, we propose a practical intelligent government complaint prediction (IGCP) framework that helps governments quickly respond to citizens' consultations and complaints via ML technologies<span>. In addition, we put forward an automatic label correction method and demonstrate its effectiveness on the performance improvement of intelligent government complaint prediction task. Specifically, the central server collects the interaction records from users and departments and automatically integrates them by the label correction approach which is designed to evaluate the similarity between different labels in data, and merge highly similar labels and corresponding samples into their most similar category. Based on those refined data, the central server quickly generates accurate solutions to complaints through text classification algorithms. The main innovation of our approach is that we turn the task of government complaint distribution into a text classification problem which is uniformly coordinated by the central server, and employ the label correction approach to correct redundant labels for training better models based on limited complaint records. To explore the influences of our approach, we evaluate its performance on real-world government service records provided by our collaborator. The experimental results demonstrate the prediction task which uses the label </span></span></span>correction algorithm achieves significant improvements on almost all metrics of the classifier.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"30 ","pages":"Article 100336"},"PeriodicalIF":3.3,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84865537","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

Data Stream Classification Based on Extreme Learning Machine: A Review 基于极限学习机的数据流分类研究综述

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2022-11-28 DOI: 10.1016/j.bdr.2022.100356

Xiulin Zheng , Peipei Li , Xindong Wu

{"title":"Data Stream Classification Based on Extreme Learning Machine: A Review","authors":"Xiulin Zheng , Peipei Li , Xindong Wu","doi":"10.1016/j.bdr.2022.100356","DOIUrl":"https://doi.org/10.1016/j.bdr.2022.100356","url":null,"abstract":"<div><p>Many daily applications are generating massive amount of data in the form of stream at an ever higher speed, such as medical data, clicking stream, internet record and banking transaction, etc. In contrast to the traditional static data, data streams are of some inherent properties, to name a few, infinite length, concept drift, multiple labels and concept evolution. Among all the data mining tasks<span><span>, classification is one of the basic topics in data stream mining and has gained more and more attentions among different research communities. Extreme Learning Machine<span> (ELM) has drawn much interests in data classification due to its high efficiency, universal approximation capability, </span></span>generalization ability<span>, and simplicity, which have greatly inspired the development of many ELM-based algorithms and their applications during the past decades. In this paper, we mainly provide a comprehensive review on ELM theoretical research and its variants in data stream classification, and categorize these algorithms from different perspectives. Firstly, we briefly introduce the basic principles of ELM and its characteristics. Secondly, we give an overview of different ELM variants to address the particular issues of data stream classification. Thirdly, we present an overview of different strategies to optimize the ELM, which have further improved the stability, accuracy and generalization ability of ELM, and briefly introduce some practical applications of ELM in data stream classification. Finally, we conduct several groups of experiments to compare the performance of ELM based models addressing the focused issues. Also, the open issues and prospects of ELM models used for stream classification are discussed, which are worthwhile to be further studied in the future.</span></span></p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"30 ","pages":"Article 100356"},"PeriodicalIF":3.3,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91599167","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: 3

Augmented Functional Analysis of Variance (A-fANOVA): Theory and Application to Google Trends for Detecting Differences in Abortion Drugs Queries 增强功能方差分析(A-fANOVA):谷歌趋势检测流产药物查询差异的理论与应用

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2022-11-28 DOI: 10.1016/j.bdr.2022.100354

Fabrizio Maturo , Annamaria Porreca

{"title":"Augmented Functional Analysis of Variance (A-fANOVA): Theory and Application to Google Trends for Detecting Differences in Abortion Drugs Queries","authors":"Fabrizio Maturo , Annamaria Porreca","doi":"10.1016/j.bdr.2022.100354","DOIUrl":"https://doi.org/10.1016/j.bdr.2022.100354","url":null,"abstract":"<div><p>The World Wide Web (WWW) has become a popular and readily accessible big data source in recent decades. The information in the WWW is offered in many different types, e.g. Google Trends, which provides deep insights into people's search queries in the Google Search engine. Analysing this kind of data is not straightforward because they usually take the form of high-dimensional data, given that the latter can be collected over extensive periods. Comparing Google Trends' means of different groups of people or Countries can help understand many phenomena and provide very appealing insights into populations' interests in specific periods and areas. However, appropriate statistical techniques should be adopted when inspecting and testing differences in such data due to the well-known curse of dimensionality. This paper suggests an original approach to dealing with Google Trends by concentrating on the search for the “<em>Cytotec</em><span>” abortion drug. The final purpose of the application is to determine if different Countries' abortion legislation can influence the research trends. This research focuses on Functional Data Analysis (FDA) to deal with high-dimensional data and proposes a generalisation of the classical functional analysis of variance model, namely the Augmented Functional Analysis of Variance (A-fANOVA). To test the existence of statistically significant differences among groups of Countries, A-fANOVA considers additional curves' characteristics provided by the velocity and acceleration of the original google queries over time. The proposed methodology appears to be intriguing for capturing additional information about curves' behaviours with the final aim of offering a monitoring tool for policy-makers.</span></p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"30 ","pages":"Article 100354"},"PeriodicalIF":3.3,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91599230","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: 4

An Embedding Model for Knowledge Graph Completion Based on Graph Sub-Hop Convolutional Network 基于图子跳卷积网络的知识图补全嵌入模型

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2022-11-28 DOI: 10.1016/j.bdr.2022.100351

Haitao He , Haoran Niu , Jianzhou Feng , Junlan Nie , Yangsen Zhang , Jiadong Ren

{"title":"An Embedding Model for Knowledge Graph Completion Based on Graph Sub-Hop Convolutional Network","authors":"Haitao He , Haoran Niu , Jianzhou Feng , Junlan Nie , Yangsen Zhang , Jiadong Ren","doi":"10.1016/j.bdr.2022.100351","DOIUrl":"https://doi.org/10.1016/j.bdr.2022.100351","url":null,"abstract":"<div><p>The research on knowledge graph completion based on representation learning<span><span> is increasingly dependent on the node structural feature in the graph. However, a large number of nodes have few immediate neighbors, resulting in the node features unable to be fully expressed. Hence, multi-hop structure features are crucial to the representation learning of nodes. GCN (Graph Convolutional Network) is a graph embedding model that can introduce the multi-hop structure. However, the multi-hop information transmitted between GCN layers suffers a lot of losses. This would lead to the insufficient mining of the node structure features and semantic feature association among entities, further reducing the efficiency of graph knowledge completion. A gate-controlled graph sub-hop </span>convolutional network<span> model for knowledge graph completion is proposed to fill these research gaps. Firstly, a graph sub-hop convolutional network based on matrix representation is designed, which can transmit multi-hop neighbor features directly to the encoded node vector to avoid a large loss of features during multi-hop transmission. On this basis, the implicit multi-hop relations are explicitly embedded into the model based on the TransE. In the process of each hop convolution, aiming at the accumulation of noise redundancy caused by the increase of the receptive field, a sub-hop gate mechanism strategy is proposed to filter information. Finally, the linear model is used to decode the encoded nodes and then complete the knowledge graph. We carried out experimental comparison and analysis on WN18RR, FB15k-237, UMLS, and KINSHIP datasets. The results show that the embedding method based on the sub-hop structural information fusion can greatly improve the results of link prediction.</span></span></p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"30 ","pages":"Article 100351"},"PeriodicalIF":3.3,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91599231","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

A Facial Expression Recognition Approach for Social IoT Frameworks 基于社交物联网框架的面部表情识别方法

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2022-11-28 DOI: 10.1016/j.bdr.2022.100353

Silvio Barra , Sanoar Hossain , Chiara Pero , Saiyed Umer

{"title":"A Facial Expression Recognition Approach for Social IoT Frameworks","authors":"Silvio Barra , Sanoar Hossain , Chiara Pero , Saiyed Umer","doi":"10.1016/j.bdr.2022.100353","DOIUrl":"10.1016/j.bdr.2022.100353","url":null,"abstract":"<div><p>Social IoT<span> has become a sensitive topic in the last years, mainly due to the attraction of social networks and the related digital activities amongst the population. These techniques are gaining even more importance in the current period, in which digital tools are the only ones allowed to maintain social distancing due to the COVID-19 restrictions. In order to aid patients and elderly people in-home healthcare context, this article explores the usage of facial patient images and emotional detection. In this regard, a Social IoT approach is proposed, which is based on a camera connected home, allowing medical examinations at a distance by keeping posted the preferred contacts of the patient. A facial expression analysis is done to infer the patient's emotional state, thus communicating to the doctor and the emergency contacts any change in the patient's state (pain, suffering, etc.). The proposed facial expression recognition system consists of three main steps: during the image preprocessing phase<span>, face detection and normalization are performed; the feature extraction process involves the computation of discriminative patterns using the Spatial Pyramid Technique; finally, an expression recognition model is built using a multi-class linear Support Vector Machine classifier. The performance of the proposed system has been tested on two challenging benchmarks for facial expression recognition, namely KDEF and GENKI-4K, which show that the proposed system overcomes state-of-the-art methods.</span></span></p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"30 ","pages":"Article 100353"},"PeriodicalIF":3.3,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81811615","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: 4