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SUMA: A Partial Materialization-Based Scalable Query Answering in OWL 2 DL SUMA: OWL 2dl中基于部分实体化的可扩展查询应答
IF 4.2 2区 计算机科学
Data Science and Engineering Pub Date : 2021-01-19 DOI: 10.1007/s41019-020-00150-0
Xiaoyu Qin, Xiaowang Zhang, Muhammad Qasim Yasin, Shujun Wang, Zhiyong Feng, Guohui Xiao
{"title":"SUMA: A Partial Materialization-Based Scalable Query Answering in OWL 2 DL","authors":"Xiaoyu Qin, Xiaowang Zhang, Muhammad Qasim Yasin, Shujun Wang, Zhiyong Feng, Guohui Xiao","doi":"10.1007/s41019-020-00150-0","DOIUrl":"https://doi.org/10.1007/s41019-020-00150-0","url":null,"abstract":"","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"86 1","pages":"229 - 245"},"PeriodicalIF":4.2,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77103071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
A Survey on Advancing the DBMS Query Optimizer: Cardinality Estimation, Cost Model, and Plan Enumeration 改进DBMS查询优化器的综述:基数估计、成本模型和计划枚举
IF 4.2 2区 计算机科学
Data Science and Engineering Pub Date : 2021-01-05 DOI: 10.1007/s41019-020-00149-7
Hai Lan, Z. Bao, Yuwei Peng
{"title":"A Survey on Advancing the DBMS Query Optimizer: Cardinality Estimation, Cost Model, and Plan Enumeration","authors":"Hai Lan, Z. Bao, Yuwei Peng","doi":"10.1007/s41019-020-00149-7","DOIUrl":"https://doi.org/10.1007/s41019-020-00149-7","url":null,"abstract":"","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"265 1","pages":"86 - 101"},"PeriodicalIF":4.2,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76537659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 49
Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM. 基于CNN和BiLSTM的推文地理位置预测
IF 4.2 2区 计算机科学
Data Science and Engineering Pub Date : 2021-01-01 Epub Date: 2021-07-08 DOI: 10.1007/s41019-021-00165-1
Rhea Mahajan, Vibhakar Mansotra
{"title":"Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM.","authors":"Rhea Mahajan,&nbsp;Vibhakar Mansotra","doi":"10.1007/s41019-021-00165-1","DOIUrl":"https://doi.org/10.1007/s41019-021-00165-1","url":null,"abstract":"<p><p>Twitter is one of the most popular micro-blogging and social networking platforms where users post their opinions, preferences, activities, thoughts, views, etc., in form of tweets within the limit of 280 characters. In order to study and analyse the social behavior and activities of a user across a region, it becomes necessary to identify the location of the tweet. This paper aims to predict geolocation of real-time tweets at the city level collected for a period of 30 days by using a combination of convolutional neural network and a bidirectional long short-term memory by extracting features within the tweets and features associated with the tweets. We have also compared our results with previous baseline models and the findings of our experiment show a significant improvement over baselines methods achieving an accuracy of 92.6 with a median error of 22.4 km at city level prediction.</p>","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"6 4","pages":"402-410"},"PeriodicalIF":4.2,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41019-021-00165-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39178111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 13
Image Preprocessing in Classification and Identification of Diabetic Eye Diseases. 图像预处理在糖尿病眼病分类与识别中的应用。
IF 4.2 2区 计算机科学
Data Science and Engineering Pub Date : 2021-01-01 Epub Date: 2021-08-17 DOI: 10.1007/s41019-021-00167-z
Rubina Sarki, Khandakar Ahmed, Hua Wang, Yanchun Zhang, Jiangang Ma, Kate Wang
{"title":"Image Preprocessing in Classification and Identification of Diabetic Eye Diseases.","authors":"Rubina Sarki,&nbsp;Khandakar Ahmed,&nbsp;Hua Wang,&nbsp;Yanchun Zhang,&nbsp;Jiangang Ma,&nbsp;Kate Wang","doi":"10.1007/s41019-021-00167-z","DOIUrl":"https://doi.org/10.1007/s41019-021-00167-z","url":null,"abstract":"<p><p>Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model's development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.</p>","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"6 4","pages":"455-471"},"PeriodicalIF":4.2,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41019-021-00167-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39335652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 38
Exploring Means to Enhance the Efficiency of GPU Bitmap Index Query Processing 探索提高GPU位图索引查询处理效率的方法
IF 4.2 2区 计算机科学
Data Science and Engineering Pub Date : 2020-11-30 DOI: 10.1007/s41019-020-00148-8
Brandon Tran, Brennan Schaffner, J. Myre, Jason Sawin, David Chiu
{"title":"Exploring Means to Enhance the Efficiency of GPU Bitmap Index Query Processing","authors":"Brandon Tran, Brennan Schaffner, J. Myre, Jason Sawin, David Chiu","doi":"10.1007/s41019-020-00148-8","DOIUrl":"https://doi.org/10.1007/s41019-020-00148-8","url":null,"abstract":"","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"160 1","pages":"209 - 228"},"PeriodicalIF":4.2,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88292955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
How Good Are Modern Spatial Libraries? 现代空间图书馆有多好?
IF 4.2 2区 计算机科学
Data Science and Engineering Pub Date : 2020-11-07 DOI: 10.1007/s41019-020-00147-9
Varun Pandey, Alexander van Renen, Andreas Kipf, A. Kemper
{"title":"How Good Are Modern Spatial Libraries?","authors":"Varun Pandey, Alexander van Renen, Andreas Kipf, A. Kemper","doi":"10.1007/s41019-020-00147-9","DOIUrl":"https://doi.org/10.1007/s41019-020-00147-9","url":null,"abstract":"","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"61 1","pages":"192 - 208"},"PeriodicalIF":4.2,"publicationDate":"2020-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76685689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 13
Blocking Techniques for Entity Linkage: A Semantics-Based Approach 实体链接的阻塞技术:基于语义的方法
IF 4.2 2区 计算机科学
Data Science and Engineering Pub Date : 2020-11-03 DOI: 10.1007/s41019-020-00146-w
Fabio Azzalini, Songle Jin, Marco Renzi, L. Tanca
{"title":"Blocking Techniques for Entity Linkage: A Semantics-Based Approach","authors":"Fabio Azzalini, Songle Jin, Marco Renzi, L. Tanca","doi":"10.1007/s41019-020-00146-w","DOIUrl":"https://doi.org/10.1007/s41019-020-00146-w","url":null,"abstract":"","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"6 1","pages":"20 - 38"},"PeriodicalIF":4.2,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76423621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 15
Parrot: A Progressive Analysis System on Large Text Collections Parrot:大型文本集的渐进式分析系统
IF 4.2 2区 计算机科学
Data Science and Engineering Pub Date : 2020-10-22 DOI: 10.1007/s41019-020-00144-y
Yazhong Zhang, Hanbing Zhang, Zhenying He, Yinan Jing, Kai Zhang, X. S. Wang
{"title":"Parrot: A Progressive Analysis System on Large Text Collections","authors":"Yazhong Zhang, Hanbing Zhang, Zhenying He, Yinan Jing, Kai Zhang, X. S. Wang","doi":"10.1007/s41019-020-00144-y","DOIUrl":"https://doi.org/10.1007/s41019-020-00144-y","url":null,"abstract":"","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"5 1","pages":"1 - 19"},"PeriodicalIF":4.2,"publicationDate":"2020-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82929566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Heterogeneous CPU-GPU Epsilon Grid Joins: Static and Dynamic Work Partitioning Strategies 异构CPU-GPU Epsilon网格连接:静态和动态工作分区策略
IF 4.2 2区 计算机科学
Data Science and Engineering Pub Date : 2020-10-21 DOI: 10.1007/s41019-020-00145-x
Benoît Gallet, M. Gowanlock
{"title":"Heterogeneous CPU-GPU Epsilon Grid Joins: Static and Dynamic Work Partitioning Strategies","authors":"Benoît Gallet, M. Gowanlock","doi":"10.1007/s41019-020-00145-x","DOIUrl":"https://doi.org/10.1007/s41019-020-00145-x","url":null,"abstract":"","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"82 1","pages":"39 - 62"},"PeriodicalIF":4.2,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81969588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 5
AGTR: Adversarial Generation of Target Review for Rating Prediction AGTR:评级预测目标评论的对抗性生成
IF 4.2 2区 计算机科学
Data Science and Engineering Pub Date : 2020-09-17 DOI: 10.1007/s41019-020-00141-1
Huilin Yu, T. Qian, Yile Liang, Bing Liu
{"title":"AGTR: Adversarial Generation of Target Review for Rating Prediction","authors":"Huilin Yu, T. Qian, Yile Liang, Bing Liu","doi":"10.1007/s41019-020-00141-1","DOIUrl":"https://doi.org/10.1007/s41019-020-00141-1","url":null,"abstract":"","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"13 1","pages":"346 - 359"},"PeriodicalIF":4.2,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73460696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 9
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