Pub Date : 2021-03-31DOI: 10.1007/s41019-021-00154-4
Jianye Yang, Wu Yao, Wenjie Zhang
{"title":"Keyword Search on Large Graphs: A Survey","authors":"Jianye Yang, Wu Yao, Wenjie Zhang","doi":"10.1007/s41019-021-00154-4","DOIUrl":"https://doi.org/10.1007/s41019-021-00154-4","url":null,"abstract":"","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87848322","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}
Pub Date : 2021-01-23DOI: 10.1007/s41019-020-00151-z
Haitao Yuan, Guoliang Li
{"title":"A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation","authors":"Haitao Yuan, Guoliang Li","doi":"10.1007/s41019-020-00151-z","DOIUrl":"https://doi.org/10.1007/s41019-020-00151-z","url":null,"abstract":"","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2021-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76395104","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}
Pub Date : 2021-01-22DOI: 10.1007/s41019-021-00152-6
G. Ulm, Simon Smith, Adrian Nilsson, E. Gustavsson, M. Jirstrand
{"title":"OODIDA: On-Board/Off-Board Distributed Real-Time Data Analytics for Connected Vehicles","authors":"G. Ulm, Simon Smith, Adrian Nilsson, E. Gustavsson, M. Jirstrand","doi":"10.1007/s41019-021-00152-6","DOIUrl":"https://doi.org/10.1007/s41019-021-00152-6","url":null,"abstract":"","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76276966","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}
Pub Date : 2021-01-05DOI: 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":null,"pages":null},"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}
Pub Date : 2021-01-01Epub Date: 2021-07-08DOI: 10.1007/s41019-021-00165-1
Rhea Mahajan, Vibhakar Mansotra
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.
{"title":"Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM.","authors":"Rhea Mahajan, 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":null,"pages":null},"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}
Pub Date : 2021-01-01Epub Date: 2021-08-17DOI: 10.1007/s41019-021-00167-z
Rubina Sarki, Khandakar Ahmed, Hua Wang, Yanchun Zhang, Jiangang Ma, Kate Wang
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.
{"title":"Image Preprocessing in Classification and Identification of Diabetic Eye Diseases.","authors":"Rubina Sarki, Khandakar Ahmed, Hua Wang, Yanchun Zhang, Jiangang Ma, 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":null,"pages":null},"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}
Pub Date : 2020-11-30DOI: 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":null,"pages":null},"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}
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