Distributed and Parallel Databases最新文献_第2页

Multi-model query languages: taming the variety of big data 多模型查询语言：驯服各种大数据

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2023-05-31 DOI: 10.1007/s10619-023-07433-1

Qingsong Guo, Chao Zhang, Shuxun Zhang, Jiaheng Lu

引用次数: 1

On combining system and machine learning performance tuning for distributed data stream applications 分布式数据流应用的系统和机器学习性能调整

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2023-05-17 DOI: 10.1007/s10619-023-07434-0

Lambros Odysseos, H. Herodotou

引用次数: 0

GTraclus: a novel algorithm for local trajectory clustering on GPUs GTraclus:一种基于gpu的局部轨迹聚类算法

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2023-05-13 DOI: 10.1007/s10619-023-07429-x

Hamza Mustafa, Clark Barrus, Eleazar Leal, L. Gruenwald

引用次数: 0

Out-of-the-box library support for DBMS operations on GPUs 对GPU上DBMS操作的开箱即用库支持

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2023-05-10 DOI: 10.1007/s10619-023-07431-3

H. Subramanian, B. Gurumurthy, Gabriel Campero Durand, David Broneske, Gunter Saake

引用次数: 0

Novel insights on atomic synchronization for sort-based group-by on GPUs gpu上基于排序的分组原子同步的新见解

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2023-04-24 DOI: 10.1007/s10619-023-07424-2

B. Gurumurthy, David Broneske, Martin Schäler, Thilo Pionteck, Gunter Saake

引用次数: 0

STIF: Intuitionistic fuzzy Gaussian membership function with statistical transformation weight of evidence and information value for private information preservation. STIF：用于私人信息保存的具有证据和信息值的统计变换权重的直觉模糊高斯隶属函数。

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2023-04-21 DOI: 10.1007/s10619-023-07423-3

G Sathish Kumar, K Premalatha

{"title":"STIF: Intuitionistic fuzzy Gaussian membership function with statistical transformation weight of evidence and information value for private information preservation.","authors":"G Sathish Kumar, K Premalatha","doi":"10.1007/s10619-023-07423-3","DOIUrl":"10.1007/s10619-023-07423-3","url":null,"abstract":"Data sharing to the multiple organizations are essential for analysis in many situations. The shared data contains the individual's private and sensitive information and results in privacy breach. To overcome the privacy challenges, privacy preserving data mining (PPDM) has progressed as a solution. This work addresses the problem of PPDM by proposing statistical transformation with intuitionistic fuzzy (STIF) algorithm for data perturbation. The STIF algorithm contains statistical methods weight of evidence, information value and intuitionistic fuzzy Gaussian membership function. The STIF algorithm is applied on three benchmark datasets adult income, bank marketing and lung cancer. The classifier models decision tree, random forest, extreme gradient boost and support vector machines are used for accuracy and performance analysis. The results show that the STIF algorithm achieves 99% of accuracy for adult income dataset and 100% accuracy for both bank marketing and lung cancer datasets. Further, the results highlights that the STIF algorithm outperforms in data perturbation capacity and privacy preserving capacity than the state-of-art algorithms without any information loss on both numerical and categorical data.","PeriodicalId":50568,"journal":{"name":"Distributed and Parallel Databases","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10121075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10073193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 1

S3QLRDF: distributed SPARQL query processing using Apache Spark—a comparative performance study S3QLRDF:使用Apache spark的分布式SPARQL查询处理-性能比较研究

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2023-01-24 DOI: 10.1007/s10619-023-07422-4

Mahmudul Hassan, S. Bansal

引用次数: 2

Remote sensing imaging analysis and ubiquitous cloud-based mobile edge computing based intelligent forecast of forest tourism demand 基于遥感影像分析和无处不在的云移动边缘计算的森林旅游需求智能预测

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2023-01-01 DOI: 10.1007/s10619-021-07343-0

Rui Zhang, Jingran Zhang, Wukui Wang

引用次数: 0

Sentimental analysis from imbalanced code-mixed data using machine learning approaches. 使用机器学习方法对不平衡代码混合数据进行情感分析。

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2023-01-01 DOI: 10.1007/s10619-021-07331-4

R Srinivasan, C N Subalalitha

{"title":"Sentimental analysis from imbalanced code-mixed data using machine learning approaches.","authors":"R Srinivasan, C N Subalalitha","doi":"10.1007/s10619-021-07331-4","DOIUrl":"https://doi.org/10.1007/s10619-021-07331-4","url":null,"abstract":"Knowledge discovery from various perspectives has become a crucial asset in almost all fields. Sentimental analysis is a classification task used to classify the sentence based on the meaning of their context. This paper addresses class imbalance problem which is one of the important issues in sentimental analysis. Not much works focused on sentimental analysis with imbalanced class label distribution. The paper also focusses on another aspect of the problem which involves a concept called \"Code Mixing\". Code mixed data consists of text alternating between two or more languages. Class imbalance distribution is a commonly noted phenomenon in a code-mixed data. The existing works have focused more on analyzing the sentiments in a monolingual data but not in a code-mixed data. This paper addresses all these issues and comes up with a solution to analyze sentiments for a class imbalanced code-mixed data using sampling technique combined with levenshtein distance metrics. Furthermore, this paper compares the performances of various machine learning approaches namely, Random Forest Classifier, Logistic Regression, XGBoost classifier, Support Vector Machine and Naïve Bayes Classifier using F1- Score.","PeriodicalId":50568,"journal":{"name":"Distributed and Parallel Databases","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10619-021-07331-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10797693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 17

Challenges and future directions for energy, latency, and lifetime improvements in NVMs nvm在能量、延迟和寿命改进方面的挑战和未来方向

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2022-09-21 DOI: 10.1007/s10619-022-07421-x

Saeed Kargar, Faisal Nawab

引用次数: 7