Distributed and Parallel Databases最新文献_第2页

Virtual machines pre-copy live migration cost modeling and prediction: a survey 虚拟机预复制实时迁移成本建模和预测:一项调查

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2022-09-01 DOI: 10.1007/s10619-021-07387-2

M. E. Elsaid, Hazem M. Abbas, C. Meinel

引用次数: 5

Four node graphlet and triad enumeration on distributed platforms 分布式平台上的四节点石墨烯和三元枚举

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2022-07-30 DOI: 10.1007/s10619-022-07416-8

Yudi Santoso, Xiaozhou Liu, Venkatesh Srinivasan, Alex Thomo

引用次数: 0

Recursive SQL and GPU-support for in-database machine learning 数据库内机器学习的递归SQL和gpu支持

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2022-07-09 DOI: 10.1007/s10619-022-07417-7

Maximilian E. Schüle, Harald Lang, M. Springer, A. Kemper, Thomas Neumann, Stephan Günnemann

引用次数: 3

Searching semantically diverse paths 搜索语义多样的路径

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2022-06-15 DOI: 10.1007/s10619-022-07413-x

Xu Teng, Goce Trajcevski, Andreas Züfle

引用次数: 0

BBoxDB streams: scalable processing of multi-dimensional data streams BBoxDB流:可扩展处理多维数据流

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2022-05-02 DOI: 10.1007/s10619-022-07408-8

Jan Kristof Nidzwetzki, R. H. Güting

引用次数: 2

DOE: database offloading engine for accelerating SQL processing DOE:加速SQL处理的数据库卸载引擎

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2022-05-01 DOI: 10.1007/s10619-023-07427-z

Wenyan Lu, Yan Chen, Jingya Wu, Yu Zhang, Xiaowei Li, Guihai Yan

引用次数: 2

MICAR: multi-inhabitant context-aware activity recognition in home environments. MICAR:家庭环境中多居民情境感知活动识别

IF 1.5 4区计算机科学

Distributed and Parallel Databases Pub Date : 2022-04-05 DOI: 10.1007/s10619-022-07403-z

Luca Arrotta, Claudio Bettini, Gabriele Civitarese

{"title":"MICAR: multi-inhabitant context-aware activity recognition in home environments.","authors":"Luca Arrotta, Claudio Bettini, Gabriele Civitarese","doi":"10.1007/s10619-022-07403-z","DOIUrl":"10.1007/s10619-022-07403-z","url":null,"abstract":"The sensor-based recognition of Activities of Daily Living (ADLs) in smart-home environments enables several important applications, including the continuous monitoring of fragile subjects in their homes for healthcare systems. The majority of the approaches in the literature assume that only one resident is living in the home. Multi-inhabitant ADLs recognition is significantly more challenging, and only a limited effort has been devoted to address this setting by the research community. One of the major open problems is called data association, which is correctly associating each environmental sensor event (e.g., the opening of a fridge door) with the inhabitant that actually triggered it. Moreover, existing multi-inhabitant approaches rely on supervised learning, assuming a high availability of labeled data. However, collecting a comprehensive training set of ADLs (especially in multiple-residents settings) is prohibitive. In this work, we propose MICAR: a novel multi-inhabitant ADLs recognition approach that combines semi-supervised learning and knowledge-based reasoning. Data association is performed by semantic reasoning, combining high-level context information (e.g., residents' postures and semantic locations) with triggered sensor events. The personalized stream of sensor events is processed by an incremental classifier, that is initialized with a limited amount of labeled ADLs. A novel cache-based active learning strategy is adopted to continuously improve the classifier. Our results on a dataset where up to 4 subjects perform ADLs at the same time show that MICAR reliably recognizes individual and joint activities while triggering a significantly low number of active learning queries.","PeriodicalId":50568,"journal":{"name":"Distributed and Parallel Databases","volume":"1 1","pages":"1-32"},"PeriodicalIF":1.5,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8980210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48545332","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: 0

Scalable probabilistic truss decomposition using central limit theorem and H-index. 基于中心极限定理和h指数的可伸缩概率桁架分解。

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2022-01-01 Epub Date: 2022-07-25 DOI: 10.1007/s10619-022-07415-9

Fatemeh Esfahani, Mahsa Daneshmand, Venkatesh Srinivasan, Alex Thomo, Kui Wu

{"title":"Scalable probabilistic truss decomposition using central limit theorem and H-index.","authors":"Fatemeh Esfahani, Mahsa Daneshmand, Venkatesh Srinivasan, Alex Thomo, Kui Wu","doi":"10.1007/s10619-022-07415-9","DOIUrl":"https://doi.org/10.1007/s10619-022-07415-9","url":null,"abstract":"Truss decomposition is a popular notion of hierarchical dense substructures in graphs. In a nutshell, k-truss is the largest subgraph in which every edge is contained in at least k triangles. Truss decomposition aims to compute k-trusses for each possible value of k. There are many works that study truss decomposition in deterministic graphs. However, in probabilistic graphs, truss decomposition is significantly more challenging and has received much less attention; state-of-the-art approaches do not scale well to large probabilistic graphs. Finding the tail probabilities of the number of triangles that contain each edge is a critical challenge of those approaches. This is achieved using dynamic programming which has quadratic run-time and thus not scalable to real large networks which, quite commonly, can have edges contained in many triangles (in the millions). To address this challenge, we employ a special version of the Central Limit Theorem (CLT) to obtain the tail probabilities efficiently. Based on our CLT approach we propose a peeling algorithm for truss decomposition that scales to large probabilistic graphs and offers significant improvement over state-of-the-art. We also design a second method which progressively tightens the estimate of the truss value of each edge and is based on h-index computation. In contrast to our CLT-based approach, our h-index algorithm (1) is progressive by allowing the user to see near-results along the way, (2) does not sacrifice the exactness of final result, and (3) achieves all these while processing only one edge and its immediate neighbors at a time, thus resulting in smaller memory footprint. We perform extensive experiments to show the scalability of both of our proposed algorithms.","PeriodicalId":50568,"journal":{"name":"Distributed and Parallel Databases","volume":" ","pages":"299-333"},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9310023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40659776","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

RETRACTED ARTICLE: Application of machine learning (ML) and internet of things (IoT) in healthcare to predict and tackle pandemic situation. 机器学习(ML)和物联网(IoT)在医疗保健中的应用，以预测和应对流行病情况。

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2022-01-01 Epub Date: 2021-08-07 DOI: 10.1007/s10619-021-07358-7

R Sitharthan, M Rajesh

引用次数: 6

MISS: finding optimal sample sizes for approximate analytics MISS:为近似分析找到最佳样本量

IF 1.2 4区计算机科学

Distributed and Parallel Databases Pub Date : 2021-10-21 DOI: 10.1007/s10619-021-07376-5

Xuebi Su, Hongzhi Wang

引用次数: 0