標(biāo)題: Titlebook: Big Data – BigData 2018; 7th International Co Francis Y. L. Chin,C. L. Philip Chen,Liang-Jie Zha Conference proceedings 2018 Springer Inter [打印本頁(yè)] 作者: CANTO 時(shí)間: 2025-3-21 19:03
書(shū)目名稱(chēng)Big Data – BigData 2018影響因子(影響力)
書(shū)目名稱(chēng)Big Data – BigData 2018影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Big Data – BigData 2018網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Big Data – BigData 2018網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Big Data – BigData 2018被引頻次
書(shū)目名稱(chēng)Big Data – BigData 2018被引頻次學(xué)科排名
書(shū)目名稱(chēng)Big Data – BigData 2018年度引用
書(shū)目名稱(chēng)Big Data – BigData 2018年度引用學(xué)科排名
書(shū)目名稱(chēng)Big Data – BigData 2018讀者反饋
書(shū)目名稱(chēng)Big Data – BigData 2018讀者反饋學(xué)科排名
作者: 凈禮 時(shí)間: 2025-3-21 22:54
Forecasting Traffic Flow: Short Term, Long Term, and When It Rainsn data into forecasting models to better predict traffic flow in rainy weather is also conducted. Dynamic regression models and neural networks are used in this experiment. In both experiments, neural networks outperformed the others overall.作者: 向外 時(shí)間: 2025-3-22 04:24 作者: 搏斗 時(shí)間: 2025-3-22 04:48 作者: BRAND 時(shí)間: 2025-3-22 09:11 作者: COWER 時(shí)間: 2025-3-22 14:04 作者: Herd-Immunity 時(shí)間: 2025-3-22 20:58
https://doi.org/10.1007/978-3-319-62797-7 nine individual cities in the percentage of positive, negative, and neutral statements, but however, there were significant differences in overall statements, where up 47.88% of all the statements were neutral, positive statements only 14.95%, while 37.16% of the statements were negative.作者: 執(zhí)拗 時(shí)間: 2025-3-22 23:09
Real-Time Analysis of Big Network Packet Streams by Learning the Likelihood of Trusted Sequencesthis paper includes (1) real-time packet data analysis, (2) learning the likelihood of trusted and untrusted packet sequences, and (3) improvement of adaptive detection against previous unknown intrusive attacks.作者: 刺穿 時(shí)間: 2025-3-23 02:03
PAGE: Answering Graph Pattern Queries via Knowledge Graph Embeddingetric that enables us to compute the plausibility of an answer. In evaluations with two popular knowledge graphs, Freebase and NELL, . demonstrated the performance increase by up?to 28% compared to baseline KGE methods.作者: prediabetes 時(shí)間: 2025-3-23 08:17 作者: 拋棄的貨物 時(shí)間: 2025-3-23 13:09
0302-9743 June 2018..The 22 full papers together with 10 short papers published in this volume were carefully reviewed and selected from 97 submissions. They are organized in topical sections such as?Data analysis, data as a service, services computing, data conversion, data storage, data centers, dataflow a作者: 同謀 時(shí)間: 2025-3-23 15:16 作者: 閑聊 時(shí)間: 2025-3-23 21:33 作者: HERE 時(shí)間: 2025-3-24 02:14
Conference proceedings 2018..The 22 full papers together with 10 short papers published in this volume were carefully reviewed and selected from 97 submissions. They are organized in topical sections such as?Data analysis, data as a service, services computing, data conversion, data storage, data centers, dataflow architectur作者: cylinder 時(shí)間: 2025-3-24 05:41 作者: Customary 時(shí)間: 2025-3-24 08:01
Big Data – BigData 2018978-3-319-94301-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 嫌惡 時(shí)間: 2025-3-24 13:19
https://doi.org/10.1007/978-3-319-94301-5Data analysis; Services computing; Data conversion; Data storage; Data centers; Dataflow architectures; Da作者: Obituary 時(shí)間: 2025-3-24 14:59
978-3-319-94300-8Springer International Publishing AG, part of Springer Nature 2018作者: Notorious 時(shí)間: 2025-3-24 21:31 作者: FLING 時(shí)間: 2025-3-25 00:49 作者: 紋章 時(shí)間: 2025-3-25 07:17 作者: labyrinth 時(shí)間: 2025-3-25 11:25
https://doi.org/10.1007/978-3-030-11671-2ed according to the layered network structure. DPI is performed against overwhelming network packet streams. By nature, network packet data is big data of real-time streaming. The DPI big data analysis, however are extremely expensive, likely to generate false positives, and less adaptive to previou作者: extemporaneous 時(shí)間: 2025-3-25 11:54 作者: GENRE 時(shí)間: 2025-3-25 18:57 作者: Microaneurysm 時(shí)間: 2025-3-25 20:34
Inverse Problems for Parabolic Equationse graphs include incorrect or incomplete information. In this paper, we present a method called . that answers graph pattern queries via knowledge graph embedding methods. . computes the energy (or uncertainty) of candidate answers with the learned embeddings and chooses the lower-energy candidates 作者: 不要嚴(yán)酷 時(shí)間: 2025-3-26 01:16
Inverse Problems for Parabolic Equationsas been designed and implemented which employs distributed blob store, custom compression, and custom query algorithm, including filtering, joins and group by. The system has been in operation at eBay for years and is described in [.]. However, large scale ingestion of data to a distributed blob sto作者: 聯(lián)合 時(shí)間: 2025-3-26 07:15 作者: chemical-peel 時(shí)間: 2025-3-26 11:37
Inverse Problems for Hyperbolic Equationsdimensional driving mechanisms and apply the behavioral and structural features to forward prediction. Firstly, by considering the effect of behavioral interest, user activity and network influence, we propose three driving mechanisms: interest-driven, habit-driven and structure-driven. Secondly, by作者: Bravura 時(shí)間: 2025-3-26 14:25 作者: 紅腫 時(shí)間: 2025-3-26 16:53
Inverse Problems for Parabolic Equationso the data warehouse through ., summary data become stale, unless the refresh of summary data is characterized by an expensive cost. The challenge gets even worst when near . are considered, even with respect to emerging .. In this paper, inspired by the well-known ., we introduce ., making use of s作者: Misgiving 時(shí)間: 2025-3-26 21:07 作者: 愛(ài)哭 時(shí)間: 2025-3-27 04:46
Biological Effects of Ion Implantation,p cluster that can effectively replicate and provide an environment for developers to easily design and implement the Spark and Hadoop Map/Reduce programming. Before running their Big Data and deep learning applications in physical multi-node Spark and Hadoop Cluster, developers can conduct Map/Redu作者: GOAT 時(shí)間: 2025-3-27 06:33 作者: 溫順 時(shí)間: 2025-3-27 11:30
https://doi.org/10.1007/b135662n petabytes of data. Thus, new technologies and approaches are needed that can efficiently perform complex and time-consuming data analytics without having to rely on expensive super machines..This paper discusses how a distributed machine learning system can be created to efficiently perform Big Da作者: Nebulizer 時(shí)間: 2025-3-27 16:15 作者: Recess 時(shí)間: 2025-3-27 21:47
Inter-Category Distribution Enhanced Feature Extraction for Efficient Text Classificationthe quality of feature extraction over the text corpus. For supervised learning over text documents, the TF-IDF (Term Frequency-Inverse Document Frequency) weighting factor is one of the most frequently used features in text classification. In this paper, we address two known limitations of TF-IDF b作者: ACRID 時(shí)間: 2025-3-28 00:25
Reversible Data Perturbation Techniques for Multi-level Privacy-Preserving Data Publicationprivacy through data perturbation provide a safe release of datasets such that sensitive information present in the dataset cannot be inferred from the published data. Existing privacy-preserving data publishing solutions have focused on publishing a single snapshot of the data with the assumption t作者: Charlatan 時(shí)間: 2025-3-28 03:12 作者: 放肆的你 時(shí)間: 2025-3-28 07:02 作者: infantile 時(shí)間: 2025-3-28 14:06 作者: inveigh 時(shí)間: 2025-3-28 15:00
PAGE: Answering Graph Pattern Queries via Knowledge Graph Embeddinge graphs include incorrect or incomplete information. In this paper, we present a method called . that answers graph pattern queries via knowledge graph embedding methods. . computes the energy (or uncertainty) of candidate answers with the learned embeddings and chooses the lower-energy candidates 作者: Osteons 時(shí)間: 2025-3-28 20:13
Distributed Big Data Ingestion at Scale for Extremely Large Community of Usersas been designed and implemented which employs distributed blob store, custom compression, and custom query algorithm, including filtering, joins and group by. The system has been in operation at eBay for years and is described in [.]. However, large scale ingestion of data to a distributed blob sto作者: 遠(yuǎn)足 時(shí)間: 2025-3-29 02:09
Convolutional Neural Network Ensemble Fine-Tuning for Extended Transfer Learninger monitoring, assault detection), safety (fire detection, distracted driving), geo-monitoring (cloud, rock and crop-disease detection). Convolutional Neural Networks(CNNs) are effective for those applications. However, they need to be trained with a huge number of examples and a consequently huge t作者: 口音在加重 時(shí)間: 2025-3-29 03:31 作者: 親愛(ài) 時(shí)間: 2025-3-29 09:50 作者: Accord 時(shí)間: 2025-3-29 12:07 作者: Coronary-Spasm 時(shí)間: 2025-3-29 16:45
The Application of Machine Learning Algorithm Applied to 3Hs Risk Assessmentcontaining Body Mass Index (BMI), Waist Circumference (WC), Hip Circumference (HC), Waist-to-hip Ratio (WHR), Waist-to-height Ratio (WHtR) and disease history, disease history of family, dietary and etc. obtained conveniently and noninvasively, this article mainly set up two models to study the appl作者: 有節(jié)制 時(shí)間: 2025-3-29 22:15 作者: Ferritin 時(shí)間: 2025-3-30 01:44 作者: 飛來(lái)飛去真休 時(shí)間: 2025-3-30 06:08 作者: 鞭打 時(shí)間: 2025-3-30 10:11
Francis Y. L. Chin,C. L. Philip Chen,Liang-Jie Zha作者: 轉(zhuǎn)折點(diǎn) 時(shí)間: 2025-3-30 13:49 作者: Intellectual 時(shí)間: 2025-3-30 19:51 作者: Asperity 時(shí)間: 2025-3-31 00:03
https://doi.org/10.1007/978-3-030-11671-2rage space for each instance of the published data. In this paper, we develop a set of reversible data perturbation techniques for large bipartite association graphs that use perturbation keys to control the sequential generation of multiple snapshots of the data to offer multi-level access based on作者: forestry 時(shí)間: 2025-3-31 04:44 作者: CODA 時(shí)間: 2025-3-31 07:33
