標題: Titlebook: Big Scientific Data Management; First International Jianhui Li,Xiaofeng Meng,Zhihui Du Conference proceedings 2019 Springer Nature Switzer [打印本頁] 作者: CT951 時間: 2025-3-21 18:51
書目名稱Big Scientific Data Management影響因子(影響力)
書目名稱Big Scientific Data Management影響因子(影響力)學(xué)科排名
書目名稱Big Scientific Data Management網(wǎng)絡(luò)公開度
書目名稱Big Scientific Data Management網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Big Scientific Data Management被引頻次
書目名稱Big Scientific Data Management被引頻次學(xué)科排名
書目名稱Big Scientific Data Management年度引用
書目名稱Big Scientific Data Management年度引用學(xué)科排名
書目名稱Big Scientific Data Management讀者反饋
書目名稱Big Scientific Data Management讀者反饋學(xué)科排名
作者: 課程 時間: 2025-3-21 22:04 作者: ESPY 時間: 2025-3-22 04:04
An Efficient Parallel Framework to Analyze Astronomical Sky Survey Data,nthetic data with raw files and HBase entries as data sources and result formats, reduced the analyze cost for scientists not familiar with parallel programming while needs to handle a mass of data. We integrate time series anomaly detection algorithms with our parallel dispatching module to achieve作者: Obedient 時間: 2025-3-22 07:47 作者: 挑剔小責(zé) 時間: 2025-3-22 12:16 作者: myelography 時間: 2025-3-22 16:40 作者: NIP 時間: 2025-3-22 19:12
Automated and Intelligent Data Migration Strategy in High Energy Physical Storage Systems,ning algorithm model to predict the evolution trend of data access heat. This paper discussed the implementation of some initial parts of the system. Then some preliminary experiments are conducted with these parts.作者: Tincture 時間: 2025-3-22 23:58
Using Hadoop for High Energy Physics Data Analysis,the local file system on data nodes instead of using Hadoop data streaming interface. This makes HEP jobs run on Hadoop possible. We also develop diverse MapReduce model for HEP jobs such as Corsika simulation, ARGO detector simulation and Medea++?reconstruction. And we develop a toolkit for users t作者: 嬉耍 時間: 2025-3-23 02:54
Multi-dimensional Index over a Key-Value Store for Semi-structured Data, unit of the range partitioned Key-value store) to utilize distributed computing and data locality. Our prototype of MD-Index is built on HBase, the standard Key-value database. Experimental results reveal that MD-Index is capable of storing and retrieving trillions of semi-structured data and achie作者: flaunt 時間: 2025-3-23 06:53 作者: 蠟燭 時間: 2025-3-23 10:18 作者: 油膏 時間: 2025-3-23 16:22
https://doi.org/10.1007/b119185ponents include three data service layers and a query engine. Each data service layer serves for a specific time period of data and query engine can provide the uniform analysis interface on different data. In addition, we also provide many applications including interactive analysis interface and d作者: gregarious 時間: 2025-3-23 21:38 作者: 精致 時間: 2025-3-23 22:21 作者: 是他笨 時間: 2025-3-24 06:17 作者: depreciate 時間: 2025-3-24 06:57
Neural Networks in Control Engineering,ssing. It uses storage structure and endpoint coprocessor of the hbase as the framework of the physical analysis operator library, uses protocol buffer to customize the client and server RPC communication and encapsulates the operators in the object-oriented data analysis framework ROOT to hbase cop作者: 津貼 時間: 2025-3-24 13:49
Introduction to Neuro-Fuzzy Systemsning algorithm model to predict the evolution trend of data access heat. This paper discussed the implementation of some initial parts of the system. Then some preliminary experiments are conducted with these parts.作者: MEAN 時間: 2025-3-24 15:09 作者: 賞錢 時間: 2025-3-24 20:04
Fundamentals of constrained optimization, unit of the range partitioned Key-value store) to utilize distributed computing and data locality. Our prototype of MD-Index is built on HBase, the standard Key-value database. Experimental results reveal that MD-Index is capable of storing and retrieving trillions of semi-structured data and achie作者: 占卜者 時間: 2025-3-25 02:25
Fundamentals of unconstrained optimization,theories, methods and tools. In the case study, the TDG of DSSC (dye-sensitized solar cell) is constructed. Furthermore, the technology dependency architecture for DSSC is constructed according to a spanning tree out of the TDG, which provides a global perspective for the research of DSSC.作者: reserve 時間: 2025-3-25 05:17 作者: 匍匐 時間: 2025-3-25 10:29 作者: 法律的瑕疵 時間: 2025-3-25 15:00 作者: Hallmark 時間: 2025-3-25 17:41 作者: assail 時間: 2025-3-25 21:24
Data Management in Time-Domain Astronomy: Requirements and Challenges,of star catalog data is larger, and the speed of data generation is faster. So, in this paper, we make a systematic and comprehensive introduction to process the data in time-domain astronomy, and valuable research questions are detailed. Then, we list candidate systems usually used in astronomy and作者: 和平主義者 時間: 2025-3-26 01:18
AstroServ: A Distributed Database for Serving Large-Scale Full Life-Cycle Astronomical Data,survey who can only find astronomical phenomena, STLF sky survey can even reveal how short astronomical phenomena evolve. The difference does not only lead the new survey data but also the new analysis style. It requires that database behind STLF sky survey should support continuous analysis on data作者: d-limonene 時間: 2025-3-26 08:14 作者: Interstellar 時間: 2025-3-26 10:41
An Efficient Parallel Framework to Analyze Astronomical Sky Survey Data,nalyzing it. There are multiple steps to the data analysis pipeline, which can be abstracted as a framework provides universal parallel high-performance data analysis. Based on ray, this paper proposed a parallel framework written in Python with an interface to aggregate and analyze homogeneous astr作者: BLA 時間: 2025-3-26 14:48
Real-Time Query Enabled by Variable Precision in Astronomy,ially, in time-domain astronomy, Short-Timescale and Large Field-of-view (STLF) sky survey not only requires real-time analysis on short-time data, but also need precise astronomical data for special phenomena. Additionally, it is important to find a partition method and build an index based on that作者: carotenoids 時間: 2025-3-26 18:13
Continuous Cross Identification in Large-Scale Dynamic Astronomical Data Flow,ation. Furthermore, transient survey projects are required to select the candidates fast from large volume data. However, traditional cross identification methods didn’t satisfy the observation of transient survey. We present a fast and efficient cross identification system for large-scale astronomi作者: anticipate 時間: 2025-3-27 00:57 作者: 發(fā)怨言 時間: 2025-3-27 03:43
EventDB: A Large-Scale Semi-structured Scientific Data Management System,ow to use these data efficiently to produce some scientific findings is a hot problem. There are many challenges in the use of these scientific big data, such as the storage, processing and sharing of the data. In this paper, we propose a data management system, EventDB, for scientific big data. Eve作者: Resection 時間: 2025-3-27 06:59 作者: Kinetic 時間: 2025-3-27 11:31
Event-Oriented Caching System for ROOT Data Analysis in HEP,ced physical experimental devices can produce a large amount of Event data up?to PB level. While Compared to these massive data generation, data storage system based on files at the moment is out of date. Event data are mostly random accessed, but searching a few specific Event in large files is an 作者: POWER 時間: 2025-3-27 16:34
Automated and Intelligent Data Migration Strategy in High Energy Physical Storage Systems,ormance data access and large volume of data storage as well. Some enterprises and research organizations are beginning to use tiered storage architectures, using tapes, disks or solid drives at the same time to reduce hardware purchase costs and power consumption. Tiered storage requires data manag作者: Obscure 時間: 2025-3-27 21:32
Using Hadoop for High Energy Physics Data Analysis,rithms/frameworks and High IO throughput are key to meet the scalability and performance requirements of HEP offline data analysis. Though Hadoop has gained a lot of attention from scientific community for its scalability and parallel computing framework for large data sets, it’s still difficult to 作者: intimate 時間: 2025-3-28 00:01 作者: Ascendancy 時間: 2025-3-28 04:43 作者: MARS 時間: 2025-3-28 09:13 作者: Truculent 時間: 2025-3-28 11:16
Big Scientific Data Management978-3-030-28061-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 史前 時間: 2025-3-28 17:45
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/185749.jpg作者: 皺痕 時間: 2025-3-28 22:08
https://doi.org/10.1007/b119185undred years after Einstein first proposed the existence of gravitational waves [., .]. Whether gravitational waves existed or not was controversial among theorists for the first 50 years, and then the actual observation came after another fifty years to develop a detector sensitive enough to observ作者: Indict 時間: 2025-3-29 02:56 作者: 委托 時間: 2025-3-29 07:01
https://doi.org/10.1007/b119185ence projects in the next five years. It brings great requirements in timeliness and accuracy of data processing, raising new challenges in heterogeneous data management and analysis. Chinese Space Science Data Center (CSSDC) is constantly exploring ways to face the potential challenges, keeping up 作者: cluster 時間: 2025-3-29 07:23 作者: BET 時間: 2025-3-29 13:41
Nanostructure Science and Technologyof star catalog data is larger, and the speed of data generation is faster. So, in this paper, we make a systematic and comprehensive introduction to process the data in time-domain astronomy, and valuable research questions are detailed. Then, we list candidate systems usually used in astronomy and作者: Pelago 時間: 2025-3-29 18:43
https://doi.org/10.1007/b119185survey who can only find astronomical phenomena, STLF sky survey can even reveal how short astronomical phenomena evolve. The difference does not only lead the new survey data but also the new analysis style. It requires that database behind STLF sky survey should support continuous analysis on data作者: 倒轉(zhuǎn) 時間: 2025-3-29 21:15
Introduction to Nanotheranosticsentire sky every 15?s, and requires data to be processed and alerted in real time within 15?s. These requirements are due to GWAC. Committed to discovering and timely capturing the development of short-time astronomical phenomena, such as supernova explosions, gamma blasts [.], and microgravity lens作者: intoxicate 時間: 2025-3-30 00:07
Developing New Modules for NS2,nalyzing it. There are multiple steps to the data analysis pipeline, which can be abstracted as a framework provides universal parallel high-performance data analysis. Based on ray, this paper proposed a parallel framework written in Python with an interface to aggregate and analyze homogeneous astr作者: 評論者 時間: 2025-3-30 05:15 作者: Confess 時間: 2025-3-30 10:44 作者: 有害 時間: 2025-3-30 14:34 作者: Ringworm 時間: 2025-3-30 18:59 作者: 大漩渦 時間: 2025-3-30 23:07
Neural Networks in Control Engineering,nical requirements for physical analysis are constantly increasing with the mass of physical events generated by high-energy physical colliders. The physical analysis for high-energy physics events refers to the selection of thousands of meaningful events from massive physical events. The analysis p作者: 漂亮才會豪華 時間: 2025-3-31 02:13
https://doi.org/10.1007/978-3-7908-1852-9ced physical experimental devices can produce a large amount of Event data up?to PB level. While Compared to these massive data generation, data storage system based on files at the moment is out of date. Event data are mostly random accessed, but searching a few specific Event in large files is an 作者: 毗鄰 時間: 2025-3-31 06:31
Introduction to Neuro-Fuzzy Systemsormance data access and large volume of data storage as well. Some enterprises and research organizations are beginning to use tiered storage architectures, using tapes, disks or solid drives at the same time to reduce hardware purchase costs and power consumption. Tiered storage requires data manag作者: 細微的差異 時間: 2025-3-31 13:02 作者: 前奏曲 時間: 2025-3-31 15:43
Nanoscale circuits and fluctuation problems, been widely used to process these data. In traditional computing model such as grid computing, computing job is usually scheduled to the sites where the input data was pre-staged in. This model will lead to some problems including low CPU utilization, inflexibility, and difficulty in highly dynamic作者: Obligatory 時間: 2025-3-31 18:44
Fundamentals of constrained optimization,s deal with the issues through R-Tree, KD-tree and space curves, but these structures are not suitable for default and discrete values of semi-structured data, and even require sampling before storage. We present MD-Index, a scalable multi-dimensional indexing system that supports high-throughput an作者: Autobiography 時間: 2025-4-1 01:42
Fundamentals of unconstrained optimization,proposes a new technology insight framework based on the text mining-Technology Dependency Graph (TDG). Firstly, an adversarial multitask learning model and distantly-supervised learning model are applied to extract the technology entities and dependency relations with a little labeled sample. Then,作者: 獨輪車 時間: 2025-4-1 04:45 作者: 搖曳 時間: 2025-4-1 08:46
978-3-030-28060-4Springer Nature Switzerland AG 2019作者: TAP 時間: 2025-4-1 10:12
Nanostructure Science and Technology point out the advantages and disadvantages of these systems. In addition, we present the key techniques needed to deal with astronomical data. Finally, we summarize the challenges faced by the design of our database prototype.作者: Embolic-Stroke 時間: 2025-4-1 14:37
