scholarly journals Towards a computing model for the LHCb Upgrade

2019 ◽  
Vol 214 ◽  
pp. 03045
Author(s):  
Concezio Bozzi ◽  
Stefan Roiser
Keyword(s):  
Data Analysis ◽  
Low Latency ◽  
Data Sets ◽  
Latency Data ◽  
Lhcb Experiment ◽  
Computing Model ◽  
And Storage

The LHCb experiment will be upgraded for data taking in the LHC Run 3. The foreseen trigger output bandwidth trigger ofa few GB/s will result in data sets of tens of PB per year, which need to be efficiently streamed and stored offline for low-latency data analysis. In addition, simulation samples of up to two orders of magnitude larger than those currently simulated are envisaged, with big impact on offline computing and storage resources. This contribution discusses the offline computing model and the required offline resources for the LHCb upgrade, as resulting from the above requirements.

Low-latency data analysis for the spherical detector Mario Schenberg

2014 ◽  
Vol 31 (8) ◽  
pp. 085012 ◽  
Author(s):  
Carlos Filipe Da Silva Costa ◽  
César Augusto Costa ◽  
Odylio Denys Aguiar
Keyword(s):  
Data Analysis ◽  
Low Latency ◽  
Latency Data ◽  
Spherical Detector

scholarly journals Visual Data Analysis Technology Based on Data Center

2022 ◽  
Vol 2146 (1) ◽  
pp. 012016
Author(s):  
Tianjun Wang ◽  
Cengceng Wang ◽  
Jiangtao Guo ◽  
dildar alim
Keyword(s):  
Data Analysis ◽  
Visual Analysis ◽  
Recognition Rate ◽  
Development Trend ◽  
Data Sets ◽  
Visual Data ◽  
Visualization Technology ◽  
Processing And Storage ◽  
And Storage ◽  
Crucial Part

Abstract Today, people are in an information explosion society, and visualization technology(VT) is an inevitable product of the development of the information society. With the emergence of multimedia products such as computers, networks, and communications, humans are paying more and more attention to data processing. Many countries in the world have already begun research in this area and have achieved remarkable results. VT is a core part of data analysis, also known as information processing and storage technology. It has a very extensive and important application in the field of data management. However, because the key information hidden in the data is often immersed in the massive data, it is necessary to filter the data information efficiently, and the visualization data analysis technology is a crucial part. This article adopts the experimental analysis method, which aims to provide a new method to solve the problems of traditional technology and the challenges that may arise in the future by further understanding the existing visual data analysis technology and development trend. According to the research results, the recognition rate of the optimized color visualization features under different classifiers is higher than that of the original emotional features. It can be seen that visual analysis technology is not limited to data sets with physical meaning, but can also be applied to abstract feature sets such as emotional features.

DTI data analysis: application of fiber tracking to group averaged data sets

2010 ◽  
Vol 41 (01) ◽  
Author(s):  
HP Müller ◽  
A Unrath ◽  
A Riecker ◽  
AC Ludolph ◽  
J Kassubek
Keyword(s):  
Data Analysis ◽  
Fiber Tracking ◽  
Data Sets ◽  
Analysis Application

HUBUNGAN HIGIENE SANITASI DENGAN KANDUNGAN ESCHERICHIA COLI PADA ES BATU INDUSTRI RUMAH TANGGA DI TEPI JALAN WAHID HASYIM 2 KECAMATAN SEMPAJA KOTA SAMARINDA TAHUN 2015

2018 ◽  
Vol 2 (1) ◽  
pp. 43
Author(s):  
Suwignyo Suwignyo ◽  
Abdul Rachim ◽  
Arizal Sapitri
Keyword(s):  
Escherichia Coli ◽  
Data Analysis ◽  
Random Sampling ◽  
Preliminary Test ◽  
Raw Material ◽  
Almost Everywhere ◽  
Ice Cube ◽  
Processing And Storage ◽  
And Storage ◽  
Cluster Random Sampling

Ice is a water that cooled below 0 °C and used for complement in drink. Ice can be found almost everywhere, including in the Wahid Hasyim Sempaja Roadside. From the preliminary test, obtained 5 samples ice cube were contaminated by Escherichia coli. The purpose of this study was to determine relationship between hygiene and sanitation with presence of Eschericia coli in ice cube of home industry at Wahid Hasyim Roadside Samarinda. This research used quantitative with survey methode. The population in this study was all of the seller in 2nd Wahid Hasyim Roadside. Sample was taken by Krejcie and Morgan so the there were 44 samples and used Cluster Random Sampling. The instruments are questionnaries, observation and laboratory test. Data analysis was carried out univariate and bivariate (using Fisher test p= 0.05). The conclusion of this study there are a relation between chosing raw material (p=0,03) and saving raw material (p=0,03) with presence of Eschericia coli. There was no relation between processing raw material into ice cube with presence of Eschericia coli (p=0,15).Advice that can be given to ice cube should maintain hygiene and sanitation of the selection, processing and storage of ice cube.

scholarly journals Updating Data-Center Network With Ultra-Low Latency Data Plane

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 2134-2144 ◽  
Author(s):  
Chengyuan Huang ◽  
Jiao Zhang ◽  
Tao Huang
Keyword(s):  
Data Center ◽  
Low Latency ◽  
Data Center Network ◽  
Latency Data ◽  
Data Plane

scholarly journals Challenges of Big Data analysis

2014 ◽  
Vol 1 (2) ◽  
pp. 293-314 ◽  
Author(s):  
Jianqing Fan ◽  
Fang Han ◽  
Han Liu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Measurement Errors ◽  
Modern Society ◽  
Big Data Analysis ◽  
Small Scale ◽  
Paradigm Change ◽  
Statistical Inferences ◽  
The One ◽  
And Storage

Abstract Big Data bring new opportunities to modern society and challenges to data scientists. On the one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This paper gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

scholarly journals A multilevel approach to big data analysis using analytic tools and actor network theory

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Tiko Iyamu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Network Theory ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Actor Network Theory ◽  
Big Data Analysis ◽  
Data Sets ◽  
Multilevel Approach ◽  
Actor Network

Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness.Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis.Method: In the study, the qualitative methods were employed from the interpretivist approach perspective.Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks.Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness.

scholarly journals Sampling Methods in Approximate Query Answering Systems

2011 ◽  
pp. 990-994 ◽  
Author(s):  
Gautam Das
Keyword(s):  
Data Analysis ◽  
Large Data ◽  
Massive Datasets ◽  
Data Repositories ◽  
Large Databases ◽  
Approximate Query Answering ◽  
Very Large Databases ◽  
Approximate Query ◽  
And Storage ◽  
Collection And Management

In recent years, advances in data collection and management technologies have led to a proliferation of very large databases. These large data repositories typically are created in the hope that, through analysis such as data mining and decision support, they will yield new insights into the data and the real-world processes that created them. In practice, however, while the collection and storage of massive datasets has become relatively straightforward, effective data analysis has proven more difficult to achieve. One reason that data analysis successes have proven elusive is that most analysis queries, by their nature, require aggregation or summarization of large portions of the data being analyzed. For multi-gigabyte data repositories, this means that processing even a single analysis query involves accessing enormous amounts of data, leading to prohibitively expensive running times. This severely limits the feasibility of many types of analysis applications, especially those that depend on timeliness or interactivity.

scholarly journals ReactomeFIViz: the Reactome FI Cytoscape app for pathway and network-based data analysis

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 146 ◽  
Author(s):  
Guanming Wu ◽  
Eric Dawson ◽  
Adrian Duong ◽  
Robin Haw ◽  
Lincoln Stein
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Graphical Models ◽  
High Throughput ◽  
Interaction Network ◽  
Large Data ◽  
Relevant Information ◽  
Data Sets ◽  
Data Types ◽  
Biological Studies

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network and human curated pathways from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

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