scholarly journals SNEWPY: A Data Pipeline from Supernova Simulations to Neutrino Signals

2021 ◽  
Vol 6 (67) ◽  
pp. 3772
Author(s):  
Amanda Baxter ◽  
Segev BenZvi ◽  
Joahan Jaimes ◽  
Alexis Coleiro ◽  
Marta Molla ◽  
...  
Keyword(s):  
Data Pipeline

HPC System Data Pipeline to Enable Meaningful Insights through Analysis-Driven Visualizations

2020 ◽  
Author(s):  
Benjamin Schwaller ◽  
Nick Tucker ◽  
Tom Tucker ◽  
Benjamin Allan ◽  
Jim Brandt
Keyword(s):  
Data Pipeline ◽  
System Data

Towards Automated Detection of Data Pipeline Faults

2020 ◽  
Author(s):  
Aiswarya Raj M ◽  
Jan Bosch ◽  
Helena Holmstrom Olsson ◽  
Tian J. Wang
Keyword(s):  
Automated Detection ◽  
Data Pipeline

HDF5-Based I/O Optimization for Extragalactic HI Data Pipeline of FAST

2020 ◽  
pp. 656-672
Author(s):  
Yiming Ji ◽  
Ce Yu ◽  
Jian Xiao ◽  
Shanjiang Tang ◽  
Hao Wang ◽  
...  
Keyword(s):  
Data Pipeline

scholarly journals A Big Data Architecture to Support Bank Digital Campaign

2019 ◽  
Vol 8 (2S7) ◽  
pp. 25-29
Keyword(s):  
Big Data ◽  
Credit Card ◽  
Latent Dirichlet Allocation ◽  
Time Windows ◽  
Shopping Center ◽  
Computing Power ◽  
Point Of Interest ◽  
Data Pipeline ◽  
Data Platform ◽  
Data Architecture

Bank marketers still have difficulties to find the best implementation for credit card promotion using above the line, particularly based on customers preferences in point of interest (POI) locations such as mall and shopping center. On the other hand, customers on those POIs are keen to have recommendation on what is being offered by the bank. On this paper we propose a design architecture and implementation of big data platform to support bank’s credit card’s program campaign that generating data and extracting topics from Twitter. We built a data pipeline that consist of a Twitter streamer, a text preprocessor, a topic extractor using Latent Dirichlet Allocation, and a dashboard that visualize the recommendation. As a result, we successfully generate topics that related to specific location in Jakarta during some time windows, that can be used as a recommendation for bank marketers to create promotion program for their customers. We also present the analysis of computing power usages that indicates the strategy is well implemented on the big data platform.

scholarly journals Real Time Data Pipeline for Twitter Trends Analysis

2021 ◽  
Vol 7 (05) ◽  
pp. 44-48
Author(s):  
Mukesh Kumar Sah., Rishabh Sharma & Amritpal Singh
Keyword(s):  
Social Media ◽  
Business Analytics ◽  
Time Data ◽  
Medical Field ◽  
Business Decisions ◽  
Data Pipeline ◽  
The Social ◽  
Enormous Amount ◽  
Healthcare Technologies ◽  
Trending Topics

In social media, Information is present in enormous amount. Extracting data from processed information from social media gives us diverse usages in various fields. In the field of Business Analytics, HealthCare, Technologies and Trending Topics in Social Media posted by the user. Extracting information from social media is providing number of benefits such as knowledge about the latest Technology, Medical field, Business Decisions, etc. Twitter is solitary of the social media which allows the user post tweets of limited number of characters and share the tweet to their followers. Twitter allows application developer to access the tweets for their motive. In the implemented methodology, Tweets are collected, and sentiment analysis is performed on them. Based on the results of sentimental analysis of Trending Topics in Twitter, suggestions can be provided to the user. In this way, the implemented system can help in improving the growth of business, healthcare, technologies and alsoNegative or Positive mentions of a product or service can be determined.

scholarly journals LSST data pipeline prototyping plans and strategy

2004 ◽  
Author(s):  
G Abdulla ◽  
J Brase ◽  
K Cook ◽  
M Miller
Keyword(s):  
Data Pipeline
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