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

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.

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Real Time Data Pipeline for Twitter Trends Analysis

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst0705006 ◽

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 ◽

LSST data pipeline prototyping plans and strategy

10.2172/15009840 ◽

2004 ◽

Author(s):

G Abdulla ◽

J Brase ◽

K Cook ◽

M Miller

Keyword(s):

Data Pipeline

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Mapping Population Vulnerability and Community Support during COVID-19

International Journal for Population Data Science ◽

10.23889/ijpds.v5i4.1409 ◽

2021 ◽

Vol 5 (4) ◽

Author(s):

Nina H Di Cara ◽

Jiao Song ◽

Valerio Maggio ◽

Christopher Moreno-Stokoe ◽

Alastair R Tanner ◽

...

Keyword(s):

Community Support ◽

Well Being ◽

Community Action ◽

Community Response ◽

Multiple Data ◽

Novel Approach ◽

Data Pipeline ◽

Complex Population ◽

Flexible Integration ◽

Interactive Map

Background Disasters such as the COVID-19 pandemic pose an overwhelming demand on resources that cannot always be met by official organisations. Limited resources and human response to crises can lead members of local communities to turn to one another to fulfil immediate needs. This spontaneous citizen-led response can be crucial to a community’s ability to cope in a crisis. It is thus essential to understand the scope of such initiatives so that support can be provided where it is most needed. Nevertheless, quickly developing situations and varying definitions can make the community response challenging to measure. Aim To create an accessible interactive map of the citizen-led community response to need during the COVID-19 pandemic in Wales, UK that combines information gathered from multiple data providers to reflect different interpretations of need and support. Approach We gathered data from a combination of official data providers and community-generated sources to create 14 variables representative of need and support. These variables are derived by a reproducible data pipeline that enables flexible integration of new data. The interactive tool is available online (www.covidresponsemap.wales) and can map available data at two geographic resolutions. Users choose their variables of interest, and interpretation of the map is aided by a linked bee-swarm plot. Discussion The novel approach we developed enables people at all levels of community response to explore and analyse the distribution of need and support across Wales. While there can be limitations to the accuracy of community-generated data, we demonstrate that they can be effectively used alongside traditional data sources to maximise the understanding of community action. This adds to our overall aim to measure community response and resilience, as well as to make complex population health data accessible to a range of audiences. Future developments include the integration of other factors such as well-being.

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