scholarly journals A new data analysis framework for the search of continuous gravitational wave signals

2018 ◽  
Vol 36 (1) ◽  
pp. 015008 ◽  
Author(s):  
O J Piccinni ◽  
P Astone ◽  
S D’Antonio ◽  
S Frasca ◽  
G Intini ◽  
...  
Keyword(s):  
Data Analysis ◽  
Gravitational Wave ◽  
Analysis Framework

scholarly journals Luiza: Analysis Framework for GLORIA

10.14311/1718 ◽  
2013 ◽  
Vol 53 (1) ◽  
Author(s):  
Aleksander Filip Żarnecki ◽  
Lech Wiktor Piotrowski ◽  
Lech Mankiewicz ◽  
Sebastian Małek
Keyword(s):  
Data Analysis ◽  
High Energy Physics ◽  
Linear Collider ◽  
International Linear Collider ◽  
High Energy ◽  
Distributed Data ◽  
Analysis Framework ◽  
Full Analysis ◽  
Distributed Data Analysis ◽  
Energy Physics

The Luiza analysis framework for GLORIA is based on the Marlin package, which was originally developed for data analysis in the new High Energy Physics (HEP) project, International Linear Collider (ILC). The HEP experiments have to deal with enormous amounts of data and distributed data analysis is therefore essential. The Marlin framework concept seems to be well suited for the needs of GLORIA. The idea (and large parts of the code) taken from Marlin is that every computing task is implemented as a processor (module) that analyzes the data stored in an internal data structure, and the additional output is also added to that collection. The advantage of this modular approach is that it keeps things as simple as possible. Each step of the full analysis chain, e.g. from raw images to light curves, can be processed step-by-step, and the output of each step is still self consistent and can be fed in to the next step without any manipulation.

The implementation of an automatic web-driven data analysis framework

2017 ◽  
Vol 2 (3/4) ◽  
pp. 150 ◽  
Author(s):  
Chun Hsiung Tseng ◽  
Yung Hui Chen ◽  
Yan Ru Jiang
Keyword(s):  
Data Analysis ◽  
Analysis Framework

K2: A Novel Data Analysis Framework to Understand US Emotions in Space and Time

2019 ◽  
Vol 13 (01) ◽  
pp. 111-133
Author(s):  
Romita Banerjee ◽  
Karima Elgarroussi ◽  
Sujing Wang ◽  
Akhil Talari ◽  
Yongli Zhang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Negative Emotion ◽  
Time Interval ◽  
Temporal Data ◽  
Analysis Framework ◽  
Spatial Clusters ◽  
Change Analysis ◽  
Spatio Temporal ◽  
Temporal Data Analysis ◽  
Very High

Twitter is one of the most popular social media platforms used by millions of users daily to post their opinions and emotions. Consequently, Twitter tweets have become a valuable knowledge source for emotion analysis. In this paper, we present a new framework, K2, for tweet emotion mapping and emotion change analysis. It introduces a novel, generic spatio-temporal data analysis and storytelling framework that can be used to understand the emotional evolution of a specific section of population. The input for our framework is the location and time of where and when the tweets were posted and an emotion assessment score in the range [Formula: see text], with [Formula: see text] representing a very high positive emotion and [Formula: see text] representing a very high negative emotion. Our framework first segments the input dataset into a number of batches with each batch representing a specific time interval. This time interval can be a week, a month or a day. By generalizing existing kernel density estimation techniques in the next step, we transform each batch into a continuous function that takes positive and negative values. We have used contouring algorithms to find the contiguous regions with highly positive and highly negative emotions belonging to each member of the batch. Finally, we apply a generic, change analysis framework that monitors how positive and negative emotion regions evolve over time. In particular, using this framework, unary and binary change predicate are defined and matched against the identified spatial clusters, and change relationships will then be recorded, for those spatial clusters for which a match occurs. We also propose animation techniques to facilitate spatio-temporal data storytelling based on the obtained spatio-temporal data analysis results. We demo our approach using tweets collected in the state of New York in the month of June 2014.

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