A “Big Data” View of the Tumor “Immunome”

Big data comprises voluminous and heterogeneous data that has a limited level of trustworthiness. This data is used to generate valuable information that can be used for decision making. However, decision making queries on Big data consume a lot of time for processing resulting in higher response times. For effective and efficient decision making, this response time needs to be reduced. View materialization has been used successfully to reduce the query response time in the context of a data warehouse. Selection of such views is a complex problem vis-à-vis Big data and is the focus of this paper. In this paper, the Big data view selection problem is formulated as a bi-objective optimization problem with the two objectives being the minimization of the query evaluation cost and the minimization of the update processing cost. Accordingly, a Big data view selection algorithm that selects Big data views for a given query workload, using the vector evaluated genetic algorithm, is proposed. The proposed algorithm aims to generate views that are able to reduce the response time of decision-making queries.

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Multi-Objective Big Data View Materialization using MOGA

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.292499 ◽

2022 ◽

Vol 13 (1) ◽

pp. 0-0

Keyword(s):

Big Data ◽

Large Scale ◽

Large Data ◽

Unstructured Data ◽

Infected Person ◽

View Selection ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

The People ◽

Data View

The COVID 19 Pandemic, has resulted in large scale of generation of Big data. This Big data is heterogeneous and includes the data of people infected with corona virus, the people who were in contact of infected person, demographics of infected person, data on corona testing, huge amount of GPS data of people location, and large number of unstructured data about prevention and treatment of COVID 19. Thus, the pandemic has resulted in producing several Zeta bytes of structured, semi-structured and unstructured data. The challenge is to process this Big data, which has the characteristics of very large volume, brisk rate of generation and modification and large data redundancy, in a time bound manner to take timely predictions and decisions. Materialization of views for Big data is one of the ways to enhance the efficiency of processing of the data. In this paper, Big data view selection problem is addressed, as a bi-objective optimization problem, using Multi-objective genetic algorithm.

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A “big data” view of the U.S. economy: Introducing the Brave-Butters-Kelley Indexes

Chicago Fed Letter ◽

10.21033/cfl-2019-422 ◽

2019 ◽

Author(s):

Scott A. Brave ◽

Ross Cole ◽

David Kelley

Keyword(s):

Big Data ◽

Data View ◽

The U.S

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Multi-Objective Big Data View Materialization Using NSGA-II

Information Resources Management Journal ◽

10.4018/irmj.2021040101 ◽

2021 ◽

Vol 34 (2) ◽

pp. 1-28

Author(s):

Akshay Kumar ◽

T. V. Vijay Kumar

Keyword(s):

Big Data ◽

Response Time ◽

Unstructured Data ◽

Materialized Views ◽

Nsga Ii ◽

View Selection ◽

Multi Objective ◽

Query Response Time ◽

View Materialization ◽

Data View

Big data views, in the context of distributed file system (DFS), are defined over structured, semi-structured and unstructured data that are voluminous in nature with the purpose to reduce the response time of queries over Big data. As the size of semi-structured and unstructured data in Big data is very large compared to structured data, a framework based on query attributes on Big data can be used to identify Big data views. Materializing Big data views can enhance the query response time and facilitate efficient distribution of data over the DFS based application. Given all the Big data views cannot be materialized, therefore, a subset of Big data views should be selected for materialization. The purpose of view selection for materialization is to improve query response time subject to resource constraints. The Big data view materialization problem was defined as a bi-objective problem with the two objectives- minimization of query evaluation cost and minimization of the update processing cost, with a constraint on the total size of the materialized views. This problem is addressed in this paper using multi-objective genetic algorithm NSGA-II. The experimental results show that proposed NSGA-II based Big data view selection algorithm is able to select reasonably good quality views for materialization.

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Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽

10.1044/leader.an5.18022013.59 ◽

2013 ◽

Vol 18 (2) ◽

pp. 59-59

Keyword(s):

Big Data

Find Out About 'Big Data' to Track Outcomes

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U.S., Canada Collaborate on Big Data in ASD Research

ASHA Leader ◽

10.1044/leader.nib4.20122015.16 ◽

2015 ◽

Vol 20 (12) ◽

pp. 16-16

Keyword(s):

Big Data

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Correlating Personality and Actual Phone Usage

Journal of Individual Differences ◽

10.1027/1614-0001/a000139 ◽

2014 ◽

Vol 35 (3) ◽

pp. 158-165 ◽

Cited By ~ 38

Author(s):

Christian Montag ◽

Konrad Błaszkiewicz ◽

Bernd Lachmann ◽

Ionut Andone ◽

Rayna Sariyska ◽

...

Keyword(s):

Big Data ◽

Social Network ◽

Mobile Phone ◽

Mobile Phones ◽

Self Report ◽

Psychological Variables ◽

New Approach ◽

Report Data ◽

Self Report Data ◽

Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

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