Efficient Data Placement and Replication for QoS-Aware Approximate Query Evaluation of Big Data Analytics

2019 ◽  
Vol 30 (12) ◽  
pp. 2677-2691 ◽  
Author(s):  
Qiufen Xia ◽  
Zichuan Xu ◽  
Weifa Liang ◽  
Shui Yu ◽  
Song Guo ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Placement ◽  
Query Evaluation ◽  
Efficient Data ◽  
Approximate Query

scholarly journals Cdep: Qos-Aware Crowd-Deduplication with Efficient Data Placement in Big Data Analytics

2021 ◽  
Vol 12 (2) ◽  
pp. 1285-1291
Author(s):  
Bosco Nirmala Priya, Et. al.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Placement ◽  
Data Deduplication ◽  
High Odds ◽  
High Data ◽  
Extra Space ◽  
Data Fragmentation ◽  
Efficient Data

In current world, on account of tremendous enthusiasm for the big data extra space there is high odds of data duplication. Consequently, repetition makes issue by growing extra room in this manner stockpiling cost. Constant assessments have shown that moderate to high data excess obviously exists in fundamental stockpiling structures in the big data specialist. Our test thinks about uncover those data plenitude shows and a lot further degree of power on the I/O way than that on hovers because of for the most part high common access an area related with little I/O deals to dull data. Furthermore, direct applying data deduplication to fundamental stockpiling structures in the big data laborer will likely explanation space struggle in memory and data fragmentation on circles. We propose a genuine exhibition arranged I/O deduplication with cryptography, called CDEP (crowd deduplication with effective data placement), and rather than a limit situated I/O deduplication. This technique achieves data sections as the deduplication system develops. It is imperative to separate the data pieces in the deduplication structure and to fathom its features. Our test assessment utilizing authentic follows shows that contrasted and the progression based deduplication calculations, the copy end proportion and the understanding presentation (dormancy) can be both improved at the same time.

scholarly journals Approximate computation for big data analytics

2021 ◽  
pp. 1-8
Author(s):  
Shuai Ma ◽  
Jinpeng Huai
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Optimal Solution ◽  
Approximate Computation ◽  
Approximate Query Processing ◽  
Data Approximation ◽  
Approximation Techniques ◽  
Approximate Query ◽  
Data Analytic

Over the past a few years, research and development has made significant progresses on big data analytics. A fundamental issue for big data analytics is the efficiency. If the optimal solution is unable to attain or unnecessary or has a price to high to pay, it is reasonable to sacrifice optimality with a "good" feasible solution that can be computed efficiently. Existing approximation techniques can be in general classified into approximation algorithms, approximate query processing for aggregate SQL queries and approximation computing for multiple layers of the system stack. In this article, we systematically introduce approximate computation, i.e. , query approximation and data approximation, for efficient and effective big data analytics. We explain the ideas and rationales behind query and data approximation, and show efficiency can be obtained with high effectiveness, and even without sacrificing for effectiveness, for certain data analytic tasks.

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

2020 ◽  
Vol 49 (5) ◽  
pp. 11-17
Author(s):  
Thomas Wrona ◽  
Pauline Reinecke
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

A Review on Big Data Analytics Tools in Context with Scalability

2019 ◽  
Vol 7 (2) ◽  
pp. 273-277
Author(s):  
Ajay Kumar Bharti ◽  
Neha Verma ◽  
Deepak Kumar Verma
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics
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