scholarly journals Data Representation in Big data via Succinct Data Structures

2018 ◽  
Vol 10 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Vinesh kumar ◽  
Dr. Amit Asthana ◽  
Sunil Kumar ◽  
Dr. Jayant Shekhar
Keyword(s):  
Big Data ◽  
Data Structures ◽  
Data Representation ◽  
Succinct Data Structures

scholarly journals Data Representation in Big data via succinct data structures

2017 ◽  
Vol 9 (02) ◽  
Author(s):  
Vinesh Kumar ◽  
Jayant Shekhar ◽  
Sunil Kumar
Keyword(s):  
Big Data ◽  
Data Structures ◽  
Time Complexity ◽  
Data Representation ◽  
Complex Problem ◽  
Main Memory ◽  
Succinct Data Structures ◽  
Efficient Data ◽  
Tree Data ◽  
Day By Day

Data Representation in memory is one of the tasks in Big data. Data representation includes several types of tree data structures through the system can access accurate and efficient data in big data. Succinct data structures can play important role in data representation while data in big-data is processed in main memory. Data representation is a very complex problem in Big Data.We proposed some solution of problems of data representation in Big data. Data processing in big data can be utilized to take a decision on data mining. We know the function and rules for query processing. We have to either change the method of processor we can change the way of representation. In this paper, different kind of tree data structures is presented for data representation in main memory of computer system for big data by using succinct data structures. Here we first compare all data structures by the table. Each method has different space and time complexity. We know that Big data information services increasing day by day. So space complexity of succinct data structures is becoming very popular in practice in this era.

Compendious and Optimized Succinct Data Structures for Big Data Store

2018 ◽  
Author(s):  
Vinesh Kumar ◽  
Amit Asthana ◽  
Sunil Kumar ◽  
Sunil Kumar
Keyword(s):  
Big Data ◽  
Data Structures ◽  
Succinct Data Structures ◽  
Data Store

The Rise of NoSQL Systems

2020 ◽  
Vol 31 (3) ◽  
pp. 67-82
Author(s):  
Akhilesh Bajaj ◽  
Wade Bick
Keyword(s):  
Big Data ◽  
Information Systems ◽  
Data Structures ◽  
Query Optimization ◽  
Transaction Processing ◽  
Data Representation ◽  
Review Article ◽  
Integrity Constraints ◽  
Relational Model ◽  
Transaction Processing Systems

Transaction processing systems are primarily based on the relational model of data and offer the advantages of decades of research and experience in enforcing data quality through integrity constraints, allowing concurrent access and supporting recoverability. From a performance standpoint, they offer joins-based query optimization and data structures to promote fast reads and writes, but are usually vertically scalable from a hardware standpoint. NoSQL (Not Only SQL) systems follow different data representation formats than relations, such as key-value pairs, graphs, documents or column-families. They offer a flexible data representation format as well as horizontal hardware scalability so that Big Data can be processed in real time. In this review article, we review recent research on each type of system, and then discuss how teaching of NoSQL may be incorporated into traditional undergraduate database courses in information systems curricula.

scholarly journals Application-Oriented Succinct Data Structures for Big Data

2019 ◽  
Vol 13 (2) ◽  
pp. 227-236
Author(s):  
Tetsuo Shibuya
Keyword(s):  
Big Data ◽  
Data Structure ◽  
Data Structures ◽  
Genome Assembly ◽  
Original Data ◽  
Succinct Data Structures ◽  
Space Requirement ◽  
Space Reduction ◽  
Big Data Applications ◽  
Application Specific

Abstract A data structure is called succinct if its asymptotical space requirement matches the original data size. The development of succinct data structures is an important factor to deal with the explosively increasing big data. Moreover, wider variations of big data have been produced in various fields recently and there is a substantial need for the development of more application-specific succinct data structures. In this study, we review the recently proposed application-oriented succinct data structures motivated by big data applications in three different fields: privacy-preserving computation in cryptography, genome assembly in bioinformatics, and work space reduction for compressed communications.

scholarly journals LevioSAM: Fast lift-over of alternate reference alignments

2021 ◽  
Author(s):  
Taher Mun ◽  
Nae-Chyun Chen ◽  
Ben Langmead
Keyword(s):  
Population Genetics ◽  
Coordinate System ◽  
Data Structures ◽  
Succinct Data Structures ◽  
Reference Coordinate System ◽  
Link Type ◽  
A Chain ◽  
Time Required ◽  
Effective Use

AbstractMotivationAs more population genetics datasets and population-specific references become available, the task of translating (“lifting”) read alignments from one reference coordinate system to another is becoming more common. Existing tools generally require a chain file, whereas VCF files are the more common way to represent variation. Existing tools also do not make effective use of threads, creating a post-alignment bottleneck.ResultsLevioSAM is a tool for lifting SAM/BAM alignments from one reference to another using a VCF file containing population variants. LevioSAM uses succinct data structures and scales efficiently to many threads. When run downstream of a read aligner, levioSAM completes in less than 13% the time required by an aligner when both are run with 16 threads.Availabilityhttps://github.com/alshai/[email protected], [email protected]

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