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

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.

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.

Analytical Study on Big Data

2018 ◽  
Vol 8 (5) ◽  
pp. 75
Author(s):  
Vivek Raich ◽  
Pankaj Maurya
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Decision Maker ◽  
Analytical Study ◽  
Large Data ◽  
Decision Makers ◽  
Continuous Increase ◽  
Analytic Methods ◽  
Data Store ◽  
Business Engineering

in the time of the Information Technology, the big data store is going on. Due to which, Huge amounts of data are available for decision makers, and this has resulted in the progress of information technology and its wide growth in many areas of business, engineering, medical, and scientific studies. Big data means that the size which is bigger in size, but there are several types, which are not easy to handle, technology is required to handle it. Due to continuous increase in the data in this way, it is important to study and manage these datasets by adjusting the requirements so that the necessary information can be obtained.The aim of this paper is to analyze some of the analytic methods and tools. Which can be applied to large data. In addition, the application of Big Data has been analyzed, using the Decision Maker working on big data and using enlightened information for different applications.

scholarly journals Applying Improved Apriori Algorithm in Figuring out the Relation between Weather Factors and Rainfall

2020 ◽  
Vol 1 (1) ◽  
pp. 23-26
Author(s):  
Siti Zulaikha ◽  
Martaleli Bettiza ◽  
Nola Ritha
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Light Intensity ◽  
Apriori Algorithm ◽  
Weather Factors ◽  
Factors Affecting ◽  
Data Store ◽  
Repeated Pattern ◽  
Major Factors ◽  
Sun Light

Data on the rainfall is compelling to study as it becomes one of the major factors affecting the weather in a certain region and various aspects of life as well. Generally, predicting rainfall is performed by analyzing data in the past in certain methods. Rainfall is prone to follow repeated pattern in sequence of time. The utilization of big data mining is expected to result in any valuable information that used to be unrevealed in the big data store. Some methods used in data mining are Apriori Algorithm and Improved Apriori Algorithm. Improved Apriori itself is to represent the database in the form of matrix to describe its relation in the database. Data used in this research is the rainfall factor in 2016 in Tanjungpinang city. Based on the test of Improved Apriori Algorithm, it was found out that the relation of the rainfall and weather factors utilizing 2 item sets, that is, if the temperature is low (24,0 - 26,0), the humidity is high (85 - 100), then the rainfall is mild. If the temperature is low (24,0 - 26,0), the light intensity is low (0 – 3), then the rainfall is heavy, and 3 item sets if the temperature is low (24,0 - 26,0), the humidity is high (85 - 100), the sun light intensity is low (0-3), then the rainfall is medium.

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