scholarly journals The Maturity Measurement of Big Data Adoption in Manufacturing Companies Using the TDWI Maturity Model

2020 ◽  
Vol 6 (1) ◽  
pp. 70
Author(s):  
Fitri Retrialisca ◽  
Umi Chotijah
Keyword(s):  
Big Data ◽  
Qualitative Methods ◽  
Data Retrieval ◽  
High Volume ◽  
Maturity Model ◽  
Manufacturing Companies ◽  
Maturity Level ◽  
Is Implementation ◽  
Organizational Ability ◽  
Big Data Technology

Background: Big data technology has been used in several sectors in Indonesia. Adoption of big technology provides great potential for research, especially achievement in the implementation of big data in manufacturing companies. The Data Warehousing Institute (TDWI) Maturity Model is a tool that can be used to measure the state of "As-is" implementation of big data using 5 main dimensions. Maturity level shows the level of organizational ability to adjust big data technology currently.Objective: This study aims to measure the level of maturity in the implementation of big data technology in manufacturing companies PT. XYZ. This measurement is considered very important because it can know the process of managing data that is structured and has a high volume of data and provides more transparent reporting. This can help the company in making decisions that provide good information, so the company can increase the trust of stakeholders.Methods: This study uses qualitative methods to analyze research data using TWDI Maturity Model tools. Interview technique is used to retrieve respondent data where interview preparation guidelines are made by paying attention to 5 dimensions and 50 indicators in TDWI.Results: The research showed that the implementation of big data technology in the company as a whole has reached the level of corporate adoption. Infrastructure, data management, and analytics dimensions have reached the corporate adoption level while the organizational and governance dimensions are still at an early adoption level.Conclusion: To measure the maturity level of adoption of big data technology in manufacturing companies can use qualitative methods with TDWI Maturity model tools, interview guides for data retrieval by considering the 5 dimensions and 50 indicators that exist in TDWI. 

scholarly journals An Approach in Big Data Analytics to Improve the Velocity of Unstructured Data Using MapReduce

2021 ◽  
Vol 10 (4) ◽  
pp. 1-25
Author(s):  
Sundarakumar M. R. ◽  
Mahadevan G. ◽  
Ramasubbareddy Somula ◽  
Sankar Sennan ◽  
Bharat S. Rawal
Keyword(s):  
Big Data ◽  
Time Delay ◽  
Data Processing ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Retrieval ◽  
High Volume ◽  
Minimum Latency ◽  
Hadoop Clusters ◽  
Search Index

Big Data Analytics is an innovative approach for extracting the data from a huge volume of data warehouse systems. It reveals the method to compress the high volume of data into clusters by MapReduce and HDFS. However, the data processing has taken more time for extract and store in Hadoop clusters. The proposed system deals with the challenges of time delay in shuffle phase of map-reduce due to scheduling and sequencing. For improving the speed of big data, this proposed work using the Compressed Elastic Search Index (CESI) and MapReduce-Based Next Generation Sequencing Approach (MRBNGSA). This approach helps to increase the speed of data retrieval from HDFS clusters because of the way it is stored in that. this method is stored only the metadata in HDFS which takes less memory during runtime compare to big data due to the volume of data stored in HDFS. This approach is reduces the CPU utilization and memory allocation of the resource manager in Hadoop Framework and imroves data processing speed, such a way that time delay has to be reduced with minimum latency.

scholarly journals An Approach in Big Data Analytics to improve the velocity of unstructured data Using Map Reduce

2021 ◽  
Vol 10 (4) ◽  
pp. 0-0
Keyword(s):  
Big Data ◽  
Time Delay ◽  
Data Processing ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Retrieval ◽  
High Volume ◽  
Map Reduce ◽  
Hadoop Clusters ◽  
Search Index

Big Data Analytics is an innovative approach for extracting the data from a huge volume of data warehouse systems. It reveals the method to compress the high volume of data into clusters by MapReduce and HDFS. However, the data processing has taken more time for extract and store in Hadoop clusters. The proposed system deals with the challenges of time delay in shuffle phase of map-reduce due to scheduling and sequencing. For improving the speed of big data, this proposed work using the Compressed Elastic Search Index (CESI) and MapReduce-Based Next Generation Sequencing Approach (MRBNGSA). This approach helps to increase the speed of data retrieval from HDFS clusters because of the way it is stored in that. this method is stored only the metadata in HDFS which takes less memory during runtime compare to big data due to the volume of data stored in HDFS. This approach is reduces the CPU utilization and memory allocation of the resource manager in Hadoop Framework and imroves data processing speed, such a way that time delay has to be reduced with minimum latency.

Hengam a MapReduce-Based Distributed Data Warehouse for Big Data

2018 ◽  
Vol 8 (1) ◽  
pp. 16-35 ◽  
Author(s):  
Mohammadhossein Barkhordari ◽  
Mahdi Niamanesh
Keyword(s):  
Big Data ◽  
Information Retrieval ◽  
Data Warehouse ◽  
Data Exchange ◽  
Data Retrieval ◽  
High Volume ◽  
Data Locality ◽  
Distributed Data ◽  
Using Data ◽  
Distributed Data Warehouse

When working with a high volume of information that follows an exponential pattern, the authors confront big data. This huge amount of information makes big data retrieval and analytics important issues. There have been many attempts to solve data analytic problems using distributed platforms, but the main problem with the proposed methods is not observing the data locality. In this article, a MapReduce-based method called Hengam is proposed. In this method, data format unification helps nodes to have data independence. The unified format leads to an increase in the information retrieval speed and prevents data exchange betoen nodes. The proposed method was evaluated using data items from an ICT company and the information retrieval time was much better than that of other open-source distributed data warehouse software.

scholarly journals An Approach in Big Data Analytics to Improve the Velocity of Unstructured Data Using MapReduce

2021 ◽  
Vol 10 (4) ◽  
pp. 0-0
Keyword(s):  
Big Data ◽  
Time Delay ◽  
Data Processing ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Retrieval ◽  
High Volume ◽  
Minimum Latency ◽  
Hadoop Clusters ◽  
Search Index

Big Data Analytics is an innovative approach for extracting the data from a huge volume of data warehouse systems. It reveals the method to compress the high volume of data into clusters by MapReduce and HDFS. However, the data processing has taken more time for extract and store in Hadoop clusters. The proposed system deals with the challenges of time delay in shuffle phase of map-reduce due to scheduling and sequencing. For improving the speed of big data, this proposed work using the Compressed Elastic Search Index (CESI) and MapReduce-Based Next Generation Sequencing Approach (MRBNGSA). This approach helps to increase the speed of data retrieval from HDFS clusters because of the way it is stored in that. this method is stored only the metadata in HDFS which takes less memory during runtime compare to big data due to the volume of data stored in HDFS. This approach is reduces the CPU utilization and memory allocation of the resource manager in Hadoop Framework and imroves data processing speed, such a way that time delay has to be reduced with minimum latency.

scholarly journals Integration and Optimization of Ancient Literature Information Resources Based on Big Data Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lingling Gu
Keyword(s):  
Big Data ◽  
Semantic Analysis ◽  
High Volume ◽  
Information Resources ◽  
Cultural Resources ◽  
Data Generation ◽  
Volume Data ◽  
Literature Information ◽  
Ancient Literature ◽  
Big Data Technology

Big data refers to a collection of data that cannot be captured, managed, and processed with conventional software tools within a certain time frame. It is a massive, high-volume, high-volume data that requires new processing models to have stronger decision-making power, insight and discovery, process optimization capabilities, growth rate, and diversified information assets. This article aims to study the integration and optimization of ancient literature information resources of big data technology, that is, to integrate and optimize ancient literature information resources through big data technology and make the literature more systematic and complete, allowing readers to find and browse literature more conveniently. This paper focuses on the literary works and the related collation, annotation, and textual research results and divides the scope of each subtopic according to the genre. The biggest difference between the information platform built in this paper and the existing ancient books database is that it has the functions of semantic analysis, subject retrieval, data generation, and so on. After text learning, the computer can automatically classify related vocabulary. Based on the effective integration of big data and cultural resources, the experimental results of this article show that, so far, through technical optimization and resource integration, the number of ancient literature reincorporated has exceeded 12,000 copies, and more than 10,000 publications have been restored. Therefore, big data technology is essential for the integration and optimization of cultural resources.

scholarly journals Data Science and Big Data Technologies Role in the Digital Economy

TEM Journal ◽  
2020 ◽  
pp. 756-762
Author(s):  
Sergey V. Novikov
Keyword(s):  
Big Data ◽  
Data Science ◽  
Predictive Analytics ◽  
Liquid Product ◽  
Digital Economy ◽  
Retail Trade ◽  
Necessary Condition ◽  
Manufacturing Companies ◽  
Big Data Technology

This article explores the role of Data Science and Big Data technology in the modern digital economy. The author states that large and medium companies from retail trade and service sector show increased interest in using them. These technologies are actively used by banks, mobile operators and large manufacturing companies to analyze data on equipment failures and to reduce downtime, which allows reducing costs. The role of Big Data technology is to be a liquid product and a necessary condition to increase the profitability of enterprises through personalized customer service and predictive analytics. For today's Russian digital economy, it is very important to legalize a single definition of Big Data and to achieve the emergence of special data exchanges.

A New Rockburst Experiment Data Compression Storage Algorithm based on Big Data Technology

2019 ◽  
pp. 561-573
Author(s):  
Yu Zhang ◽  
Yan-Ge Wang ◽  
Yan-Ping Bai ◽  
Yong-Zhen Li ◽  
Zhao-Yong Lv ◽  
...  
Keyword(s):  
Big Data ◽  
Data Compression ◽  
Experiment Data ◽  
Big Data Technology

Assessing The COBIT Maturity Model in Manufacturing Company

2019 ◽  
Vol 5 (2) ◽  
pp. 109-115
Author(s):  
Johanes Fernandes Andry ◽  
Gunawan Wang ◽  
Gusti Ngurah Suryantara ◽  
Devi Yurisca Bernanda
Keyword(s):  
Information Systems ◽  
Business Processes ◽  
Standard Procedure ◽  
It Governance ◽  
Maturity Model ◽  
Maturity Level ◽  
It Alignment ◽  
Business Goals ◽  
Manufacturing Company ◽  
Effectiveness And Efficiency

PT Hema Indonesia is manufacturing company established in 2001 and has continued to grow. Nowadays the company has supported business processes in various companies, such as the use of information systems. The purpose of this research is to get an overview of the performance of information systems in order to determine the extent of maturity level which is currently running, with a few aspects to consider such as effectiveness and, efficiency. Implementing IT governance, however, is a challenge to organizations. To ensure IT alignment with business goals use standard COBIT. The analytical tool used is the standard procedure COBIT issued by ISACA. In this paper the method to be used is COBIT 4.1. Coverage of Audit IT Domain are Plan Organize (PO), such as PO4, PO5, PO7 and PO8. The conclusion that can be drawn from the research that has been done is IT governance at the company has been done, although still run optimally within each IT process contained in the sub domain average on level repeatable and defined proses.

IT GOVERNANCE AUDIT AT PT PERUSAHAAN GAS NEGARA USING COBIT FRAMEWORK

2018 ◽  
Vol 23 (2) ◽  
pp. 95-106
Author(s):  
Mahendra Sunt Servanda ◽  
Achmad Benny Mutiara
Keyword(s):  
Information And Communication Technology ◽  
Communication Technology ◽  
It Governance ◽  
Maturity Model ◽  
Maturity Level ◽  
Efficiency And Effectiveness ◽  
Information And Communication ◽  
Level 3 ◽  
It Usage ◽  
A Company

The use of information and communication technology in a company gives an important contribution for the achievement of business objectives. PT Perusahaan Gas Negara, especially in the Business Solutions and Services Operations (BSSO), plays a significant role in the utilization of information and communication technology assets to PT Perusahaan Gas Negara. It takes a good IT governance for BSSO to improve the efficiency and effectiveness of IT usage. Audit of IT governance maturity using COBIT 4.1. Maturity model level used to determine the maturity level of IT usage in the enterprise with a scale of 0 (non-existent) to 5 (optimized). This study focused on two domains namely Plan and Organise (PO) and Monitor and Evaluate (ME) model to measure the maturity level of IT maturity levels in PT Perusahaan Gas Negara. From this study, the results of the maturity level domain PO is 3.13 and ME is 2.98, it can be given the conclusion that the maturity level of IT governance at PT PGN is in level 3 (defined). At this level means that all the procedures in the company are standardized and documented, but the company is still not able to detect the deviations that have occurred.

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