A Framework for Managing Big Data in Enterprise Organizations

2020 ◽  
Vol 12 (1) ◽  
pp. 84-97
Author(s):  
Youssef Ahmed ◽  
Walaa Medhat ◽  
Tarek El Shishtawi
Keyword(s):  
Big Data ◽  
Data Management ◽  
Rapid Growth ◽  
Decision Makers ◽  
Data Service ◽  
Correct Decision ◽  
Management Framework ◽  
Critical Problems ◽  
Business Needs ◽  
Main Components

Big Data management is trending research that seeks to find a framework that will give support to decision makers in governments and enterprises organizations. For the rapid growth of data, dealing with Big Data with respect to management and finding new values has drawn attention recently. Strategies should be established together with the goals, vision, and objectives of an organization to manage Big Data. Big data management frameworks are the main components for the implementation of Big Data service. Many organizations that deals with Big Data have three critical problems, how to manage Big Data, how can Big Data create new values reference to its strategies and business needs, and how it can take the correct decision in the correct time. In this article, the authors propose a Big Data management framework that will handle all Big Data operation beginning with collecting data until making analysis and how new value can be created. The proposed framework also takes care of other factors such as organization strategies, governance, and security.

Emerging Trends and Techniques in Cloud-Based Data Analytics

2021 ◽  
pp. 37-47
Author(s):  
Shaila S. G. ◽  
Monish L. ◽  
Lavanya S. ◽  
Sowmya H. D. ◽  
Divya K.
Keyword(s):  
Social Media ◽  
Cloud Computing ◽  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Huge Data ◽  
Emerging Trends ◽  
Media Applications

The new trending technologies such as big data and cloud computing are in line with social media applications due to their fast growth and usage. The big data characteristic makes data management challenging. The term big data refers to an immense collection of both organised and unorganised data from various sources, and nowadays, cloud computing supports in storing and processing such a huge data. Analytics are done on huge data that helps decision makers to take decisions. However, merging two conflicting design principles brings a challenge, but it has its own advantage in business and various fields. Big data analytics in the cloud places rigorous demands on networks, storage, and servers. The chapter discusses the importance of cloud platform for big data, importance of analytics in cloud and gives detail insight about the trends and techniques adopted for cloud analytics.

scholarly journals Efficient IoT Data Management for Geological Disasters Based on Big Data-Turbocharged Data Lake Architecture

2021 ◽  
Vol 10 (11) ◽  
pp. 743
Author(s):  
Xiaohui Huang ◽  
Junqing Fan ◽  
Ze Deng ◽  
Jining Yan ◽  
Jiabao Li ◽  
...  
Keyword(s):  
Big Data ◽  
Data Management ◽  
Decision Makers ◽  
Prototype System ◽  
Data Repositories ◽  
Metadata Model ◽  
Parallel Data ◽  
Interactive Environment ◽  
Data Ingestion ◽  
Distributed Cache

Multi-source Internet of Things (IoT) data, archived in institutions’ repositories, are becoming more and more widely open-sourced to make them publicly accessed by scientists, developers, and decision makers via web services to promote researches on geohazards prevention. In this paper, we design and implement a big data-turbocharged system for effective IoT data management following the data lake architecture. We first propose a multi-threading parallel data ingestion method to ingest IoT data from institutions’ data repositories in parallel. Next, we design storage strategies for both ingested IoT data and processed IoT data to store them in a scalable, reliable storage environment. We also build a distributed cache layer to enable fast access to IoT data. Then, we provide users with a unified, SQL-based interactive environment to enable IoT data exploration by leveraging the processing ability of Apache Spark. In addition, we design a standard-based metadata model to describe ingested IoT data and thus support IoT dataset discovery. Finally, we implement a prototype system and conduct experiments on real IoT data repositories to evaluate the efficiency of the proposed system.

scholarly journals Sports Big Data: Management, Analysis, Applications, and Challenges

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhongbo Bai ◽  
Xiaomei Bai
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Management ◽  
Rapid Growth ◽  
Growth Trend ◽  
Research Directions ◽  
Research Issues ◽  
Big Data Applications ◽  
Rich Information ◽  
Sports Data

With the rapid growth of information technology and sports, analyzing sports information has become an increasingly challenging issue. Sports big data come from the Internet and show a rapid growth trend. Sports big data contain rich information such as athletes, coaches, athletics, and swimming. Nowadays, various sports data can be easily accessed, and amazing data analysis technologies have been developed, which enable us to further explore the value behind these data. In this paper, we first introduce the background of sports big data. Secondly, we review sports big data management such as sports big data acquisition, sports big data labeling, and improvement of existing data. Thirdly, we show sports data analysis methods, including statistical analysis, sports social network analysis, and sports big data analysis service platform. Furthermore, we describe the sports big data applications such as evaluation and prediction. Finally, we investigate representative research issues in sports big data areas, including predicting the athletes’ performance in the knowledge graph, finding a rising star of sports, unified sports big data platform, open sports big data, and privacy protections. This paper should help the researchers obtaining a broader understanding of sports big data and provide some potential research directions.

scholarly journals Implementing Data Strategy

2020 ◽  
Vol 24 ◽  
Author(s):  
Radhakrishnan Balakrishnan ◽  
Satyasiba Das ◽  
Manojit Chattopadhyay
Keyword(s):  
Big Data ◽  
Data Management ◽  
Value Creation ◽  
Customer Value ◽  
Data Extraction ◽  
Management Framework ◽  
Value Propositions ◽  
Four Levels ◽  
Using Data ◽  
Transactional Data

With the arrival of Big Data, organizations have started building data-enabled customer value propositions to increase monetizing and cost-saving opportunities. Organizations have to implement a set of guidelines, procedures, and processes to manage, process and transform data that could be leveraged for value creation. This study has approached the journey of an organization towards data-enabled value creation through four levels of data processing, such as data extraction, data transformation, value creation, and value delivery. This study has critical inferences on using data management solutions such as RDBMS, NoSQL, NewSQL, Big Data and real-time reporting tools to support transactional data in internal systems, and other types of data in external systems such as Social Media. The outcome of this study is a methodological technology independent data management framework an organization could use when building a strategy around data. This study provides guidelines for defining an enterprise-wide data management solution, helping both the academicians and practitioners.

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 DaLiF: a data lifecycle framework for data-driven governments

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Iftikhar Hussain Shah ◽  
Vassilios Peristeras ◽  
Ioannis Magnisalis
Keyword(s):  
Big Data ◽  
Data Management ◽  
Good Governance ◽  
Business Community ◽  
Data Driven ◽  
Governmental Organizations ◽  
Data Lifecycle ◽  
The Government ◽  
Data Ecosystem ◽  
Governance Transparency

AbstractThe public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.

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