Big Data and Analytics—A Journey Through Basic Concepts to Research Issues

2015 ◽  
pp. 291-306 ◽  
Author(s):  
Manjula Ramannavar ◽  
Nandini S. Sidnal
Keyword(s):  
Big Data ◽  
Research Issues ◽  
Basic Concepts

Big Data and Global Software Engineering

2019 ◽  
pp. 131-163 ◽  
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Big Data ◽  
Potential Effect ◽  
The Internet ◽  
Research Issues ◽  
Open Research ◽  
Fundamental Goal ◽  
Innovative Work ◽  
Different Dimensions ◽  
Global Software Engineering ◽  
The Internet Of Things

A large vault of terabytes of information created every day from present-day data frameworks and digital innovations, for example, the internet of things and distributed computing. Investigation of this enormous information requires a ton of endeavors at different dimensions to separate learning for central leadership. An examination is an ebb-and-flow territory of innovative work. The fundamental goal of this paper is to investigate the potential effect of enormous information challenges, open research issues, and different instruments related to it. Subsequently, this article gives a stage to study big data at various stages. It opens another skyline for analysts to build up the arrangement in light of the difficulties, and open research issues. The article comprehended that each large information stage has its core interest. Some of this is intended for bunch handling while some are great at constant scientific. Each large information stage likewise has explicit usefulness. Unique procedures were utilized for the investigation.

Big Data and Global Software Engineering

2022 ◽  
pp. 1249-1274
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Big Data ◽  
Potential Effect ◽  
The Internet ◽  
Research Issues ◽  
Open Research ◽  
Fundamental Goal ◽  
Innovative Work ◽  
Different Dimensions ◽  
Global Software Engineering ◽  
The Internet Of Things

A large vault of terabytes of information created every day from present-day data frameworks and digital innovations, for example, the internet of things and distributed computing. Investigation of this enormous information requires a ton of endeavors at different dimensions to separate learning for central leadership. An examination is an ebb-and-flow territory of innovative work. The fundamental goal of this paper is to investigate the potential effect of enormous information challenges, open research issues, and different instruments related to it. Subsequently, this article gives a stage to study big data at various stages. It opens another skyline for analysts to build up the arrangement in light of the difficulties, and open research issues. The article comprehended that each large information stage has its core interest. Some of this is intended for bunch handling while some are great at constant scientific. Each large information stage likewise has explicit usefulness. Unique procedures were utilized for the investigation.

Applications of Big Data in Large and Small Systems

2021 ◽  
pp. 20-37
Author(s):  
Archana Purwar ◽  
Indu Chawla
Keyword(s):  
Big Data ◽  
Soft Computing ◽  
New Technology ◽  
Electronic Devices ◽  
Small Systems ◽  
Innovative Methods ◽  
Research Challenges ◽  
Basic Understanding ◽  
Basic Concepts ◽  
Tools And Techniques

Nowadays, big data is available in every field due to the advent of computers and electronic devices and the advancement of technology. However, analysis of this data requires new technology as the earlier designed traditional tools and techniques are not sufficient. There is an urgent need for innovative methods and technologies to resolve issues and challenges. Soft computing approaches have proved successful in handling voluminous data and generating solutions for them. This chapter focuses on basic concepts of big data along with the fundamental of various soft computing approaches that give a basic understanding of three major soft computing paradigms to students. It further gives a combination of these approaches namely hybrid soft computing approaches. Moreover, it also poses different applications dealing with big data where soft computing approaches are being successfully used. Further, it comes out with research challenges faced by the community of researchers.

scholarly journals A Survey on Smart Analytics: Method, Tools, and Open Research Issues

2020 ◽  
Vol 3 (1) ◽  
pp. 54-64
Author(s):  
Nova Nurviana
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Research Issues ◽  
Open Research

Smart analytic nowadays was very popular because today big data is booming. Back several years ago, people were fascinating with statictics which surveys or cencus become a gun for describing or forecasting some issues as of decision making or planning. By migrating to big data, smart analytic is very needed to filter which data is useful and producing some worthy information.

Database Systems for Big Data Storage and Retrieval

2018 ◽  
pp. 76-100 ◽  
Author(s):  
Venkat Gudivada ◽  
Amy Apon ◽  
Dhana L. Rao
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Database Systems ◽  
Query Languages ◽  
Research Issues ◽  
Storage And Retrieval ◽  
Emerging Trends ◽  
Big Data Applications ◽  
Data Application ◽  
Big Data Application

Special needs of Big Data applications have ushered in several new classes of systems for data storage and retrieval. Each class targets the needs of a category of Big Data application. These systems differ greatly in their data models and system architecture, approaches used for high availability and scalability, query languages and client interfaces provided. This chapter begins with a description of the emergence of Big Data and data management requirements of Big Data applications. Several new classes of database management systems have emerged recently to address the needs of Big Data applications. NoSQL is an umbrella term used to refer to these systems. Next, a taxonomy for NoSQL systems is developed and several NoSQL systems are classified under this taxonomy. Characteristics of representative systems in each class are also discussed. The chapter concludes by indicating the emerging trends of NoSQL systems and research issues.

Bootstrapping Urban Planning

2020 ◽  
pp. 217-246
Author(s):  
Ankur Lohachab
Keyword(s):  
Big Data ◽  
Urban Planning ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Sensitive Information ◽  
Ongoing Research ◽  
Research Issues ◽  
Advantages And Disadvantages ◽  
Efficient Management ◽  
Different Characteristics

Rapid growth of embedded devices and population density in IoT-based smart cities provides great potential for business and opportunities in urban planning. For addressing the current and future needs of living, smart cities have to revitalize the potential of big data analytics. However, a colossal amount of sensitive information invites various computational challenges. Moreover, big data generated by the IoT paradigm acquires different characteristics as compared to traditional big data because it contains heterogeneous unstructured data. Despite various challenges in big data, enterprises are trying to utilize its true potential for providing proactive applications to the citizens. In this chapter, the author finds the possibilities of the role of big data in the efficient management of smart cities. Representative applications of big data, along with advantages and disadvantages, are also discussed. By delving into the ongoing research approaches in securing and providing privacy to big data, this chapter is concluded by highlighting the open research issues in the domain.

Data Mining for the Internet of Things: Literature Review and Challenges

2021 ◽  
Vol 9 (VII) ◽  
pp. 3351-3362
Author(s):  
Dr. Mohd Zuber
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Internet Of Things ◽  
The Internet ◽  
Mining System ◽  
Research Issues ◽  
Data Mining Algorithms ◽  
Huge Data ◽  
Mining Algorithms ◽  
The Internet Of Things

The huge data generate by the Internet of Things (IOT) are measured of high business worth, and data mining algorithms can be applied to IOT to take out hidden information from data. In this paper, we give a methodical way to review data mining in knowledge, technique and application view, together with classification, clustering, association analysis and time series analysis, outlier analysis. And the latest application luggage is also surveyed. As more and more devices connected to IOT, huge volume of data should be analyzed, the latest algorithms should be customized to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

Big Data and Digital Analytics

2022 ◽  
pp. 571-589
Author(s):  
Sumathi Doraikannan ◽  
Prabha Selvaraj
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Digital Data ◽  
Detailed Knowledge ◽  
Data Sets ◽  
Business Values ◽  
Business Operations ◽  
It Systems ◽  
Basic Concepts ◽  
And Storage

Data becomes big data when then the size of data exceeds the ability of our IT systems in terms of 3Vs (volume, velocity, and variety). When the data sets are large and complex, it becomes a great difficult task for handling such voluminous data. This chapter will provide a detailed knowledge of the major concepts and components of big data and also the transformation of big data in to business operations. Collection and storage of big data will not help out in creation of business values. Values and importance are created once when the action starts on data by performing an analysis. Hence, this chapter provides a view on various kinds of analysis that can be done with big data and also the differences between traditional analytics and big data analytics. The transformation of digital data into business values could be in terms of reports, research analyses, recommendations, predictions, and optimizations. In addition to the concept of big data, this chapter discuss about the basic concepts of digital analytics, methods, and techniques for digital analysis.

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