PhD Thesis on Big Data Analytics

Big data is denoted as the variety, volume, and velocity of information assets in high form along with processes such as decision-making, innovations in information processing, and the cost-effective process. The objectives of big data analytics are deployed with advanced analytics techniques through various and massive data sets such as the unstructured, structured, and semi-structured data collected from various sources in several sizes such as zettabytes, terabytes, etc. This page is all about latest information for reference about phd thesis on big data analytics.

Introduction to Big Data Analytics

Big data can capture traditional relational databases and data sets of any size. In addition, it is far enough to process and regulate the data in low latency. Mobile, social, internet of things and artificial intelligence are deployed to drive the complexity of data using some forms and data sources. For your reference, we have enlisted some components that are created in real-time and on a very large scale.

  • Social media
  • Web
  • Transactional applications
  • Log files
  • Networks
  • Video and audio
  • Devices
  • Sensors

The applications of big data analytics include data from external sources and internal systems like demographic data and weather data from third-party information service providers. Streaming analytics applications are included in this process of the big data environment to perform real-time analytics as the user in served data to the Hadoop systems using some engines based on stream processing and that includes the storm, flink, and spark for latest phd thesis on big data analytics.

How does it work?

  • Data analysis
    • Deep learning
      • It is similar to the human learning patterns along with the functions of machine learning and artificial intelligence and it is deployed to recognize the patterns and layer algorithms in the abstract and complex data
  • Data mining
    • It is categorized as the large datasets for the creation of data clusters, recognizing the patterns and anomalies
  • Predictive analytics
    • The data based on history is used to predict the future and find the risks and opportunities
  • Data cleaning
    • It is used to enhance the quality of data and that too acquire strong results and it provides the correct format from the duplicative and irrelevant data
  • Data processing
    • It includes two significant processing such as
      • Stream processing
        • It is considered as the small batches of data and it reduces the time delay in the data analysis and data collection
      • Batch processing
        • It is the massive data block through time and it is used for the turnaround time among the data analysis and collection
  • Data collection
    • IoT sensors are used to store the collected data from various sources of structured and unstructured data. The storage process includes mobile applications and cloud storage

Components of Big Data Analytics

Big data analytics use both the logical and physical structure of data in high volumes and they are accessed, managed, stored, processed, ingested, and more through the following elements.

  • Data sources
  • Data storage
  • Real-time message ingestion
  • Batch processing
  • Stream processing
  • Analytical data store
  • Analytics and reporting

The architecture of Big Data Analytics

The modules included in the architecture of big data analytics include four logical layers and they are listed in the following.

  • Composition layer
    • It is used to receive the results based on the big data analysis layer and it is also called a business intelligence layer
  • Analysis layer
    • In this layer the analytical tools are used to extract the business intelligence in big data
  • Management and storage layer
    • It is mainly used for the data to a comprehensible format for the data analysis tool to store the data
  • Big data sources layer
    • It is capable to regulate the real-time processing of big data sources and batch processing and includes the process such as
      • IoT devices
      • SaaS application
      • Relational database management systems
      • Data warehouse

Applications of Big Data Analytics

Big data is deployed to extract the business value using the massive data volume analysis that is functional for the capabilities, modeling, and analytics. Now, let’s have look at the significant applications based to design PhD thesis on big data analytics in the following.

  • Fraud detection and prevention
    • The analytical models are created to enhance the reliability of the decision-making process in machine learning
  • Resource optimization
    • Analytics process is deployed to determine the number of resources that are allocated to provide various scenarios such as
      • Location
      • Time
  • Exploration and discovery
    • The analytical results are deployed to recognize the features of cost measures for the new product in the market

Research Challenges in Big Data Analytics

The issues in big data management are functioning among the storage, collection, and integration of data with minimal requirements, and they are enlisted in the following for your reference.

  • Challenges in data analysis and visualization
  • Data storage, data capture, and quality of data
  • Data privacy issues
  • Data security issues

Research Solutions in Big Data Analytics

Our research professionals are well-equipped to provide research solutions for the above-mentioned research challenges in big data analytics. The following is about the relevant solutions for the research limitations that occurred in PhD thesis on big data analytics.

  • Big data analytics is deployed to enhance the automatic models and to develop the user experience
  • It can prove resources in the increasing traffic to solve
  • The network traces are used to develop the automatic models that is functioning to configure and monitor the cellular network

Types of Big Data Analytics

For your reference, our technical experts have highlighted the types that are occurred in the process of big data analytics.

  • Prescriptive analytics
  • Predictive analytics
  • Diagnostic analytics
  • Descriptive analytics

Processes of Big Data Analytics

  • Protecting quality of service
  • Systems management
  • Data governance
  • Connecting to data sources

The aforementioned are the significant processes that are functioning while implementing the PhD thesis on big data analytics. In addition, we have enlisted the topical and contemporary research areas in the big data analytics research field.

Research Areas in Big Data Analytics

  • Cloud
  • Artificial intelligence (AI)
  • Machine learning
  • Data mining
  • Internet of things (IoT)

Our research professionals have stated the research topics based on the analysis of research areas in big data analytics.

Research Topics in Big Data Analytics

  • Frequent pattern mining from heterogeneous database
  • Privacy preservation for recommendation systems design
  • Security issues in Hadoop’s massive data
  • Addressing bid data classification and clustering problem
  • Fault data detection and recovery

The above-mentioned research topics are organized as their research area and it is beneficial for the research scholars to go through them. It is used for scholars to drop an idea and start the research. Now, let’s have a look at some of the notable research trends in big data analytics for research scholars along with the future research directions in the same field.

Recent Trends in Big Data Analytics

  • Quantum computing
  • Data as a service

Future Research Directions

  • Predictive analytics
    • It is deployed to implement the modern data to identify the possible hazards because it can predict the future and it is efficient to correct the analysis process

So far, we have discussed the significance of PhD thesis on big data analytics and the topical research areas in this field and the above-mentioned are the emerging research trends that are gaining widespread implications. We must aware of the current trends and developments in the research area before presenting the doctoral degree. In the following, we have enlisted significant components such as algorithms, protocols, simulation models, tools, and datasets to develop research projects in big data analytics.

Algorithms in Big Data Analytics

  • Applications of game theory
  • Self-organizing maps (SOM)
  • A priori algorithms
  • Combinatorial algorithms
  • Clustering algorithms
  • Neural networks (NN)
  • Random forest
  • Naive Bayes classifier (NBC)
  • K Nearest neighbor (KNN)
  • Support vector machine (SVM)
  • Decision trees (DT)
  • Linear regression

Protocols in Big Data Analytics

  • Using a VPN to securely access and transfer big data
  • A secure way to go about the deployment of big data in business

Simulation Models in Big Data Analytics

  • Discrete event simulation
  • Monte Carlo and risk analysis simulation
  • Agent-based modeling and simulation

Tools and Software in Big Data Analytics

  • R
    • It is one of the programming languages deployed in the process of big data analysis and using the massive data sets through Hadoop it provides an easy solution for the process
  • Apache mahout
    • The mahout includes large datasets, java libraries, algorithms, and the application of large-scale machine learning. It offers optimized algorithms based on the machine learning task conversion

Datasets in Big Data Analytics

  • Place#Hashtag twitter dataset: COVID-19 Hashtags
    • It is the type of dataset that is developed through the GeoCOVID19 tweets datasets. Network analysis is functioning through the parallel datasets among the hashtags and countries. The users can utilize the CSV files where it is required

Performance Metrics in Big Data Analytics

  • Precision
  • Accuracy
  • Recall

The above-mentioned performance metrics are used to analyze the research results of the big data analytics thesis. For your quick understanding, our research professionals have enlisted the answers to the following questions that are essential to implement the research in big data analytics.

Questions

What are the techniques for big data analytics?

  • Divide and conquer analysis related to the velocity of data.
  • Precision analysis related to the veracity of data
  • Deep analysis related to the veracity of data
  • High dimensional analysis related to a variety of data
  • Association analysis related to unknown data sampling
  • Ensemble analysis related to a large volume of data

What are the big data analytics tools?

  • Apache spark
    • It is an open-source process and is denoted as the cluster computing framework deployed in real-time processing. It is used as the interface for programming and the open-source community. It is ensured implicit data parallelism and fault tolerance
  • R and python
    • It is used in the data analytics process and it is an open-source tool deployed in the analytics and statistics with the interpreted languages and for the simple syntax and dynamic semantics

What are the latest trends in big data?

  • Natural language processing
  • Edge computing

What are the research areas in big data?

  • Scalable storage systems for big data
  • Data mining tools and techniques for big data
  • Security, and privacy issues in big data
  • Software and tools for massive big-data processing
  • Scalable architectures for massively parallel data processing

Which ML algorithms are implemented on big data?

  • Semi-supervised clustering
    • Clustering is utilizing the regulation domain data for the enhancement of clustering and it includes the alteration of domain data
  • Supervised learning
    • Naive Bayes
    • Maximum entropy method (MaxENT)
    • Support vector machines (SVM)
  • Unsupervised learning
    • Self-organizing Maps
    • Clustering
      • K-means
      • Density-based learning
      • Hierarchal learning
    • Adaptive Resonance Theory

At the end of this article, we guarantee to provide unique research service and 100% plagiarism-free research work for the PhD research scholars. For more research requirements the research scholars can contact our research professionals at any time. We have 150+ research experts and developers to enrich your research PhD thesis on big data analytics. So, join hands with our experts to get succeed in your research career.

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