Research Gaps in Big Data & Computational models

Future Research Direction on Computational Models for Big Data and Research Opportunities for PhD Scholars

Introduction

“Research gaps” is quite hard, isn’t it? That, too, in the ocean-like field of big data. It may be a little frustrating, but it is a rewarding process for sure. The rapid increase in the domains of computational models, both as standalone and interdisciplinary aspects, has led to a situation like picking a needle in a haystack when you must seek research gaps. No worries! We got you. Know the contemporary trends of Big Data.

Overview

Fundamentally, a computational model is a simulation that operates based on algorithms to represent real-world systems and analyze and predict their behavior. Concisely, the events or hypothetical situations cannot be analyzed or simulated practically with real-world input; instead, the algorithms use human input data and variables to simulate and predict. Hence, data processing has expanded to a variety of forms and form factors. Their significance and recommendations are discussed in detail

Background

Classical computational models were merely just neutral representations that entirely depended on human input and could not learn and evolve on their own. However, right after the introduction of machine learning and AI, the models began to learn actively from both their developers and users. The integration of big data opened doors for high efficiency and optimization with large datasets, which caused a cultural shift in lifestyle. It paved the way for greater scope in numerous sectors which resulted in the emergence of a new era in computer science.

Significance of big data

The need for big data

Big Data and Computational Models have always been the core of evolution for data processing. Without it, we could have ended up with devices that resembled a cellular phone that could not process data efficiently. Imagine typing the algorithm for its classical logic gates for hours only to print “Hello world!” nostalgic yet frustrating, isn’t it?

In all the vital sectors that influence the multiple facets of society, such as economics, healthcare, finance, business, and management, big data has somehow become indispensable. Very obviously, the automated computation procedure reduced the time, cost, and effort to a significant portion, which is good. But we must look at how these things can be developed even more.

The need for research in computational models and future directions

While they are cutting-edge and cost-effective in their essence, trust and validity are still unexplored.

One of the biggest setbacks is the lack of adequate information on the study and behavior of computational models.

Behaviour of models are usually hidden under its processing layer,; certain areas in data processing need enough study and research that are still in development. Let’s look at them.

Existing computational models and their role

The pre-existing data computational models and the nature of their framework are very diverse and offer a spectrum of applications in demanding fields that require high velocity and high volumes of Large Data Sets. Particularly, they are used for better optimization, simulating real-world assumptions, predictive analysis, and understanding complex systems. In conciseness, their ability to advance and comprehend both technical and practical concepts became essential for research and development.

The leading types of computational models include algorithmic models, mathematical models, agent-based models, and AI and machine learning models.

Challenges in existing computational models

Model tuning

Many of these models demanda highly complex and repetitive tuning process to produce accurate output, which requires a highly efficient computational resource

Resource demanding

The simulations that involvelarge amounts of data sets require hardware that keeps up wellwith the model’s data processing needs, which can be expensive.

Ethical concerns

The pre-existing models have huge concerns regarding fairness, bias, privacy, and ethical usage.These issues need immediate addressing in order to make them stable and applicable more deliberately.

Emerging computational models

Graph neural networks

Graph Neural Networks serve in analyzing and predicting the outcomes or relations using the link and node information to derive valuable insights. Applying GNN to specific domains needs customization and adaptability, and they are dynamic, meaning they change their node and links frequently. Their ambiguity in being both centralized and decentralized has more open-ended questions about their flexibility and scalability.

Moreover, GNNs are anticipated to be integrated with education as an interdisciplinary approach for improved learning experiences with more customized teaching suitable to the individual.

Explainable AI

Not even a decade has passed since AI came into play, yet we have Explainable AI now. Making AI transparent and trustable is the holy grail for research scholars and enthusiasts, as it will open doors for further research and improvements in AI and machine learning in relation to big data. This geometrical progression in the evolution of computational models is indeed a place that needs sufficient examination.

Quantum computing

Using the characteristics of quantum mechanics, a new emerging computational model is in the early stages of development, which holds high hopes for improved efficiency and optimization. Like any computational model, this also has some areas for improvement and development, which can ultimately lead to breakthroughs. Combining Quantum Computing with other domains as an interdisciplinary initiative is a great choice for exploring the possibilities of quantum computing.

Edge computing

One of the modified frameworks of Big Data is Edge computing, which processes data in the nearest proximity to the source and reduces bandwidth issues and latency. This will enhance the security of the data as it does not require traveling to a centralized data center. Such a model will deal with the privacy challenges of the other models.

This expanding field of big data continues to grow and finds itself on a continuous path of progression. It promises a huge number of opportunities for exciting breakthroughs that have yet to be discovered by researchers. By looking at the specific areas for further exploration, we can conclude what exactly the research gaps are with extensive study.

Future Directions for Research/PhD scholars

We saw that the emerging trends in computational models in Big Data, which are still in development, require an extensive study dedicatedto each framework.This will eventually provide more prospects for development, but not without proper research.
Soon after the arrival of Artificial Intelligence, Machine Learning Computational Models were forced to rethink their purpose, causing a new shift in Big Data.Studying their behavior required empirical research and a customized ecosystem.
Machine learning models increasingly use users’ data, making it crucial to protect their privacy.Creating methods to protect user data from getting exposed while still allowing the models to function efficiently will make sure the user’s data is anonymous and enhance overall data security. Machine Learning models increasingly use personal data of the users, making it crucial to protect the privacy of the users. Creating methods to protect user data from getting exposed while still allowing the models to function efficiently, will make sure the user’s data is anonymous and enhance overall data security.
Learning models learn from both users and developers with integration to a large combination of labeled and unlabeled datasets to find hidden patterns. This made it impossible to foresee their behavior, which resulted in less accountability and validity. So, this is where the Explainable AI creates hope for understanding the Machine’s behavior, which will eventually lead to transparency in their analysis and prediction. Creating a metric for XAI’s behavior pattern that includes its planning, the logic behind planning, and execution steps to communicate the hidden compiling pattern. Making AI transparent will create a chain reaction of improvements in Machine language and AI, almost another cultural shift in Computer Language.
A similar approach was taken for the GNN models not to make them accountable but to integrate them with education as an initiative to process realeal-time data for customisededucational patterns for students according to their cognitive skills. However, there are some potential opportunities to make it more effective in terms of accuracy with deep learning methodologies.The issue with imbalanced datasets and domain-dependent datasets can be resolved with further research into domain adaptation methods.

Future research direction

Future Directions	Scope
Enhancing Ethical Considerations of Machine Learning Models	Improving ethical practices of machine learning and protecting user data privacy byimplementing bias detection tools and data anonymization
Enhancing the Explainable AI Models	Implementing transparency in AI data processing with interpretability methods like LIME and SHAP and ensuring the privacy of user data with K-anonymity privacy protection concept.
Addressing the current issues in GNN Models	Resolving GNN’s limitations by adapting and for imbalanced datasets with re-sampling methods.
Scaling up the data collection to improve pedagogical insights	Integrating student-driven data with models to improve insights on students’ behavior and pedagogical practices with predictive analysis for performance prediction.

Conclusion

This exciting realm of big data comprises countless elements that continuously perpetuate fresh perspectives so often.

While they may ignite curiosity, they need extensive research and study for stability and sustainability in terms of applicability. Seeing these incomplete assumptions made on those components may create an urge to explore these data processing models with the objective of finding a solid conclusion. But they obviously require a huge amount of effort and time.

However, by uncovering the research potential with the right expert guidance, will make such tedious task simple and effective. PhD Assistance offers standardized dissertation services from start to finish.