The huge collection of data (like organization data) is called big data. At present time, big data technology is creating the best impact in many industrial and technological sectors. Since it supports any size of data in any format from any source. The initiators who use advanced data can quickly recognize the benefits in multiple ways. As well, some of them are product optimization, efficient processes, improved customer services, varying environmental visibility, etc. This article springs with new information on Big Data PhD Topics, Research Areas, Issues, Trends, Directions, Tools, etc.!!!
In the era of the digital world, digital information is growing fast which is collectively addressed as big data. In recent days, the management of big data has become a challenging task in all fields. Therefore, it grabs the attraction of scholars to produce new big data solutions for reliable processing and management of big data. We hope that you also have the same interest in big data. We are here to support you in all stages of big data PhD Topics Research with a development service. Before getting into the topic deep, first, know the following key terms of big data. Since it is more important to begin the big data machine learning study.
Fundamentals of Big Data
- Data Quality
- Streaming, Validated and Fixed
- Data Source
- Public, Private, External, and Internal
- Data Storage
- Portable, Distant-Access, Shared, and Frameworks
- Data Association
- Correlations, Superset and Subset
- Data Value
- Unique, Specialized, and Generic
- Data Structure
- Proprietary, Unstructured, Structured, Semi-Structured and Table-based
Now, we can see the general architecture of big data. Here, we have mentioned to you about different layers of big data systems starting from infrastructure to application layer. Particular, each specific set of features and functionalities to perform. Our developers have constructed an infinite number of projects in big data. So, we are familiar with possible technical issues among layers. And also, we have designed different suitable modern solutions for many research issues.
Layers of Big Data Architecture
- Infrastructure layer
- It is the first and foremost layer that comprises a required network, hardware, and software devices
- All these collections are used to acquire the data and forward them to the Hadoop cluster.
- In this, software ranges between OS and other common tools for Hadoop cluster observation
- Data Repository layer
- It is a second layer that handles data mobility in distributed environ
- Mainly, it has a chief repository for data storage as HDFS for Hadoop
- As well, it also includes data distribution tools such as Flume and Sqoop
- Further, it enables NoSQL databases in a different form of achieves
- For instance: HBase and Accumulo
- Data Refinery layer
- It is a third layer that offers a parallel processing framework
- By the by, it is used to process and manipulate data
- For instance: MapReduce and Yarn
- Data Factory layer
- The classes of the layer are referred to as workers
- It enables to monitor and manage Hadoop completely
- Further, it also empowers users to make new jobs through SQL
- Then, SQL input is translated into MapReduce jobs
- For instance: Pig, Spark, Oozie, and Hive
- Data Fusion
- It creates the platform for processing business-oriented big data solutions
- Moreover, it comprises both data visualization and analytics tools for performing all sorts of big data operations
- By the by, it enables machine learning technology
- For instance: Mahout, Tableau, Pentohoe, Datameter, etc.
- Application / Service Layer
- It is the last layer that has extensive tools to meet service requirements
- Further, it also handles the request, cost, and expenditure of requests
For your information, here we have given two main research issues of big data analytics. Presently, these issues gain more attention among the current big data research community. Although technologies are improving, they are the most common issues in many big data applications/services. To know the appropriate problem-solving techniques for the below issues, communicate with us. Similarly, we also provide the best solutions for your handpicked project.
What are the Research issues in big data analytics?
- Massive data objects / values
- Objects represent the separate datasets
- When the object is very large, the processing data through classic algorithms and hardware are complex
- As well, it collects data from a single source only
- Massive data sources
- Big data collected from various sources are hard to manage
- Also, it is larger to fit on the respective disk
- As well, the data are in a different format which compiles in individual physical site/repository
Beyond the above list of research issues, we also support you in other research challenges of big data analytics. Although different algorithms are proposed for these research challenges, still looking for the best and most effective solutions. Our developers are passionate in default to provide the best research solutions for any kind of research problem. So, we have already framed effective solutions for all these problems. If required, we also design new algorithms/techniques to settle the critical issues.
Research Ideas in Big Data Analytics
- Noise-filled Data
- Mainly, it signifies unnecessary and unreadable data which have no meaning
- By the by, it has insufficient algorithms for task optimization
- As well, it detects the noisy points through similarity and clustering techniques
- Unlabeled data
- When the data is growing tremendously, the possibility of unlabeled data may increases
- It completely tedious job to identify the unlabeled data from millions of data
- As a result, it may lead to low accuracy while dealing with incorrect data on the training model
- So, it is required to create in-built mechanisms to handle unlabeled data in all algorithms
- Also, it is benefited in data classification
- Lack of Stability in Classification
- Relatively, if there is more data in one or more classes then it creates an imbalance in training data
- So, it is necessary to balance the data through efficient sampling techniques
- Lost values
- Majorly, it may affect the accuracy and robustness of the models
- Further, it also creates issues in cooperating filtering and clustering algorithms which are majorly based on similarity computations
- So, it is required to solve by rows elimination/imputation techniques
- Greater dimensionality
- When the ratio between feature and instance increases, the high dimensionality will happen
- Largely, it uses feature selection approach to reduce high dimensionality in data
- Further, it uses different dimension reduction algorithms like Principal Component Analysis (PCA)
Next, we can see the primary processes of big data. These processes are common for many real-time big data applications. Actually, the main aim of big data analytics is to collect and process vast data. Further, it needs to classify the processed/analyzed data for user benefits. Here, we have specified the primary processes with their associated key tasks. Our developers are expertized in every process of big data to provide you with keen assistance in development.
What are the Most Important Big Data Technologies?
- Regression
- It is introduced to analyze the relations among dependent and independent variables
- It enables to inspect of the changing value of a dependent variable concerning value of the independent variable
- For instance: Find the future mobile money transactions through existing transaction format, amount, type, location, money subscription, etc.
- Clustering and Segmentation
- It segments the large-scale data into multiple smaller groups based on certain similar patterns/features
- For instance: classification of customers
- Itemset Mining and Association
- It detects the statistical relations among dataset variables
- For instance: Provide incentives to banking users based on transaction amount, volume, and app utilization level
- Correlation and Similarity
- It is used to signify the undirected clustering techniques
- It also computes the similarities among cluster element through similarity-scoring algorithms
- Classification
- It categorizes the data into pre-defined classes through certain attributes
- It can be pre-selected in prior or classified by clustering model
- For instance: segmenting new clients in a particular category
In addition, we have given you the recent research trends in big data analytics. As a point of fact, big data analytics applications are increasing more in several fields. Since every field is currently handling massive digital data. Here, we have listed only the top 5 big data analytics research perceptions. Beyond these trends, there are various new dimensions of big data. We are ready to share more big data PhD topics with you from our latest collections.
Current Trends in Big Data Analytics
- Large-scale Data Perseverance and Protection
- Massive Information Processing and Management
- Essential Features Mining and Searching in Huge Data
- Massive Information Analytics and Optimization in Social Networks
- Tools and Frameworks for Big Data Processing and Maintenance
Now, we can see the development tools for the big data analytics field. Due to big technological advancements, it is widely improved in development tools and technologies also. Therefore, one should be more conscious of selecting suitable tools for their projects. Our developers will guide you to select the best-fitting tools for your project based on project intentions. So, interact with our experts to know more interesting information about big data development.
Big Data Analytics Tools List
- Programming and Statistical Analysis
- R
- MATLAB
- MLib
- Scala
- Python
- Storage Frameworks
- Hadoop Distributed File System
- Processing Frameworks
- Mahout
- Flume
- MapReduce
- Apache Tez
- Storm
- Zookeeper
- YARN
- Pig
- Oozie
- Management Frameworks
- Hive
- Drill
- Sqoop
- Hbase
- Kafka
- NoSQL
- Casandra
From the above list of big data analytics tools, here we are going to see a few important tools in detail. This helps you to be aware of recent demanding big data tools for PhD / MS projects. In this, we have listed the main purpose and functions of each tool. While selecting the best tool for you, we consider the sophisticated infrastructure of phd implementation tools, services, toolboxes, libraries, modules, etc. So, our recommended tool surely meets your project expectation and generates accurate results.
Big Data Analytics Tools & Techniques
- Cascading
- It works as an abstraction layer in Hadoop
- It reduces the Mapreduce complexities
- It provides JVM language to create data processing procedure
- Hive
- It is a relational model that executes SQL interface
- It works with data warehousing applications
- It creates the infrastructure over Hadoop
- It enables to give query and summarization
- Avro
- It is primarily used in Apache Hadoop
- It provides data interchange and serialization services
- It executes the services either collectively or individually
- Oozie
- It is developed in Java and executes on Java servlet
- It collects and correlates the Hadoop works/events
- It enables to create of web-applications
- It maintains and saves the workflow characterization
- Big Top
- It is used for analyzing and validating Hadoop environ
- HBase
- It is a distributed non-relational database that executes on HDFS
- It came after Google’s Big Table which developed in Java
- It is an instance of NoSQL datastore
- Apache Pig
- It is a framework that supports high level language
- It is mainly used for data analysis and assessment
- It comprises a sequence of MapReduce programs
- It is close to SQL operations
Now, we can see the significant techniques of big data analytics. These techniques are widely recognized in many recent big data applications. All these techniques give the best results in big data clustering, regression, classification, etc. As well, our developers will give flawless guidance in choosing techniques for each operation of big data. More than these algorithms, we also facilitate you in other emerging techniques to satisfy recent developments.
Big Data Analytics PhD Topics
- Bayesian Learning Prototypes
- Uncertainty Estimation
- Similarity
- Approximation Learning
- Generative Modeling
- GAN
- Gaussian
- Bayesian Networks
- Autoencoders
- HMM, etc.
- Machine Learning Approaches
- Reinforcement Learning
- Supervised Learning
- Constrained
- Unsupervised Learning, etc.
- Active Learning
- Defective Labels
- Semi-supervised
- Datasets of Partial Labels
- Artificial Intelligence
- FACT
- Significant Features
- SHAP
- LIME, etc.
Last but not least, now we can see different research ideas of big data analytics. All these ideas are collected from our recent big data study. Further, these research directions signify the latest big data PhD topics and research interests. In specific, all these areas are moving in the direction of future technologies. So, this list of topics has a high degree of future research scope. To know more about both current and future research directions of big data analytics, communicate with us. We will let you know your expected information from our experts.
Future Research Directions of Big Data Analytics
- Customer behavior Analysis in Big-Sale
- Mixed Data Fusion from Multiple Sources
- Energy Utilization Control in Distributed System
- Deep Learning Techniques for Features Analysis
- Real-time Investigation of Heterogeneous Data
- Network Optimization over Huge-scale Data
- Security and Privacy Challenges in Big Data
- Mobile Crowdsourcing Services over Massive Data
- Fast Travel Estimation over Large Data Transmission
- Accurate User Service Recommendation in Big Data
Overall, we provide more Big Data PhD Topics from the latest and futuristic research areas. Then, we provide both code development support in apt development tools and technologies. To the great extent, we also extend our support in proposal writing, literature study writing, paper writing, paper publication, and thesis/dissertation writing. In other words, we provide a comprehensive PhD research service in the big data field.
