An Academic presentation byDr. Nancy Agnes, Head, Technical Operations, PhdassistanceGroup  www.phdassistance.comEmail: info@phdassistance.com

W H A T A R E T H E R E S E A R C HS C O P E S I N B I G D A T A A N A L Y S I SA N D P R O B L E M F A C E D B Y T H ER E S E A R C H E R W H I L EP R E P A R I N G A T H E S I S O N B I GD A T A A N A L Y T I C S

In-Brief

Introduction

Research Scope in Big Data Analytics

Research problems in Big Data analysis

Selecting PhD Research Topics in Big Data Analytics

Confusion while selecting a Big Data tool

Conclusion

Outline

TODAY'S DISCUSSION

Big data is a collection of large and complex data sets which are difficult toprocess with on-hand database management tools. Scalability andperformance in big data analysis can be achieved with the help of

parallelization techniques. Parallelization and divide and conquer are thebest algorithms used to handle big data. Many Organizations uses online bigdata applications which are economical and effective, but it lay way for many

security threats.

In-Brief

In recent days big data analytics become a mostpopular research area.

It has acquired a lot of attention in academic as well asindustries both private and public sectors.

It is developing as an emerging at the same timechallenging research area.

Many scientists defined big data in different ways;Gartner defines it as high volume, high velocity and awide variety of information that requires newprocessing form which permits enhanced decisionmaking, insight discovery and process optimization.

Introduction

Contd..

It is a collection of large and complex data sets that are difficult to process in on-hand database management tools.

Every day worldwide trillions of data are created on social media networkingsites, from scientific experiments, over the mobile conversation, network sensorsand other sources.

So we require new tool and technique which can help in big data organize, storeand analyze.

Contd..

It is an emerging research area with lots of research scopes, where primaryresearch carried out, but a lot are expecting to be in future.

PhD Projects in Big Data will increase the scope of your research work.

Present database management systems areinadequate to store large flood of big data.

Researchers have suggested that commercial DBMSare unsuitable for processing a large amount of dataand suggesting new big database managementsystem which will be economical and scalable.

Scalability and performance in big data analysis can beachieved with the help of parallelization techniquesand algorithms.

ResearchScope in BigDataAnalytics

Contd..

Many researchers have defined new theories, technologies and methods formanaging and analyzing big data.

Present day's database management systems are insufficient to store increasingflood of big data.

Hence, arise a need for hierarchical storage architectures which are efficient inhandling storage challenges of Big Data.

Existing data processing algorithms are excellent in handling homogeneous andstatic data, but present-day data are created from numerous resources which areheterogeneous and dynamic.

Contd..

Scalable data processing algorithms are in need to process such types of data.

Speed is the main criteria need to consider while processing queries in big data.

Indexing is the best choice in a complex query processing case.

Parallelization and divide and conquer are the best algorithms used to handlingbig data.

Many Organizations uses online big data applications which are economical andeffective, but it makes way for many security threats.

Contd..

Security is the main concern for the researcher as well as organizations.

The PhD Program Big Data Analytics is an interdisciplinary, i.e., comprises morethan one branch STEM, PhD program concentrating on system and technologyused for processing data and information.

Unlike other data science program it comprises the human and social implications ofinformation, technology, bringing in critical components of cognition, ethics, biasesand storytelling into a strong, big data analytics curriculum.

Scalability- Scale up or increase the structure ofsystem architecture to meet parallel data processing.

Adoption and analytics of big data for cloudcomputing platforms-It reduce the cost of complexanalytics in the cloud.

Security and Confidentiality problems

Effective storing and transmission problem

Graph databases

Let us discuss some of the research problems in Big Dataresearch topics

ResearchProblems inBig DataAnalysis

Contd..

PhD Thesis on Big Data Analytics which act as a major research sector concerningthe future; it is one of the popular emerging research areas.

Main things need to consider while writing PhD Projects in Big Data is selectingPhD Research Topics in Big Data Analytics.

Contd..

There are many topics on PhD Big Data domain; you haveto look for topics which are more interesting and related tofuture research enhancement with open source tools.

Big Data handles a large amount of data collecting,processing and transmitting it through modern technology.

It made way to analyze and manage large datasets andhow to create, use and apply it in different fields likescience and physics.

SelectingPhDResearchTopics inBig DataAnalytics

Contd..

Clear sight over big data and its application help you to identify a good research topicand significant research problem statement needs to be addressed in future, whichwill bring scope to your research work.

Here some of the PhD Research Topics in Big Data Analytics Enhancing FaultDetection for Big Data Analytics through Evolutionary Computation.

An effective approach to detecting a failure in big data analytics using clustering andclassification technique.

Contd..

An effective technique applied for improving classification problem using aclustering approach.

Incorporating the Apriori Algorithm with MapReduce approach to RemoveClassification problems

Confusion often arises while selecting the tool for big dataanalysis and storage area, which will be the besttechnology for storage HBase or Cassandra.

Hadoop Map Reduction or spark, which will be the betteroption for data analytics and storage?

These are some of the common queries that need to beconsidered.

But most of the times many ends up being confused andselect inappropriate technology which will lead to wastageof resources, money, time and effort.

ConfusionwhileSelecting aBig DataTool

Contd..

The best way to handle it is by getting professional help.

You can get help from professionals who are experts in big data tools.

Our PhD Guidance in Big Data Helps in all aspect like choosing the topic, toolselection and other features.

Based on their advice, and you can select the best tools.

Thesis preparation is a long and complicated process.

Many research works often fail due to lack of knowledgein a particular research field, and confusion may arisein handling different tools, you can get support fromprofessionals who are experts in big data tools.

And you can also get guidance from our PhD Guidancein Big Data service to identify the right approach foryour Research and Thesis work.

Conclusion

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