Brijesh M. Profile

5,0

(3 recenzii)

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$30 USD/oră

porirua,

new zealand

$30 USD/oră

Momentan, aici e ora 9:31 p.m.

S-a înregistrat pe noiembrie 18, 2013

2 Recomandări

Brijesh M.

@brijeshbmehta

5,0

(3 recenzii)

2,2

100%

$30 USD/oră

porirua,

new zealand

$30 USD/oră

100 %

Proiecte finalizate

100 %

Conform bugetului

100 %

La timp

Indisponibil

Rata de angajare repetată

Big Data Consultant - Articles Content Writer

I am comfortable with Installation and configuration of big data related tools and technologies such as, Hadoop, Spark, Pig, Hive, and NoSQL databases. I can work on data management as well. You can ask for anything related to Hadoop stack. I am also working on Kubernetes, and Docker. I am also a talented and experienced Articles Writer. I have good experience with Article writing. I am a hard worker and enjoy working at all critical conditions. If hired by you I will be responsible to complete the task within the stipulated time frame and with utmost confidence.

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Recenzii

Modificările au fost salvate

Se afișează 1 – 3 din 3 recenzii

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5,0

₹1.000,00 INR

Big Data - Offline Trainer

Brijesh is a brilliant freelancer who understands the task very fast very friendly and customer oriented would love to work with him for future projects

Sahaana P.

@Sahaana74

•

4 ani în urmă

5,0

$140,00 AUD

Hadoop BigData - Urgent...

Nice work done by Brijesh M.

Sandeep C.

@schoudhary1553

•

4 ani în urmă

5,0

$15,00 USD

MongoDB cluster configuration

Calm personality and very helpful. Really enjoyed working with him. Hire this guy, you won't be disappointed! Highly recommended.

System Admin

Linux

Database Administration

MongoDB

Open Source S.

@sharingknowledg7

•

5 ani în urmă

Experiență

Automaton AI Infosystem Pvt Ltd, Pune, India

mai 2019 - dec. 2020 (1 an, 7 luni)

Configure, manage and maintain big data storage infrastructure. Develop storage architectures to store large amount of data. Recently configured MongoDB cluster with shards.

College of Technology and Engineering, Udaipur, India

ian. 2018 - mar. 2019 (1 an, 2 luni)

Taught Under Graduate and Post Graduate students of Engineering. Recruited under National Project Implementation Unit (NPIU) - Technical Education Quality Improvement Programme III (TEQIP III) sponsored by world bank. Taught subjects like, Distributed Computing / Systems, Network Programming, Database Systems, Distributed Database Systems. Worked as an Information Technology Infrastructure Coordinator and Technical Events Coordinator.

Sardar Vallabhbhai National Institute of Technology, Surat, India

aug. 2014 - dec. 2017 (3 ani, 4 luni)

Research contribution towards the scalability of existing Privacy Preserving Data Publishing Approaches such as k-Anonymity and l-Diversity. Worked on converting unstructured data to the structured data using Conditional Random Fields (CRF) based Named Entity Recognition (NER) approach. Apache Hadoop 1x cluster was built for the experiments. Implemented our proposed approaches using Apache Hadoop (MapReduce), Apache Pig and Python.

Educație

Doctorate

National Institute of Technology Surat,

India

2014 – 2019

(5 ani)

Master of Technology

National Institute of Technology Surat,

India

2009 – 2011

(2 ani)

Bachelor of Engineering

Saurashtra University,

India

2003 – 2007

(4 ani)

Calificări

Cognitive Class

2019

Cognitive Class

2019

MongoDB University

2019

Publicații

A Novel approach as Multi-place Watermarking for Security in Database

SAM'11, 10th International Conference on Security and Management, World-comp

Discussion related to database watermarking and existing techniques. Proposed a novel approach of inserting watermarking at two different places in database for copyright protection

Watermarking for security in database: A review

2014 Conference on IT in Business, Industry and Government (CSIBIG), IEEE

A brief survey of database watermarking schemes.

Privacy Preserving Unstructured Big Data Analytics: Issues and Challenges

Procedia Computer Science, Elsevier

Discussion related to privacy issues and challenges with respect to unstructured big data.

Towards Privacy Preserving Big Data Analytics

Sixth International Conference on Advanced Computing & Communication Technologies,ResearchPublishing

Discussion related to privacy issues with big data. Comparison of existing privacy preserving techniques with 3Vs of big data.

Privacy preserving big data publishing: a scalable k-anonymization approach using MapReduce

IET Software

A novel scalable approach based on k-anonymization is proposed for large volume of data in Privacy Preserving Big Data Publishing (PPBDP). The proposed algorithm is implemented using Apache Pig and Python over Apache Hadoop cluster. Based on empirical analysis it is found that the proposed approach have low information loss and very low running time as compared to existing approaches.

Toward Scalable Anonymization for Privacy-Preserving Big Data Publishing

Recent Findings in Intelligent Computing Techniques, Springer

Scalable k-Anonymization (SKA) approach is proposed over Hadoop cluster for Privacy Preserving Big Data Publishing (PPBDP). The proposed algorithm is implemented using Apache Hadoop (MapReduce) and Python.

Scalable l-Diversity: An Extension to Scalable k-Anonymity for Privacy Preserving Big DataPublishing

International Journal of Information Technology and Web Engineering (IJITWE), IGI Global

Scalable k-Anonymization (SKA) approach is extended for l-diversity as Scalable l-Diversity (SLD) for Privacy Preserving Big Data Publishing (PPBDP). The proposed algorithm is implemented using Apache Pig and Python over Apache Hadoop cluster.

Towards privacy preserving unstructured big data publishing

Journal of Intelligent & Fuzzy Systems, IOS Press

Scalable k-Anonymization (SKA) is improvised in terms of data utility as Improved Scalable k-Anonymization (ImSKA) for Privacy Preserving Big Data Publishing (PPBDP). Conditional Random Fields (CRF) based Named Entity Recognition (NER) is used to convert unstructured data to structured data. The proposed algorithm is implemented using Apache Pig and Python over Apache Hadoop cluster.

Improved l-Diversity: Scalable Anonymization Approach for Privacy Preserving Big Data Publishing

Journal of King Saud University-Computer and Information Sciences, Elsevier

Scalable l-Diversity (SLD) is improvised in terms of data utility as Improved Scalable l-Diversity (ImSLD) for Privacy Preserving Big Data Publishing (PPBDP). The proposed algorithm is implemented using Apache Pig and Python over Apache Hadoop cluster.