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How do you choose Dissertation topics on Big Data? There are many dissertation topics and the possibilities while choosing topic is almost endless. This is why choosing dissertation topic is a difficult task for researchers. Here’s we provides some steps for selecting a topics for a dissertation, you can follow these steps and make a good dissertation topic.
Steps for Topic Selection:
- Generate new ideas or find a new ideas rather than pick just one
- Test your each idea through some reputed journal papers.
- Refine your ideas, once you have knowledge about your choices
The following are some Dissertation topics on Big Data which we currently working on:
- Learning Platform for Primary school pupils
- Data Centre Consolidation based on open source cloud platforms
- Benchmarking the clouds
- Building a secure distributed environment for information sharing across organizations
- Building a scalable application based on Hibernate/Spring/J2EE
- Building a Grid or Web Service(RESTful/SOAP)
- Mining the Big Data
For your every Dissertation Topics on Big Data, you need to concentrate on the following suggestions:
- Sensors, Internet, Machines, etc.
- Web log, Images/audio, RFID, Videos, Sensor Data, etc.
- Technologies, Support data storage, etc.
- Programming framework, Processing framework, etc.
- Patterns in data, Decision making, Predictive Analytics, etc.
- Humans, Business processes, Applications etc.
Today’s Top Dissertation Topics on Big Data:
- Opening Up Digital Archives to Identify Sensitive Content Over the Usage of Analytics
- Convolutional Networks for Aerial Images Based Large Scale Solar Panel Mapping
- Trolls and Control Terror Awareness Level Identification Using Scalable Paradigm in Social Networks
- Scaling Morphological Tagging Based Character to Fourteen Languages
- Dynamic Feature Selection and Generation for Music Recommendation on Heterogeneous Graph
- Research on Large Scale Water Monitoring Application in Spatial-Temporal Data for Identify Dynamic Changes based on Noisy Labels
- Evaluate Code Level Performance Tuning Impacts on Power Efficiency
- Efficient Data Access Schemes on HPC Clusters with Heterogeneous Storage for Spark and Hadoop
- Hierarchical and Hybrid Outlier Detection Strategy for Protect Large Scale Data
- Rule Based Diagnosis and Hierarchical Correlation Based Performance Analysis for Big Data Paradigms
- Analyze Data Partitioning and Data Replication Performance using BEOWULF Approach in Cloud
- Parallel Clustering Method for Spark Paradigm Based Non-Disjoint Large Scale Data Partitioning
- Rare Failure Events Prediction on Large Scale manufacturing and Complex Interaction Using Classification Trees
- Labeling Actors by Integrated Information within and Through Multiple Views in Multi-View Social Networks
- Dynamic Distributed Data Structure Using Spatial Data Mining Algorithms for Efficient Data Distribution between Cluster Nodes