Dissertation Topics on Big Data
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Dissertation Topics on Big Data
Dissertation Topics on Big Data grant our momentous research guidance for students and research intellectuals in each and every point of their research. On these days, we prepared thousands of big data projects by uptrend research concepts. In project development phase, we are using wide numerous data sets such as data matrix, graphs & networks (Social, web and molecular structures), ordered data (Temporal data, genetic sequence data, multimedia & image data, spatial data, video data, sequential data), and relational record. Prepare for the Dissertation topic is not going to be easy, but we will make it for you….. Don’t miss an opportunity to contacting us……….
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
I have seen many people asking for help in data mining forums and on other websites about how to choose a good thesis topicin data mining. Therefore, in this this post, I will address this question.
The first thing to consider is whether you want to design/improve data mining techniques, apply data mining techniques or do both. Personally, I think that designing or improving data mining techniques is more challenging than using already existing techniques. Moreover, you can make a more fundamental contribution if you work on improving data mining techniques instead of applying them. However, you need to be aware that improving data mining techniques may require better algorithmic and/or mathematics skills.
The second thing to consider is what kind of techniques you want to apply or design/improve? Data mining is a broad field consisting of many techniques such as neural networks, association rule mining algorithms, clustering and outlier detection. You should try to get some overview of the different techniques to see what you are more interested in. To get a rough overview of the field, you could read some introduction books on data mining such as the book by Tan, Steinbach & Kumar (Introduction to data mining) or read websites and articles related to data mining. If your goal is just to apply data mining techniques to achieve some other purpose (e.g. analysing cancer data) but you don’t know which one yet, you could skip this question.
The third thing to consider is which problems you want to solve or what you want to improve. This requires more thoughts. A good way is to look at recent good data mining conferences (KDD, ICDM, PKDD, PAKDD, ADMA, DAWAK, etc.) and journals (TKDE, TKDD, KAIS, etc.), or to attend conferences, if possible, and talk with other researchers. This helps to see what are the current popular topics and what kind of problems researchers are currently trying to solve. It does not mean that you need to work on the most popular topic. Working on a popular topic (e.g. social network mining) has several advantages. It is easier to get grants or in some case to get your papers accepted in special issues, workshops, etc. However, there are also some “older” topics that are also interesting even if they are not the current flavor of the day. Actually, the most important is that you find a topic that you like and will enjoy working on it for perhaps a few years of your life. Finding a good problem to work on can require to read several articles to understand what are the limitations of current techniques and decide what can be improved. So don’t worry. It is normal that it takes time to find a more specific topic.
Fourth, one should not forget that helping to choose a thesis topic is also the job of the professor that supervise the Master or Ph.D Students. Therefore, if you are looking for a thesis topic, it is good to talk with your supervisor and ask for suggestions. He should help you. If you don’t have a supervisor yet, then try to get a rough idea of what you like, and try to meet/discuss with professors that could become your supervisors. Some of them will perhaps have some research projects and ideas that they could give you if you work with them. Choosing a supervisor is a very important and strategic decision that every graduate student has to make. For more information about choosing a supervisor, you can read this post : How to choose a research advisor for M.Sc. / Ph.D ?
Lastly, I would like to discuss the common question “please give me a Ph.D. topic in data mining“, that I read on websites and that I sometimes receive in my e-mails. There are two problems with this question. The first problem is that it is too general. As mentioned, data mining is a very broad field. For example, I could suggest you some very specific topics such as detecting outliers in imbalanced stock market data or to optimize the memory efficiency of subgraph mining algorithms for community detection in social networks. But will you like it? It is best to choose something by yourself that you like. The second problem with the above question is that choosing a topic is the work that a researcher should do or learn to do. In fact, in research, it is equally important to be able to find a good research problem as it is to find a good solution. Therefore, I highly recommend to try to find a research topic by yourself, as it is important to develop this skill to become a successful researcher. If you are a student, when searching for a topic, you can ask your research advisor to guide you.
Also, just for fun, here is a Ph.D thesis title generator.
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