Something that *you* want to do. Whatever anyone else suggests is totally irrelevant, you are about to spend 1-2 years on this so choose something that you care about and want to spend your time on. If in doubt, read. Decide based on what you find you can engage with in terms of your reading.
When my grad students struggled with topics I would give them a 5-question survey to help narrow some of the choices. Here's a modified version.
Q1: What most excites you every day? What is that one thing that makes you feel happy, accomplished, or like you've not wasted your time by working with the item or issue.
An example, for me, would be risk avoidance. It is a theory that people will take affirmative steps to avoid risk. On one hand, when risk is high there is less chance of performing a difficult task. So I'd use this theory as the basis for my research.
Q2: What topic or aspect of a topic will keep you interested for a decent length of time? In other words, is there a topic that is broad enough that it will provide plenty of research options, while at the same time deep enough to allow you to do deep research or experimentation in order to answer your research questions.
As an example, if I stay with my broad idea of risk avoidance - a valid topic for Information Systems -- then I may find the general topic too broad. The broader the topic, the more work must be done to narrow the topic to a reasonable research topic. Thus, under #1 we find the major topic area and then in #2 we begin to narrow it to fit the standards for your research question.
Q3: Find the problem with the topic. It is easy to throw out random ideas to get you started on selecting a Tropic; however, many of these overly broad suggestions fail because they don't focus enough on a problem or on finding the topic that will help add to the overall understanding of the topic.
To remedy this problem you should take the time to find the problem within the topic. As with my example on risk avoidance, the problem may be one of a capable student avoiding certain classes because of the risk to the overall grade point average. The student in question wishes to go to a top school with active research components, but if they avoid certain tough classes-those where the risk to harming the GPA is high, then they fail in their education and improving preparedness.
In this scenario, we see that we have a topic that we can narrow by focusing on a particular problem. As one can quickly see, the key is not to just selecting the topic but in being able to find the internal problem from the topic. As an example,The suggested topics of networking and data mining are both interesting and likely provide a very broad selection of topics within the subject. But we can see quickly that the broad topic of data mining will have too many subtopics into many alternative pass a researcher can take. Such overly broad topics can create problems more than they repair them.
Working much like we would if pouring sugar through a funnel, we want to take our selection from the broad topic spectrum through the narrowing to get to the problem and then move on in the final toward the solution.
Q4: what do you have time to do for your research project? A surprising number of graduate students fumble the topic right away when they choose one that is too broad and will take too much time to complete the research needed. A good rule of thumb is to set aside 10% of your time for your preliminary inquiries, 60% of your time for the actual research, 20% of your time writing the paper, and the remainder of your time in proofreading, editing, and rewrites.
The key to a good research paper is to manage the time necessary for each of the components in preparing the paper. As we can see the majority of time should be spent in the research. But before you get to that large block of time you must first perform a series of preliminary task. Among these tasks are the need to select a topic, the problem, the research method, and to begin a literature review. The literature review should help to narrow your topic and to begin to focus on specific problems to which you can research and (hopefully) find a solution.
Q5: Do you make a good plan, list of expectations, or topics to be completed during the research? These are all part of a well-defined approach to your topic, the research, and ultimately the final product you will produce.
Within the plan should be points where you step back and consider what your progress has become. Remember, these type of projects are often time sensitive and making sure you have time to complete the particular process or component of the work is an important consideration when attempting to stay on track.
This is especially important when you consider how easily a small change in one area can create big results for later areas of your research. An example of this would be a change in the type of risk avoidance issue you've chosen and any changes you may make to that subject as you prepare for the next stage. In this instance. We started very broadly with the term risk avoidance, began to narrow in shape it by defining the subjects within which it will fit, then began formulating our plan for research in the final product. If at any stage in the process you change any component you may find an even bigger issue of time management, so you want to be aware of such potential outcomes and be ready to adjust reasonably when they arise.
I propose research in the use of new information, information and Internet technologies, techniques for processing large information sets in database systems, in the computing cloud - data collected from IT systems working in educational institutions or health care.
I would ask around the faculty to see what their interests are. Unless you're looking to go to another university, you're going to have a lot easier time finding someone under whom you can do research, if you adjust your research interest to those of the faculty members.
Big Data will be applied in both health and education. Most medical diagnoses will be based on the future use of artificial intelligence, and artificial intelligence is built based on previous experiences, diagnoses and symptoms of people's diseases. All this information collected in the cloud and BIG DATA can be used to help or even diagnose diseases by medical robots. For this, analitics software capable of making accurate diagnoses has to be developed.