I'm currently working on a minor problem in information extraction. The idea behind the system is relatively simple and contains small modifications of existing algorithms. However in the process, I ended up creating a whole new dataset of a different kind - which did not exist before. This dataset would foster new novel research in my subdomain (call it problem X).
How can I stress the quality of my data and upcoming innovations it can drive and inspire rather than worrying about the simplicity of my algorithms (and reiteration and experimentation of other non-novel ideas)? There has been lot of significant research in problem X recently and my method doesn't stand anywhere in comparison with state of the art.
However I expect some great upcoming works (from the whole research community) in Problem X in upcoming couple of years. If I don't publish this dataset, someone else would definitely produce something similar within a year (think of it as need of the hour) along with some hi-fi "mathy" solutions to Problem X though I just experimented with existing data mining tools and libraries.
I have one more week to go. What should I do that would improve my chances of getting this simple work on Problem X but with an awesome new dataset published?
Raw data of this kind is available publicly in massive amount but considered as noise, though it's easy for human perception and conveys a lot of sense.
My contribution in creating dataset is annotations and analysis (basic statistical distributions etc)
My approach to solve Problem X is not mathematically complex - but at par with regular Tier-II papers being published in this area.
There IS a need of this kind of systems in the industry. (any citations would disclose my solution)