  We are living in an information era from Twitter to Fitocracy every episode
of peoples life is converted to numbers. That abundance of data is also
available in information technologies. From Stackoverflow to GitHub many big
data sources are available about trends in Information Technologies. The aim of
this research is studying information technology trends and compiling useful
information about those technologies using big data sources mentioned above.
Those collected information might be helpful for decision makers or information
technology professionals to decide where to invest their time and money. In
this research we have mined and analyzed StackExchange and GitHub data for
creating meaningful predictions about information technologies. Initially
StackExchange and GitHub data were imported into local data repositories. After
the data is imported, cleaning and preprocessing techniques like tokenization,
stemming and dimensionality reduction are applied to data. After preprocessing
and cleaning keywords, their relations are extracted from data. Using those
keywords data, four main knowledge areas and their variations, i.e., 20
Programming Languages, 8 Database Applications, 4 Cloud Services and 3 Mobile
Operating Systems, are selected for analysis of their trends. After the
keywords are selected, extracted patterns are used for cluster analysis in
Gephi. Produced graphs are used for the exploratory analysis of the programming
languages data. After exploratory analysis, time series of usage are created
for selected keywords. Those times series are used as training and testing data
for forecasts created using R forecast library. After making forecasts, their
accuracy are tested using Mean Magnitude of Relative Error and Median Magnitude
of Relative Error.
