Abstract: As enterprise computing becomes more and more
complex, the costs and technical challenges of IT system maintenance
and support are increasing rapidly. One popular approach to managing
IT system maintenance is to prepare and use a FAQ (Frequently Asked
Questions) system to manage and reuse systems knowledge. Such a
FAQ system can help reduce the resolution time for each service
incident ticket. However, there is a major problem where over time the
knowledge in such FAQs tends to become outdated. Much of the
knowledge captured in the FAQ requires periodic updates in response
to new insights or new trends in the problems addressed in order to
maintain its usefulness for problem resolution. These updates require a
systematic approach to define the exact portion of the FAQ and its
content. Therefore, we are working on a novel method to
hierarchically structure the FAQ and automate the updates of its
structure and content. We use structured information and the
unstructured text information with the timelines of the information in
the service incident tickets. We cluster the tickets by structured
category information, by keywords, and by keyword modifiers for the
unstructured text information. We also calculate an urgency score
based on trends, resolution times, and priorities. We carefully studied
the tickets of one of our projects over a 2.5-year time period. After the
first 6 months we started to create FAQs and confirmed they improved
the resolution times. We continued observing over the next 2 years to
assess the ongoing effectiveness of our method for the automatic FAQ
updates. We improved the ratio of tickets covered by the FAQ from
32.3% to 68.9% during this time. Also, the average time reduction of
ticket resolution was between 31.6% and 43.9%. Subjective analysis
showed more than 75% reported that the FAQ system was useful in
reducing ticket resolution times.
Abstract: Mostly the real life signals are time varying in nature. For proper characterization of such signals, time-frequency representation is required. The STFT (short-time Fourier transform) is a classical tool used for this purpose. The limitation of the STFT is its fixed time-frequency resolution. Thus, an enhanced version of the STFT, which is based on the cross-level sampling, is devised. It can adapt the sampling frequency and the window function length by following the input signal local variations. Therefore, it provides an adaptive resolution time-frequency representation of the input. The computational complexity of the proposed STFT is deduced and compared to the classical one. The results show a significant gain of the computational efficiency and hence of the processing power. The processing error of the proposed technique is also discussed.
Abstract: In this paper, we propose a novel time-frequency distribution (TFD) for the analysis of multi-component signals. In particular, we use synthetic as well as real-life speech signals to prove the superiority of the proposed TFD in comparison to some existing ones. In the comparison, we consider the cross-terms suppression and the high energy concentration of the signal around its instantaneous frequency (IF).