Log messages provide a valuable source of runtime information for ensuring the safety and consistency of systems. Recently, many machine learning and deep learning methods have been proposed to automatically detect anomalous log messages, obviating the need for manual detection by experts. However, we find that in practice, the effectiveness of existing learning-based methods is severely affected by incomplete information and distribution shift. Specifically, each log message can actually be parsed into a fixed number of key information fields, while existing methods analyze log messages using only the log event information and ignore other useful information fields that can be critical to anomaly detection. Further, the distribution of real-world log messages changes continuously due to the dynamic nature of the runtime environment and thus, a detection model conventionally trained based on the unrealistic i.i.d. assumption may not provide the expected and consistent performance. In this paper, we present a robust and transferable anomaly detection framework RT-Log to address the above problems. To perform a comprehensive analysis of log messages, we introduce an adaptive relation modeling technique, which captures feature interactions among log information fields selectively and dynamically for effective and interpretable log representations. To establish its robustness and transferability, we propose a general environment generalization technique for learning the environment invariant representations that can generalize across different runtime environments. We evaluate the anomaly detection performance of RT-Log on large real-world datasets. Extensive experimental results demonstrate that RT-Log consistently outperforms state-of-the-art methods by a significant margin under different settings.

Data processing and analytics are fundamental and pervasive. Algorithms play a vital role in data processing and analytics where many algorithm designs have incorporated heuristics and general rules from human knowledge and experience to improve their effectiveness. Recently, reinforcement learning, deep reinforcement learning (DRL) in particular, is increasingly explored and exploited in many areas because it can learn better strategies in complicated environments it is interacting with than statically designed algorithms. Motivated by this trend, we provide a comprehensive review of recent works focusing on utilizing deep reinforcement learning to improve data processing and analytics. First, we present an introduction to key concepts, theories, and methods in deep reinforcement learning. Next, we discuss deep reinforcement learning deployment on database systems, facilitating data processing and analytics in various aspects, including data organization, scheduling, tuning, and indexing. Then, we survey the application of deep reinforcement learning in data processing and analytics, ranging from data preparation, natural language interface to healthcare, fintech, etc. Finally, we discuss important open challenges and future research directions of using deep reinforcement learning in data processing and analytics.

The existing method for sampling a modulated signal is the same as that for a non-modulated message signal. Therefore, the quadrature sampling rate for a modulated signal must be larger than its bandwidth. However, a new algorithm, which can recover completely a DC-free message signal from an FM signal at a sampling rate less than its bandwidth, is presented. The new sampling rate limit is also discussed. The investigation shows that the current sampling theorem does not present an optimal sampling rate for recovering the message signal from an FM signal and further modification of the theorem might be needed.

A new orthogonal multicarrier bandwidth modulation (OMBM) scheme is proposed in this paper. The distinct feature of this scheme is that the frequency domain data is used to decide the number of orthogonal subcarriers for wireless transmissions. Different bandwidth of the OMBM symbol in the time domain reflects on the different data in the frequency domain. A discrete Fourier transform (DFT) based minimum mean square error (MMSE) estimator is employed to conduct the bandwidth detection. Analysis and simulation results demonstrate that the OMBM scheme is a flexible modulation structure and not sensitive to noise interference. Some properties and possible application of the OMBM code are also introduced.