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Debugging and error resolution has become increasingly time-consuming and difficult for all domains of software development. Error logs have become very important when it comes to debugging and error resolution. To remedy the problems presented in the logs, typically, a search on online forums would shed light on the solution. We present a novel approach to utilizing these logs in conjunction with external Question and Answer forums to compute and expedite resolution by suggesting a solution to runtime errors. Since log format is non-standard and use cases can vary widely, our architecture allows for extreme customization for the intended ecosystem as well as a great degree of fine-tuning. We evaluated our solution in a case study and made our implementation open-source for the community. 

In modern computing, log files provide a wealth of information regarding the past of a system, including the system failures and security breaches that cost companies and developers a fortune in both time and money. While this information can be used to attempt to recover from a problem, such an approach merely mitigates the damage that has already been done. Detecting problems, however, is not the only information that can be gathered from log files. It is common knowledge that segments of log files, if analyzed correctly, can yield a good idea of what the system is likely going to do next in real-time, allowing a system to take corrective action before any negative actions occur. In this paper, the authors put forth a systematic map of this field of log prediction, screening several hundred papers and finally narrowing down the field to approximately 30 relevant papers. These papers, when broken down, give a good idea of the state of the art, methodologies employed, and future challenges that still must be overcome. Findings and conclusions of this study can be applied to a variety of software systems and components, including classical software systems, as well as software parts of control, or the Internet of Things (IoT) systems. 

The Arctic is entering a new ecological state, with alarming consequences for humanity. Animal-borne sensors offer a window into these changes. Although substantial animal tracking data from the Arctic and subarctic exist, most are difficult to discover and access. Here, we present the new Arctic Animal Movement Archive (AAMA), a growing collection of more than 200 standardized terrestrial and marine animal tracking studies from 1991 to the present. The AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. With AAMA-based case studies, we document climatic influences on the migration phenology of eagles, geographic differences in the adaptive response of caribou reproductive phenology to climate change, and species-specific changes in terrestrial mammal movement rates in response to increasing temperature.