System log files contains messages emitted from several modules within a system and carries valuable information about the system state such as device status and error conditions and also about the various tasks within the system such as program names, execution path, including function names and parameters, and the task completion status. For customers with remote support, the system collects and transmits these logs to a central enterprise repository, where these are monitored for alerts, problem forecasting, and troubleshooting. Very large log files limit the interpretability for the support engineers. For an expert, a large volume of log messages may not pose any problem; however, an inexperienced person may get flummoxed due to the presence of a large number of log messages. Often it is desired to present the log messages in a comprehensive manner where a person can view the important messages first and then go into details if required. In this article, we present a user-friendly log viewer where we first hide the unimportant or inconsequential messages from the log file. A user can then click a particular hidden view and get the details of the hided messages. Messages with low utility are considered inconsequential as their removal does not impact the end user for the aforesaid purpose such as problem forecasting or troubleshooting. We relate the utility of a message to the probability of its appearance in the due context. We present machine-learning-based techniques that computes the usefulness of individual messages in a log file. We demonstrate identification and discarding of inconsequential messages to shrink the log size to acceptable limits. We have tested this over real-world logs and observed that eliminating such low value data can reduce the log files significantly (30% to 55%), with minimal error rates (7% to 20%). When limited user feedback is available, we show modifications to the technique to learn the user intent and accordingly further reduce the error.

Space management is the activity of monitoring and ensuring adequate free space on all volumes in a clustered storage system. Volumes that exceed used space limits are typically relieved by migrating a part of their data to other under utilized volumes. Without deduplication, space reclamation is simple as one has to just migrate as much data as the desired space reclamation. However, in deduped volumes there is no direct relation between the logical size of the file and the physical space occupied by it. Therefore, optimal space reclamation is hard as: a)migrating few files may produce little or zero bytes of free space, but still incur significant network costs. b)migrating a heavily shared file destroys the disk sharing relationships in that volume and increases the physical space consumption of that dataset.

We consider the problem of speeding up Entity Recognition systems that exploit existing large databases of structured entities to improve extraction accuracy. These systems require the computation of the maximum similarity scores of several overlapping segments of the input text with the entity database. We formulate a Batch-Top-K problem with the goal of sharing computations across overlapping segments. Our proposed algorithm performs a factor of three faster than independent Top-K queries and only a factor of two slower than an unachievable lower bound on total cost. We then propose a novel modification of the popular Viterbi algorithm for recognizing entities so as to work with easily computable bounds on match scores, thereby reducing the total inference time by a factor of eight compared to stateof- the-art methods.