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Organizations routinely accumulate semi-structured log datasets generated asthe output of code; these datasets remain unused and uninterpreted, and occupywasted space - this phenomenon has been colloquially referred to as "data lake"problem. One approach to leverage these semi-structured datasets is to convertthem into a structured relational format, following which they can be analyzedin conjunction with other datasets. We present Datamaran, an tool that extractsstructure from semi-structured log datasets with no human supervision.Datamaran automatically identifies field and record endpoints, separates thestructured parts from the unstructured noise or formatting, and can tease apartmultiple structures from within a dataset, in order to efficiently extractstructured relational datasets from semi-structured log datasets, at scale withhigh accuracy. Compared to other unsupervised log dataset extraction toolsdeveloped in prior work, Datamaran does not require the record boundaries to beknown beforehand, making it much more applicable to the noisy log files thatare ubiquitous in data lakes. Datamaran can successfully extract structuredinformation from all datasets used in prior work, and can achieve 95%extraction accuracy on automatically collected log datasets from GitHub - asubstantial 66% increase of accuracy compared to unsupervised schemes fromprior work. Our user study further demonstrates that the extraction results ofDatamaran are closer to the desired structure than competing algorithms.

Navigating the Data Lake with Datamaran: Automatically Extracting Structure from Log Datasets