UMBC ebiquity

Parallelizing Natural Language Techniques for Knowledge Extraction from Cloud Service Level Agreements

Authors: Sudip Mittal, Karuna Pande Joshi, Claudia Pearce, and Anupam Joshi

Book Title: 2015 IEEE International Conference on Big Data

Date: October 19, 2015

Abstract: To efficiently utilize their cloud based services, consumers have to continuously monitor and manage the Service Level Agreements (SLA) that define the service performance measures. Currently this is still a time and labor intensive process since the SLAs are primarily stored as text documents. We have significantly automated the process of extracting, managing and monitoring cloud SLAs using natural language processing techniques and Semantic Web technologies. In this paper we describe our prototype system that uses a Hadoop cluster to extract knowledge from unstructured legal text documents. For this prototype we have considered publicly available SLA/terms of service documents of various cloud providers. We use established natural language processing techniques in parallel to speed up cloud legal knowledge base creation. Our system considerably speeds up knowledge base creation and can also be used in other domains that have unstructured data.

Type: InProceedings

Tags: knowledge extraction, distributed systems, data mining

Google Scholar: search

Number of downloads: 299

 

Available for download as


size: 170492 bytes
 

Related Projects:

Active Project

 ALDA: Automated Legal Document Analytics.