Exploit unstructured data using deep analytics to optimise enterprise IT asset management

Abstract

Unstructured data pose a huge risk towards supporting critical goals of sustainable reconciliation of inventory due to over spending and audit exposure. They are inherently fragmented, incomplete and without restriction. Hence domain specific explicit information extracted from unstructured data may not be complete or contextually accurate or both for reconciliation. It requires inference to extract implicit information to make the extracted information usefully complete. Domain reconciliation modelled in relational database is static, requires deep domain knowledge prior to modelling and depends on surface commonalities of data labels and values between source and target. These design constraints make relational model-based reconciliation unfit for reconciling entities extracted from unstructured data. In this paper we propose an ontology-based semantic information model and semantic reconciliation mediator to extract valid entity information from unstructured data in knowledge format and reconcile them using pattern-based reconciliation. This agile, superior way of integrating information makes it possible to support unstructured information processing through inference and incorporates effects of temporal events that impact the ownership and usage rights of resources as well. Copyright © 2013 Inderscience Enterprises Ltd.