A Logic Programming Approach to Integration Network Inference

TL;DR

This paper introduces a logic programming method to infer and reconstruct enterprise integration networks from raw network mining data, enabling better visibility into complex IT landscapes.

Contribution

It presents a novel approach using first-order logic for network inference and reports on its application to real-world enterprise data.

Findings

Effective reconstruction of integration networks from raw data

Application to real enterprise landscapes demonstrated feasibility

Enhanced visibility into complex IT environments

Abstract

The discovery, representation and reconstruction of (technical) integration networks from Network Mining (NM) raw data is a difficult problem for enterprises. This is due to large and complex IT landscapes within and across enterprise boundaries, heterogeneous technology stacks, and fragmented data. To remain competitive, visibility into the enterprise and partner IT networks on different, interrelated abstraction levels is desirable. We present an approach to represent and reconstruct the integration networks from NM raw data using logic programming based on first-order logic. The raw data expressed as integration network model is represented as facts, on which rules are applied to reconstruct the network. We have built a system that is used to apply this approach to real-world enterprise landscapes and we report on our experience with this system.