From network mining to large scale business networks

Abstract

The vision of Large Scale Network Analysis (LSNA) states on large amounts of network data, which are produced by social media applications like Facebook, Twitter, and the competitive domain of biological networks as well as their needs for network data extraction and analysis. That raises data management challenges which are addressed by biological, data mining and linked (web) data management communities. So far, mainly these domains were considered when identifying research topics and measuring approaches and progress. We argue that an important domain, the Business Network Management (BNM), representing business and (technical) integration data, implicitely linked and available in enterprises, has been neglected. Not only do enterprises need visibilities into their business networks, they need ad-hoc analysis capabilities on them.In this paper, we introduce BNM as domain, which comes with large scale network data. We discuss how linked business data can be made explicit by what we called Network Mining (NM) from dynamic, heterogeneous enterprise environments to combine it to a (cross-) enterprise linked business data network and state on its different facets w.r.t large network analysis and highlight challenges and opportunities.

Towards a Business Network Management

“…The virtualization level L1 corresponds to the first two types of Network Mining (NM), i.e. discovery and conformance, in [10] and covers the discovery, extraction and domain specific analysis of relevant data from dynamic, distributed and heterogeneous enterprise landscapes. Fig.…”

“…This work builds on previous work conducted in [10], which discusses how to come from Network Mining to large scale business networks. The contribution of this paper starts with the definition of Business Network Virtualization, which is used to extend the definition of Business Network towards the Business Network Management.…”

“…In previous work, we introduced the domain of Network Mining (NM) [10], which allows to discover this information from the different business process and integration domains. The extracted raw data contains the information necessary to reconstruct the different network perspectives as well as to semantically link them into one business network [11].…”

“…In this position paper, we introduce a new enterprise and linked data domain, the Business Network Management, which helps enterprises to get insight into their business networks. Based on previous work in the area of Network Mining [10], we show how to bridge the gap from the NM raw data towards reconstructed business networks. For that, we use the concept of a virtualized business network, which extends the capabilities of NM from discovery to enrichment and re-deployment of information as required for the management of business networks.…”

Advanced Data Processing in the Business Network System

“…Yet simple questions about the network (e.g., which business processes require which interfaces, which integration artifacts are obsolete) remain difficult to answer, which makes the operation and lifecycle management like data migration, landscape optimization and evolution hard and more expensive increasing with the number of the systems. To change that, Network Mining (NM) systems are used to discover and extract raw data [13] -be it technical data (e.g., configurations of integration products like Enterprise Service Bus (ESB) [8]) or business data (e.g., information about a supplier in a Supplier Relationship Management (SRM) product). The task at hand is to provide a system, that automatically discovers and reconstructs the "as-is" BN from the incomplete, fragmented, cross-domain NM data and make it accessible for visualization and analysis.…”