A Transaction-Oriented Architecture for Enterprise Systems

A Transaction-Oriented Architecture for Enterprise Systems

Simon Polovina
Copyright: © 2013 |Pages: 11
DOI: 10.4018/ijiit.2013100105
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Many enterprises risk business transactions based on information systems that are incomplete or misleading, given that 80-85% of all corporate information remains outside of their processing scope. It highlights that the bulk of information is too unstructured for these systems to process, but must be taken into account if those systems are to provide effective support. Computer technology nonetheless continues to become more and more predominant, illustrated by SAP A.G. recognising that 65-70% of the world's transactions are run using their technology. Using SAP as an illustrative case study, and by bringing in the benefits of technologies such as Service-Oriented Architecture (SOA), Business Process Management (BPM), Enterprise Architecture Frameworks (EA) and Conceptual Structures, a practical roadmap is identified to a Transaction-Oriented Architecture (TOA) that is predicated on the Transaction Concept. This concept builds upon the Resources-Events-Agents (REA) modelling pattern that is close to business reality. Enterprise systems can thus better incorporate that missing 80-85% of hitherto too-unstructured information thereby allowing enterprise systems vendors such as SAP, their competitors, customers, suppliers and partners to do an ever better job with the world's transactions.
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Accordingly there has been a substantial push to Service-Oriented Architecture (SOA) and an eco-system that in SAP’s case is epitomised by the Enterprise Services Workplace (ESW) (SAP A.G., 2012). Allied to these approaches is the integration of Business Intelligence (BI), particularly in handing the proliferation of data (Economist, 2010) and in conjunction with novel database querying tools such as Hadoop Impala (Cloudera, Inc., 2013). In SAP's case, there has been the emergence of the High-Performance Analytic Appliance (SAP HANA) architecture (Word, 2013; SAP A.G., 2013). A continuation of BI is to apply semantic technologies that structure unstructured data. These information extraction technologies take knowledge management a stage further by discovering knowledge hitherto hidden in that data, thereby capturing much more of that elusive 80-85% of corporate information. The Combining and Unifying BI with Semantic Technologies project (CUBIST) is an exemplar of extracting meaning from structured and unstructured data to discover knowledge (CUBIST project, 2013).

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