Integrated Business and Production Process Data Warehousing

Integrated Business and Production Process Data Warehousing

Dirk Draheim (University of Lunsbruck, Austria) and Oscar Mangisengi (BWIN Interactive Entertainment and AG & SMS Data System, Austria)
DOI: 10.4018/978-1-60566-232-9.ch005
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Nowadays tracking data from activity checkpoints of unit transactions within an organization’s business processes becomes an important data resource for business analysts and decision-makers to provide essential strategic and tactical business information. In the context of business process-oriented solutions, business-activity monitoring (BAM) architecture has been predicted as a major issue in the near future of the business-intelligence area. On the other hand, there is a huge potential for optimization of processes in today’s industrial manufacturing. Important targets of improvement are production efficiency and product quality. Optimization is a complex task. A plethora of data that stems from numerical control and monitoring systems must be accessed, correlations in the information must be recognized, and rules that lead to improvement must be identified. In this chapter we envision the vertical integration of technical processes and control data with business processes and enterprise resource data. As concrete steps, we derive an activity warehouse model based on BAM requirements. We analyze different perspectives based on the requirements, such as business process management, key performance indication, process and state based-workflow management, and macro- and micro-level data. As a concrete outcome we define a meta-model for business processes with respect to monitoring. The implementation shows that data stored in an activity warehouse is able to efficiently monitor business processes in real-time and provides a better real-time visibility of business processes.
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In the continuously changing business environment nowadays manufacturing organizations can benefit from one unified business environment that brings production process data and business data together in real-time. We believe in a balanced view on all activities in the modern enterprise. A focus on the mere administration side of businesses is to narrow. The production process must be incorporated into information management from the outset, because excellence in production is a fundament of today’s businesses (Hayes & Wheelright, 1984). In fact, manufacturers generate incredible amounts of raw data in production processes, however, they are often not used efficiently yet. The rationales of turning production process data into information in industrial manufacturing are to improve production processes, competitiveness, and product qualities; enabling management to understand where inefficiencies exist and to optimize production processes, and to prepare smart business decisions for high-level management, such as to provide an accurate picture of occurrences on the production process. As a result, the need for highly integrated control and information systems as data resources for Business Intelligence (BI) applications is essential to addressing the emerging challenges.

Data Warehousing currently is almost identical to BI tools for supporting decision-making. A data warehouse (DW) stores historical data, which are integrated from different data sources, and it is organized into multidimensional data (Kimball, Ross & Merz, 2002; Inmon, 2002). Data in a DW is dynamically processed by an On-Line Analytical Processing (OLAP) tool (Codd, Codd & Salley, 1993) for high-level management to make decisions. Although DWs have been developed over a decade, they are still inadequate for answering the needs of BI applications. DW does not provide data based on events and lacks process-context. DW stores end measures, i.e., aggregated reference data, rather than process checkpoints (Creese, 2005). However, those processes, events, or activities always occur in business processes as well as production processes.

Workflow management (WfM) systems (Hollingworth, 1995) have been developed in the last decade to help automating business processes of organizations. Today’s workflow technology products known as business process management suites (Miers, Harmon & Hall, 2006) enable the tracking of data in business processes. Furthermore, Business Activity Monitoring (BAM) - a current business intelligence trend (Dresner, 2002; Mangisengi, Pichler, Auer, Draheim & Rumetshofer, 2007) – enables monitoring business process activities of an organization.

Based on our experience in successfully implementing an activity warehouse for monitoring business activities for integrating enterprise applications (Mangisengi et al., 2007), we argue that the workflow technology supported by Service-Oriented Architecture (SOA) and BAM technology are potential technologies for industrial manufacturing to optimize and improve production processes and business processes as well as product quality measures.

In this paper we envision the vertical integration of technical processes and control data with business processes and enterprise resource data. This paper presents a meta-model of an activity warehouse for integrating business and production process data in industrial manufacturing. We approach BAM requirements for deriving the meta-model of the activity warehouse.

This work is structured as follows. The next section gives related work. Then, research background and motivation are presented. Afterwards, we present production process data based on BAM requirements. Furthermore, we present a meta-model of integrated business and production process data. Finally, a conclusion and future research are given in the last section.

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Editorial Advisory Board
Table of Contents
David Taniar
Chapter 1
Laila Niedrite, Maris Solodovnikova Treimanis, Liga Grundmane
There are many methods in the area of data warehousing to define requirements for the development of the most appropriate conceptual model of a data... Sample PDF
Development of Data Warehouse Conceptual Models: Method Engineering Approach
Chapter 2
Stefano Rizzi
In the context of data warehouse design, a basic role is played by conceptual modeling, that provides a higher level of abstraction in describing... Sample PDF
Conceptual Modeling Solutions for the Data Warehouse
Chapter 3
Hamid Haidarian Shahri
Entity resolution (also known as duplicate elimination) is an important part of the data cleaning process, especially in data integration and... Sample PDF
A Machine Learning Approach to Data Cleaning in Databases and Data Warehouses
Chapter 4
Maurizio Pighin, Lucio Ieronutti
Data Warehouses are increasingly used by commercial organizations to extract, from a huge amount of transactional data, concise information useful... Sample PDF
Interactive Quality-Oriented Data Warehouse Development
Chapter 5
Dirk Draheim, Oscar Mangisengi
Nowadays tracking data from activity checkpoints of unit transactions within an organization’s business processes becomes an important data resource... Sample PDF
Integrated Business and Production Process Data Warehousing
Chapter 6
Jorge Loureiro, Orlando Belo
OLAP queries are characterized by short answering times. Materialized cube views, a pre-aggregation and storage of group-by values, are one of the... Sample PDF
Selecting and Allocating Cubes in Multi-Node OLAP Systems: An Evolutionary Approach
Chapter 7
Jorge Loureiro, Orlando Belo
Globalization and market deregulation has increased business competition, which imposed OLAP data and technologies as one of the great enterprise’s... Sample PDF
Swarm Quant' Intelligence for Optimizing Multi-Node OLAP Systems
Chapter 8
Franck Ravat, Olivier Teste, Ronan Tournier
With the emergence of Semi-structured data format (such as XML), the storage of documents in centralised facilities appeared as a natural adaptation... Sample PDF
Multidimensional Anlaysis of XML Document Contents with OLAP Dimensions
Chapter 9
Hanene Ben-Abdallah, Jamel Feki, Mounira Ben Abdallah
Despite their strategic importance, the wide-spread usage of decision support systems remains limited by both the complexity of their design and the... Sample PDF
A Multidimensional Pattern Based Approach for the Design of Data Marts
Chapter 10
Concepción M. Gascueña, Rafael Guadalupe
The Multidimensional Databases (MDB) are used in the Decision Support Systems (DSS) and in Geographic Information Systems (GIS); the latter locates... Sample PDF
A Multidimensional Methodology with Support for Spatio-Temporal Multigranularity in the Conceptual and Logical Phases
Chapter 11
Francisco Araque, Alberto Salguero, Cecilia Delgado
One of the most complex issues of the integration and transformation interface is the case where there are multiple sources for a single data... Sample PDF
Methodology for Improving Data Warehouse Design using Data Sources Temporal Metadata
Chapter 12
Shi-Ming Huang, John Tait, Chun-Hao Su, Chih-Fong Tsai
Data warehousing is a popular technology, which aims at improving decision-making ability. As the result of an increasingly competitive environment... Sample PDF
Using Active Rules to Maintain Data Consistency in Data Warehouse Systems
Chapter 13
Marcin Gorawski, Wojciech Gebczyk
This chapter describes realization of distributed approach to continuous queries with kNN join processing in the spatial telemetric data warehouse.... Sample PDF
Distributed Approach to Continuous Queries with kNN Join Processing in Spatial Telemetric Data Warehouse
Chapter 14
Maria Luisa Damiani, Stefano Spaccapietra
This chapter is concerned with multidimensional data models for spatial data warehouses. Over the last few years different approaches have been... Sample PDF
Spatial Data Warehouse Modelling
Chapter 15
Jérôme Darmont
Performance evaluation is a key issue for designers and users of Database Management Systems (DBMSs). Performance is generally assessed with... Sample PDF
Data Warehouse Benchmarking with DWEB
Chapter 16
Lars Frank, Christian Frank
A Star Schema Data Warehouse looks like a star with a central, so-called fact table, in the middle, surrounded by so-called dimension tables with... Sample PDF
Analyses and Evaluation of Responses to Slowly Changing Dimensions in Data Warehouses
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