Background

Business Process (BP) "consists of a set of activities that are performed in
coordination in an organizational and technical environment. These activities
jointly realize a business goal."

Business Process Intelligence (BPI) is an upcoming buzzword in the enterprise
application area as a kind of Business Intelligence 2.0. Business Intelligence in
the traditional sense covers the spotting, extraction and analyzing of business
data. BI aims to support decision support for the operative, tactical and strategic
focus. As contrast BPI focuses on the business process data of a enterprise.
It manages "process execution quality by providing [...] analysis, prediction,
monitoring, control and optimization"[5]. For example instead analyzing the
sales revenue by products, it focuses on the productivity of the sale process
itself.

Key Performance Indicators (KPIs) are quantitative measures to capture the
performance of a business process. Each KPI is linked to a strategic goal. A
strategic goal hereby is a quanti able, measurable and result-oriented high-level
goal. By de ning av KPI one can measure if a aspect of the process serves the
overall strategic goal.

Business Activity Monitoring (BAM) is the part of BPI and aims to the real-
time monitoring of business processes to get visibility for business performance
(e.g. KPIs). Consequently, process owner and process participants get able to
react on business changes fast and more e cient.

Workflow Management Systems (WFMSs) manage and control business pro-
cesses. Consequently, they automate business tasks, mange process-related in-
formations and integrate information in the enterprise.

Audit Trail Data in general is a sequence of steps generated by a system to
document the real processing. In the WFMS, it is a set of event logs produced.
A event captured in logs represents a activity state change during the execution
of a process. This paper introduce a reference architecture for a data warehouse
specialized on audit trails logs.

Data Warehouse (DWH) is "a subject-oriented, integrated, time-variant, non-
volatile collection of data in support of management's decision-making pro-
cess". The DWH's main task is to give a uni ed view on the operational data
of a company. Thereby it o ers a variety of aggregation and analysis feature
to support the decision-making. As contrast to the traditional weekly reporting
DWs, nowadays warehouse have to face near-real time analyses requirements.
For example on going research presents the zero-latency data warehouse.

Operational Data Store (ODS) contains, as contrast to the DWH, "volatile,
current-valued, detailed-only collection of data". It's main focus is to deliver
up-to-the-second fresh information. Thereby, it enable o er near-real-time data
analytics.

Online Analytical Processing(OLAP) applications enables enterprise users to
gain insight into their data by fast access to a variety of views of data organized
to inflect the multidimensional nature of enterprise data. Thereby the data get
summarized, consolidated and synthesized according to multiple dimensions.


References:

• Agrawal, R., Gupta, A., Sarawagi, S.: Modeling multidimensional databases. In:Data Engineering, 1997. Proceedings. 13th International Conference on. pp. 232{243. IEEE (1997)
• Inmon, W.: What is a data warehouse? In: PRISM Tech Topic (1992)
• B., I.: The operational data store. infodb. evaltech.com (1995)
• Tho, M., Tjoa, A.: Zero-latency data warehousing for heterogeneous data sources and continuous data streams. In: Proc. of the Fifth Int. Conf. on Information Integration and Web-based Applications Services. Citeseer (2003)
• Weske, M.: Business Process Management: Concepts, Languages, Architectures.
• Springer, softcover reprint of hardcover 1st ed. 2007 edn. (11 2010), http://amazon.com/o/ASIN/3642092640/