Josef Schiefer

A Comparison of Data Warehouse Development Methodologies Case Study of the Process Warehouse

Building a data warehouse is a very challenging issue because compared to software engineering it is quite a young discipline and does not yet offer well-established strategies and techniques for the development process. Current data warehouse development methods can fall within three basic groups: data-driven, goal-driven and user-driven. All three development approaches have been applied to the Process Warehouse that is used as the foundation of a process-oriented decision support system, which aims to analyse and improve business processes continuously. In this paper we evaluate all three development methodologies by various assessment criteria. The aim is to establish a link between the methodology and the requirement domain.

Sense & response service architecture (SARESA): an approach towards a real-time business intelligence solution and its use for a fraud detection application

The dynamic business environment of many organizations require massive monitoring of their processes in real-time in order to proactively respond to exceptional situations and to take advantage of time-sensitive business opportunities. The ability to sense and interpret events about a changing business environment requires an event-driven IT infrastructure for pwerforming fast and well-informed decisions and putting them into action. However, traditional Business Intelligence (BI) and Data Warehousing technologies do not directly address time sensitive monitoring and analytical requirements. We introduce an enhanced BI architecture that covers the complete process to sense, interpret, predict, automate and respond to business environments and thereby aims to decrease the reaction time needed for business decisions. We propose an event-driven IT infrastructure to operate BI applications which enable real-time analytics across corporate business processes, notifies the business of actionable recommendations or automatically triggers business operations, and effectively closing the gap between Business Intelligence systems and business processes. A scenario from the area of mobile phone fraud detection was chosen for building a prototype that illustrates the proposed approach by using current available IT technologies.

Event-driven rules for sensing and responding to business situations

Event-based systems have been developed and used to implement networked and adaptive business environments based on loosely coupled systems in order to respond faster to critical business events. In this paper, we introduce a rule management system which is able to sense and evaluate events in order to respond to changes in a business environment or customer needs. It enables users to graphically compose comprehensive event-triggered rules, which can be used to control the processing of services. For the definition of a rule set, users can independently define event conditions, event patterns and correlation-related information which can be combined for modeling complex business situations. We have fully implemented the proposed system with a service-oriented approach and illustrate our approach with an order management business case.

Concepts and models for typing events for event-based systems

Event-based systems are increasingly gaining widespread attention for applications that require integration with loosely coupled and distributed systems for time-critical business solutions. In this paper, we show concepts and models for representing, structuring and typing events. We discuss existing event models in the field and introduce the event model of the event-based system SARI for illustrating various typing concepts. The typing concepts cover topics such as type inheritance and exheritance, dynamic type inferencing, attribute types, as well as the extendibility and addressability of events. We show how the typing concepts evolved and depend on the implemented event-based systems which use different approaches for the event processing such as graphical approaches, or approaches, that use Java code, SQL code, or ECA (event-condition-action) rules.

Management and Controlling of Time-Sensitive Business Processes with Sense a Respond

The dynamic business environment of many organizations require to monitor their business, IT and organizational processes in real-time in order to proactively respond to exceptions and to take advantage of time-sensitive business opportunities. The ability to sense and interpret events about a changing business environment or customer needs require an event-driven IT infrastructure for making fast and well-informed decisions and putting them into action. In this paper we introduce sense & respond loops that support a complete business intelligence process to sense, interpret, predict, automate and respond to business processes and aim to decrease the time it takes to make the business decisions. Our approach enables real-time analytics across corporate business processes, notifies the business of actionable recommendations or automatically triggers business operations, effectively closing the gap between business intelligence systems and business processes. We propose a system for executing and managing sense & respond loops and illustrate our approach with a supply chain business scenario

Enhanced business intelligence - supporting business processes with real-time business analytics

In the 21st century, organizations are experiencing environmental changes characterized by indistinct organizational boundaries and fast-paced change. As a result firms need appropriate decision support infrastructures in order to face these challenges. Current data warehousing and business intelligence approaches are widely accepted as a middleware layer for state-of-the-art decision support. However, they do not provide sufficient support in dealing with the upcoming challenges, such as real-time and closed loop decision making. In this paper, we suggest an architecture for enhanced business intelligence that aims to increase the value of business intelligence by reducing action time and interlinking business processes into decision making.

Event Cloud - Searching for Correlated Business Events

Market players that can respond to critical business events faster than their competitors will end up as winners in the fast moving economy. Event-based systems have been developed and used to implement networked and adaptive business environments based on loosely coupled systems. In this paper, we introduce Event Cloud, a system that allows searching for business events in a variety of contexts that also take the relationships between events into consideration. Event Cloud supports knowledge workers in their daily operations in order to perform investigations and analyses based on historical events. It enables users to search in large sets of historical events which are correlated and indexed in a data staging process with an easy-to-use search interface. For improving the search results, we propose an index based ranking system. We present an architecture for the Event Cloud system, which supports a continuous near real-time integration of business events with the aim of decreasing the time it takes to make them available for searching purposes. We have fully implemented the proposed architecture and discuss implementation details.

An agent-based architecture for analyzing business processes of real-time enterprises

As the desire for business intelligence capabilities for e-business processes expands, existing workflow management systems and decision support systems are not able to provide continuous, real-time analytics for decision makers. Business intelligence requirements may appear to be different across the various industries, but the underlying requirements are similar nformation that is integrated, current, detailed, and immediately accessible. In this paper we introduce an agent-based architecture that supports a complete business intelligence process to sense, interpret, predict, automate and respond to business processes and aims to decrease the time it takes to make business decisions. In fact, there should be almost zero-latency between the cause and effect of a business decision. Our architecture enables analysis across corporate business processes notifies the business of auctionable recommendations or automatically triggers business operations, effectively closing the gap between business intelligence systems and business processes.

Process information factory: a data management approach for enhancing business process intelligence

With access to critical performance indicators of business processes, executives, business managers and staff members can play a crucial role in improving the speed and effectiveness of an organizations business operations. The monitoring and analysis of business processes are complicated by the variety of organizational units and information systems involved in the execution of these processes. In this paper, we present a process information factory as a solution for managing performance data of business processes. The purpose of the process information factory is to provide a data foundation for a process-driven decision support system to monitor and improve business processes continuously.

Process data store: A real-time data store for monitoring business processes

With access to real-time information on critical performance indicators of business processes, managers and staff members can play a crucial role in improving the speed and effectiveness of an organization’s business operations. While the investments in data warehouse technologies have resulted in considerable information processing efficiencies for the organizations, there is still a significant delay in the time required for mission critical information to be delivered in a form that is usable to managers and staff. In this paper we introduce an architecture for business process monitoring based on a process data store which is a data foundation for operational and tactical decision-making by providing real-time access to critical process performance indicators to improve the speed and effectiveness of workflows. The process data store allows to identify and react to exceptions or unusual events that happened in workflows by sending out notifications or by directly changing the current state of the workflow. Our proposed architecture allows to transform and integrate workflow events with minimal latency providing the data context against which the event data is used or analyzed.