Robert M. Bruckner

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.

Capturing Delays and Valid Times in Data Warehouses—Towards Timely Consistent Analyses

Real-world changes are generally discovered delayed by computer systems. The typical update patterns for traditional data warehouses on an overnight or even weekly basis increase this propagation delay until the information is available to knowledge workers. Typically, traditional data warehouses focus on summarized data (at some level) rather than detailed data.For active data warehouse environments, detailed data about entities is required for checking the data conditions and triggering actions to automize routine decision tasks. Hence, keeping data current (by minimizing the latency from when data is captured until it is available to knowledge workers) and consistent in that context is a difficult task.In this paper we present an approach for modeling conceptual time consistency problems and introduce a data model that deals with timely delays. It supports knowledge workers in finding out, why (or why not) an active system responded to acertain state of the data. Therefore, the model enables analytical processing of detailed data (enhanced by valid time) based on a knowledge state at a specific time. All states that were not yet known by the system at that point in time are consistently ignored. This enables timely consistent analyses by considering that the validity of detailed data and aggregates can be restricted to time intervals only, due to frequent updates and late-arriving information.

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.

Managing Time Consistency for Active Data Warehouse Environments

Real-world changes are generally discovered delayed by computer systems. The typical update patterns for traditional data warehouses on an overnight or even weekly basis enlarge this propagation delay until the information is available to knowledge workers. Typically, traditional data warehouses focus on summarized data (at some level) rather than detail data. For active data warehouse environments, also detailed data about individual entities are required for checking the data conditions and triggering actions. Hence, keeping data current and consistent in that context is not an easy task. In this paper we present an approach for modeling conceptual time consistency problems and introduce a data model that deals with timely delays. It supports knowledge workers, to find out, why (or why not) an active system responded to a certain state of the data. Therefore the model enables analytical processing of detail data (enhanced by valid time) based on a knowledge state at a specified instant of time. All states that were not yet knowable to the system at that point in time are consistently ignored.

A framework for a multidimensional OLAP model using Topic Maps

The goal of a data warehouse is to integrate applications at the data level. Data warehouse architectures are developing in response to our increasing data and information requirements. The traditional notion of data warehouses is evolving into a federated warehouse augmented by a set of processes and services to support integrated and consistent access to heterogeneous, decentralized warehouse systems. The evolution of data warehouses into knowledge repositories requires an architecture supporting the information acquisition from heterogeneous sources. We propose a framework using XML Topic Maps (XTM) as a foundation for the combination of Web OLAP and data warehouse resources by integrating schema information and addressing semantic heterogeneity. This aims at providing, managing and exploiting a set of integrated data warehouses for knowledge management and decision support.

Holistic software process performance measurement from the stakeholders' perspective

Organisations need to measure the performance of their software development process, in order to control, manage and improve it continuously. Current measurement approaches lack adequate metrics. This work improves software performance measurement with a stakeholder approach that fosters balanced and goal-oriented metrics. This approach has been implemented in a multi-national organisation. The metrics gathered have been actively utilised throughout the organisation for more than one year. The measurement approach has been verified through interviews with key project members.

On Efficient and Effective Association Rule Mining from XML Data

In this paper, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently and effectively. In XAR-Miner, raw XML data are first transform ed to either an Indexed Content Tree (IX-tree) or M ulti-relational databases (Multi-DB), depending on the size of XML document and memory constraint of the system, for efficient data selection in the AR mining. Concepts that are relevant to the AR mining task are generalized to produce generalized meta-patterns. A suitable metric is devised for measuring the degree of concept generalization in order to prevent under-generalization or over-generalization. Resultant generalized meta-patterns are used to generate large ARs that meet the support and confidence levels. An efficient AR mining algorithm is also presented based on candidate AR generation in the hierarchy of generalized meta-patterns. The experiments show that XAR-Miner is more efficient in performing a large number of AR mining tasks from XML docume nts than the state-of-the-art method of repetitively scanning through XML documents in order to perform each of the mining tasks.

Building XML Data Warehouse Based on Frequent Patterns in User Queries

With the proliferation of XML-based data sources available across the Internet, it is increasingly important to provide users with a data warehouse of XML data sources to facilitate decision-making processes. Due to the extremely large amount of XML data available on web, unguided warehousing of XML data turns out to be highly costly and usually cannot well accommodate the users needs in XML data acquirement. In this paper, we propose an approach to materialize XML data warehouses based on frequent query patterns discovered from historical queries issued by users. The schemas of integrated XML documents in the warehouse are built using these frequent query patterns represented as Frequent Query Pattern Trees (FreqQPTs). Using hierarchical clustering technique, the integration approach in the data warehouse is flexible with respect to obtaining and maintaining XML documents. Experiments show that the overall processing of the same queries issued against the global schema become much efficient by using the XML data warehouse built than by directly searching the multiple data sources.

Measuring knowledge with workflow management systems

Expert knowledge is captured in process design. In organisations, knowledge becomes embedded in routines, processes and practices as well as norms, and can be evaluated by the decisions or actions to which it leads, e.g. measurable efficiencies, speed or quality gains. Knowledge develops over time, through experience that includes what we absorb from courses, books and mentors, as well as from informal learning. In this paper, we analyse workflow history and demonstrate that workflow management systems enable knowledge measurement.

Uncovering Information Hidden in Web Archives: A Glimpse at Web Analysis Building on Data Warehouses

The Internet has turned into an important aspect of our information infrastructure and society, with the Web forming a part of our cultural heritage. Several initiatives thus set out to preserve it for the future. The resulting Web archives are by no means only a collection of historic Web pages. They hold a wealth of information that waits to be exploited, information that may be substantial to a variety of disciplines. With the time-line and metadata available in such a Web archive, additional analyzes that go beyond mere information exploration become possible. In the context of the Austrian On-Line Archive (AOLA), we established a Data Warehouse as a key to this information. The Data Warehouse makes it possible to analyze a variety of characteristics of the Web in a flexible and interactive manner using on-line analytical processing (OLAP) techniques. Specifically, technological aspects such as operating systems and Web servers used, the variety of file types, forms or scripting languages encountered, as well as the link structure within domains, may be used to infer characteristics of technology maturation and impact or community structures.