Is There a Difference Between the Theoretical and Practical Complexity of UML?

An on-going and major problem faced by information systems developers and business users alike is reaching a clear and consensual understanding of the system by both groups. This can be difficult because the businesses are (typically) process driven, while the systems are (increasingly) object-oriented. Enter modeling. Modeling is one way of presenting complex information in a way that enhances or eases understanding. But, even models can be extremely complex, and the underlying tools and modeling languages are not any less complex. This chapter investigates the possibility that modeling languages can be simplified by considering that not all of the “words” in the language are used all of the time. If theoretical (maximum) represents all the words in a modeling language, then this chapter suggests that there might exist a more use-based (we name it practical complexity) subset that represents an easier to learn and use subcomponent of the language.

Data Quality

This chapter presents results from a large-scale survey of Australian CPA members regarding data quality. The research investigates and reports major stakeholders’ opinions on the importance of critical success factors affecting data quality and the actual performance on each of those factors. The results reveal whether dissimilarly sized organizations differ in the way they measure the importance and performance of critical success factors for data quality in accounting information systems.

Cover Stories for Key Attributes—Expanded Database Access Control

In this chapter, we extend the multi-level secure (MLS) data model to include nonkey related cover stories so that key attributes can have different values at different security levels. MLS data models require the classification of data and users into multiple security levels. In MLS systems, cover stories allow information provided to users at lower security levels to differ from information provided to users at higher security levels. Previous versions of the MLS model did not permit cover stories for key attributes because the key is used to relate the various cover stories for a particular entity. We present the necessary model changes and modifications to the relational algebra, which are required to implement cover stories for keys. We demonstrate the improvements made by these changes, illustrate the increased expressiveness of the model, and determine the soundness of a database, based on the described concepts.

Applying UML for Modeling the Physical Design of Data Warehouses

In previous work, we have shown how to use unified modeling language (UML) as the primary representation mechanism to model conceptual design, logical design, modeling of extraction, transformation, loading (ETL) processes, and defining online analytical processing (OLAP) requirements of data warehouses (DW). Continuing our work on using UML throughout the DW development lifecycle, in this chapter, we present our modeling techniques of physical design of DW using component diagrams and deployment diagrams of UML. Our approach allows the DW designer to anticipate important physical design decisions that may reduce the overall development time of a DW such as replicating dimension tables, vertical and horizontal partitioning of a fact table, and the use of particular servers for certain ETL processes. We illustrate our techniques with a case study.

Effective Processing of XML-Extended OLAP Queries Based on a Physical Algebra

In today’s OLAP systems, physically integrating fast-changing data, for example, stock quotes, into a cube is complex and time-consuming. This data is likely to be available in XML format on the World Wide Web (WWW); thus, instead of physical integration, making XML data logically federated with OLAP systems is desirable. In this chapter, we extend previous work on the logical federation of OLAP and XML data sources by presenting simplified query semantics, a physical query algebra and a robust OLAP-XML query engine as well as the query evaluation techniques. Performance experiments with a prototypical implementation suggest that the performance for OLAP-XML federations is comparable to queries on physically integrated data.

Efficient Placement and Processing in Shared-Nothing Data Warehouses

Some businesses generate giga or even terabytes of historical data that can be organized and analyzed for better decision making. This poses issues concerning systems and software for efficient processing over such data. While the traditional solution to this problem involves costly hardware and software, we focus on strategies for running large data warehouses over low-cost, non-dedicated nodes in a local-area network (LAN) and non-proprietary software. Once such a technology is in place, every data warehouse will be able to run in a small cost environment, but the system must be able to choose its placement and processing for maximum efficiency. We discuss the basic system architecture and the design of the data placement and processing strategy. We compare the shortcomings of a basic horizontal partitioning for the environment, with a simple design that produces efficient placements. Our discussion and results provide important insight into how low-cost efficient data warehouse systems can be obtained.

Factors Affecting Design Decisions for Customer Relationship Management Data Warehouses

Customer relationship management (CRM) is a strategy that integrates concepts of knowledge management, data mining, and data warehousing in order to support an organization’s decision-making process to retain long-term and profitable relationships with its customers. Key factors for successfully implementing CRM (e.g., data quality issues, organizational readiness, customer strategies, selection of appropriate KPIs, and the design of the data warehouse model) are discussed with the main thrust of the chapter focusing on CRM analyses and the impact of those analyses on CRM data warehousing design decisions. This chapter then presents a robust multidimensional starter model that supports CRM analyses. Additional research contributions include the introduction of two new measures, percent success ratio and CRM suitability ratio by which CRM models can be evaluated, the identification/ classification of CRM queries, and a preliminary heuristic for designing data warehouses to support CRM analyses.

Towards an Ontology for Information Systems Development—A Contextual Approach

This chapter presents an ISD ontology, which aims to provide an integrated conceptualization of ISD through anchoring it upon a contextual approach. The ISD ontology is composed of concepts, relationships, and constraints referring to purposes, actors, actions, and objects of ISD. It is presented as a vocabulary with explicit definitions and in meta models in a UML-based ontology representation language. We believe that although not complete the ISD ontology can promote the achievement of a shared understanding of contextual aspects in ISD. It can be used to analyze and compare existing frameworks and meta models and as a groundwork for engineering new ISD methods, and parts thereof.

Supporting the Full BPM Life-Cycle Using Process Mining and Intelligent Redesign

Business process management (BPM) systems provide a broad range of facilities to enact and manage operational business processes. Ideally, these systems should provide support for the complete BPM life-cycle: (re)design, configuration, execution, control, and diagnosis of processes. However, based on an extensive evaluation of the FileNet P8 BPM Suite, we show that existing BPM tools are unable to support the full life-cycle. There are clearly gaps between the various phases (e.g., users need to transfer or interpret information without any support) and some of the phases (e.g., the redesign and diagnosis phases) are not supported sufficiently. This chapter shows that techniques for process mining and intelligent redesign can be used to offer better support for the (re)design and diagnosis phases and, thus, close the BPM life-cycle. We also briefly report on the work done in the context of the ProM tool, which is used as framework to experiment with such techniques.

Ontological Analysis of KAOS using Separation of References

Goal modeling is emerging as a central requirements engineering (RE) technique. Unfortunately, current goal-oriented languages are not interoperable with one another or with modeling languages that address other modeling perspectives. This problematic because the emerging generation of model-driven information systems is likely to depend on coordinated use of several modeling languages to represent different perspectives of the enterprise and its proposed information system. The chapter applies a structured approach to describe a well-known goal oriented language, KAOS, by mapping it onto a philosophically grounded ontology. The structured approach facilitates language interoperability because when other languages are described using the same approach, they become mapped onto the same ontology. The approach thereby provides an intermediate language for comparison, consistency checking, update reflection, view synchronization and, eventually, model-to-model translation, both between goal-oriented languages and between different languages.