In the face of knowledge deficits in and poor outcome assessments of Organisation Transformation (OT), there is a need for a better understanding of the relationship between change leader interventions and qualitative organisational schema change, the collective knowledge structures that must be replaced or significantly elaborated if OT is to be realised. Previous research on this relationship has (a) focused on imposed structural interventions and given little attention to large-scale human process interventions, (b) given little attention to the radical structural interventions frequently involved in the transformation of public organisations, (c) given little scrutiny to how organisational schema have been conceptualised, (d) given little scrutiny to recent propositions on schema change dynamics that may be contentious, and (e) given little consideration to the change management contexts in which leader influence may be neutralised. In the light of these gaps in the literature, this thesis investigates, from the perspective of change recipients, the relationship between complex large-scale change leader interventions and qualitative organisational schema change in change management contexts thought to be inimical to leader influence. In particular, how efficacious are change leader interventions in realising qualitative organisational schema change in such contexts? An interpretive longitudinal case study design was used to address this question. The case organisation is a spatially differentiated technically-oriented public Professional Bureaucracy located in Queensland. In this context, this thesis investigates, over a three-year period, the creation and evolution of three schema change contexts, or change trajectories, created by two temporally disconnected yet functionally inter-related change leader interventions. Data collection techniques included focus group interviews, semi-structured interviews, and secondary sources. Data were collected from several sites, including Head Office functions and Regional and District offices, across Queensland. Data were collected on four occasions across the three-year period from early 2000 to late 2002. The results reveal that (a) while there are no panaceas, public managers need more sophisticated intervention theories based on a knowledge of the relative efficacy of different interventions rather than relying on, predominantly, structural interventions, (b) viewing organisational schema in one-dimensional rather than multidimensional terms masks both the complexity of organisational schema change and the possibility of partial rather than configurational schema change, (c) while inter-schema conflict or dialectical processes were apparent, successful schema change was better explained by teleological processes than by dialectical processes, and (d) change leaders can have a powerful influence on OT in change management contexts thought to be inimical to change leader influence yet their influence is linked to high investments of time and effort.

In the thesis, we provide a solution to solve a schema change problem. In a data warehouse system, if schema changes occur in a data source, the overall system will lose the consistency between the data sources and the data warehouse. These schema changes will render the data warehouse obsolete. We have developed three stages to handle schema changes occurring in databases. They are change detection, diagnosis, and handling. Recommendations are generated by DB-agent to information DW-agent to notify the DBA what and where a schema change affects the star schema. In the study, we mainly handle seven schema changes in a relational database. All of them, we not only handle non-adding schema changes but also handling adding schema changes. A non-adding schema change in our experiment has high correct mapping rate as using a traditional mappings between a data warehouse and a database. For an adding schema change, it has many uncertainties to diagnosis and handle. For this reason, we compare similarity between an adding relation or attribute and the ontology concept or concept attribute to generate a good recommendation. The evaluation results show that the proposed approach is capable to detect these schema changes correctly and to recommend the DBA about the changes appropriately.

"Change is inevitable even for persistent information. Effectively managing change of persistent information, which includes the specification, execution and the maintenance of any derived information, is critical and must be addressed by all database systems. Today, for every data model there exists a well-defined set of change primitives that can alter both the structure (the schema) and the data. Several proposals also exist for incrementally propagating a primitive change to any derived information (or view). However, existing support is lacking in two ways. First, change primitives as presented in literature are very limiting in terms of their capabilities allowing users to simply add or remove schema elements. More complex types of changes such the merging or splitting of schema elements are not supported in a principled manner. Second, algorithms for maintaining derived information often do not account for the potential heterogeneity between the source and the target. The goal of this dissertation is to provide solutions that address these two key issues. The first part of this dissertation addresses the challenge of expressing a rich complex set of changes. We propose the SERF (Schema Evolution through an Extensible, Re-usable and Flexible) framework that allows users to perform a wide range of complex user-defined schema transformations. Our approach combines existing schema evolution primitives using OQL (object query language) as the glue logic. Within the context of this work, we look at the different domains in which SERF can be applied, including web site management. To further enrich our framework, we also investigate the optimization and verification of SERF transformations. The second part of this dissertation addresses the problem of maintaining views in the face of source changes when the source and the view are not in the same data model. With today's increasing heterogeneity in information structure, it is critical that maintenance of views addresses the data model boundaries. However, view definitions that go across data models are limited to hard-coded algorithms, thereby making it difficult to develop general maintenance algorithms. We provide a two-step solution for this problem. We have developed a cross algebra, that defines views such that there is no restriction that forces the view and the source data models to be the same. We then define update propagation algorithms that can propagate changes from source to target irrespective of the exact translation and the data models. We validate our ideas by applying them to translation and change propagation between the XML and relational data models."

Data warehousing is becoming an increasingly important technology for information integration and data analysis. Given the dynamic nature of modern distributed environments, both source data updates and schema changes are likely to occur autonomously and even concurrently in different data sources. Current approaches to maintain a data warehouse in such dynamic environments sequentially schedule maintenance processes to occur in isolation. Furthermore, each maintenance process is handling the maintenance of one single source update. This limits the performance of current data warehouse maintenance systems in a distributed environment where the maintenance of source updates endures the overhead of network delay as well as IO costs for each maintenance query. In this thesis work, we propose two different optimization strategies which can greatly improve data warehouse maintenance performance for a set of source updates in such dynamic environments. Both strategies are able to support source data updates and schema changes. The first strategy, the parallel data warehouse maintainer, schedules multiple maintenance processes concurrently. Based on the DWMS_Transaction model, we formalize the constraints that exist in maintaining data and schema changes concurrently and propose several parallel maintenance process schedulers. The second strategy, the batch data warehouse maintainer, groups multiple source updates and then maintains them within one maintenance process. We propose a technique for compacting the initial sequence of updates, and then for generating delta changes for each source. We also propose an algorithm to adapt/maintain the data warehouse extent using these delta changes. A further optimization of the algorithm also is applied using shared queries in the maintenance process. We have designed and implemented both optimization strategies and incorporated them into the existing DyDa/TxnWrap system. We have conducted extensive experiments on both the parallel as well as the batch processing of a set of source updates to study the performance achievable under various system settings. Our findings include that our parallel maintenance gains around 40 ~ 50% performance improvement compared to sequential processing in environments that use single-CPU machines and little network delay, i.e, without requiring any additional hardware resources. While for batch processing, an improvement of 400 ~ 500% improvement compared with sequential maintenance is achieved, however at the cost of less frequent refreshes of the data warehouse content.

Data Warehouses (DW) have to continuously adapt to evolving business requirements, which implies structure modification (schema changes) and data migration requirements in the system design. However, it is challenging for designers to control the performance and cost overhead of different schema change implementations. In this paper, we demonstrate SCIT, a tool for DW designers to test and implement different logical design alternatives in a two-fold manner. As a main functionality, SCIT translates common DW schema modifications into directly executable SQL scripts for relational database systems, facilitating design and testing automation. At the same time, SCIT assesses changes and recommends alternative design decisions to help designers improve logical designs and avoid common dimensional modeling pitfalls and mistakes. This paper serves as a walk-through of the system features, showcasing the interaction with the tool’s user interface in order to easily and effectively modify DW schemata and enable schema change analysis.