The mediated data integration (MeDInt) : An approach to the integration of database and legacy systems

The information required for decision making by executives in organizations is normally scattered across disparate data sources including databases and legacy systems. To gain a competitive advantage, it is extremely important for executives to be able to obtain one unique view of information in an accurate and timely manner. To do this, it is necessary to interoperate multiple data sources, which differ structurally and semantically. Particular problems occur when applying traditional integration approaches, for example, the global schema needs to be recreated when the component schema has been modified. This research investigates the following heterogeneities between heterogeneous data sources: Data Model Heterogeneities, Schematic Heterogeneities and Semantic Heterogeneities. The problems of existing integration approaches are reviewed and solved by introducing and designing a new integration approach to logically interoperate heterogeneous data sources and to resolve three previously classified heterogeneities. The research attempts to reduce the complexity of the integration process by maximising the degree of automation. Mediation and wrapping techniques are employed in this research. The Mediated Data Integration (MeDint) architecture has been introduced to integrate heterogeneous data sources. Three major elements, the MeDint Mediator, wrappers, and the Mediated Data Model (MDM) play important roles in the integration of heterogeneous data sources. The MeDint Mediator acts as an intermediate layer transforming queries to sub-queries, resolving conflicts, and consolidating conflict-resolved results. Wrappers serve as translators between the MeDint Mediator and data sources. Both the mediator and wrappers arc well-supported by MDM, a semantically-rich data model which can describe or represent heterogeneous data schematically and semantically. Some organisational information systems have been tested and evaluated using the MeDint architecture. The results have addressed all the research questions regarding the interoperability of heterogeneous data sources. In addition, the results also confirm that the Me Dint architecture is able to provide integration that is transparent to users and that the schema evolution does not affect the integration

Metadata queries for complex database systems

Federated Database Management Systems (FDBS) are very complex. Component databases can be heterogeneous, autonomous and distributed, accounting for these different characteristics in building a FDBS is a difficult engineering problem. The Common Data Model (CDM) is what is used to represent the data in the FDBS. It must be semantically rich to correctly represent the data from diverse component databases which differ in structure, datamodel, semantics and content. In this research project we look at the complexity of the FDBS and examine which datamodel is most suited for th e CDM. A good metad a ta interface and query language is essential for th e CDM because merging component databases into the FDBS and maintaining and building the FDBS rely on a complete metadata interface and query language. In this research project we analyse the metadata interface and query language of the Object-Relational datamodel with a view to use it as the CDM. Distributed Component databases in a FDBS need to be merged in to the FDBS, current tools can not completely automate this process, we examine these problems and present a mobile solution

A New Design for Open and Scalable Collaboration of Independent Databases in Digitally Connected Enterprises

“Digitally connected enterprises” refers to e-business, global supply chains, and other new business designs of the Knowledge Economy; all of which require open and scalable information supply chains across independent enterprises. Connecting proprietarily designed and controlled enterprise databases in these information supply chains is a critical success factor for them. Previous connection designs tend to rely on “hard-coded” regimes, which do not respond well to disruptions (including changes and failures), and do not afford these enterprises sufficient flexibility to join simultaneously in multiple supply chain regimes and share information for the benefit of all. The paper develops a new design: It combines matchmaking with global database query, and thereby supports the interoperation of independent databases to form on-demand information supply chains. The design provides flexible (re-)configuration to decrease the impact of disruption, and proactive control to increase collaboration and information sharing. More broadly, the papers results contribute to a new Information System design method for massively extended enterprises, and facilitate new business designs using digital connections at the level of databases

Constructing and Validating Feature Models Using Relational, Document, and Graph Databases

Building a software product line (SPL) is a systematic strategy for reusing software within a family of related systems from some application domain. To define an SPL, a domain analyst must identify the common and variable aspects of a family of systems and capture them for later use in construction of specific products. To do so, Feature-Oriented Domain Analysis (FODA) introduced the feature model as an abstraction to represent the common and variable aspects, using a feature diagram to depict the model visually. However, this abstraction is often difficult for developers to use because most tools rely on specialized theories, notations, or technologies

Model Management Systems: Proposed Model Representations and Future Designs

The availability of microcomputers, modeling langauges and general purpose spreadsheets has resulted in an increase in the use of models for decision making within organizatons. Decision makers with microcomputers on their desks and spreadsheet and modeling software can create models rapidly. Problems with model redundancy, consistency, integrity and security have prompted an increased interest in the design of model management systems (MMS). Several model management designs have been discussed in the literature. Different model representation techniques have been proposed. These include formal logic, semantic inheritance networks, frames, and relational representations. The approaches to model management are evaluated in respect to their model manipulation and model storage functions. A framework for the design of MMS is proposed based on the system design objectives and the system domain complexity. Advantages and disadvantages of each model representation method are identified. Application domains for the classifications are proposed which focus on the strengths and weaknesses of the model representation for supporting model storage and model manipulation functions. An example of the design of a MMS using the classification is presented

Towards a unified view of design data and knowledge representation

Adequate information modeling in non-standard application areas (e.g. engineering applications such as CAD/CAM, VLSI design or knowledgebased applications) requires the abstraction concepts of classification, aggregation, generalization, and association. The Molecule-Atom Data model (MAD) designed for the effective support of such an information model is justified and described with its essential properties and features. MAD offers dynamic object definition and object handling, based on direct and symmetric management of network structures and recursiveness. These generic mechanisms can be used to map the above mentioned abstraction concepts in a straight-forward manner. Thus, the mapping of a wide variety of semantic and object-oriented modeling constructs, including complex objects with shared subobjects, becomes feasible. All these concepts are illustrated by means of some vivid examples taken from the areas of CAD/CAM and knowledge-based applications

An Approach to Integrate Heterogeneous Data Sources

To gain a competitive advantage, it is extremely important for executives to be able to obtain one unique view of information, normally scattered across disparate data sources, in an accurate and timely manner. To interoperate data sources which differ structurally and semantically, particular problems occur, for example, problems of changing schema in data sources will affect the integrated schema. In this paper, conflicts between heterogeneous systems are investigated and existing approaches to integration are reviewed. This research introduces a new mediated approach employing the Mediated Data Integration Mediator (MeDInt), and wrapping techniques as the main components for the integration of databases and legacy systems. The MeDInt mediator acts as an intermediate medium transforming queries to subqueries, integrating result data and resolving conflicts. Wrappers then transform sub-queries to specific local queries so that each local system is able to understand the queries. This framework is currently being developed to make the integration process more widely accessible by using standard tools. A prototype is implemented to demonstrate the model

Object orientation within the PRIMA-NDBS

In the following we wish to highlight the design and implementation concepts of a non-standard database system (NDBS) called PRIMA and its provision for object orientation

EIS: using the metadatabase approach for data integration and OLAP.

by Ho Kwok-Wai.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 121-126).Abstract also in Chinese.ABSTRACT --- p.IITABLE OF CONTENTS --- p.VLIST OF FIGURES --- p.XACKNOWLEDGMENTS --- p.XIIChapter CHAPTER 1 --- INTRODUCTION --- p.1Chapter 1.1 --- Need support in data integration --- p.2Chapter 1.2 --- Need support in On-line Analytical Processing (OLAP) --- p.4Chapter 1.3 --- The proposed research --- p.5Chapter 1.4 --- Scope of the study --- p.6Chapter 1.5 --- Organization of the Thesis --- p.7Chapter CHAPTER 2 --- LITERATURE REVIEW --- p.8Chapter 2.1 --- Executive Information System (EIS) --- p.9Chapter 2.1.1 --- Definition --- p.9Chapter 2.1.2 --- Goals of Executive Information System --- p.10Chapter 2.1.3 --- Role of Executive Information System --- p.11Chapter 2.1.4 --- General characteristics of Executive Information System --- p.12Chapter 2.1.4.1 --- A separate executive database --- p.12Chapter 2.1.4.2 --- Data aggregation facilities --- p.12Chapter 2.1.4.3 --- Drill-Down (and Roll-Up) --- p.13Chapter 2.1.4.4 --- Trend analysis --- p.13Chapter 2.1.4.5 --- Highly user-friendly interfaceChapter 2.1.4.6 --- Flexible menu-based data retrieval --- p.14Chapter 2.1.4.7 --- High quality of business graphics --- p.14Chapter 2.1.4.8 --- Simple modeling facilities --- p.15Chapter 2.1.4.9 --- Communications --- p.15Chapter 2.1.4.10 --- Automated links to other databases --- p.15Chapter 2.1.4.11 --- Briefing book --- p.16Chapter 2.1.5 --- Architecture of Executive Information System --- p.16Chapter 2.1.6 --- Potential problems of Executive Information System --- p.18Chapter 2.2 --- On-line Analytical Processing (OLAP) --- p.20Chapter 2.2.1 --- Limitations of OLAP --- p.21Chapter 2.2.2 --- Integration of heterogeneous distributed systems and databases --- p.21Chapter 2.3 --- Data Warehousing (DW) --- p.23Chapter 2.3.1 --- Definition --- p.24Chapter 2.3.1.1 --- Subject-Orientation --- p.24Chapter 2.3.1.2 --- Integration --- p.25Chapter 2.3.1.3 --- Time Variancy --- p.26Chapter 2.3.1.4 --- Nonvolatile --- p.27Chapter 2.3.2 --- Goal of Data Warehousing --- p.28Chapter 2.3.3 --- Architecture of Data Warehousing --- p.28Chapter 2.3.3.1 --- Integrator --- p.29Chapter 2.3.3.2 --- Monitor --- p.30Chapter 2.3.3.3 --- Data Warehouse --- p.31Chapter 2.3.4 --- Application in EIS --- p.31Chapter 2.3.5 --- Problems associated with Data Warehouse --- p.33Chapter 2.4 --- The Metadatabase Approach --- p.35Chapter 2.4.1 --- Goals of the Metadatabase Approach --- p.36Chapter 2.4.2 --- Structure of the Metadatabase Approach --- p.37Chapter 2.4.3 --- Metadatabase Approach functionalities --- p.40Chapter 2.4.4 --- TSER Modeling Technique --- p.42Chapter 2.4.4.1 --- The Functional Model --- p.43Chapter 2.4.4.1.1 --- Subject --- p.43Chapter 2.4.4.1.2 --- Context --- p.43Chapter 2.4.4.2 --- The Structural Model --- p.44Chapter 2.4.4.2.1 --- Entity --- p.44Chapter 2.4.4.2.2 --- Plural Relationship (PR) --- p.45Chapter 2.4.4.2.3 --- Functional Relationship (FR) --- p.45Chapter 2.4.4.2.4 --- Mandatory Relationship (MR) --- p.45Chapter 2.4.4.3 --- Metadatabase Repository --- p.46Chapter CHAPTER 3 --- RESEARCH METHODOLOGY --- p.48Chapter 3.1 --- Literature review --- p.49Chapter 3.2 --- Architecture construction --- p.50Chapter 3.3 --- Algorithm and methods development --- p.50Chapter 3.4 --- Prototyping --- p.51Chapter 3.5 --- Analysis and evaluation --- p.51Chapter CHAPTER 4 --- MULTIDIMENSIONAL DATA ANALYSIS --- p.53Chapter 4.1 --- Multidimensional Analysis Unit (MAU) --- p.54Chapter 4.2 --- New steps for multidimensional data analysis --- p.57Step 1 Indicator Selection --- p.57Step 2 Dimensions Determination --- p.58Step 3 Dimensions Selection --- p.58Step 4 MAU Sub-view Materialization --- p.59Step 5 On-line Analytical Processing (OLAP) --- p.59Chapter CHAPTER 5 --- NEW ARCHITECTURE FOR EXECUTIVE INFORMATION SYSTEM --- p.60Chapter 5.1 --- Evolution of EIS architecture --- p.60Chapter 5.2 --- Objectives of the new EIS architecture --- p.63Chapter 5.3 --- The new EIS architecture --- p.65Chapter 5.3.1 --- The Metadatabase Management System (MDBMS) --- p.67Chapter 5.3.2 --- The ROLAP/MDB Interface --- p.68Chapter 5.3.2.1 --- The Indicator Browser --- p.69Chapter 5.3.2.2 --- The Dimension Selector --- p.70Chapter 5.3.2.3 --- The Multidimensional Data Analyzer --- p.70Chapter 5.3.3 --- The ROLAP/MDB Analyzer --- p.71Chapter 5.3.3.1 --- The Dimension Determination Module --- p.71Chapter 5.3.3.2 --- The MAU Schema Saver --- p.72Chapter 5.3.3.3 --- The MQL Generator --- p.72Chapter 5.3.3.4 --- The MAU Sub-view Materializer --- p.72Chapter 5.3.3.5 --- The ROLAP/MDB Processor --- p.73Chapter CHAPTER 6 --- ALGORITHM AND METHODS FOR THE NEW EIS ARCHITECTURE.… --- p.74Chapter 6.1 --- Indicator Browser --- p.74Chapter 6.2 --- Determining dimensions and storing MAU Schema --- p.77Chapter 6.3 --- Dimensions selection --- p.82Chapter 6.4 --- Materialize MAU Sub-view --- p.82Chapter 6.5 --- Multidimensional data analysis in relational manner --- p.85Chapter 6.5.1 --- SQL statements for three dimensional slide operation --- p.87Chapter 6.5.2 --- SQL statements for n-dimensional slide operation --- p.89Chapter 6.5.3 --- SQL statements for n-dimensional dice operation --- p.91Chapter 6.5.4 --- Rotation --- p.92Chapter 6.5.5 --- Drill-Down (and Roll-Up) --- p.94Chapter CHAPTER 7 --- A CASE STUDY USING THE PROTOTYPED EIS --- p.97Chapter 7.1 --- A Business Case --- p.97Chapter 7.2 --- Multidimensional data analysis --- p.98Step 1 Indicator selection --- p.99Step 2 & 3 Dimension determination & MAU Schema storage --- p.100Step 4 Dimension specification --- p.102Step 5 MAU Sub-view formation --- p.104Step 6 Multidimensional data analysis operations --- p.104Chapter CHAPTER 8 --- EVALUATION OF THE NEW EIS ARCHITECTURE --- p.110Chapter 8.1 --- Improvements --- p.110Chapter 8.1.1 --- Adaptability --- p.111Chapter 8.1.2 --- Flexibility --- p.112Chapter 8.2 --- New features of the new EIS architecture --- p.113Chapter 8.2.1 --- Access on-line production data --- p.113Chapter 8.2.2 --- Facilitate data-mining --- p.114Chapter 8.3 --- Processing efficiency problem --- p.114Chapter 8.3.1 --- MAU Schema Saver for reusability --- p.115Chapter 8.3.2 --- Dimension Selector to scale down data retrieval --- p.116Chapter 8.3.3 --- MAU Sub-view materialization for reusability --- p.116Chapter 8.3.4 --- Incorporate data warehouse to reduce access to local systems --- p.117Chapter 8.4 --- Summary --- p.117Chapter CHAPTER 9 --- CONCLUSION --- p.118Chapter CHAPTER 10 --- DIRECTION OF FUTURE STUDIES --- p.120REFERENCES --- p.121APPENDIX --- p.127Global Information Resources Dictionary (GIRD) --- p.12