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

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

Resolving horizontal partitioning and schematic variances using metadatabase approach.

by Poon, Koon-hei.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 80-83).Abstracts in English and Chinese.Chapter CHAPTER 1 --- INTRODUCTION --- p.6Chapter CHAPTER 2 --- LITERATURE REVIEW --- p.13Chapter 2.1. --- BACKGROUND --- p.13Chapter 2.2. --- example systems --- p.20Chapter 2.2.1 --- Multibase --- p.20Chapter 2.2.2. --- Mermai d --- p.23Chapter 2.2.3. --- The Metadatabase Approach --- p.26Chapter 2.3. --- SUMMARY --- p.29Chapter CHAPTER 3 --- THE METADATABASE APPROACH --- p.31Chapter 3.1. --- Two-Stage Entity Relationship (TSER) model --- p.31Chapter 3.2. --- The GIRD --- p.34Chapter 3.3. --- The Metadatabase system in action --- p.36Chapter 3.3. --- global query formulations and processing in the metadatabase system --- p.37Chapter CHAPTER 4 --- PROBLEM OUTLINES FOR HORIZONTAL PARTITIONING AND ITS VARIANTS --- p.39Chapter 4.1. --- Horizontal partitioning --- p.39Chapter 4.2. --- Level of abstraction --- p.41Chapter 4.3. --- Schematic variances --- p.42Chapter 4.4. --- Summary --- p.43Chapter 4.5. --- The Scenario --- p.44Chapter 4.6. --- Populating the Metadatabase --- p.48Chapter CHAPTER 5 --- THE ENHANCEMENTS FOR GLOBAL QUERY WITH HORIZONTAL PARTITIONED DATA OBJECTS --- p.51Chapter 5.1. --- Identifying partitioned data objects --- p.51Chapter 5.2. --- Additional metadata for the horizontal partitioned data objects --- p.52Chapter 5.3. --- Complications of horizontal partitioning problem --- p.54Chapter 5.3.1. --- Level of abstraction --- p.55Chapter 5.3.2. --- Schematic variances --- p.57Chapter 5.4. --- Global query with horizontal partitioning data objects --- p.59Chapter 5.5. --- Housing the new metadata --- p.68Chapter 5.6. --- Example --- p.72Chapter CHAPTER 6 --- ANALYSIS --- p.75Chapter CHAPTER 7 --- CONCLUSION AND FUTURE WORKS --- p.78REFERENCES --- p.80APPENDICES --- p.84Chapter A. --- GIRD Definitions --- p.84Chapter A1. --- GIRD Model --- p.84Chapter A2. --- GIRD/SER Contents --- p.84Chapter A3. --- GIRD/OER Constructs --- p.87Chapter A4. --- Definition of Meta-attributes --- p.89Chapter B. --- Problems Representations in Relation Algebra --- p.96Chapter B1. --- Horizontal problem --- p.96Chapter B2. --- Level of abstraction --- p.96Chapter B3. --- Schematic Variance --- p.97Chapter C. --- Details of local systems --- p.9

Information System Integrati on: A Metadata Management Approach

This paper deal s with the integration and control of organizational information systems. The framework for this integration and control is that of Information Resource Management (IRM) - the management not only of information but al so of the processes that specify, generate, di stri bute, and consume information. Within the context of IRM, the concept of metadata -- data about data -- is introduced. A tool to manage metadata, the Information Resource Dictionary System, is defined and a data model and data architecture are presented for the system. Al so, support tool s to aid in information integration are discussed. Lastly, the Enterprise Administration function is proposed to ensure not only the proper use of the Information Resource Dictionary System but also the impl ementation of corporate i nformation system integration and control policies

Įmonės metaduomenų modelio formavimas remiantis veiklos modeliu

Straipsnyje nagrinėjami įmonės metaduomenų modelio formavimo pagal veiklos modelį principai. Analizuojami veiklos modeliavimo metodai, lyginamos žinomos veiklos modelių notacijos jų perteikiamų metaduomenų požiūriu. Nagrinėjamos veiklos valdymo modelio, kuris sudaromas remiantis elementariu valdymo ciklu (EMC), savybės metaduomenų aspektu. Atliktas tokio veiklos valdymo modelio savybių įvertinimas metaduomenų požiūriu rodo, kad EMC pagrindu sudarytas veiklos modelis perteikia daugiau metaduomenų, formaliai yra turtingesnis už kitus veiklos modeliavimo metodus. Pateikta metaduomenų gavimo iš veiklos modelio schema, aprašyti metaduomenų gavimo iš veiklos modelio proceso etapai.Pagrindiniai žodžiai: informacijos sistemos, veiklos modeliavimas, įmonės metaduomenys, veiklos valdymo modelis, elementarus valdymo ciklas (EMC), metaduomenų modelio formavimas.Modeling the Enterprise Metadata Model Based on the Business ModelSaulius Gudas, Gražina Kalibataitė SummaryThe article analyzes the principles of enterprise metadata modeling and the modeling methods in metadata terms. The business models and their notation-conveyed formalized description of metadata are proposed. The relative performance management model, which follows the elementary management cycle (EMC), is analyzed. The operational manage ment of the properties of the metadata model shows that the EMC-based business model reflects more metadata than any other business modeling technique. A conceptual scheme of metadata extraction from the operational model by the process steps is described. 18px;">&nbsp

Maintaining Integrity Constraints in Semantic Web

As an expressive knowledge representation language for Semantic Web, Web Ontology Language (OWL) plays an important role in areas like science and commerce. The problem of maintaining integrity constraints arises because OWL employs the Open World Assumption (OWA) as well as the Non-Unique Name Assumption (NUNA). These assumptions are typically suitable for representing knowledge distributed across the Web, where the complete knowledge about a domain cannot be assumed, but make it challenging to use OWL itself for closed world integrity constraint validation. Integrity constraints (ICs) on ontologies have to be enforced; otherwise conflicting results would be derivable from the same knowledge base (KB). The current trends of incorporating ICs into OWL are based on its query language SPARQL, alternative semantics, or logic programming. These methods usually suffer from limited types of constraints they can handle, and/or inherited computational expensiveness. This dissertation presents a comprehensive and efficient approach to maintaining integrity constraints. The design enforces data consistency throughout the OWL life cycle, including the processes of OWL generation, maintenance, and interactions with other ontologies. For OWL generation, the Paraconsistent model is used to maintain integrity constraints during the relational database to OWL translation process. Then a new rule-based language with set extension is introduced as a platform to allow users to specify constraints, along with a demonstration of 18 commonly used constraints written in this language. In addition, a new constraint maintenance system, called Jena2Drools, is proposed and implemented, to show its effectiveness and efficiency. To further handle inconsistencies among multiple distributed ontologies, this work constructs a framework to break down global constraints into several sub-constraints for efficient parallel validation

Complex adaptive systems based data integration : theory and applications

Data Definition Languages (DDLs) have been created and used to represent data in programming languages and in database dictionaries. This representation includes descriptions in the form of data fields and relations in the form of a hierarchy, with the common exception of relational databases where relations are flat. Network computing created an environment that enables relatively easy and inexpensive exchange of data. What followed was the creation of new DDLs claiming better support for automatic data integration. It is uncertain from the literature if any real progress has been made toward achieving an ideal state or limit condition of automatic data integration. This research asserts that difficulties in accomplishing integration are indicative of socio-cultural systems in general and are caused by some measurable attributes common in DDLs. This research’s main contributions are: (1) a theory of data integration requirements to fully support automatic data integration from autonomous heterogeneous data sources; (2) the identification of measurable related abstract attributes (Variety, Tension, and Entropy); (3) the development of tools to measure them. The research uses a multi-theoretic lens to define and articulate these attributes and their measurements. The proposed theory is founded on the Law of Requisite Variety, Information Theory, Complex Adaptive Systems (CAS) theory, Sowa’s Meaning Preservation framework and Zipf distributions of words and meanings. Using the theory, the attributes, and their measures, this research proposes a framework for objectively evaluating the suitability of any data definition language with respect to degrees of automatic data integration. This research uses thirteen data structures constructed with various DDLs from the 1960\u27s to date. No DDL examined (and therefore no DDL similar to those examined) is designed to satisfy the law of requisite variety. No DDL examined is designed to support CAS evolutionary processes that could result in fully automated integration of heterogeneous data sources. There is no significant difference in measures of Variety, Tension, and Entropy among DDLs investigated in this research. A direction to overcome the common limitations discovered in this research is suggested and tested by proposing GlossoMote, a theoretical mathematically sound description language that satisfies the data integration theory requirements. The DDL, named GlossoMote, is not merely a new syntax, it is a drastic departure from existing DDL constructs. The feasibility of the approach is demonstrated with a small scale experiment and evaluated using the proposed assessment framework and other means. The promising results require additional research to evaluate GlossoMote’s approach commercial use potential