Despite the success of general Internet search engines, information retrieval remains an incompletely solved problem. Our research focuses on supporting domain experts when they search domain-specific libraries to satisfy targeted information needs. The semantic components model introduces a schema specific to a particular document collection. A semantic component schema consists of a two-level hierarchy, document classes and semantic components. A document class represents a document grouping, such as topic type or document purpose. A semantic component is a characteristic type of information that occurs in a particular document class and represents an important aspect of the document’s main topic. Semantic component indexing identifies the location and extent of semantic component instances within a document and can supplement traditional full text and keyword indexing techniques. Semantic component searching allows a user to refine a topical search by indicating a preference for documents containing specific semantic components or by indicating terms that should appear in specific semantic components. We investigate four aspects of semantic components in this research. First, we describe lessons learned from using two methods for developing schemas in two domains. Second, we demonstrate use of semantic components to express domainspecific concepts and relationships by mapping a published taxonomy of questions asked by family practice physicians to the semantic component schemas for two document collections about medical care. Third, we report the results of a user study, showing that manual semantic component indexing is comparable to manual keyword indexing with respect to time and perceived difficulty and suggesting that semantic component indexing may be more accurate and consistent than manual keyword indexing. Fourth, we report the results of an interactive searching study, demonstrating the ability of semantic components to enhance search results compared to a baseline system without semantic components. In addition, we contribute a formal description of the semantic components model, a prototype implementation of semantic component indexing software, and a prototype implementation adding semantic components to an existing commercial search engine. Finally, we analyze metrics for evaluating instances of semantic component indexing and keyword indexing and illustrate use of a session-based metric for evaluating multiple-query search sessions.

M. Tech. Business Information Systems / Enterprise Application Integration (EAI) is an integration framework that encapsulates a set of services and technologies that forms a middle tier to enable integration of varied systems and applications across the enterprise. It is a mechanism to combine processes, software, standards, and hardware to seamlessly integrate two or more enterprise application systems allowing them to operate as a unit. There are various challenges with EAI in terms of the approach, the architecture, implementation and its operation in a business environment. Even more so in a Third-party logistics (3PL) environment, where its function is directly associated with the performance of the business. Third Party Logistic is a supply chain service provider that carries out supply chain activities on behalf of a client, also known as a shipper, for part, or all of their supply chain management. However, regardless of the value put on the 3PL, IT capabilities shippers are still not satisfied with the performance, and there are no specific guidelines for EAI usage in 3PL environment. The goal of this study was to design an Enterprise Application Integration framework for Third-Party logistics.

Semantic interoperability is essential on the Semantic Web to enable different information systems to exchange data. Ontology matching has been recognised as a means to achieve semantic interoperability on the Web by identifying similar information in heterogeneous ontologies. Existing ontology matching approaches have two major limitations. The first limitation relates to similarity metrics, which provide a pessimistic value when considering complex objects such as strings and conceptual entities. The second limitation relates to the role of description logic reasoning. In particular, most approaches disregard implicit information about entities as a source of background knowledge. In this thesis, we first present a new similarity function, called the degree of commonality coefficient, to compute the overlap between two sets based on the similarity between their elements. The results of our evaluations show that the degree of commonality performs better than traditional set similarity metrics in the ontology matching task. Secondly, we have developed the Knowledge Organisation System Implicit Mapping (KOSIMap) framework, which differs from existing approaches by using description logic reasoning (i) to extract implicit information as background knowledge for every entity, and (ii) to remove inappropriate correspondences from an alignment. The results of our evaluation show that the use of Description Logic in the ontology matching task can increase coverage. We identify people interested in ontology matching and reasoning techniques as the target audience of this work

Retaining business value in a legacy commercial enterprise resource planning system today often entails more than just maintaining the software to preserve existing functionality. This type of system tends to represent a significant capital investment that may not be easily scrapped, replaced, or re-engineered without considerable expense. A legacy system may need to be frequently extended to impart new behavior as stakeholder business goals and technical requirements evolve. Legacy ERP systems are growing in prevalence and are both expensive to maintain and risky to evolve. Humans are the driving factor behind the expense, from the engineering costs associated with evolving these types of systems to the labor costs required to operate the result. Autonomic computing is one approach that addresses these challenges by imparting self-adaptive behavior into the evolved system. The contribution of this dissertation aims to add to the body of knowledge in software engineering some insight and best practices for development approaches that are normally hidden from academia by the competitive nature of the retail industry. We present a formal architectural pattern that describes an asynchronous, low-complexity, and autonomic approach. We validate the pattern with two real-world commercial case studies and a reengineering simulation to demonstrate that the pattern is repeatable and agnostic with respect to the operating system, programming language, and communication protocols. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2015. / FAU Electronic Theses and Dissertations Collection

Designing and developing enterprise applications is a complex and resource intensive process, as it often must address thousands of requirements. At the same time, the software architecture of most enterprise applications at their core have many features and structures in common. Designers from different teams do not normally share design elements because of the competitive and proprietary nature of development, and enterprise applications design and development teams end up re-inventing the wheel when tackling a new product. My objective is to formulate new design patterns for enterprise application architectures that assist software architects with reusable solutions to improve design quality and productivity. I achieve this by presenting seven patterns, each providing a solution to a specific challenge or a problem that is common to many enterprise applications. The Business Object Pattern provides a generic approach to design extensible Business Objects and their frameworks for enterprise applications. The pattern covers a number of concepts, including the Dynamic business object, the Static business object, constraints for validity, editability, and attribute visibility, as well as the mechanisms for workflow. The Business Object Life Cycle Pattern introduces the concept of stages which comprise a business object’s life cycle, and their relation to the business object’s integrity during that life cycle. The Simple Change History Pattern provides a concept of enforcing record keeping of the owner and date of the last change performed on a given business data object. The Business Data Object Versioning Pattern offers a solution by introducing a new version of a given business data object which allows for preservation of the original data. The Change History Record Pattern defines a solution for cases when there is a need to capture detailed information about the changes performed on a given business object, such as who made the changes, when, and what changes were made. The Permission Based Granular Access Control Pattern offers a basic approach for access control to objects and their attributes. Finally, the Money Object Pattern offers a language neutral approach to internationalization and globalization of business applications which require multi-currency capability. It is hoped that applying these patterns will provide many advantages, ranging from quicker delivery times to a more reliable software, and ultimately help achieve a systematic approach to designing and building complex enterprise applications. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

In order to remain competitive, the integration of their information systems is an imperative for many large organisations. Applications that originally have been developed independently are now required to interoperate to support new or different functions of the enterprise. Although the mechanisms for application interoperation exist provided by the technology, due to the sheer number and complexity of the running systems, integration solutions � centralised or distributed�appropriate at the local level do not translate successfully to the whole enterprise. Centralised integration approaches often satisfy only some of the integration requirements, they are very expensive, and are fraught with danger since they imply an �all or nothing� approach. Distributed approaches, on the other hand, suffer from complexity and scalability problems as the number of system interfaces to be implemented and the number of execution-time invocations grows with the number of component applications. This dissertation makes a contribution to the field of Enterprise Application Integration (EAI) within the framework of distributed systems technology. Based on real-life case studies experience, we present here a federated approach that controls the size and complexity of the integration effort by reusing existing systems as much as possible and reducing the number of interacting applications. Only selected local elements are exposed to the organisational milieu, and a consistent supporting infrastructure is provided to make systems interactions possible. Our approach provides a flexible and scalable strategy to enterprise integration, avoiding the shortcomings of traditional approaches. We respect existing organisational structures, and demonstrate how appropriate federation infrastructure and protocols enable the interoperation of existing systems. The three main facets of enterprise knowledge are systematically incorporated into the integration effort: a) by the use of domain ontologies to support data integration; b) by the development of a methodology to include business rules; and c) by the development of FEW, a federated workflow model to implement the business processes of the organisation.

Since the advent of RPC Stub Generator, software tools that translate a high level specification into executable programs have been instrumental in facilitating the development of distributed software systems. Developers write programs at a high level abstraction with high readability and reduced initial development cost. However, existing approaches to building code generation tools for such systems have difficulties evolving these tools to meet challenges of new standards, new platforms and languages, or changing product scopes, resulting in generator tools with limited lifespan. The difficulties in evolving generator tools can be characterized as a combination of three challenges that appear inherently difficult to solve simultaneously: the abstraction mapping challenge translating a high-level abstraction into a low-level implementation), the interoperable heterogeneity challenge stemming from multiple input and output formats, and the flexible customization challenge to extend base functionality for evolution or new applications. The Clearwater approach to code generation uses XML-based technologies and software tools to resolve these three challenges with three important code generation features: specification extensibility, whereby an existing specification format can accommodate extensions or variations at low cost; generator pliability, which allows the generator to operator on an extensible specification and/or support multiple and new platforms; and flexible customization, which allows an application developer to make controlled changes to the output of a code generator to support application-specific goals. The presentation will outline the Clearwater approach and apply it to meet the above three challenges in two domain areas. The first area is information flow applications (e.g., multimedia streaming and event processing), a horizontal domain in which the ISG code generator creates QoS-customized communication code using the Infopipe abstraction and specification language. The second area is enterprise application staging (e.g., complex N-tier distributed applications), a vertical domain in which the Mulini code generator creates multiple types of source code supporting automatic staging of distributed heterogeneous applications in a data center environment. The success of applying Clearwater to these domains shows the effectiveness of our approach.

Implementing self-management for enterprise systems is difficult. First, the scale and complexity of such systems makes it hard to understand and interpret system behavior or worse, the root causes of certain behaviors. Second, it is not clear how the goals specified at a system-level translate to component-level actions that drive the system. Third, the dynamic environments in which such systems operate requires self-management techniques that not only adapt the system but also adapt their own decision making processes. Finally, to build a self-management solution that is acceptable to administrators, it should have the properties of tractability and trust, which allow an administrator to both understand and fine-tune self-management actions. This dissertation work introduces, implements, and evaluates iManage, a novel system state-space based framework for enabling self-management of enterprise-scale systems. The system state-space, in iManage, is defined to be a collection of monitored system parameters and metrics (termed system variables). In addition, from amongst the system variables, it identifies the variables of interest, which determine the operational status of a system, and the controllable variables, which are the ones that can be deterministically modified to affect the operational status of a system. Using this formal representation, we have developed and integrated into iManage techniques that establish a probabilistic model relating the variables of interest and the controllable variables under the prevailing operational conditions. Such models are then used by iManage to determine corrective actions in case of SLA violations and/or to determine per-component ranges for controllable variables, which if independently adhered to by each component, lead to SLA compliance. To address the issue of scale in determining system models, iManage makes use of a novel state-space partitioning scheme that partitions the state-space into smaller sub-spaces thereby allowing us to more precisely model the critical system aspects. Our chosen modeling techniques are such that the generated models can be easily understood and modified by the administrator. Furthermore, iManage associates each proposed self-management action with a confidence-attribute that determines whether the action in question merits autonomic enforcement or not.

Thesis (Ph. D.)--University of New South Wales, 2005. / Also available online.

Thesis (Ph. D.)--Computing, Georgia Institute of Technology, 2008. / Committee Member: Ahamad, Mustaque; Committee Member: Eisenhauer, Greg; Committee Member: Milojicic, Dejan; Committee Member: Pu, Calton; Committee Member: Schwan, Karsten.