An Object-Oriented Heterogeneous Database Architecture

Many data management environments face a critical need to integrate heterogeneous data-data that are stored in varying locations using various data management systems with diverse data formats and schemas. To address this problem, the database research community has developed the concept of a heterogeneous database system (HDB) that provides users with the illusion of a single unified database. However, HDBs rely on the implicit assumption that all data to be integrated into the HDB are stored in full-fledged database management systems (DBMS). This assumption leaves environments that need to integrate non-DBMS data unserved by HDB systems. Furthermore, HDBs are complex software solutions that are not easily lmplementable by database developers wrestling with heterogeneous data. This thesis presents a new, easily implemented HDB architecture that is suitable for integrating non-DBMS data. The key to our architecture is using an object-oriented database management system (OODBMS) as an implementation tool. Rather than developing an HDB from scratch, we leverage the power and facilities of the underlying OODBMS to provide a query language, application programmer interface, interactive query interface, concurrency control, etc. Using object-oriented technology gives us an additional benefit-our HDB becomes an object-oriented HDB (OOHDB) providing users with greater data model expressivity along with a powerful behavioral component. The OOHDB architecture we present is independent of a particular OODBMS and can be implemented using a number of commercial OODBMSs for a variety of data management environments. We describe one implementation of our architecture using the GemStone OODBMS for accessing heterogeneous materials science data. This implementation demonstrates how easily the architecture can be implemented. We use this implementation to analyze the performance of the architecture and examine the effectiveness of strategies for enhancing performance. We conclude that for many environments with heterogeneous non-DBMS data, our OOHDB architecture provides a good solution that is easy to implement using commercial OODBMS technology

DRIVER Technology Watch Report

This report is part of the Discovery Workpackage (WP4) and is the third report out of four deliverables. The objective of this report is to give an overview of the latest technical developments in the world of digital repositories, digital libraries and beyond, in order to serve as theoretical and practical input for the technical DRIVER developments, especially those focused on enhanced publications. This report consists of two main parts, one part focuses on interoperability standards for enhanced publications, the other part consists of three subchapters, which give a landscape picture of current and surfacing technologies and communities crucial to DRIVER. These three subchapters contain the GRID, CRIS and LTP communities and technologies. Every chapter contains a theoretical explanation, followed by case studies and the outcomes and opportunities for DRIVER in this field

A Shared Ontology Approach to Semantic Representation of BIM Data

Architecture, engineering, construction and facility management (AEC-FM) projects involve a large number of participants that must exchange information and combine their knowledge for successful completion of a project. Currently, most of the AEC-FM domains store their information about a project in text documents or use XML, relational, or object-oriented formats that make information integration difficult. The AEC-FM industry is not taking advantage of the full potential of the Semantic Web for streamlining sharing, connecting, and combining information from different domains. The Semantic Web is designed to solve the information integration problem by creating a web of structured and connected data that can be processed by machines. It allows combining information from different sources with different underlying schemas distributed over the Internet. In the Semantic Web, all data instances and data schema are stored in a graph data store, which makes it easy to merge data from different sources. This paper presents a shared ontology approach to semantic representation of building information. The semantic representation of building information facilitates finding and integrating building information distributed in several knowledge bases. A case study demonstrates the development of a semantic based building design knowledge base

Pattern Reification as the Basis for Description-Driven Systems

One of the main factors driving object-oriented software development for information systems is the requirement for systems to be tolerant to change. To address this issue in designing systems, this paper proposes a pattern-based, object-oriented, description-driven system (DDS) architecture as an extension to the standard UML four-layer meta-model. A DDS architecture is proposed in which aspects of both static and dynamic systems behavior can be captured via descriptive models and meta-models. The proposed architecture embodies four main elements - firstly, the adoption of a multi-layered meta-modeling architecture and reflective meta-level architecture, secondly the identification of four data modeling relationships that can be made explicit such that they can be modified dynamically, thirdly the identification of five design patterns which have emerged from practice and have proved essential in providing reusable building blocks for data management, and fourthly the encoding of the structural properties of the five design patterns by means of one fundamental pattern, the Graph pattern. A practical example of this philosophy, the CRISTAL project, is used to demonstrate the use of description-driven data objects to handle system evolution.Comment: 20 pages, 10 figure

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

High-accuracy Geant4 simulation and semi-analytical modeling of nuclear resonance fluorescence

Nuclear resonance fluorescence (NRF) is a photonuclear interaction that enables highly isotope-specific measurements in both pure and applied physics scenarios. High-accuracy design and analysis of NRF measurements in complex geometries is aided by Monte Carlo simulations of photon physics and transport, motivating Jordan and Warren (2007) to develop the G4NRF codebase for NRF simulation in Geant4. In this work, we enhance the physics accuracy of the G4NRF code and perform improved benchmarking simulations. The NRF cross section calculation in G4NRF, previously a Gaussian approximation, has been replaced with a full numerical integration for improved accuracy in thick-target scenarios. A high-accuracy semi-analytical model of expected NRF count rates in a typical NRF measurement is then constructed and compared against G4NRF simulations for both simple homogeneous and more complex heterogeneous geometries. Agreement between rates predicted by the semi-analytical model and G4NRF simulation is found at a level of ${\sim}1\%$ in simple test cases and ${\sim}3\%$ in more realistic scenarios, improving upon the ${\sim}20\%$ level of the initial benchmarking study and establishing a highly-accurate NRF framework for Geant4.Comment: 16 pages, 6 figures, revised for peer revie