Extensible Management System Research Articles

Leveraging Linked Data for Knowledge Management: A Proposal for the Aerospace Industry

Within a given subculture or industry, knowledge proliferates insofar as those outside the sphere of knowledge lack an understanding of the terms, concepts, and ideas specific to the domain. Moreover, collaborative work within large industries suffers from a lack of understanding between collaborators and from the use of nonstructured legacy knowledge artifacts. The aerospace and defense industries are no exception; indeed, they present examples of some of the largest industries with extensive projects and a diverse array of applications. This paper presents a state-of-the-art literature review of knowledge management and knowledge collaboration systems in the aerospace and defense industries. Based on the review findings, a proposal for an extensible and queryable knowledge management system for aerospace industry workspaces using the Web Ontology Language of the Semantic Web is presented.

EVKG: An interlinked and interoperable electric vehicle knowledge graph for smart transportation system

AbstractOver the past decade, the electric vehicle (EV) industry has experienced unprecedented growth and diversification, resulting in a complex ecosystem. To effectively manage this multifaceted field, we present an EV‐centric knowledge graph (EVKG) as a comprehensive, cross‐domain, extensible, and open geospatial knowledge management system. The EVKG encapsulates essential EV‐related knowledge, including EV adoption, EV supply equipment, and electricity transmission network, to support decision‐making related to EV technology development, infrastructure planning, and policy‐making by providing timely and accurate information and analysis. To enrich and contextualize the EVKG, we integrate the developed EV‐relevant ontology modules from existing well‐known knowledge graphs and ontologies. This integration enables interoperability with other knowledge graphs in the Linked Data Open Cloud, enhancing the EVKG's value as a knowledge hub for EV decision‐making. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV‐related questions, providing critical insights into the EV ecosystem. Our EVKG provides an efficient and effective approach for managing the complex and diverse EV industry. By consolidating critical EV‐related knowledge into a single, easily accessible resource, the EVKG supports decision‐makers in making informed choices about EV technology development, infrastructure planning, and policy‐making. As a flexible and extensible platform, the EVKG is capable of accommodating a wide range of data sources, enabling it to evolve alongside the rapidly changing EV landscape.

The JSUB task submission and management software

In near future, new high energy physics experiments with challenging data volume are coming into operations or are planned in IHEP, China. A DIRAC-based distributed computing system is being enabled to process the huge amount of data to be obtained in these experiments. To encourage better utilization of computing resources, it’s essential to give users handy tools that saves them from troubles of mastering various technical details of distributed computing and let them have comfortable control over their tasks.In this conference, we present the design and development of a extensible task submission and management system called JSUB, which provides users from different experiments with consistent solutions of managing massive jobs. JSUB covers functionalities including user interface, task creation and submission, run-time workflow control, task monitoring and management. Major features of this software include friendliness with extensions into different user scenarios, flexible task setting, and parametric job submission.Currently, the JSUB software has been developed and put into use for JUNO and CEPC experiments. The software is also highly extensible to other HEP experiments. The designs and techniques could be interesting to other experiments which also use DIRAC as workload management system. The software has only been previously reported inside JUNO collaboration, and this is the first time to be presented to a wider audience.

NewWave: Workflow engine

• Pharo. • Workflow engine. • NewWave Pharo project. • Business process management. • Business process modeling. An ever-increasing demand for information systems in the last few decades brought many new opportunities but also presented new challenges for companies in the landscape of doing business globally. In these terms, software systems need to adapt almost instantaneously to new requirements, opportunities, and customer expectations. Process-aware systems have become integral part in business information system solutions, finding their place at different locations. This paper presents a workflow engine - NewWave , created with the goal to enable an extensible workflow management system, to facilitate easy specification and implementation of application business logic with focus on simplicity and immediate feedback. Through seamless integration with Pharo applications, libraries, tools, and leveraging Pharo's live environment, the NewWave enables users to create dynamically adaptable process models to facilitate the flexible adaptation of business logic.

Distributed secondo: an extensible and scalable database management system

This paper describes a novel method to couple a standalone database management system (DBMS) with a highly scalable key-value store. The system employs Apache Cassandra as data storage and the extensible DBMS Secondo as a query processing engine. The resulting system is a distributed, general-purpose DBMS which is highly scalable and fault tolerant. The logical ring of Cassandra is used to split up input data into smaller units of work (UOWs), which can be processed independently. A decentralized algorithm is responsible to assign the UOWs to query processing nodes. In case of a node failure, UOWs are recalculated on a different node. All the data models (e.g. relational, spatial and spatio-temporal) and functions (e.g. filter, aggregates, joins and spatial-joins) implemented in Secondo can be used in a scalable way without changing the implementation. Many aspects of the distribution are hidden from the user. Existing sequential queries can be easily converted into parallel ones.

LORIS: a web-based data management system for multi-center studies

Longitudinal Online Research and Imaging System (LORIS) is a modular and extensible web-based data management system that integrates all aspects of a multi-center study: from heterogeneous data acquisition (imaging, clinical, behavior, and genetics) to storage, processing, and ultimately dissemination. It provides a secure, user-friendly, and streamlined platform to automate the flow of clinical trials and complex multi-center studies. A subject-centric internal organization allows researchers to capture and subsequently extract all information, longitudinal or cross-sectional, from any subset of the study cohort. Extensive error-checking and quality control procedures, security, data management, data querying, and administrative functions provide LORIS with a triple capability (1) continuous project coordination and monitoring of data acquisition (2) data storage/cleaning/querying, (3) interface with arbitrary external data processing “pipelines.” LORIS is a complete solution that has been thoroughly tested through a full 10 year life cycle of a multi-center longitudinal project1 and is now supporting numerous international neurodevelopment and neurodegeneration research projects.

Context-awareness for heterogeneous access management

Abstract. The increasing availability of both static and dynamic context information has steadily been driving the development of context-aware communication systems. Adapting system behavior according to current context of the network, the user, and the terminal can yield significant end-to-end performance improvements. In this paper, we present a concept for how to use context information, in particular location information and movement prediction, for Heterogeneous Access Management (HAM). In a first step, we outline the functional architecture of a distributed and extensible context management system (CMS) that defines the roles, tasks, and interfaces of all modules within such a system for large-scale context acquisition and dissemination. In a second step, we depict how the available context information can be exploited for optimizing terminal handover decisions to be made in a multi-RAT (radio access technology) environment. In addition, the utilized method for predicting terminal location as well as the objective functions used for evaluating and comparing system performance are described. Finally, we present preliminary simulation results demonstrating that HAM systems that include current and future terminal context information in the handover decision process clearly outperform conventional systems.

Federated Web-accessible Clinical Data Management within an Extensible NeuroImaging Database

Managing vast datasets collected throughout multiple clinical imaging communities has become critical with the ever increasing and diverse nature of datasets. Development of data management infrastructure is further complicated by technical and experimental advances that drive modifications to existing protocols and acquisition of new types of research data to be incorporated into existing data management systems. In this paper, an extensible data management system for clinical neuroimaging studies is introduced: The Human Clinical Imaging Database (HID) and Toolkit. The database schema is constructed to support the storage of new data types without changes to the underlying schema. The complex infrastructure allows management of experiment data, such as image protocol and behavioral task parameters, as well as subject-specific data, including demographics, clinical assessments, and behavioral task performance metrics. Of significant interest, embedded clinical data entry and management tools enhance both consistency of data reporting and automatic entry of data into the database. The Clinical Assessment Layout Manager (CALM) allows users to create on-line data entry forms for use within and across sites, through which data is pulled into the underlying database via the generic clinical assessment management engine (GAME). Importantly, the system is designed to operate in a distributed environment, serving both human users and client applications in a service-oriented manner. Querying capabilities use a built-in multi-database parallel query builder/result combiner, allowing web-accessible queries within and across multiple federated databases. The system along with its documentation is open-source and available from the Neuroimaging Informatics Tools and Resource Clearinghouse (NITRC) site.

JAX Colony Management System (JCMS): an extensible colony and phenotype data management system

The Jackson Laboratory Colony Management System (JCMS) is a software application for managing data and information related to research mouse colonies, associated biospecimens, and experimental protocols. JCMS runs directly on computers that run one of the PC Windows® operating systems, but can be accessed via web browser interfaces from any computer running a Windows, Macintosh®, or Linux® operating system. JCMS can be configured for a single user or multiple users in small- to medium-size work groups. The target audience for JCMS includes laboratory technicians, animal colony managers, and principal investigators. The application provides operational support for colony management and experimental workflows, sample and data tracking through transaction-based data entry forms, and date-driven work reports. Flexible query forms allow researchers to retrieve database records based on user-defined criteria. Recent advances in handheld computers with integrated barcode readers, middleware technologies, web browsers, and wireless networks add to the utility of JCMS by allowing real-time access to the database from any networked computer.

Enabling Grid Access to Mass Storage

The purpose of the EDG Storage Element (SE) is to provide a uniform Grid interface to mass storage systems. We needed an extensible and flexible architecture, and chose one based on message processing. Not only has this architecture provided a solid framework for meeting changing requirements, it also provides scope for building an extensible and scalable storage management system. While distributed SEs in a Grid are managed by higher level replication services, our request processing model opens possibilities for distributing a single SE across several nodes, allowing further opportunities for optimisation. In this paper, we discuss applications of distributed, transactional processing in mass storage, and its implications for optimisations based on access patterns.

A goal-driven auto-configuration tool for the distributed workflow management system mentorlite

The Mentor-lite prototype has been developed within the research project “Architecture, Configuration, and Administration of Large Workflow Management Systems” funded by the German Science Foundation (DFG). It has evolved from its predecessor Mentor [1], but aims at a simpler architecture. The main goal of Mentor-lite has been to build a light-weight, extensible, and tailorable workflow management system (WFMS) with small footprint and easy-to-use administration capabilities. Our approach is to provide only kernel functionality inside the workflow engine, and consider system components like history management and worklist management as extensions on top of the kernel. The key point to retain the light-weight nature is that these extensions are implemented as workflows themselves. The workflow specifications are interpreted at runtime, which is a crucial prerequisite for flexible exception handling and dynamic modifications during runtime. The interpreter performs a stepwise execution of the workflow specification according to its formal semantics. For each step, the activities to be performed by the step are determined and started. Mentor-lite supports a protocol for distributed execution of workflows spread across multiple workflow engines. This support is crucial for workflows that span large, decentralized enterprises with largely autonomous organizational units or even cross multiple enterprises to form so-called “virtual enterprises”. A communication manager is responsible for sending and receiving synchronization messages between the engines. In order to guarantee a consistent global state even in the presence of site or network failures, we have built reliable message queues using the CORBA Object Transaction Services. For administration, Mentor-lite provides a Java-based workbench for workflow design, workflow partitioning across multiple workflow servers, and a Java-based runtime monitoring tool.

Supporting Software Synthesis of Communication Infrastructures for Embedded Real-Time Applications

Although there are many commercial real-time micro-kernel available on the market today, the common prejudices against their use for implementing control algorithms are still justified. Some of them are too complex and overhead-prone while others are tiny and efficient but don't provide all those services that are actually required by real-time applications. This is especially true regarding communication support for distributed and embedded control systems.This paper focuses on a new design concept for real-time communication system (RCOS) platforms matching the quality and functional needs of a given distributed and embedded application. The approach is based on a library operating system (Dreams**Distributed Real-Time Extensible Application Management System) which allows for extremely fine-grained customizations to avoid systemlevel overhead. We will outline the architecture of Dreams, show how customization is applied for interprocessor communication and present first results that demonstrate the impact of customization on system overhead.

Meta-Modeling Concepts and Tools for Model Management: A Systems Approach

We present a new framework for model management based on system concepts and theory. Underlying the framework is a set of meta-modeling concepts that are useful in capturing the semantics of the modeling process in a modeling environment. These concepts include the notions of a general-model type, type specialization, atomic and composite model versions, model instances, and parameterized versions. We describe these concepts both conceptually and formally and then briefly present a Model Description Language (MDL) that embodies them. While other researchers have suggested some of these concepts primarily in different contexts, this paper makes at least four valuable contributions: (1) the identification of fundamental issues and principles related to model management; (2) the development and enhanced treatment of meta-modeling concepts specifically for model management; (3) the synthesis of those concepts into a coherent, unifying framework for model management; and (4) a demonstration of the practicality of those concepts through a prototype system implementation. Our framework proposes a graph-oriented, nonprocedural, and hierarchical approach for model composition. The framework also supports both model-solver independence and model-data independence. Moreover, it offers general solutions to two critical issues in model management: model-model linkage and model-data linkage. We argue that the system framework can serve as a guide for an effective design of a flexible and extensible model management system. An architecture of such a system and its prototype implementation—called SYMMS—are briefly described. Examples are presented to illustrate the features and advantages of our approach.

Open OODB

The Open OODB project, part of the DARPA Persistent Object Base (POB) Program, is an effort to build an open, extensible object-oriented database management system (OODB) in which database functionality can be tailored for particular applications within an incrementally improvable framework. The system is designed to serve both as a platform for research and as a testbed that can meet the needs of demanding, next generation database applications. The Open OODB project goals are to describe the design space of OODBs; build an architectural framework that enables configuring independently useful modules to form an OODB; verify the suitability of this open approach by implementing an OODB to these specifications; and identify opportunities for building consensus that can lead to much-needed OODB standards. The motivating factors in this approach were that our previous experience in object-oriented databases had convinced us that different applications have differing requirements, and that a monolithic system is unlikely to meet the needs of many demanding kinds of applications.

Extensions to Starburst

Starburst is a research prototype of an extensible relational database management system that is under development at the IBM Almaden Research Center. Through extensions to Starburst, we are incorporating the advanced structuring and data behavior features offered by object-oriented database management systems, while retaining the significant gains in data independence and data integrity of the relational model and upward compatibility with its standard access language, SQL. Some of the advanced features supported by Starburst extensions, described in this paper, include hierarchies of user-defined types and functions, large unstructured and structured complex objects, and user-defined rules to respond to changes in the database.

Implementation of extended indexes in POSTGRES

The vaunted "Spartan simplicity" of the relational model can limit the usefulness of the relational database management system (RDBMS) for non-traditional applications. For example, there is no natural way to model a keyword index for bibliographic informational retrieval (IR), or any other index whose key values are computed from column values, in a standard RDBMS. The extended indexing proposed in [LYNC88a] is intended to support applications that require indexes on computed values. This paper reports on an implementation of this type of indexing using the POSTGRES extensible database management system [STON86a], focusing on two issues: general problems, and the features in POSTGRES that proved helpful in the solution of these problems.

A rule-based view of query optimization

The query optimizer is an important system component of a relational database management system (DBMS). It is the responsibility of this component to translate the user-submitted query - usually written in a non-procedural language - into an efficient query evaluation plan (QEP) which is then executed against the database. The research literature describes a wide variety of optimization strategies for different query languages and implementation environments. However, very little is known about how to design and structure the query optimization component to implement these strategies. This paper proposes a first step towards the design of a modular query optimizer . We describe its operations by transformation rules which generate different QEPs from initial query specifications. As we distinguish different aspects of the query optimization process, our hope is that the approach taken in this paper will contribute to the more general goal of a modular query optimizer as part of an extensible database management system.