How to integrate library data into GoTriple

This article covers the new service offered by HeBIS, which gives libraries the opportunity to optimize workflows with regard to title selection and acquisition. It consists of a highly integrated seamless system using the German National Library's data incorporated within the HeBIS Central union database and the functionalities of OCLC -Pica software (CBS,OWC,ACQ).

Data exchange and virtual data integration have been the subject of several investigations in the recent literature. At the same time, the notion of peer data management has emerged as a powerful abstraction of many forms of flexible and dynamic data-centere ddistributed systems. Although research on the above issues has progressed considerably in the last years, a clear understanding on how to combine data exchange and data integration in peer data management is still missing. This is the subject of the present paper. We start our investigation by first proposing a novel framework for peer data exchange, showing that it is a generalization of the classical data exchange setting. We also present algorithms for all the relevant data exchange tasks, and show that they can all be done in polynomial time with respect to data complexity. Based on the motivation that typical mappings and integrity constraints found in data integration are not captured by peer data exchange, we extend the framework to incorporate these features. One of the main difficulties is that the constraints of this new class are not amenable to materialization. We address this issue by resorting to a suitable combination of virtual and materialized data exchange, showing that the resulting framework is a generalization of both classical data exchange and classical data integration, and that the new setting incorporates the most expressive types of mapping and constraints considered in the two contexts. Finally, we present algorithms for all the relevant data management tasks also in the new setting, and show that, again, their data complexity is polynomial. Copyright 2007 ACM.

The increasing global demand on freshwater is resulting in nations improving their terrestrial water monitoring and reporting systems to better understand the availability, and quality, of this valuable resource. A barrier to this is the inability for stakeholders to share information relating to water observations data: traditional hydrological information systems have relied on internal custom data formats to exchange data, leading to issues in data integration and exchange. Organisations are looking to information standards to assist in data exchange, integration and interpretation to lower costs in use, and re-use, of monitoring data. The WaterML2.0 Standards Working Group (SWG), working within the Open Geospatial Consortium (OGC) and in cooperation with the joint OGC-World Meteorological Organisation (WMO) Hydrology Domain Working Group (HDWG), has developed an open standard for the exchange of water observation data. The focus of the standard is time-series data, commonly used for hydrological applications such as flood forecasting, environmental reporting and hydrological infrastructure, where a lack of standards inhibits efficient re-use and automation. This paper describes the development methodology and principles of WaterML2.0, key parts of its information model, implementation scenarios, evaluation and future work. WaterML2.0 was adopted by the OGC as an official standard in September 2012.

Within the agricultural knowledge-based bio-economy, information sharing is an important issue. Information systems for agricultural supply food chain network are not standardized. This reduces efficiency in the exchange of information in agri-business processes. To address these problems, agriXchange, an EU-funded coordination and support action was setup to develop a sustainable network system for common data exchange in the agricultural sector. The overall objectives are to: a) establish a platform on data exchange in agriculture in the EU, b) develop a reference framework for interoperability of data exchange, and c) identify the main challenges for harmonizing data exchange. Analysis of the situation concerning data exchange in agriculture in individual EU member states (including Switzerland) is an integral component of this harmonization support action. In this paper the results of the investigation of the state-of-the art around agricultural data exchange in EU member states is reported. This research on data exchange and data integration was carried out in 27 EU member states and Switzerland. The investigation employed experts to quantitatively and qualitatively inquire about agricultural data exchange in the EU. A framework was developed to inquire the different integration levels, within as well as between enterprises in agriculture. Based on the analysis of the state of the art, the challenges for future research and trends data exchange in European agriculture were identified. The results showed that there are substantial differences across the EU in relation to the level of data integration and standardization. Member states can be divided into different groups from; none or hardly any data integration to quite well developed infrastructures such as France, Germany, The Netherlands and Denmark. The most important findings identified were with the aging population of farmers which manifests itself through the lack of adaption and investments in new technology, especially in Southern and Eastern countries. Availability of mobile and broadband infrastructure was a major problem in rural areas for most countries in a quantitative sense, but for ICT developed agricultural countries more of a quality of service problem. Cost of acquiring data exchange capable equipment, data exchange formats, proprietary data formats and complexity in machines was also a major concern. As a recommendation, it was noted that open networks with flexible relationships between network partners will facilitate successful integration of systems. The importance of agricultural data exchange in the EU has broadly been recognized, however all service providers and users need to be convinced about the benefits. Finally, focus should be on putting research information into practice to demonstrate how data harmonization processes can work, however, this should be kept flexible and hence keep the rigidity of (formal) standardization processes minimal in agricultural data harmonization.

BackgroundThe One Health approach aims to balance and optimize the health of humans, animals, and ecosystems, recognizing that shared health outcomes are interdependent. A One Health approach to disease surveillance, control, and prevention requires infrastructure for coordinating, collecting, integrating, and analyzing data across sectors, incorporating human, animal, and environmental surveillance data, as well as pathogen genomic data. However, unlike data interoperability problems faced within a single organization or sector, data coordination and integration across One Health sectors requires engagement among partners to develop shared goals and capacity at the response level. Successful examples are rare; as such, we sought to develop a framework for local One Health practitioners to utilize in support of such efforts.MethodsWe conducted a systematic scientific and gray literature review to inform development of a One Health data integration framework. We discussed a draft framework with 17 One Health and informatics experts during semi-structured interviews. Approaches to genomic data integration were identified.ResultsIn total, 57 records were included in the final study, representing 13 pre-defined frameworks for health systems, One Health, or data integration. These frameworks, included articles, and expert feedback were incorporated into a novel framework for One Health data integration. Two scenarios for genomic data integration were identified in the literature and outlined.ConclusionsFrameworks currently exist for One Health data integration and separately for general informatics processes; however, their integration and application to real-time disease surveillance raises unique considerations. The framework developed herein considers common challenges of limited resource settings, including lack of informatics support during planning, and the need to move beyond scoping and planning to system development, production, and joint analyses. Several important considerations separate this One Health framework from more generalized informatics frameworks; these include complex partner identification, requirements for engagement and co-development of system scope, complex data governance, and a requirement for joint data analysis, reporting, and interpretation across sectors for success. This framework will support operationalization of data integration at the response level, providing early warning for impending One Health events, promoting identification of novel hypotheses and insights, and allowing for integrated One Health solutions.

The "ARISE/NISR Workshop on Exchange and Integration of Data" was held at the IBM Center for Advanced Studies, Toronto Lab., between October 7-9, 2004.

State-subsidized programs develop medical data integration centers in Germany. To get infection disease (ID) researchers involved in the process of data sharing, common interests and minimum data requirements were prioritized. In 06/2019 we have initiated the German Infectious Disease Data Exchange (iDEx) project. We have developed and performed an online survey to determine prioritization of requests for data integration and exchange in ID research. The survey was designed with three sub-surveys, including a ranking of 15 data categories and 184 specific data items and a query of available 51 data collecting systems. A total of 84 researchers from 17 fields of ID research participated in the survey (predominant research fields: gastrointestinal infections n=11, healthcare-associated and antibiotic-resistant infections n=10, hepatitis n=10). 48% (40/84) of participants had experience as medical doctor. The three top ranked data categories were microbiology and parasitology, experimental data, and medication (53%, 52%, and 47% of maximal points, respectively). The most relevant data items for these categories were bloodstream infections, availability of biomaterial, and medication (88%, 87%, and 94% of maximal points, respectively). The ranking of requests of data integration and exchange is diverse and depends on the chosen measure. However, there is need to promote discipline-related digitalization and data exchange.

This article presents a comprehensive framework for human-AI collaborative workflow optimization in automation-heavy industries, addressing the limitations of fully automated approaches while leveraging the complementary strengths of human judgment and artificial intelligence. We introduce the Collaborative Workflow Intelligence Framework (CWIF), which establishes structured information flows and decision authority boundaries between human operators and AI components across manufacturing, logistics, and financial services domains. Through industry-specific applications, we demonstrate how this collaborative approach enhances production scheduling, quality control, supply chain efficiency, transportation optimization, and financial risk assessment while maintaining appropriate human oversight. Our methodology provides practical guidance for system architecture design, data integration, performance evaluation, and phased implementation, with particular attention to ethical considerations including worker autonomy and skills development. The framework balances operational efficiency with human expertise, creating systems that suggest process improvements and identify inefficiencies while preserving human decision authority in complex and consequential domains. This collaborative paradigm represents a significant advance over traditional automation approaches, offering organizations a path to workflow optimization that enhances rather than replaces human capabilities while addressing the technical, organizational, and ethical challenges of AI implementation.

Publishing library catalog records as Linked Open Data is a challenge to many libraries because there is no community-driven best practice that each individual library can easily follow and implement into its workflow. Publishing library data as Linked Open Data is common practice for many national libraries, notably the British Library, French National Library, and the German National Library as well as for metadata aggregators and service providers, such as Europeana and the Online Computer Library Center (OCLC). However, the ways in which these institutions execute

Purpose The lack of interfaces for system integration in the Operating Room (OR) has been recognized as one of the major factors to impede efficient OR turnover procedures as well as efficient intraoperative data handling and ergonomics in the OR. A software interface for application-layer connection between data processing units in the OR is presented. Methods The requirements for data exchange in the OR were derived, mostly from the literature. A c++ class library was developed according to these requirements. The efficiency of the data exchange and the limits of the system were investigated by a series of experiments. An examplary application was built using the library to show its applicability under realistic conditions. Results We present the software library TiCoLi for automatic OR network configuration, device and service discovery (“surgical plug-and-play”), message exchange, data streaming, and remote access to device parameters and functionalities through an Ethernet network. The system is based on a modular, object-oriented software design, which guarantees high flexibility regarding the adaptation of its subsystems to the needs of a particular clinical application. Conclusions The TiCoLi package is the first open source software library which was developed for system integration in the OR which covers message exchange, data streaming, and auto configuration.

The Trials and Tribulations of Health Information Sharing: The Turbulent Rise of the Rhio

Schema mapping composition is a fundamental operation in schema management and data exchange. The mapping composition problem has been extensively studied for a number of mapping languages, most notably source-to-target tuple-generating dependencies (s-t tgds). An important class of s-t tgds are local-as-view (LAV) tgds. This class of mappings is prevalent in practical data integration and exchange systems, and recent work by ten Cate and Kolaitis shows that such mappings possess desirable structural properties.It is known that s-t tgds are not closed under composition. That is, given two mappings expressed with s-t tgds, their composition may not be definable by any set of s-t tgds (and, in general, may not be expressible in first-order logic). Despite their importance and extensive use in data integration and exchange systems, the closure properties of LAV composition remained open to date. The most important contribution of this paper is to show that LAV tgds are closed under composition, and provide an algorithm to directly compute the composition.An important application of our composition result is that it helps to understand if given a LAV mapping Mst from schema S to schema T, and a LAV mapping Mts from schema T back to S, the composition of Mst and Mts is able to recover the information in any instance of S. Arenas et al. formalized this notion and showed that general s-t tgds mappings always have a recovery. Hence, a LAV mapping always has a recovery. However, the problem of testing whether a given Mts is a recovery of Mst is known to be undecidable for general s-t tgds. In contrast, in this paper we show the tractability of the problem for LAV mappings, and give a polynomial-time algorithm to solve it.

Rapid advances in information technology are changing the way technical data is created, stored, and used. These advances have created opportunities to reduce costs and improve productivity in both the administration of data and in the acquisition and operation of weapon systems. To realize these benefits, effectively administered data dictionary systems are required. Data dictionary systems are databases that store and manage data related to the structure and use of information resources. These databases will facilitate the access and exchange of data with the DoD, within industry, and between the DoD and industry during the acquisition and operation of the weapon system. The access, exchange, and integration of data that the data dictionary supports will revolutionize weapon system engineering, manufacturing, and support. To realize the full potential of data dictionaries, three requirements need to be satisfied: (1) Establish an organization to administer and manage technical data and data dictionaries; (2) Implement and administer data and procedural standards that will govern weapon system life cycle data and activities; and (3) Establish a data management process that will provide needed services to technical data users

It is very important to facilitate the access and the integration of data and information for the acquisition, accumulation, modeling and transformation of environmental data into information required for decision making and planning of the environment. Examples on the use of new WEB-technology for access and exchange of marine data are given. The use of XML-applications for retrieval of new information is presented.

Collaborative field development well-planning concepts have been subject of several studies in upstream industry, with limited supporting workflows that are often based on case by case studies, which are not necessarily scalable in limited turnaround time due to complexities of larger field development projects. The traditional workflows are highly linear and inefficient, due to lag times while various functions perform their due diligence, reconsider assumptions that eventually increases risk of not achieving the optimal solutions and thus reducing the efficiency. This Paper discusses how all relevant field development workflows were brought under a single collaborative solution environment with efficient workflows, data integration and its successful deployment for optimum pad-based field development implementation for a large brown field. In traditional field development well planning, the workflows such as reservoir targets creation, field development well planning scenarios, wellbore positioning & drillability, surface location optimization and costing are performed manually in siloes. It is indeed challenging to govern and manage data efficiently across these workflows in an integrated way to serve various domains and departments. With larger brown fields, further disintegration of functions across multiple reservoirs, well construction parameters and reservoir developmental strategies occurs. The proposed approach is driven by multidisciplinary collaborative solution environment built on automation and optimization technologies enabled by automated cross-domain data integration across several data sources. The above stated workflows have been automated in a single solution workspace that can quickly respond to risk or changes. This smart collaborative environment has been adapted to optimize the pad-based onshore mature field development for a large field across the planned drilling schedule of wells in order to mitigate the impact of increasing surface congestion in the field due to planned facilities, wellheads whilst considering geological uncertainties. This analysis has been performed while maintaining constraints such as identification of optimized size, number and orientation of pads, slot allocation for pad & off-pad well locations, long term development scenarios including EOR pilots, anti-collision and proximity to production network nodes as well as minimizing CAPEX while remaining in-line with HSE standards. This collaborative solution environment has successfully demonstrated value of data integration with appropriate workflows thus improving communication, accountability and transparency in planning stages of field development. Accelerated field development well planning has reduced uncertainty and multiple iterations of scenarios were generated due to cross-collaboration between teams to evaluate cost and business impacts on a significantly reduced timeline for optimizing field development. This paper illustrates implementation of collaborative solution and respective automated workflows, data integration framework and its synergy with current working environment. The case study explains the successful adoption, workflow collaboration between asset development, drilling & engineering by visualizing various constraints and outlined benefits & value.