Information Silos Research Articles

Assessing the design of road traffic death information systems in Iran: A participatory systems approach

AimTo describe and analyze the information architecture and information pathways of the road traffic death recording, registration and reporting system in Guilan Province, northernIran. MethodsWe used Business Process Mapping, a qualitative approach. This participatory and iterative approach consists of a document review, key informant interviews, development of a process map and a participatory workshop with key stakeholders to illuminate and validate the findings. We classified the tasks performed in the system into three phases: (1) Identification and recording; (2) Notification and registration, and (3) Production of statistics. ResultsWe identified 13 stakeholders, with operating and influencing roles in the process of identification, registration and production of statistics about road traffic deaths in Guilan province. The three main sources of road traffic death statistics are the Ministry of Health and Medical Education, the National Organization for Civil Registration and the Forensic Medicine Organization. Our results reveal a highly fragmented system with minimal cross-sectoral data exchange. Each stakeholder operates in a silo resulting in delays and redundancies in the operating system. In the absence of an effective communication among stakeholders, the information exchange was dependent on the family of the deceased. These fragmented information silos alter the compilation of cause of death statistics and result in under-reporting and discrepancies in road traffic deaths figures. ConclusionsDesigning a comprehensive road traffic information system that provides accurate and timely information requires an understanding of the information flow and the entangled web of different stakeholders operating in the system. Participatory systems approaches such as process mapping can assist in capturing the complexity of the system and the integration process by facilitating stakeholders’ engagement and ownership in improving the design of the system.

Design-Manufacturing-Operation & Maintenance (O&M) Integration of Complex Product Based on Digital Twin

This paper presents a complex product design-manufacturing-operations and maintenance integration method based on digital twin technology. This method aims to solve the problem of information silos in the design, manufacturing and operation and maintenance phases of complex products in the context of intelligent manufacturing and to integrate the design, manufacturing and operation and maintenance processes of complex products. To address the integration needs of complex product design, manufacturing, operation and maintenance business integration, a framework for complex product design-manufacturing-operation and maintenance integration based on the digital twin is first proposed, in addition to designing a model and operation mechanism for combining the virtual and real of the digital twin model. Then, the implementation of multistage collaborative design technology, data intelligent sensing technology, and data integration and fusion technology for the digital twin-based design-manufacturing-operations and maintenance integration processes are analyzed and discussed. Finally, a case study involving the fault prediction of key components of the bogie of an EMU demonstrated the integrated mode of operation in the design-manufacture-operation and maintenance process of the EMU. It verified the effectiveness of the proposed framework, process and methodology.

An In-Memory Data-Cube Aware Distributed Data Discovery Across Clouds for Remote Sensing Big Data

With the booming of high-resolution Earth observation and Open-Data efforts, petabyte-scale Earth observation data have been available for free access. Due to the unprecedented availability of big data deluge, regional to global spatio-temporal analysis has been significantly challenged with the huge computational barriers, the tedious cycles of “download-preprocess-store-analyze” leading to excessive data downloading overhead, and the acquisition-oriented 2D file-based structure which is not fit for spatio-temporal analysis. The Earth Observation Data Cube (EODC) paradigm revolutionizes the traditional way of storing, managing, and analyzing spatio-temporal RS data, and solves problems of easy-to-use of RS data to a certain extent. However, different EODC solutions are becoming “Information Silos”. Therefore, the sharing and joint use of remote sensing (RS) data across EODCs have become extremely challenging. To address the above challenges, we proposed a method of in-memory distributed data cube auto-discovery and retrieval across Clouds. We construct a distributed in-memory data orchestration across Clouds to shield the heterogeneity of the EODC storage solutions, solving “Information Silos” problems. And we put forward a Larger-sites-first and Spatio-temporal Aware RS data discovery strategy, which can automatically discover data across Clouds for requirements. Based on the data cube paradigm, this paper proposes a Quality-first data filtering strategy, which can help users to filter out high-quality data covering the target spatio-temporal range from the huge amount of data, and solve the problem of data cube joint retrieval and efficient use across Clouds. In addition, we have confirmed that our method is effective and efficient through comparative experiments.

Exploring the Construction of a Social Credit System Based on Blockchain Technology and Digital Trust

The degree of information transmission and trust are closely related, and in socio-economic activities, information transmission often faces difficulties such as fragmentation, distortion, inadequacy, and asymmetry. With the development of information technology, social relationships have been reconstructed, and traditional trust mechanisms based on acquaintances and institutional trust can no longer meet the transactional needs of socio-economic activities. Therefore, a new form of digital trust has emerged, with blockchain technology as an important foundation, which can construct more direct, efficient, and reliable trust relationships. The advantages of blockchain technology in information sharing, data authentication, and automated transactions are suitable for solving the difficulties of narrow credit data scope, poor timeliness, difficult aggregation, information silos, and unclear digital property rights in the construction of a social credit system. Based on this, a practical approach to the construction of a social credit system based on digital trust and blockchain technology is proposed.

Italian Immigration in the American West: 1870–1940

Italian Immigration in the American West: 1870–1940

Multimodal analysis and the oncology patient: Creating a hospital system for integrated diagnostics and discovery

We propose that an information technology and computational framework that would unify access to hospital digital information silos, and enable integration of this information using machine learning methods, would bring a new paradigm to patient management and research. This is the core principle of Integrated Diagnostics (ID): the amalgamation of multiple analytical modalities, with evolved information technology, applied to a defined patient cohort, and resulting in a synergistic effect in the clinical value of the individual diagnostic tools. This has the potential to transform the practice of personalized oncology at a time at which it is very much needed.In this article we present different models from the literature that contribute to the vision of ID and we provide published exemplars of ID tools. We briefly describe ongoing efforts within a universal healthcare system to create national clinical datasets. Following this, we argue the case to create “hospital units” to leverage this multi-modal analysis, data integration and holistic clinical decision-making. Finally, we describe the joint model created in our institutions.

Construction and Optimization of Co-occurrence-attribute-interaction Model for Column Semantic Recognition

PDF HTML XML Export Cite reminder Construction and Optimization of Co-occurrence-attribute-interaction Model for Column Semantic Recognition DOI: 10.21655/ijsi.1673-7288.00294 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Government data governance is undergoing a new phase of transition from ``physical data aggregation'' to ``logical semantic unification''. Thus far, long-term ``autonomy'' of government information silos, leads to a wide spectrum of metadata curation issues, such as attributes with the same names but having different meanings, or attributes with different names but having the same meanings. Instead of either rebuilding/modifying legacy information systems or physically aggregating data from government information silos, logical semantic unification solves this problem by unifying the semantic expression of the metadata in government information silos and achieves the standardized metadata governance. This paper focuses on the logical semantic unification that semantically aligns the metadata in each government information silo with the existing standard metadata. Specifically, the names of the standard metadata are abstracted as semantic labels, and the column projections of silo relational data are semantically recognized to semantically align column names with the standard metadata and ultimately achieve the standardized governance of silo metadata. The existing semantic recognition techniques based on column projection fail to capture the column order-independent features of relational data and the correlation features among attributes and semantic labels. To address the above problem, we propose a two-phase model based on a prediction phase and a correction phase. In the prediction phase, a Co-occurrence-Attribute-Interaction (CAI) model is proposed to guarantee the column order-independent property by employing the parallelized self-attention mechanism; in the correction phase, a correction mechanism is introduced to optimize the prediction results of the CAI model by utilizing the co-occurrence of semantic labels. Experiments are conducted on a government benchmark dataset and several public English datasets, such as Magellan, and the results show that the two-phase model with a correction mechanism outperforms the current optimal model in macro-average and weighted average by up to 20.03% and 13.36%, respectively. Reference Related Cited by

A novel system for medical equipment supply chain traceability based on alliance chain and attribute and role access control

With the increasing sales of the medical device market, the medical incidents caused by passive medical devices are increasing, which needs to be solved from the medical device supply chain. In addition, there are complex rights retrieval and assignment problems in the medical device supply chain system, and the traditional Role-based Access Control (RBAC) model is no longer suitable for the actual application environment of the supply chain.This article combines the traditional RBAC model and the Attribute-based Access Control (ABAC) model for access control management in the medical device supply chain to overcome the limitations abovementioned, taking advantage of the ABAC model’s flexibility to achieve granular and dynamic management of permissions, as also the RBAC model to simplify the permissions management of the entire system. Blockchain technology’s traceability, non-tampering, and decentralization features are essential to eliminate passive and inferior medical equipment sales activities, solving the problem of mutual mistrust and ‘Information Silo’ between the two sides of medical device transactions. The access control model is implemented in the blockchain’s smart contract, providing granular and dynamic authority management for the medical equipment supply chain and guaranteeing the security and privacy of medical device information in the supply chain. Three smart contracts on Hyperledger Fabric are designed for device management information, storage of implemented access control models, and implementation of access control policies. Experimental results show that the entire system is stable, ensuring high throughput and high security and promising.

A Federated Domain Adaptation Algorithm Based on Knowledge Distillation and Contrastive Learning

Smart manufacturing suffers from the heterogeneity of local data distribution across parties, mutual information silos and lack of privacy protection in the process of industry chain collaboration. To address these problems, we propose a federated domain adaptation algorithm based on knowledge distillation and contrastive learning. Knowledge distillation is used to extract transferable integration knowledge from the different source domains and the quality of the extracted integration knowledge is used to assign reasonable weights to each source domain. A more rational weighted average aggregation is used in the aggregation phase of the center server to optimize the global model, while the local model of the source domain is trained with the help of contrastive learning to constrain the local model optimum towards the global model optimum, mitigating the inherent heterogeneity between local data. Our experiments are conducted on the largest domain adaptation dataset, and the results show that compared with other traditional federated domain adaptation algorithms, the algorithm we proposed trains a more accurate model, requires fewer communication rounds, makes more effective use of imbalanced data in the industrial area, and protects data privacy.

Current Challenges in the Adoption of Digital Visual Management at Construction Sites: Exploratory Case Studies

In the construction industry, digitalisation has led to increasing efforts to improve construction management using digital visual management (VM) devices. Although the amount of research on digital VM (DVM) in the design phase and in the management of construction sites has also increased, its implementation during the production phase and by construction crews remains limited. The objective of this study is to explore the adoption of DVM in construction sites, assess construction workers’ experiences regarding digital and analogue VM devices, and understand the challenges that hinder the adoption of such devices. This study used a mixed method approach, combining qualitative and quantitative research. Data included visual site explorations, surveys of construction workers and crew managers, and unstructured interviews with site managers and development directors to assess the use of DVM devices in construction sites, the need for them and their current implementation. The findings showed that VM should be conveniently located near the job site instead of the office site, which is the current situation. Construction crews who experienced more production and schedule disruptions reported that VM supported their work, compared with crews that had fewer disruptions. VM devices on construction sites are analogue, and their usage continues to be in construction site management, which perpetuates information silos during construction projects. The findings of this study provide insights into the development and deployment of DVM devices on construction sites. Construction workers’ need for visual information close at hand is of interest to both scholars and practitioners in future research and development.

Integrated care with digital health innovation: pressing challenges

PurposeDigital health care has emerged as one of the most important means to deliver integrated care by care providers in recent years. As the use of digital health increases, there are some pressing issues such as interoperability of data across different healthcare information systems, regulatory environment and security and privacy of patient’s information which need to be discussed and addressed in order to reduce information silos and to ensure efficient and seamless use of digital health technologies. The purpose of this paper is to address these issues.Design/methodology/approachIn this paper the authors outline the key concepts of interoperability, key challenges pertaining in achieving interoperability and concepts of security and privacy in context of digital health models of integrated care.FindingsThe study suggests that standardization of digital health information systems and connecting existing systems to health network, addressing privacy and security related issues through a comprehensive but supportive regulatory environment and educating citizens and healthcare providers are some of the ways to achieve effective use of digital health in models of integrated care.Originality/valueAlthough the concepts of privacy and interoperability are not new, however, as per best of the authors’ knowledge, this is the first attempt to discuss the challenges and possible actions to meet the objective of achieving integrated care through digital innovation.

The Advent of the New Architectural Profession through BIM

The profession of architecture mainly involves the construction of building information; this is achieved within the knowledge paradigm of our society. Therefore, a shift in the knowledge paradigm can lead to the advent of new architectural professions; the Renaissance and the current era are periods of such shifts. However, during the process of such a shift, it is difficult to notice the essential nature of the change, such as the emergence of a new architectural profession utilizing building information modeling (BIM). This study is unique in that it uses the correlation of building information with knowledge to uncover the essential nature of the architectural profession. Furthermore, Nonaka’s knowledge creation process (SECI) model is used to reveal the knowledge paradigm of our society. The analysis results show that knowledge creation in society has increased explosively since it has become possible—through the printing revolution of the Renaissance period—to share knowledge that previously remained at the experience level. Accordingly, this has led to the advent of an architectural profession involving today’s intellectuals, who can construct and express building information by using conceptualized knowledge rather than experience. Nowadays, machines have emerged as new agents of knowledge processing through the digital revolution, innovatively strengthening connections between information silos. As a result, a new architectural profession has emerged, focused on improving the performance of buildings by using simulations with machine-readable building information as well as with one’s own knowledge. Further, this new architectural profession, by harnessing the newly developed hybrid intelligence of machines and humans, is expected to overcome the former limits of the profession.

Research on internetware: Review and prospect

<p indent="0mm">Software is the core component of IT industry and an important “infrastructure” that supports social operations in the digital economy era. Since the 21st century, the internet has evolved into a global ubiquitous computing environment and open platform. Its open, dynamic, and uncontrollable nature requires the corresponding changes in the basic form and characteristics of software, the conceptual framework, and the logical connotation, and thus leads to substantial challenges to software theory, methods, and technologies. In 2000, researchers from China proposed the term “internetware”, which indicates a new software paradigm for the internet computing. After more than <sc>20 years</sc> of efforts, a series of important research achievements have been made in aspects of the basic model, development methodology, runtime support, quality assessment and assurance of internetware, and resulted in systematic innovation results and produced a wide range of academic and industrial impacts. In internetware, the software model consists of a set of autonomous software entities distributed and/or decentralized over the internet and other extensions like internet of things and 4G/5G, together with a set of connectors for enabling collaborations among these entities in various ways. Internetware software entities are able to sense dynamic changes of the underlying environments, and continuously adapt to these changes by means of structural and behavioral maintenance and evolution. From the micro perspective, internetware software entities collaborate with each other on demand and on the fly. From the macro perspective, the entities can self-organize to form an application or community of interest and even decentralized autonomous organizations. As a result, the development and evolution of a software application with internetware can be viewed as continuous and iterative composition of various “disordered” resources into “ordered” software applications. Thus, software development with internetware is a process being bottom-up, inside-out, spiral, and to some extent, similar with the complex adaptive systems. For example, the internetware paradigm proposed the theory of software architecture modeling (called ABC methodology) covering the whole life-cycle, where the core artifacts and activities of every single stage are unified into the software architecture model and its iterative refinement and transformation. In this way, the internetware paradigm greatly improves the efficiency and quality of software development and evolution. In addition, the internetware paradigm expands the software architecture from the development phase to the runtime, by proposing the concept of runtime software architecture (RSA). The RSA has been widely applied for a large number of information “silo” systems to enable the functionality and data interoperability. This solution is a significantly disruptive technical invention, or namely the “black box” mode, based on the client-driven resource reflection mechanism to achieve automatic recovery of the system runtime architecture and automatic generation of data access interfaces. Compared to the traditional “white box” interoperability solution, the internetware paradigm eliminates the need of accessing source code, documentation, and original development team, and improves the interoperability efficiency between information silos, with more than 100X acceleration. This article reviews the research and practice of internetware from the perspective of software paradigm, following the internet computing environment and its extensions as a clue. It also discusses the future research outlook of internetware, especially for the ubiquitous computing environments and data centric technologies.

Hybrid modelling for remote process monitoring and optimisation

Process simulation is used to develop a digital twin representation of chemical processes typically for process optimization or what-if scenarios. However, it is known to be computationally expensive and there is an increasing need for process remote monitoring and optimization. This is where machine learning models shine, where they can run up to several orders of magnitude faster than their equivalent first principle process simulation models. Most of the previous work in this area tend to demonstrate the ability of machine learning models to accurately capture complex, non-linear relationships between process parameters in various chemical processes. Two important aspects are rarely discussed, namely the construction of machine learning models using operating data and the deployment of these models. In this paper, we address these two aspects and review the different information silos in industrial settings that need to be considered when working with hybrid models (combining process simulation and machine learning). This is illustrated by a case study for an operating natural gas dehydration unit, covering data management, process simulation, machine learning and visualization. We demonstrate how the hybrid models can be constructed and packaged as an online monitoring and a prediction dashboard. Several unique challenges are also highlighted including the reliability of field data and operational deviations due to operability and controllability - all of which need to be understood in order to successfully translate operating systems to process simulation and machine learning models that are reliable and accurate. While the exact implementation may vary from project to project, the current work serves as an example and highlights the important considerations to make when working with such systems.

Addressing the Gap in Data Communication from Home Health Care to Primary Care during Care Transitions: Completeness of an Interoperability Data Standard.

In a future where home health care is no longer an information silo, patient information will be communicated along transitions in care to improve care. Evidence-based practice in the United States supports home health care patients to see their primary care team within the first two weeks of hospital discharge to reduce rehospitalization risk. We sought to identify a parsimonious set of home health care data to be communicated to primary care for the post-hospitalization visit. Anticipating electronic dataset communication, we investigated the completeness of the international reference terminology, Logical Observation Identifiers Names and Codes (LOINC), for coverage of the data to be communicated. We conducted deductive qualitative analysis in three steps: (1) identify home health care data available for the visit by mapping home health care to the information needed for the visit; (2) reduce the resulting home health care data set to a parsimonious set clinicians wanted for the post-hospitalization visit by eliciting primary care clinician input; and (3) map the parsimonious dataset to LOINC and assess LOINC completeness. Our study reduced the number of standardized home health care assessment questions by 40% to a parsimonious set of 33 concepts that primary care team physicians wanted for the post-hospitalization visit. Findings indicate all home health care concepts in the parsimonious dataset mapped to the information needed for the post-hospitalization visit, and 84% of the home health care concepts mapped to a LOINC term. The results indicate data flow of parsimonious home health care dataset to primary care for the post-hospitalization visit is possible using existing LOINC codes, and would require adding some codes to LOINC for communication of a complete parsimonious data set.

What is misinformation and disinformation? Understanding multi-stakeholders’ perspectives in the Asia Pacific

ABSTRACT Misinformation and disinformation on digital platforms can cause serious harms by undermining the integrity of elections, destabilising political systems, and derailing public health messages. Yet, key decision-makers still struggle to agree on definitions of these terms. This dissensus can frustrate holistic approaches needed to tackle the harms caused by online falsehoods. This article focuses on the Asia Pacific and draws upon a comprehensive set of expert interviews in Singapore and Indonesia to investigate how digital platforms, civil society actors, academics, and journalists conceptualise misinformation and disinformation. We find existing definitions have been developed in ‘information silos’. In response, we map stakeholders’ key definitional indicators of misinformation and disinformation to identify overlap and difference and to explicate the role of harm in their conceptualisations. This framework is used to recast working definitions of misinformation and disinformation with the intention of assisting greater stakeholder collaboration needed to mitigate its societal harms.

Ridership and Operations Visualization Engine: An Integrated Transit Performance and Passenger Journey Visualization Engine

Transit agencies collect a vast amount of data on vehicle positions, passenger loading and, increasingly, origin–destination flows. Collecting and synthesizing these data to support operations and planning are significant challenges and can be constrained by information silos within transit agencies. In this paper, an open-source bus performance and journey visualization dashboard, Ridership and Operations Visualization Engine, is presented, which integrates multiple disparate data sources into a flexible and iterative analysis tool. It differs from existing commercial products by including origin–destination flows along with standard performance metrics, and is designed to be adaptable and relevant to any transit agency. Two case studies are presented to demonstrate the functionality of the dashboard: planning transit priority infrastructure and evaluating network design changes. The dashboard was developed in partnership with Chicago Transit Authority and Massachusetts Bay Transportation Authority, and practical details from the installation and maintenance procedures are included for prospective users.

Differential Game Model of Information Sharing among Supply Chain Finance Based on Blockchain Technology

In the development of traditional supply-chain finance, the information asymmetry of all parties in the supply chain has become the primary problem hindering its development. Blockchain technology is an effective method to solve the problem of information silos. Based on differential game theory, this paper constructs a game model of supply-chain financial information-sharing behavior based on blockchain technology. Three scenarios of independent decision, the cost-subsidy mechanism of financial institutions, and dealers and collaborative decisions are studied, and the theoretical model is verified through a simulation algorithm. The results show that information sharing in supply-chain finance based on blockchain technology is much higher than that of traditional supply-chain finance, and the use of blockchain technology can promote more sustainable development of supply chains. Blockchain technology can effectively solve the information-silo effect, and the information sharing cost-subsidy mechanism can effectively relieve the cost pressure of information sharing and optimize the supply-chain structure. In addition, the amount of information and benefits shared among the three parties of supply-chain finance based on blockchain technology and the overall benefits show an increasing and stabilized trend over time. This study provides a reference for supply-chain finance members to reasonably choose the optimal strategic behavior.

Decision-Making Using Big Data Relevant to Sustainable Development Goals (SDGs)

Policymakers, practitioners, and researchers around the globe have been acting in a coordinated manner, yet remaining independent, to achieve the seventeen Sustainable Development Goals (SDGs) defined by the United Nations. Remarkably, SDG-centric activities have manifested a huge information silo known as big data. In most cases, a relevant subset of big data is visualized using several two-dimensional plots. These plots are then used to decide a course of action for achieving the relevant SDGs, and the whole process remains rather informal. Consequently, the question of how to make a formal decision using big data-generated two-dimensional plots is a critical one. This article fills this gap by presenting a novel decision-making approach (method and tool). The approach formally makes decisions where the decision-relevant information is two-dimensional plots rather than numerical data. The efficacy of the proposed approach is demonstrated by conducting two case studies relevant to SDG 12 (responsible consumption and production). The first case study confirms whether or not the proposed decision-making approach produces reliable results. In this case study, datasets of wooden and polymeric materials regarding two eco-indicators (CO2 footprint and water usage) are represented using two two-dimensional plots. The plots show that wooden and polymeric materials are indifferent in water usage, whereas wooden materials are better than polymeric materials in terms of CO2 footprint. The proposed decision-making approach correctly captures this fact and correctly ranks the materials. For the other case study, three materials (mild steel, aluminum alloys, and magnesium alloys) are ranked using six criteria (strength, modulus of elasticity, cost, density, CO2 footprint, and water usage) and their relative weights. The datasets relevant to the six criteria are made available using three two-dimensional plots. The plots show the relative positions of mild steel, aluminum alloys, and magnesium alloys. The proposed decision-making approach correctly captures the decision-relevant information of these three plots and correctly ranks the materials. Thus, the outcomes of this article can help those who wish to develop pragmatic decision support systems leveraging the capacity of big data in fulfilling SDGs.

Protecting their turf: When and why supervisors undermine employee boundary spanning.

The benefits boundary spanners offer organizations by bridging information silos are well documented. However, informational boundary spanning also implies crossing organizational territories, as employees seek advice from others outside their supervisors' control. Applying a territoriality theory lens, we develop new insights about when and why supervisors may view their subordinates' informational boundary-spanning activities unfavorably and attempt to undermine boundary spanners. We argue that undermining results from supervisors perceiving the boundary spanning of their employees as weakening their control over their organizational territory. We further argue that subordinates who seek advice across organizational boundaries without also seeking advice from their supervisor are more likely to be seen by their supervisors as having negative intentions when engaging in boundary spanning, which increases their risk of being undermined. We find support for our arguments in a field study and in a scenario experiment. Our results provide new insights into the potentially negative reactions from supervisors toward employees who engage in boundary spanning. We discuss how these insights contribute to the boundary spanning literature, to territoriality theory, and to the leadership literature. (PsycInfo Database Record (c) 2022 APA, all rights reserved).