Data Interoperability Problem Research Articles

An ontology-based methodology to establish city information model of digital twin city by merging BIM, GIS and IoT

With the development of digital city and smart city construction, the City Information Model (CIM) has played a critical role as a container of spatial–temporal data to establish the Digital Twin City. For a digital twin city, a virtual high-fidelity CIM model that corresponds closely to the real physical world is the premise and cornerstone of its construction. Therefore, the integration of BIM, GIS and IoT has become the preferred topic for researchers and has received much more attention from a wide academic circle. However, traditional integration mainly focuses on the conversion of both IFC and CityGML, and IoT data are also often used as visualizations. More importantly, the underlying data formats of GIS, BIM and IoT are still independent of each other without a unified data structure expression, so real data-driven analysis and decision-making cannot be implemented. This study aims to establish a general CIM ontology to integrate heterogeneous BIM, GIS and IoT data. First, the related work of BIM, GIS and IoT integration is studied and analyzed. A comparison of three mainstream approaches, data conversion, standard extension and data linking, is conducted, and it illustrates the advantages of ontology techniques in solving data interoperability problems. Second, a technical framework of BIM, GIS and IoT data integration based on ontology technology is proposed. The approach is mainly divided into five steps: geometry processing, data instantiation, ontology construction, ontology mapping and querying application. On the basis of the CIM ontology, an application ontology is built for a specific application domain to illustrate rule-based mapping, querying and inferring. Finally, the case study shows that the Ontology-based methodology in this paper has contributed to establish a general pattern for CIM data integration by mapping and linking concepts from the semantic level. It avoids changes in the original data sources and the missing data problem.

Provenance Data Management in Health Information Systems: A Systematic Literature Review.

This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and technologies related to provenance data management in health information systems (HISs). The SLR developed here seeks to answer the questions that contribute to describing the results. An SLR was performed on six databases using a search string. The backward and forward snowballing technique was also used. Eligible studies were all articles in English that presented on the use of different methods, techniques, models, methodologies, and technologies related to provenance data management in HISs. The quality of the included articles was assessed to obtain a better connection to the topic studied. Of the 239 studies retrieved, 14 met the inclusion criteria described in this SLR. In order to complement the retrieved studies, 3 studies were included using the backward and forward snowballing technique, totaling 17 studies dedicated to the construction of this research. Most of the selected studies were published as conference papers, which is common when involving computer science in HISs. There was a more frequent use of data provenance models from the PROV family in different HISs combined with different technologies, among which blockchain and middleware stand out. Despite the advantages found, the lack of technological structure, data interoperability problems, and the technical unpreparedness of working professionals are still challenges encountered in the management of provenance data in HISs. It was possible to conclude the existence of different methods, techniques, models, and combined technologies, which are presented in the proposal of a taxonomy that provides researchers with a new understanding about the management of provenance data in HISs.

Multi-Source Information Fusion Technology and Its Application in Smart Distribution Power System

Compared to traditional measurement devices, the micro-synchrophasor measurement unit (D-PMU or μPMU) in the distribution power system has great differences in data acquisition frequency, data format, data dimension, time-stamped information, etc. Hence, it is imperative to research the integration mechanism of heterogeneous data from multiple sources. Based on the analysis of the current technology of multi-source information fusion, this paper proposes a novel approach, which considers two aspects: the interoperability of multi-source data and the real-time processing of large-scale streaming data. To solve the problem of data interoperability, we have modified the model of D-PMU data and established a unified information model. Meanwhile, an advanced distributed processing technology has been deployed to solve the problem of real-time processing of streaming data. Based on this approach, a smart distribution power system wide-area measurement and control station can be established, and the correctness and practicality of the proposed method are verified by an on-field project.

DISTRIBUTED INTEROPERABILITY SOLUTIONS SMART HEALTHCARE SYSTEM FOR MULTI-PATIENT VITAL SIGNS MONITORING AND FORECAST OF CRITICAL ALERTS

In current healthcare systems, doctors and paramedics have to individually observe the readings on attached devices to judge a patient’s condition. A smart healthcare system may generate an opinion about the patient by compiling data from various vital sign monitoring devices. However, multi-vendor devices face the data interoperability problem because of the varying standardsused. Current solutions rely on centralized cloud/fog-based servers for interoperability which is a barrier to real-time multi-patient monitoring. This research presents an Edge-computing based distributed interoperability framework for smart healthcare devices and presents a system that continuously monitors the patients’ vital signs, ensemble the results for display in the nursing office, or in the doctor’s wallet. A healthcare setup was emulated for testing the proposed solution. Results are compared to the centralized authority-based system, which showed that the proposed solution performed better in terms of response time, with the advantage of utilizing the local resources to achieve data interoperability. We used deep learning techniques to learn patients’ critical situation from the vital signs monitoring database and predicted the critical situations to alert about the criticalpatients with 86% accuracy and 91% precision. Our model achieved a sensitivity of 90% and specificity of 72%. Hence a good overall performance has been achieved.

A Conceptual Model for Development of Small Farm Management Information System: A Case of Indonesian Smallholder Chili Farmers

Farm Management Information Systems (FMIS) assists farmers in managing their farms more effectively and efficiently. However, the use of FMIS to support crop cultivation is, at the present time, relatively expensive for smallholder farmers. Due to some handicaps, providing an FMIS that is suitable for small-holder farmers is a challenge. To analyze this gap, this study followed 3 steps, namely: (1) identified commodity and research area, (2) performed Farmers’ Information Needs Assessment (FINA), and (3) developed the conceptual model using the Soft System Methodology. Indonesian smallholder chili farmers are used as a case study. The most required information of smallholder’ farmers was identified through a qualitative questionnaire. Despite this, not all identified information needs could be accurately mapped. Thus, this indicates the need for a new FMIS conceptual model that is suitable for smallholder farmers. This study proposes an FMIS conceptual model for farm efficiency that incorporates five layers, namely farmers’ information needs, data quality assessment, data extraction, SMM (split, match and merge), and presentation layer. SMM layer also provides a method to comprehensively tackle three main problems in data interoperability problems, namely schema heterogeneity, schema granularity, and mismatch entity naming.

Integrating building and context information for automated zoning code checking: a review

Interoperability approaches have attracted much attention in the AEC/FM industry with the increased interest in Building Information Modeling (BIM) studies since 2000’s. Especially, the integration of BIM with GIS is crucial for areas, which need detailed information on buildings and their surroundings. Automated code compliance checking against zoning codes is an area that requires both zoning data and building design data. In an ideal automated zoning code checking process, building codes should be retrieved from the responsible authority, data regarding the neighborhood the project is located in should be retrieved directly from the local municipality’s GIS, and the building project should be supplied by the designer as a BIM file. The checking process should be able to work with a combination of GIS and BIM data and generate a compliance report. Although recent BIM-GIS integration efforts have been successful in some areas, BIM-GIS integration studies in the context of automated zoning code compliance checking are limited, and the data interoperability problem in this field still needs to be addressed. This paper intends to (1) provide a critical review and analysis of the current BIM and GIS integration studies for building permit processes, (2) present the opportunities that the implementation of integrating BIM and GIS might bring to the automated zoning compliance checking domain and (3) identify promising integration approaches for future efforts.

A prospective interoperable distributed e-Health system with loose coupling in improving healthcare services for developing countries

A loosely coupled distributed system enables interoperability in healthcare structures and can be used to create flexible architecture. The key limitation in adopting electronic health (e-Health) services in developing countries is the health data interoperability problem. Most developing countries are being confronted with the utilization of e-Health systems for their health care services. This study aims to develop a prospective e-Health system for developing countries to improve the effectiveness of health care services. This paper presents an enhanced e-Health framework using distributed memory and heterogeneous data handling mechanisms with loose coupling. The distributed e-Health has been designed using e-Health standards that are compatible with health level 7 (HL7) and can facilitate interoperability among health information systems. The Message Passing Interface (MPI) has been implemented to store regular backups of heterogeneous health data. The HL7 standards can also handle security threats. Data interchange from one e-Health system server to another has been conducted based on the experimental results. The distributed database has been made accessible from a remote server by using loosely coupled technology. It also provided the e-Health data recovery features and handled an extensive database through a distributed environment. According to the study, the developed system can deliver a low-cost service to the intended developing-country community effectively. • Entails distributed technology into the e-Health system and keep track of the health records of different organizations independently for developing countries. • Providing potential interoperability among health information systems using health level 7 (HL7) based e-Health standard. • Employs a decentralized, distributed database to address the challenges of a centralized database in sustaining e-Health care. • Using the message passing interface (MPI) regular backups of heterogeneous health data has been performed to avoid system failure.

Applying Class Distance to Decide Similarity on Information Models for Automated Data Interoperability

In the world of the Internet of Things (IoT), heterogeneous systems and devices need to be connected and exchange data with others. How data exchange can be automatically realized becomes a critical issue. An information model (IM) is frequently adopted and utilized to solve the data interoperability problem. Meanwhile, as IoT systems and devices can have different IMs with different modeling methodologies and formats such as UML, IEC 61360, etc., automated data interoperability based on various IMs is recognized as an urgent problem. In this paper, we propose an approach to automate the data interoperability, i.e. data exchange among similar entities in different IMs. First, similarity scores among entities are calculated based on their syntactic and semantic features. Then, in order to precisely get similar candidates to exchange data, a concept of class distance calculated with a Virtual Distance Graph (VDG) is proposed to narrow down obtained similar properties for data exchange. Through analyzing the results of a case study, the class distance based on VDG can effectively improve the precisions of calculated similar properties. Furthermore, data exchange rules can be generated automatically. The results reveal that the approach of this research can efficiently contribute to resolving the data interoperability problem.

A Semantic Model for Interchangeable Microservices in Cloud Continuum Computing

The rapid growth of new computing models that exploit the cloud continuum has a big impact on the adoption of microservices, especially in dynamic environments where the amount of workload varies over time or when Internet of Things (IoT) devices dynamically change their geographic location. In order to exploit the true potential of cloud continuum computing applications, it is essential to use a comprehensive set of various intricate technologies together. This complex blend of technologies currently raises data interoperability problems in such modern computing frameworks. Therefore, a semantic model is required to unambiguously specify notions of various concepts employed in cloud applications. The goal of the present paper is therefore twofold: (i) offering a new model, which allows an easier understanding of microservices within adaptive fog computing frameworks, and (ii) presenting the latest open standards and tools which are now widely used to implement each class defined in our proposed model.

Enhancing energy management at district and building levels via an EM-KPI ontology

Abstract The use of information and communication technologies facilitates energy management (EM) at both district and building levels, but also generates a considerable amount of data. To gain insights into such data, it is essential to resolve the cross-domain data interoperability problem and determine an approach to exchange performance information and insightful data among various stakeholders. This paper developed an EM-KPI (key performance indicator) ontology to exchange key performance information and data for districts and buildings. The ontology contains two components: namely KPIs and EM master data; these, respectively, represent the multi-level performance information for energy performance tracking and the key data for data exploitation. Through a demonstration, a sample linked dataset generated using the data correlation predefined in the ontology is presented. The linked data analysis proves the feasibility of the ontology for exchanging data among different stakeholders and for exploring insights in relation to performance improvement.

SWRL rule-selection methodology for ontology interoperability

Data interoperability represents a great challenge for today's enterprises. Indeed, they use various information systems, each relying on several different models for data representation. Ontologies and notably ontology matching have been recognized as interesting approaches for solving the data interoperability problem. In this paper, we focus on improving the performance of queries addressed over ontology alignments expressed through SWRL rules. Indeed, when considering the context of executing queries over complex and numerous alignments, the number of SWRL rules highly impacts the query execution time. Moreover, when hybrid or backward-chaining reasoning is applied, the query execution time may grow exponentially. Still, the reasoners involved deliver performant results (in terms of execution time) when applied over reduced and simpler rule sets. Based on this statement, and to address the issue of improving the query execution time, we describe a novel approach that allows, for a given query, to ignore unnecessary rules. The proposed Rule Selector (RS) is a middleware between the considered systems and the reasoner present on the triple store side. Through the benchmarks realized we prove that our approach allows considerably minimizing query execution time.

An Approach for Secure Semantic Data Integration at Data as a Service (DaaS) Layer

 Abstract—Data integration provides a uniform view of a set of heterogeneous data sources and facilitates users to query without any knowledge of the underlying heterogeneous data sources. In current era, Service Oriented Architecture and Cloud computing together has enabled users to access services over the Internet at a low cost. Cloud computing model provides a layer which is responsible for providing data to the other layers and services i.e., Data as a service (DaaS) layer. The issue of providing an integrated view of data can be handled using Semantic data; the data stored in a way that is understandable by machines and integratable without human intervention. However, integrating data using semantic web technology without enforcing any access management will raise privacy and confidentiality concerns. Different data owners store data in heterogeneous format based on their requirements. This leads to the data interoperability problem. This research proposes a framework that would allow data from different sources to be integrated using the concept of semantic data, thus resolving the issue of interoperability and also devises an access control system for defining explicit privacy constraints.

Postmarketing Safety Study Tool: A Web Based, Dynamic, and Interoperable System for Postmarketing Drug Surveillance Studies.

Postmarketing drug surveillance is a crucial aspect of the clinical research activities in pharmacovigilance and pharmacoepidemiology. Successful utilization of available Electronic Health Record (EHR) data can complement and strengthen postmarketing safety studies. In terms of the secondary use of EHRs, access and analysis of patient data across different domains are a critical factor; we address this data interoperability problem between EHR systems and clinical research systems in this paper. We demonstrate that this problem can be solved in an upper level with the use of common data elements in a standardized fashion so that clinical researchers can work with different EHR systems independently of the underlying information model. Postmarketing Safety Study Tool lets the clinical researchers extract data from different EHR systems by designing data collection set schemas through common data elements. The tool interacts with a semantic metadata registry through IHE data element exchange profile. Postmarketing Safety Study Tool and its supporting components have been implemented and deployed on the central data warehouse of the Lombardy region, Italy, which contains anonymized records of about 16 million patients with over 10-year longitudinal data on average. Clinical researchers in Roche validate the tool with real life use cases.

Formal Models and Algorithms for XML Data Interoperability

In this paper, we study the data interoperability problem of web services in terms of XML schema compatibility. When Web Service A sends XML messages to Web Service B, A is interoperable with B if B can accept all messages from A. That is, the XML schema R for B to receive XML instances must be compatible with the XML schema S for A to send XML instances, Le., A is a subschema of B. We propose a formal model called Schema Automaton (SA) to model W3C XML Schema (XSD) and develop several algorithms to perform different XML schema computations. The computations include schema minimization, schema equivalence testing, subschema testing, and subschema extraction. We have conducted experiments on an e-commerce standard XSD called xCBL to demonstrate the practicality of our algorithms. One experiment has refuted the claim that the xCBL 3.5 XSD is backward compatible with the xCBL 3.0 XSD. Another experiment has shown that the xCBL XSDs can be effectively trimmed into small subschemas for specific applications, which has significantly reduced the schema processing time.

Toward Generic, Immersive, and Collaborative Solutions to the Data Interoperability Problem which Target End-Users

In this paper, we describe our vision of a "Just-in-time" initiative to solve the Data Interoperability Problem (a.k.a. INTEROP.) We provide an architectural overview of our initiative which draws upon existing technologies to develop an immersive and collaborative approach which aims at empowering data stakeholders (e.g., data producers and data consumers) with integrated tools to interact and collaborate with each other while directly manipulating visual representations of their data in an immersive environment (e.g., implemented via Second Life.) The semantics of these visual representations and the operations associated with the data are supported by ontologies defined using the Common Logic Framework (CL). Data operations gestured by the stakeholders, through their avatars, are translated to a variety of generated resources such as multi-language source code, visualizations, web pages, and web services. The generality of the approach is supported by a plug-in architecture which allows expert users to customize tasks such as data admission, data manipulation in the immersive world, and automatic generation of resources. This approach is designed with a mindset aimed at enabling stakeholders from diverse domains to exchange data and generate new knowledge.

A Short History of the Last 15 Year's Quest for It Interoperability in the Petroleum E&P Industry

This paper gives a personal view on the drivers of the different actors of the E&P petroleum Industry (companies, vendors, certification organisations) to obtain a good level interoperability between data and applications. This is illustrated by a synthetic view of several interoperability projects dealing with subsurface aspects (geology, geophysics, reservoir). In fact, the oil and gas profession started to address subsurface data interoperability problems back in the 1990's with the well-known POSC consortium, which led to the reference Epicentre model. Since those days, many projects have been undertaken, dealing with all sorts of applications (data exchange, software interoperability, workflow modelling etc.) and using all sorts of technologies, from conventional database technologies to more advanced semantic models on top of XML. This article will help the reader find his/her way among the POSC, Epicentre, RESCUE, OpenSpirit, EpiSEM and WITSML projects for this key application domain which is only at the beginning of its life.

Assessment of a Semantic Statistical Approach to Detecting Land Cover Change Using Inconsistent Data Sets

A semantic, statistical approach to reconciling data with different ontologies is introduced. It was applied to UK land cover datasets from 1990 and 2000 in order to identify land cover change. The approach combined expression of expert opinion about how the semantics of the two datasets relate with spectral homogeneity metadata. A sample of the changes identified was assessed by field validation. Change was identified in 41 percent of the visited parcels, and all of the false positives were found to be due to classification error in either dataset. Thus, the approach reliably identifies inconsistency between two datasets, and the results indicate the suitability of uncertainty formalisms. The inclusion of extensive objectlevel metadata by the data producers greatly facilitates practical solutions to problems of data interoperability.