Search results

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

Government administrative data have enormous potential for public and individual benefit through improved educational and health services to citizens, medical research, environmental and climate interventions and better use of scarce energy resources. The purpose of this study (part of the Administrative Data Research Centre in England, ADRC-E) was to examine perspectives about the sharing, linking and re-use (secondary use) of government administrative data. This study seeks to establish an analytical understanding of risk with regard to administrative data.

This qualitative study focused on the secondary use of government administrative data by academic researchers. Data collection was through 44 semi-structured interviews plus one focus group, and was supported by documentary analysis and a literature review. The study draws on the views of expert data researchers, data providers, regulatory bodies, research funders, lobby groups, information practitioners and data subjects.

This study discusses the identification and management of risk in the use of government administrative data and presents a risk framework.

This study will have resonance with records managers, risk managers, data specialists, information policy and compliance managers, citizens groups that engage with data, as well as all those responsible for the creation and management of government administrative data.

First, this study identifies and categorizes the risks arising from the research use of government administrative data, based on policy, practice and experience of those involved. Second, it identifies mitigating risk management activities, linked to five key stakeholder communities, and it discusses the locus of responsibility for risk management actions. The conclusion presents the elements of a new risk framework to inform future actions by the government data community and enable researchers to exploit the power of administrative data for public good.

The purpose of this paper is to assess the level of the organizational goal accomplishment by assessing the reliance relationship between organizational data and organizational goals.

The evaluation of the organizational goals is based on design and operational level, which can serve in ranking of the organizational goals achievement and hence assist the decision-making process in achieving the organizational goals. To achieve this aim, the authors propose an ontology to develop the relationship between organizational data and organizational goals.

Data goals dependency shows the dependency relationship between organizational data and organizational goals. At the same time, data goals dependency assists the process of identifying data attributes, where the authors suggest that these data attributes are relevant in relation to the organizational goals.

The contribution of this paper will serve as the first step to evaluate the relevance of organizational data to assist decision-making in relation to the organizational goals.

In the world of big data, data integration technology is crucial for maximising the capability of data-driven decision-making. Integrating data from multiple sources drastically expands the power of information and allows us to address questions that are impossible to answer using a single data source. Record Linkage (RL) is a task of identifying and linking records from multiple sources that describe the same real world object (e.g. person), and it plays a crucial role in the data integration process. RL is challenging, as it is uncommon for different data sources to share a unique identifier. Hence, the records must be matched based on the comparison of their corresponding values. Most of the existing RL techniques assume that records across different data sources are structured and represented by the same scheme (i.e. set of attributes). Given the increasing amount of heterogeneous data sources, those assumptions are rather unrealistic. The purpose of this paper is to propose a novel RL model for unstructured data.

In the previous work (Jurek-Loughrey, 2020), the authors proposed a novel approach to linking unstructured data based on the application of the Siamese Multilayer Perceptron model. It was demonstrated that the method performed on par with other approaches that make constraining assumptions regarding the data. This paper expands the previous work originally presented at iiWAS2020 [16] by exploring new architectures of the Siamese Neural Network, which improves the generalisation of the RL model and makes it less sensitive to parameter selection.

The experimental results confirm that the new Autoencoder-based architecture of the Siamese Neural Network obtains better results in comparison to the Siamese Multilayer Perceptron model proposed in (Jurek et al., 2020). Better results have been achieved in three out of four data sets. Furthermore, it has been demonstrated that the second proposed (hybrid) architecture based on integrating the Siamese Autoencoder with a Multilayer Perceptron model, makes the model more stable in terms of the parameter selection.

To address the problem of unstructured RL, this paper presents a new deep learning based approach to improve the generalisation of the Siamese Multilayer Preceptron model and make is less sensitive to parameter selection.

Family health can be studied using the 1994–1995 National Health Interview Survey Disability Supplement by linking children to their mothers and other family members. However, the data item required to link is missing for 13% of children. We found that unlinked children and their probable mothers differed in many respects from their counterparts who could be linked, and exclusion of these mothers and their children from the analysis could bias results by introducing error due to incomplete coverage of the target population. We developed and validated a simple algorithm to match these children with their probable mother.

This chapter proposes an optimized innovative information technology as a means for achieving operational functionalities of real-time portable electronic health records, system interoperability, longitudinal health-risks research cohort and surveillance of adverse events infrastructure, and clinical, genome regions – disease and interventional prevention infrastructure. In application to the Dod-VA (Department of Defense and Veteran's Administration) health information systems, the proposed modernization can be carried out as an “add-on” expansion (estimated at $288 million in constant dollars) or as a “stand-alone” innovative information technology system (estimated at $489.7 million), and either solution will prototype an infrastructure for nation-wide health information systems interoperability, portable real-time electronic health records (EHRs), adverse events surveillance, and interventional prevention based on targeted single nucleotide polymorphisms (SNPs) discovery.

There has been an increase in the use of registers and record linkages to study migrant mental health. However, the accuracy of these registers and the degree to which they are representative of the migrant population in Northern Ireland (NI) are unclear. The purpose of this paper is to explore: the coverage of the NI migrant population in general practitioner (GP) data and Census records; the issues faced by migrants in terms of registering and accessing the local health system; and the reporting of racial hate crimes against migrants to police.

Two focus groups of professionals (n=17) who worked with migrants were conducted. Group discussions were guided by a research-informed topic guide, and the data were analysed using thematic analysis.

Three main themes emerged: issues with the use of GP registration, Census and hate crime data for researching migrant mental health; barriers to health service use (e.g. low cultural awareness among health staff and access to interpreters); and risk factor exposure and mental health status in migrant communities (e.g. poverty, isolation and poor working conditions).

Record linkage and registry studies of migrant health and well-being using Census and health service sources need to be mindful of the likelihood that some migrants may be missed. The possible underrepresentation of migrants in health registers may be explained by reduced use of such services which may be caused my encountering staff with limited cultural competency and the inability to access an interpreter promptly.

The purpose of this paper is to explore the relationship between big data and knowledge management (KM).

The study adopts a qualitative research methodology and a case study approach was followed by conducting nine semi-structured interviews with open-ended and probing questions.

Useful predictive knowledge can be generated through big data to help companies improve their KM capability and make effective decisions. Moreover, combination of tacit knowledge of relevant staff with explicit knowledge obtained from big data improvises the decision-making ability.

The focus of the study was on oil and gas sector, and, thus, the research results may lack generalizability.

This paper fulfills an identified need of exploring the relationship between big data and KM which has not been discussed much in the literature.

The purpose of this paper is to identify the concepts, component parts and relationships between vocabularies, linked data and knowledge graphs (KGs) from the perspectives of data and knowledge transitions.

This paper uses conceptual analysis methods. This study focuses on distinguishing concepts and analyzing composition and intercorrelations to explore data and knowledge transitions.

Vocabularies are the cornerstone for accurately building understanding of the meaning of data. Vocabularies provide for a data-sharing model and play an important role in supporting the semantic expression of linked data and defining the schema layer; they are also used for entity recognition, alignment and linkage for KGs. KGs, which consist of a schema layer and a data layer, are presented as cubes that organically combine vocabularies, linked data and big data.

This paper first describes the composition of vocabularies, linked data and KGs. More importantly, this paper innovatively analyzes and summarizes the interrelatedness of these factors, which comes from frequent interactions between data and knowledge. The three factors empower each other and can ultimately empower the Semantic Web.