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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.

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.

Information and communication technologies can transform how services can be and are delivered as has already happened in other arenas, such as civil aviation, financial services and retailing. Most modern health care is heavily dependent on e-health, including record keeping, targeted information sharing and digital diagnostic and imaging techniques. However, there remains little scientific knowledge base for optimal system content and function in primary health care, particularly for children. Models of Child Health Appraised (MOCHA) aimed to establish the current e-health situation in children’s primary care services. Electronic health records (EHRs) are in regular use in much of northern and western Europe and in some newer European Union Member States, but other countries lag behind. MOCHA investigated the use of unique identifiers, the use of case-based public health EHRs and the capability of record linkage, linkage of information with school health data and monitoring of social media influences, such as health websites and health apps. A widespread lack of standards underlined a lack of research enquiry into this issue in terms of children’s health data and health knowledge. Health websites and apps are a growing area of healthcare delivery, but there is a worrying lack of safeguards in place. The challenge for policy-makers and practitioners is to be aware and to lead on the innovative harnessing of new technologies, while protecting child users against new harms.

The relationship between employment services and employment outcomes has been the subject of research for a number of years (Bond et al., 2001; Drake et al., 1996). More recently, the competitive employment of service recipients has become an important indicator of community mental health program and service system performance. The National Association of State Mental Health Program Directors’ President’s Task Force on Performance Measures, for instance, recognized the importance of monitoring employment rates for adults with serious mental illness: “For payers, this is the payoff…Monitoring this outcome for populations with mental illness…is critical. This was considered a critical outcome to track.” For similar reasons, the new federal Performance Partnership (Block) Grant program (Federal Register, 2002) requires annual reporting by all states of employment rates for recipients of publicly funded mental health services.

While efforts to mainstream collection of ethnicity in routine health datasets have gathered pace since the Race Relations (Amendment) Act 2000, relatively little attention has focused on the capture of migrants, and data on asylum seekers and refugees are even more sparse. There is negligible coverage in the key datasets for primary and secondary care, and only a few of the new contract datasets to support the National Service Frameworks accord importance to the migrant population. Some of the communicable disease data collections record country of birth, but its incompleteness is a drawback. Given the growing size of the non‐UK‐born population and the accumulating evidence on the health and health care needs of the migrant population, country of birth merits a place alongside other access variables such as age, sex, ethnicity, socioeconomic status and place of residence in routine data collection. While record linkage studies offer some scope, and there is currently a focus on obtaining improved migration statistics in the forthcoming census and other data flows, health and social care sources remain a neglected area.

The purpose of this paper is to collect and understand the nature of real cases of author name variants that have often appeared in bibliographic digital libraries (DLs) as a case study of the name authority control problem in DLs.

To find a sample of name variants across DLs (e.g. DBLP and ACM) and in a single DL (e.g. ACM), the approach is based on two bipartite matching algorithms: Maximum Weighted Bipartite Matching and Maximum Cardinality Bipartite Matching.

First, the authors validated the effectiveness and efficiency of the bipartite matching algorithms. The authors also studied the nature of real cases of author name variants that had been found across DLs (e.g. ACM, CiteSeer and DBLP) and in a single DL.

To the best of the authors knowledge, there is less research effort to understand the nature of author name variants shown in DLs. A thorough analysis can help focus research effort on real problems that arise when the authors perform duplicate detection methods.

The chapter considers the ethical problems engendered by random assignment and privacy concerns in randomised controlled experiments and cluster randomised trials. The particular focus is on procedural, legislative and technical approaches to reducing or avoiding the problems. Examples are given from a variety of disciplines including health and education, though the main emphasis is on research in crime and delinquency.

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.

With the progress of new technologies of information and communication, more and more producers of data exist. On the other hand, the web forms a huge support of all these kinds of data. Unfortunately, existing data is not proper due to the existence of the same information in different sources, as well as erroneous and incomplete data. The aim of data integration systems is to offer to a user a unique interface to query a number of sources. A key challenge of such systems is to deal with conflicting information from the same source or from different sources. We present, in this paper, the resolution of conflict at the instance level into two stages: references reconciliation and data fusion. The reference reconciliation methods seek to decide if two data descriptions are references to the same entity in reality. We define the principles of reconciliation method then we distinguish the methods of reference reconciliation, first on how to use the descriptions of references, then the way to acquire knowledge. We finish this section by discussing some current data reconciliation issues that are the subject of current research. Data fusion in turn, has the objective to merge duplicates into a single representation while resolving conflicts between the data. We define first the conflicts classification, the strategies for dealing with conflicts and the implementing conflict management strategies. We present then, the relational operators and data fusion techniques. Likewise, we finish this section by discussing some current data fusion issues that are the subject of current research.