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Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers.

This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords.

Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich.

This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.

The emergence of machine learning has opened a new way for researchers. It allows them to supplement the traditional manual methods for conducting a literature review and turning it into smart literature. This study aims to present a framework for incorporating machine learning into financial statement fraud (FSF) literature analysis. This framework facilitates the analysis of a large amount of literature to show the trend of the field and identify the most productive authors, journals and potential areas for future research.

In this study, a framework was introduced that merges bibliometric analysis techniques such as word frequency, co-word analysis and coauthorship analysis with the Latent Dirichlet Allocation topic modeling approach. This framework was used to uncover subtopics from 20 years of financial fraud research articles. Furthermore, the hierarchical clustering method was used on selected subtopics to demonstrate the primary contexts in the literature on FSF.

This study has contributed to the literature in two ways. First, this study has determined the top journals, articles, countries and keywords based on various bibliometric metrics. Second, using topic modeling and then hierarchy clustering, this study demonstrates the four primary contexts in FSF detection.

In this study, the authors tried to comprehensively view the studies related to financial fraud conducted over two decades. However, this research has limitations that can be an opportunity for future researchers. The first limitation is due to language bias. This study has focused on English language articles, so it is suggested that other researchers consider other languages as well. The second limitation is caused by citation bias. In this study, the authors tried to show the top articles based on the citation criteria. However, judging based on citation alone can be misleading. Therefore, this study suggests that the researchers consider other measures to check the citation quality and assess the studies’ precision by applying meta-analysis.

Despite the popularity of bibliometric analysis and topic modeling, there have been limited efforts to use machine learning for literature review. This novel approach of using hierarchical clustering on topic modeling results enable us to uncover four primary contexts. Furthermore, this method allowed us to show the keywords of each context and highlight significant articles within each context.

Many libraries and archives maintain collections of research documents, such as administrative records, with paper-based formats that limit the documents' access to in-person use. Digitization transforms paper-based collections into more accessible and analyzable formats. As collections are digitized, there is an opportunity to incorporate deep learning techniques, such as Document Image Analysis (DIA), into workflows to increase the usability of information extracted from archival documents. This paper describes the authors' approach using digital scanning, optical character recognition (OCR) and deep learning to create a digital archive of administrative records related to the mortgage guarantee program of the Servicemen's Readjustment Act of 1944, also known as the G.I. Bill.

The authors used a collection of 25,744 semi-structured paper-based records from the administration of G.I. Bill Mortgages from 1946 to 1954 to develop a digitization and processing workflow. These records include the name and city of the mortgagor, the amount of the mortgage, the location of the Reconstruction Finance Corporation agent, one or more identification numbers and the name and location of the bank handling the loan. The authors extracted structured information from these scanned historical records in order to create a tabular data file and link them to other authoritative individual-level data sources.

The authors compared the flexible character accuracy of five OCR methods. The authors then compared the character error rate (CER) of three text extraction approaches (regular expressions, DIA and named entity recognition (NER)). The authors were able to obtain the highest quality structured text output using DIA with the Layout Parser toolkit by post-processing with regular expressions. Through this project, the authors demonstrate how DIA can improve the digitization of administrative records to automatically produce a structured data resource for researchers and the public.

The authors' workflow is readily transferable to other archival digitization projects. Through the use of digital scanning, OCR and DIA processes, the authors created the first digital microdata file of administrative records related to the G.I. Bill mortgage guarantee program available to researchers and the general public. These records offer research insights into the lives of veterans who benefited from loans, the impacts on the communities built by the loans and the institutions that implemented them.

A policy of surveillance which interferes with the fundamental right to a private life requires credible justification and a supportive evidence base. The authority for such interference should be clearly detailed in law, overseen by a transparent process and not left to the vagaries of administrative discretion. If a state surveils those it governs and claims the interference to be in the public interest, then the evidence base on which that claim stands and the operative conception of public interest should be subject to critical examination. Unfortunately, there is an inconsistency in the regulatory burden associated with access to confidential patient information for non-health-related surveillance purposes and access for health-related surveillance or research purposes. This inconsistency represents a systemic weakness to inform or challenge an evidence-based policy of non-health-related surveillance. This inconsistency is unjustified and undermines the qualities recognised to be necessary to maintain a trustworthy confidential public health service. Taking the withdrawn Memorandum of Understanding (MoU) between NHS Digital and the Home Office as a worked example, this chapter demonstrates how the capacity of the law to constrain the arbitrary or unwarranted exercise of power through judicial review is not sufficient to level the playing field. The authors recommend ‘levelling up’ in procedural oversight, and adopting independent mechanisms equivalent to those adopted for establishing the operative conceptions of public interest in the context of health research to non-health-related surveillance purposes.

Knowledge- and communication-intensive domains still long for a better support of creativity that considers legal requirements, compliance rules and administrative tasks as well, because current systems focus either on knowledge representation or business process management. The purpose of this paper is to discuss our model of integrated knowledge and business process representation and its presentation to users.

The authors follow a design science approach in the environment of patent prosecution, which is characterized by a highly standardized, legally prescribed process and individual knowledge study. Thus, the research is based on knowledge study, BPM, graph-based knowledge representation and user interface design. The authors iteratively designed and built a model and a prototype. To evaluate the approach, the authors used analytical proof of concept, real-world test scenarios and case studies in real-world settings, where the authors conducted observations and open interviews.

The authors designed a model and implemented a prototype for evolving and storing static and dynamic aspects of knowledge. The proposed solution leverages the flexibility of a graph-based model to enable open and not only continuously developing user-centered processes but also pre-defined ones. The authors further propose a user interface concept which supports users to benefit from the richness of the model but provides sufficient guidance.

The balanced integration of the data and task perspectives distinguishes the model significantly from other approaches such as BPM or knowledge graphs. The authors further provide a sophisticated user interface design, which allows the users to effectively and efficiently use the graph-based knowledge representation in their daily study.

In this paper, the authors focus on participatory budgeting (PB) as an organizational issue in local government. The aim of this study is to analyze the premises of PB becoming institutionalized in local governance by scrutinizing the factors that enable or disable PB as a collaborative innovation process, and that in turn build innovative capacity. The authors study how the collaborative innovation process constructs the innovation capacity of local government and further how this enables PB to institutionalize in local government. With this study, the authors deepen the understanding of PB as a collaborative innovation process that may encounter obstacles and hindrances, but also enablers and drivers for creating and transforming sustainable collaborative practices in local government.

In this paper, the authors present a case study from one Finnish local government, the city of Lahti. The authors employ multifaced empirical data collected from the city of Lahti in two PB rounds between 2019 and 2022. Data include surveys for citizens and employees and municipal councilors and altogether 24 interviews with employees and councilors. The authors also gathered data by observing the PB process in Lahti, taking parts in workshops and meetings between 2019 and 2022. Municipal documents were likewise used as data in this study.

The institutionalization of PB in a local government organization requires organizational innovation capacity, not only individuals who are keen, motived and committed to work on PB. However, the Lahti case shows that successful PB results from the citizens' viewpoint can on one hand be reached while simultaneously the sustainability of PB needs more organizational commitment and support that materializes into managerial activities. The authors found that adequate resourcing is a key question in the institutionalization of PB.

In the present study, the authors approach the often-neglected topic of PB from the professional viewpoint in public administration. The originality of the empirical setting is the multifaced data collection during the first two rounds of PB in Lahti. It is highly relevant to analyze PB in its early stages as the organizational difficulties and resistance are at the time at their highest. This study offers a unique perspective on to the initialization of a novel participatory method in a city where no such efforts on this scale have been implemented before.

The purpose of this paper is to introduce and identify the basic components, tasks and application areas of expert systems (ESs) as a decision support system that has been increasingly used in the business world lately and explore its potential for improving the effectiveness of administrative decisions in the public sector. Empirically, the paper explains the role of ESs in fostering decision-making processes at the Ministry of Investment and International Cooperation (MIIC) in Egypt.

The design of this research is descriptive in the theoretical section and quantitative in the empirical one. Theoretically, the study adopted both the analytical approach and systems approach to demonstrate main concepts and relationships, while it conducted an empirical study to investigate the correlations in practice.

The research concluded that the usage of ESs is deemed to be on the top of the technical solutions that might help public organizations develop their management quality and maintain competitive strength. In addition, the results proved that ESs contribute to administrative decisions at MIIC.

The paper provides profitable findings and recommendations which can be applied by Egyptian public executives, in an attempt to ensure high quality and successful decisions using modern technology.

This study has valuable implications for theory and practice together, as it offers numerous contributions to literature in the area of concern.

There is significant national interest in tackling issues surrounding the needs of vulnerable children and adults. This paper aims to argue that much value can be gained from the application of new data-analytic approaches to assist with the care provided to vulnerable children. This paper highlights the ethical and information governance issues raised in the development of a research project that sought to access and analyse children’s social care data.

The paper documents the process involved in identifying, accessing and using data held in Birmingham City Council’s social care system for collaborative research with a partner organisation. This includes identifying the data, its structure and format; understanding the Data Protection Act 1998 and 2018 (DPA) exemptions that are relevant to ensure that legal obligations are met; data security and access management; the ethical and governance approval process.

The findings will include approaches to understanding the data, its structure and accessibility tasks involved in addressing ethical and legal obligations and requirements of the ethical and governance processes.

The aim of this research is to highlight the potential use of use new data-analytic techniques to examine the flow of children’s social care data from referral, through the assessment process, to the resulting service provision. Data held by Birmingham City Council are used throughout, and this paper highlights key ethical and information governance issues which were addressed in preparing and conducting the research. The findings provide insight for other data-led studies of a similar nature.

This paper aims to contribute to the debate about the value of blockchain for supply chain management by assessing empirical evidence on the relationship between blockchain and supply chain performance.

The authors conducted a structured review of the academic literature to identify and assess papers providing empirical insight on operational blockchain applications. The authors complement the findings from this review with primary empirical data from 11 interviews with blockchain providers, users and experts involved in four recent projects.

The paper presents an integrated research framework that illustrates the impact of blockchain on supply chain performance. The findings highlight that blockchain can affect supply chain performance directly – via one of its core technological features – and indirectly via the broader business project through which blockchain technology is implemented.

Insights from this paper should provide managers with a more nuanced understanding of how blockchain technology can be leveraged to address important supply chain management challenges.

Prior research addressing the relationship between blockchain and supply chain performance mostly discusses potential performance effects of blockchain, presents individual blockchain applications and/or provides little explanation for how the core technological features of blockchain affect supply chain performance. This paper systematically assesses the ways in which blockchain can affect supply chain performance. In doing so, it goes beyond the initial hype around blockchain technology while countering some of the more recent critiques.

The purpose of this paper is to develop a usable configuration management for Archistar, which utilizes secret sharing for redundantly storing data over multiple independent storage clouds in a secure and privacy-friendly manner. Selecting the optimal secret sharing parameters, cloud storage servers and other settings for securely storing the secret data shares, while meeting all of end user’s requirements and other restrictions, is a complex task. In particular, complex trade-offs between different protection goals and legal privacy requirements need to be made.

A human-centered design approach with structured interviews and cognitive walkthroughs of user interface mockups with system administrators and other technically skilled users was used.

Even technically skilled users have difficulties to adequately select secret sharing parameters and other configuration settings for adequately securing the data to be outsourced.

Through these automatic settings, not only system administrators but also non-technical users will be able to easily derive suitable configurations.

The authors present novel human computer interaction (HCI) guidelines for a usable configuration management, which propose to automatically set configuration parameters and to solve trade-offs based on the type of data to be stored in the cloud. Through these automatic settings, not only system administrators but also non-technical users will be able to easily derive suitable configurations.