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This article provides an account of how databases can be effectively used in entrepreneurship research. Improved quality and access to large secondary databases offer paths to answer questions of great theoretical value. I present an overview of theoretical, methodological, and practical difficulties in working with database data, together with advice on how such difficulties can be overcome. Conclusions are given, together with suggestions of areas where databases might provide real and important contributions to entrepreneurship research.

There appears to be no empirical-based method in the literature for estimating if an engineering change proposal (ECP) will occur or the dollar amount incurred. This paper aims to present an empirically based approach to address this shortfall.

Using the cost assessment data enterprise database, 533 contracts were randomly selected via a stratified sampling plan to build two regression models: one to predict the likelihood of a contract experiencing an ECP and the other to determine the expected median per cent increase in baseline contract cost if an ECP was likely. Both models adopted a stepwise approach. A validation set was placed aside prior to any model building.

Not every contract incurs an ECP; approximately 80 per cent of the contracts in the database did not have an ECP. The likelihood of an ECP and the additional amount incurred appears to be statistically independent of acquisition phase, branch of service, commodity, contract type or any other factor except for the basic contract amount and the number of contract line item numbers; both of these later variables equally affected the contract percentage increase because of an ECP. The combined model overall bested current anecdotal approaches to ECP withhold.

This paper both serves as a published reference point for ECP withholds in the archival forum and presents an empirically based method for determining per cent ECP withhold to use.

Data protection requirements heavily increased due to the rising awareness of data security, legal requirements and technological developments. Today, NoSQL databases are increasingly used in security-critical domains. Current survey works on databases and data security only consider authorization and access control in a very general way and do not regard most of today’s sophisticated requirements. Accordingly, the purpose of this paper is to discuss authorization and access control for relational and NoSQL database models in detail with respect to requirements and current state of the art.

This paper follows a systematic literature review approach to study authorization and access control for different database models. Starting with a research on survey works on authorization and access control in databases, the study continues with the identification and definition of advanced authorization and access control requirements, which are generally applicable to any database model. This paper then discusses and compares current database models based on these requirements.

As no survey works consider requirements for authorization and access control in different database models so far, the authors define their requirements. Furthermore, the authors discuss the current state of the art for the relational, key-value, column-oriented, document-based and graph database models in comparison to the defined requirements.

This paper focuses on authorization and access control for various database models, not concrete products. This paper identifies today’s sophisticated – yet general – requirements from the literature and compares them with research results and access control features of current products for the relational and NoSQL database models.

Enterprise knowledge graphs (EKG) in resource description framework (RDF) consolidate and semantically integrate heterogeneous data sources into a comprehensive dataspace. However, to make an external relational data source accessible through an EKG, an RDF view of the underlying relational database, called an RDB2RDF view, must be created. The RDB2RDF view should be materialized in situations where live access to the data source is not possible, or the data source imposes restrictions on the type of query forms and the number of results. In this case, a mechanism for maintaining the materialized view data up-to-date is also required. The purpose of this paper is to address the problem of the efficient maintenance of externally materialized RDB2RDF views.

This paper proposes a formal framework for the incremental maintenance of externally materialized RDB2RDF views, in which the server computes and publishes changesets, indicating the difference between the two states of the view. The EKG system can then download the changesets and synchronize the externally materialized view. The changesets are computed based solely on the update and the source database state and require no access to the content of the view.

The central result of this paper shows that changesets computed according to the formal framework correctly maintain the externally materialized RDB2RDF view. The experiments indicate that the proposed strategy supports live synchronization of large RDB2RDF views and that the time taken to compute the changesets with the proposed approach was almost three orders of magnitude smaller than partial rematerialization and three orders of magnitude smaller than full rematerialization.

The main idea that differentiates the proposed approach from previous work on incremental view maintenance is to explore the object-preserving property of typical RDB2RDF views so that the solution can deal with views with duplicates. The algorithms for the incremental maintenance of relational views with duplicates published in the literature require querying the materialized view data to precisely compute the changesets. By contrast, the approach proposed in this paper requires no access to view data. This is important when the view is maintained externally, because accessing a remote data source may be too slow.

The purpose of the study is to ascertain whether or not faculty members would be motivated to use e-Databases for research considering the impact of the Technology Acceptance Model2 (TAM2) cognitive instrumental processes of job relevance, output quality and result demonstrability.

The survey research design was applied. The selection of samples was based on a multistage sampling technique involving; purposive, simple/systematic random and total enumeration procedures. Five colleges and departments each were selected from the three universities that provided the setting for the conduct of this study, out of which a sample of 135 was drawn from the total population of 209. The questionnaire method was used for data gathering. Ninety-five percent return rate of the administered instrument was observed. Descriptive and inferential statistical tools were employed for data analyses.

Job relevance, output quality and result demonstrability are motivators of faculty use of e-Databases for research with result demonstrability wielding the strongest influence. Use of e-Databases for research is based on the usefulness level perceived of them. Faculty are highly predisposed to using the technology for research with the chances of getting published in reputable journal outlets ranked highest among other factors that influence faculty use of e-Databases.

The conceptualization of TAM2 cognitive instrumental processes as system characteristics and motivators of e-Databases use among faculty towards research engagement advances the understanding of intention to use e-Databases for research.

In order to assess the state of health of Europe’s children, or to appraise the systems and models of healthcare delivery, data about children are essential, with as much precision and accuracy as possible by small group characteristic. Unfortunately, the experience of the Models of Child Health Appraised (MOCHA) project and its scientists shows that this ideal is seldom met, and thus the accuracy of appraisal or planning work is compromised. In the project, we explored the data collected on children by a number of databases used in Europe and globally, to find that although the four quinquennial age bands are common, it is impossible to represent children aged 0–17 years as a legally defined group in statistical analysis. Adolescents, in particular, are the most invisible age group despite this being a time of life when they are rapidly changing and facing increasing challenges. In terms of measurement and monitoring, there is little progress from work of nearly two decades ago that recommended an information system, and no focus on the creation of a policy and ethical framework to allow collaborative analysis of the rich anonymised databases that hold real-world people-based data. In respect of data systems and surveillance, nearly all systems in European society pay lip service to the importance of children, but do not accommodate them in a practical and statistical sense.

For aiding computer security experts in their study, log files are a crucial piece of information. Especially the time domain is very important for us because in most cases, timestamps are the only linking points between events caused by attackers, faulty systems or simple errors and their corresponding entries in log files. With the idea of storing and analyzing this log information in graph databases, we need a suitable model to store and connect timestamps and their events. This paper aims to find and evaluate different approaches how to store timestamps in graph databases and their individual benefits and drawbacks.

We analyse three different approaches, how timestamp information can be represented and stored in graph databases. For checking the models, we set up four typical questions that are important for log file analysis and tested them for each of the models. During the evaluation, we used the performance and other properties as metrics, how suitable each of the models is for representing the log files’ timestamp information. In the last part, we try to improve one promising looking model.

We come to the conclusion, that the simplest model with the least graph database-specific concepts in use is also the one yielding the simplest and fastest queries.

Limitations to this research are that only one graph database was studied and also improvements to the query engine might change future results.

In the study, we addressed the issue of storing timestamps in graph databases in a meaningful, practical and efficient way. The results can be used as a pattern for similar scenarios and applications.

The technical feasibility of using Benford's law to assist internal auditors in reviewing the integrity of high-volume data sets is analysed. This study explores whether Benford's distribution applies to the set of numbers represented by the quantity of records (size) that comprise the different tables that make up a state-owned enterprise's (SOE) enterprise resource planning (ERP) relational database. The use of Benford's law streamlines the search for possible abnormalities within the ERP system's data set, increasing the ability of the internal audit functions (IAFs) to detect anomalies within the database. In the SOEs of emerging economies, where groups compete for power and resources, internal auditors are better off employing analytical tests to discharge their duties without getting involved in power struggles.

Records of eight databases of an SOE in Argentina are used to analyse the number of records of each table in periods of three to 12 years. The case develops step-by-step Benford's law application to test each ERP module records using Chi-squared (χ²) and mean absolute deviation (MAD) goodness-of-fit tests.

Benford's law is an adequate tool for performing integrity tests of high-volume databases. A minimum of 350 tables within each database are required for the MAD test to be effective; this threshold is higher than the 67 reported by earlier researches. Robust results are obtained for the complete ERP system and for large modules; modules with less than 350 tables show low conformity with Benford's law.

This study is not about detecting fraud; it aims to help internal auditors red flag databases that will need further attention, making the most out of available limited resources in SOEs. The contribution is a simple, cheap and useful quantitative tool that can be employed by internal auditors in emerging economies to perform the first scan of the data contained in relational databases.

This paper provides a tool to test whether large amounts of data behave as expected, and if not, they can be pinpointed for future investigation. It offers tests and explanations on the tool's application so that internal auditors of SOEs in emerging economies can use it, particularly those that face divergent expectations from antagonist powerful interest groups.

This study demonstrates that even in the context of limited information technology tools available for internal auditors, there are simple and inexpensive tests to review the integrity of high-volume databases. It also extends the literature on high-volume database integrity tests and our knowledge of the IAF in Civil law countries, particularly emerging economies in Latin America.

The purpose of this paper is to evaluate innovations in intellectual property rights (IPR) databases, techniques and software tools, with an emphasis on selected new developments and their contribution towards achieving advantages for IPR management (IPRM) and wider social benefits. Several industry buzzwords are addressed, such as IPR-linked open data (IPR LOD) databases, blockchain and IPR-related techniques, acknowledged for their contribution in moving towards artificial intelligence (AI) in IPRM.

The evaluation, following an original framework developed by the authors, is based on a literature review, web analysis and interviews carried out with some of the top experts from IPR-savvy multinational companies.

The paper presents the patent databases landscape, classifying patent offices according to the format of data provided and depicting the state-of-art in the IPR LOD. An examination of existing IPR tools shows that they are not yet fully developed, with limited usability for IPRM. After reviewing the techniques, it is clear that the current state-of-the-art is insufficient to fully address AI in IPR. Uses of blockchain in IPR show that they are yet to be fully exploited on a larger scale.

A critical analysis of IPR tools, techniques and blockchain allows for the state-of-art to be assessed, and for their current and potential value with regard to the development of the economy and wider society to be considered. The paper also provides a novel classification of patent offices and an original IPR-linked open data landscape.