Search results

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.

This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase.

In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed.

Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed.

To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

Data service (DS) is a special software service that enables data access in cloud environment and provides a unified data model for cross-origination data integration and data sharing. The purpose of the work is to automatically compose DSs and quickly generate data view to satisfy users' various data requirements (DRs).

The paper proposes an automatic DS composition and view generation approach. DSs are organized into DS dependence graph (DSDG) based on their inherent dependences, and DSs can be automatically composed using the DSDG according to user's DRs. Then, data view will be generated by interpreting the composed DS.

Experimental results with real cross-origination data sets show the proposed approaches have high efficiency and good quality for DS composition and view generation.

The authors propose a DS composition algorithm and a data view generation algorithm according to users' DRs.

Since the first definition of open innovation (OI), the indivisible relationship between this concept and entrepreneurship was undeniable. However, the exact mechanisms by which an entrepreneurial approach may benefit OI processes and vice versa are not yet fully understood. The study aims to offer an accurate map of the knowledge evolution of the OI–entrepreneurship relationship and interesting gaps to be filled in the future.

The study adopted a bibliometric analysis, coupled with a systematic literature review performed over a data set of 106 peer-reviewed articles published from 2005 to 2020 to identify thematic clusters.

The results show five thematic clusters: entrepreneurial opportunities, organisational opportunities, strategic partnership opportunities, institutional opportunities and digital opportunities for OI. Investigating each of them, the authors created a framework that highlights future avenues for further developing the topic.

This study is the first of its kind to systematise, analyse and critically interpret the literature concerned with the topic of the OI–entrepreneurship.

Virtual regional service provider is a key management mechanism created to realize the health service system development plans and a mechanism established to bring about integration of all elements of the health system. However, a virtual service provider office (VSPO) to support the work of the executive has not yet been formally established, and there are no operations practitioners deployed yet, who are necessary to develop the competency of regional operating officer (ROO) in each province. The purpose of this paper is to analyze training program factors to develop the competency of ROO in the VSPO in Thailand.

This is a descriptive research that used structural equation model. The research sample consisted of 274 executives and 664 practitioners in the Vice Chief of the provincial health office under the 12 regions; executives’ questionnaire and a VSPO questionnaire were used as tools for this study; data were statistically analyzed by three methods, namely, exploratory factor analysis (EFA), second-order confirmatory factor analysis (second-order CFA), and path analysis (PA).

The variable model was composed of ten factors, selected from 40 variables, which are as follows: service plan and personal administration, summary to present for administrators in the VSPO, network management and team building, summary to adjust strategies, key performance indicator (KPI) and action plan, new management skills, system thinking, analytical thinking, synthesis thinking, conflict management style, and leadership; presentation of methods and data for monitoring, presentation skill, conference management skill; researching, learning skill, communication skill exercise, action plan workshop, preparation to study in the fields workshop; fiscal and monetary, internal control and risk management; project management, monitoring, and data definition; and positive thinking, conflict management process, negotiation skill contingency management.

Ten factors of the variable model of training program factors to develop the competency of ROO in the VSPO in Thailand had high construct validity and they were analyzed using three methods, i.e. EFA, second-order CFA and PA, that were appropriate to be used for developing a training program.

In this paper, the author examines the role of uncertainty due to pandemic on the predictability of sectoral stock returns in South Africa. This is motivated by the ongoing global pandemic, COVID-19, in predicting sector stock returns.

The study considers estimation of dynamic panel data with dynamic common correlated effects estimator and two pair-wise forecast measures, namely Campbell and Thompson (2008) and Clark and West (2007) tests in dealing with the nested predictive models.

The results show that pandemic uncertainty has a negative and statistically significant effect on the different sector returns, implying that sector stock returns decline as the pandemic outbreak becomes more pronounced. While the single predictor model consistently outperforms the historical average model both for in-sample and out-of-sample, controlling for other macroeconomic variables effect improves the forecast accuracy of infectious diseases uncertainty. These results are consistently robust to both the in-sample and out-of-sample forecast periods, outliers and heterogeneity. These results have implications for portfolio diversification strategies, which we set aside for future research.

The empirical literature is satiated with studies on how news can predict economic and financial variables, however, the role of uncertainty due to infectious diseases in the stock return predictability especially at the sectoral level is less understudied, this is the main contribution of the study.

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.