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Any XML schema definition can be organized according to one of the following design styles: “Russian Doll”, “Salami Slice”, “Venetian Blind” and “Garden of Eden” (with the additional “Bologna” style actually representing absence of style). Conversion from a design style to another can facilitate the reuse and exchange of schema specifications encoded using the XML schema language. Without any computer-aided engineering support, style conversions must be performed very carefully as they are difficult and error-prone operations. The purpose of this paper is to efficiently deal with such XML schema design style conversions.

A general approach, named StyleVolution, for automatic management of XML schema design style conversions, is proposed. StyleVolution is equipped with a suite of seven procedures: four for converting a valid XML schema from any other design style to the “Garden of Eden” style, which has been chosen as a normalized XML schema format, and three for converting from the “Garden of Eden” style to any of the other desired design styles.

Procedures, algorithms and methods for XML schema design style conversions are presented. The feasibility of the approach has been shown through the encoding (using the XQuery language) and the testing (with the Altova XMLSpy 2019 tool) of a suite of seven ready-to-use procedures. Moreover, four test procedures are provided for checking the conformance of a given input XML schema to a schema design style.

The proposed approach implements a new technique for efficiently managing XML schema design style conversions, which can be used to make any given XML schema file to conform to a desired design style.

Changes in market and production profiles require a more flexible concept in manufacturing. Computer integrated manufacturing (CIM) describes an integrative concept for joining business and manufacturing islands. In this context, database technology is the key technology for implementing the CIM philosophy. However, CIM applications are more complex and thus more demanding than traditional database applications such as business and administrative applications. Systematically analyses the database requirements for CIM applications including business and manufacturing tasks. Special emphasis is given on integration requirements due to the distributed, partly isolated nature of CIM applications developed over the years. An illustrative sampling of current efforts in the database community to meet the challenge of non‐standard applications such as CIM is presented.

This study aims to adjust Thesis-type properties on Schema.org using metadata models and standards (MS) (Bibframe, electronic thesis and dissertations [ETD]-MS, Common European Research Information Format [CERIF] and Dublin Core [DC]) to enrich the Thesis-type properties for better description and processing on the Web.

This study is applied, descriptive analysis in nature and is based on content analysis in terms of method. The research population consisted of elements and attributes of the metadata model and standards (Bibframe, ETD-MS, CERIF and DC) and Thesis-type properties in the Schema.org. The data collection tool was a researcher-made checklist, and the data collection method was structured observation.

The results show that the 65 Thesis-type properties and the two levels of Thing and CreativeWork as its parents on Schema.org that corresponds to the elements and attributes of related models and standards. In addition, 12 properties are special to the Thesis type for better comprehensive description and processing, and 27 properties are added to the CreativeWork type.

Enrichment and expansion of Thesis-type properties on Schema.org is one of the practical applications of the present study, which have enabled more comprehensive description and processing and increased access points and visibility for ETDs in the environment Web and digital libraries.

This study has offered some new Thesis type properties and CreativeWork levels on Schema.org. To the best of the authors’ knowledge, this is the first time this issue is investigated.

Knowledge graphs (KGs) are structured knowledge bases that represent real-world entities and are used in a variety of applications. Many of them are created and curated from a combination of automated and manual processes. Microdata embedded in Web pages for purposes of facilitating indexing and search engine optimization are a potential source to augment KGs under some assumptions of complementarity and quality that have not been thoroughly explored to date. In that direction, this paper aims to report results on a study that evaluates the potential of using microdata extracted from the Web to augment the large, open and manually curated Wikidata KG for the domain of touristic information. As large corpora of Web text is currently being leveraged via large language models (LLMs), these are used to compare the effectiveness of the microdata enhancement method.

The Schema.org taxonomy was used as the source to determine the annotation types to be collected. Here, the authors focused on tourism-related pages as a case study, selecting the relevant Schema.org concepts as point of departure. The large CommonCrawl resource was used to select those annotations from a large recent sample of the World Wide Web. The extracted annotations were processed and matched with Wikidata to estimate the degree to which microdata produced for SEO might become a valuable resource to complement KGs or vice versa. The Web pages themselves can also serve as a context to produce additional metadata elements using them as context in pipelines of an existing LLMs. That way, both the annotations and the contents itself can be used as sources.

The samples extracted revealed a concentration of metadata annotations in only a few of the relevant Schema.org attributes and also revealed the possible influence of authoring tools in a significant fraction of microdata produced. The analysis of the overlapping of attributes in the sample with those of Wikidata showed the potential of the technique, limited by the disbalance of the presence of attributes. The combination of those with the use of LLMs to produce additional annotations demonstrates the feasibility of the approach in the population of existing Wikidata locations. However, in both cases, the effectiveness appears to be lower in the cases of less content in the KG, which are arguably the most relevant when considering the scenario of an automated population approach.

The research reports novel empirical findings on the way touristic annotations with a SEO orientation are being produced in the wild and provides an assessment of their potential to complement KGs, or reuse information from those graphs. It also provides insights on the potential of using LLMs for the task.

A shared cognitive schema is the fundamental source of tacit understanding within a team. This study aims to address how such a shared cognitive schema emerges and evolves in an interdisciplinary research team.

This study uses an exploratory single case study to analyze the emergence and evolution of a shared cognitive schema in an interdisciplinary research team systematically. The authors spent more than two years collecting data from the IAM team via semistructured interviews, archival data and observation. Subsequently, a framework for the resulting mechanism model was developed by analyzing the data using a three-step process.

This study shows that as the interdisciplinary research team develops, the shared cognitive schema passes through three stages: overlapping cognitive schema, complementary cognitive schema and synergetic cognitive schema. The mechanisms of overlap, complement and synergy play important roles. The convergent roles of partner-based recruiting, knowledge categorization and following the existing institution facilitate the overlapping of knowledge structures. Complementary cognitive schema sharing is facilitated by interdisciplinary member selection, knowledge stock expansion and the effects of accomplished mentors. The synergetic behaviors of group voice, interactive cognition and adaptive learning facilitate synergetic cognitive schema sharing.

This study is the first to discuss the emergence and evolution of a shared cognitive schema at the microlevel of knowledge structure and belief structure. It offers a new theoretical perspective on the development rules of scientific research teams and provides practical enlightenment regarding the establishment and operation of interdisciplinary research teams.

The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data.

A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail.

MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance.

The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.

Most literature on workflow (WF) adaptation considered the control flow correctness like absence of dead lock, live-lock, etc. during adaptation. The data aspect of WF adaptation like data flow, database schema changes and their correctness are less studied. When the WF schema is modified, their data flow and the database schema changes. The existing approaches used for adapting these data changes in the underlying database schema are time consuming and/or affect the old data persistence. The purpose of this paper is to concern the dynamic adaptation of the WF schema and implementing its data changes in the existing database schema.

A conceptual framework developed to adapt on-the-fly, the concomitant data changes during WF adaptation. The framework consists a set of data schema compliance criteria (DSC) which identify the data changes that can be directly accommodated in the existing database schema. Data adaptation algorithm (DAA) is developed to handle the data changes that does not conform to the DSC in the existing database schema.

In this approach the existing database schema is dynamically evolved without re-creating it, after WF schema adaptation. Therefore the WF schema changes can be implemented on-the-fly without stopping the running system. It also ensures the persistence of old data residing in the existing database.

A novel approach developed to adapt the data changes in the existing database schema, without requiring recreation or migration the data. This automated consistency checking of data attribute changes in the database schema and implement them dynamically.

Graph rewriting concerns the technique of transforming a graph; it is thus natural to conceive its application in the evolution of graph databases. This paper aims to propose a two-step framework where rewriting rules formalize instance or schema changes, ensuring graph’s consistency with respect to constraints, and updates are managed by ensuring rule applicability through the generation of side effects: new updates which guarantee that rule application conditions hold.

This paper proposes Schema Evolution Through UPdates, optimized version (SetUp_OPT), a theoretical and applied framework for the management of resource description framework (RDF)/S database evolution on the basis of graph rewriting rules. The framework is an improvement of SetUp which avoids the computation of superfluous side effects and proposes, via SetUpoptND, a flexible and extensible package of solutions to deal with non-determinism.

This paper shows graph rewriting into a practical and useful application which ensures consistent evolution of RDF databases. It introduces an optimised approach for dealing with side effects and a flexible and customizable way of dealing with non-determinism. Experimental evaluation of SetUpoptND demonstrates the importance of the proposed optimisations as they significantly reduce side-effect generation and limit data degradation.

SetUp originality lies in the use of graph rewriting techniques under the closed world assumption to set an updating system which preserves database consistency. Efficiency is ensured by avoiding the generation of superfluous side effects. Flexibility is guaranteed by offering different solutions for non-determinism and allowing the integration of customized choice functions.

Web applications are subject to continuous changes and rapid evolution triggered by increasing competition, especially in commercial domains such as electronic commerce. Unfortunately, usually they are implemented without producing any useful documentation for subsequent maintenance and evolution. Thereof, the maintenance of such systems becomes a challenging problem as the complexity of the web application grows. Reverse engineering has been heralded as one of the most promising technologies to support effective web application maintenance. This paper aims to present a reverse engineering approach that helps understanding existing undocumented web applications to be maintained or evolved.

The proposed approach provides reverse engineering rules to generate a conceptual schema from a given domain ontology by using a set of transformation rules. The reverse engineering process consists of four phases: extracting useful information; identifying a set of ontological constructs representing the concepts of interest; enriching the identified set by additional constructs; and finally deriving a conceptual schema.

The advantage of using ontology for conceptual data modeling is the reusability of domain knowledge. As a result, the conceptual data model will be made faster, easier and with fewer errors than creating it in usual way. Designers can use the extracted conceptual schema to gain a better understanding of web applications and to assist in their maintenance.

The strong point of this approach is that it relies on a very rich semantic reference that is domain ontology. However, it is not possible to make a straightforward transformation of all elements from a domain ontology into a conceptual data model because ontology is semantically richer than data conceptual models.

The purpose of this paper is to expand the understanding of the process by which organizational routines emerge in entrepreneurial ventures. The emphasis is on the role of management and interaction in shaping shared schemata among members of the enterprise.

This paper uses a longitudinal interpretive and exploratory case study based on semi-structured interviews, archival material and naturalistic observation at a startup enterprise in China.

Focusing on the process through which shared schemata emerge to lay the foundation for routines in new firms, the authors find shared schemata emerge through a three-stage process: individual schemata emergence, partially shared schemata emergence and organizationally shared schemata emergence. Analogical transfer, strong foundational leadership and horizontal interaction among employees facilitate the development of individual schemata and their evolution into the shared schemata underlying organizational routines.

This paper contributes to the understanding of routine formation in entrepreneurial ventures by creating a framework of the stages of development of organizational routines, as well as the role management plays in each stage. This contribution fits within the emergent field of microfoundations, linking individual actions and cognition to organizational outcomes and adding to this the contribution of social interaction.

Managers in new Chinese enterprises could benefit from understanding the importance of routinization and the managerial approaches which facilitate routine formation. This will increase the likelihood of firm survival as well as the competitive strength of the firm.

To date, there has been little research on how routines arise in entrepreneurial ventures, and none on explicitly the role for management and interaction in fostering routinization.