A customized semantic service retrieval methodology for the digital ecosystems environment

With the emergence of the Web and its pervasive intrusion on individuals, organizations, businesses etc., people now realize that they are living in a digital environment analogous to the ecological ecosystem. Consequently, no individual or organization can ignore the huge impact of the Web on social well-being, growth and prosperity, or the changes that it has brought about to the world economy, transforming it from a self-contained, isolated, and static environment to an open, connected, dynamic environment. Recently, the European Union initiated a research vision in relation to this ubiquitous digital environment, known as Digital (Business) Ecosystems. In the Digital Ecosystems environment, there exist ubiquitous and heterogeneous species, and ubiquitous, heterogeneous, context-dependent and dynamic services provided or requested by species. Nevertheless, existing commercial search engines lack sufficient semantic supports, which cannot be employed to disambiguate user queries and cannot provide trustworthy and reliable service retrieval. Furthermore, current semantic service retrieval research focuses on service retrieval in the Web service field, which cannot provide requested service retrieval functions that take into account the features of Digital Ecosystem services. Hence, in this thesis, we propose a customized semantic service retrieval methodology, enabling trustworthy and reliable service retrieval in the Digital Ecosystems environment, by considering the heterogeneous, context-dependent and dynamic nature of services and the heterogeneous and dynamic nature of service providers and service requesters in Digital Ecosystems.The customized semantic service retrieval methodology comprises: 1) a service information discovery, annotation and classification methodology; 2) a service retrieval methodology; 3) a service concept recommendation methodology; 4) a quality of service (QoS) evaluation and service ranking methodology; and 5) a service domain knowledge updating, and service-provider-based Service Description Entity (SDE) metadata publishing, maintenance and classification methodology.The service information discovery, annotation and classification methodology is designed for discovering ubiquitous service information from the Web, annotating the discovered service information with ontology mark-up languages, and classifying the annotated service information by means of specific service domain knowledge, taking into account the heterogeneous and context-dependent nature of Digital Ecosystem services and the heterogeneous nature of service providers. The methodology is realized by the prototype of a Semantic Crawler, the aim of which is to discover service advertisements and service provider profiles from webpages, and annotating the information with service domain ontologies.The service retrieval methodology enables service requesters to precisely retrieve the annotated service information, taking into account the heterogeneous nature of Digital Ecosystem service requesters. The methodology is presented by the prototype of a Service Search Engine. Since service requesters can be divided according to the group which has relevant knowledge with regard to their service requests, and the group which does not have relevant knowledge with regard to their service requests, we respectively provide two different service retrieval modules. The module for the first group enables service requesters to directly retrieve service information by querying its attributes. The module for the second group enables service requesters to interact with the search engine to denote their queries by means of service domain knowledge, and then retrieve service information based on the denoted queries.The service concept recommendation methodology concerns the issue of incomplete or incorrect queries. The methodology enables the search engine to recommend relevant concepts to service requesters, once they find that the service concepts eventually selected cannot be used to denote their service requests. We premise that there is some extent of overlap between the selected concepts and the concepts denoting service requests, as a result of the impact of service requesters’ understandings of service requests on the selected concepts by a series of human-computer interactions. Therefore, a semantic similarity model is designed that seeks semantically similar concepts based on selected concepts.The QoS evaluation and service ranking methodology is proposed to allow service requesters to evaluate the trustworthiness of a service advertisement and rank retrieved service advertisements based on their QoS values, taking into account the contextdependent nature of services in Digital Ecosystems. The core of this methodology is an extended CCCI (Correlation of Interaction, Correlation of Criterion, Clarity of Criterion, and Importance of Criterion) metrics, which allows a service requester to evaluate the performance of a service provider in a service transaction based on QoS evaluation criteria in a specific service domain. The evaluation result is then incorporated with the previous results to produce the eventual QoS value of the service advertisement in a service domain. Service requesters can rank service advertisements by considering their QoS values under each criterion in a service domain.The methodology for service domain knowledge updating, service-provider-based SDE metadata publishing, maintenance, and classification is initiated to allow: 1) knowledge users to update service domain ontologies employed in the service retrieval methodology, taking into account the dynamic nature of services in Digital Ecosystems; and 2) service providers to update their service profiles and manually annotate their published service advertisements by means of service domain knowledge, taking into account the dynamic nature of service providers in Digital Ecosystems. The methodology for service domain knowledge updating is realized by a voting system for any proposals for changes in service domain knowledge, and by assigning different weights to the votes of domain experts and normal users.In order to validate the customized semantic service retrieval methodology, we build a prototype – a Customized Semantic Service Search Engine. Based on the prototype, we test the mathematical algorithms involved in the methodology by a simulation approach and validate the proposed functions of the methodology by a functional testing approach

Ontology Based Approach for Services Information Discovery using Hybrid Self Adaptive Semantic Focused Crawler

Focused crawling is aimed at specifically searching out pages that are relevant to a predefined set of topics. Since ontology is an all around framed information representation, ontology based focused crawling methodologies have come into exploration. Crawling is one of the essential systems for building information stockpiles. The reason for semantic focused crawler is naturally finding, commenting and ordering the administration data with the Semantic Web advances. Here, a framework of a hybrid self-adaptive semantic focused crawler – HSASF crawler, with the inspiration driving viably discovering, and sorting out administration organization information over the Internet, by considering the three essential issues has been displayed. A semi-supervised system has been planned with the inspiration driving subsequently selecting the ideal limit values for each idea, while considering the optimal performance without considering the constraint of the preparation of data set. DOI: 10.17762/ijritcc2321-8169.15072

A Framework for understanding & classifying Urban Data Business Models

Governments’ objective to transition to ‘Smart Cities’ heralds new possibilities for urban data business models to address pressing city challenges and digital transformation imperatives. Urban data business models are not well understood due to such factors as the maturity of the market and limited available research within this domain. Understanding the barriers and challenges in urban data business model development as well as the types of opportunities in the ecosystem is essential for incumbents and new entrants. Therefore, the aim of this paper is to develop a framework for understanding and classifying Urban Data Business Models (UDBM). This paper uses an embedded case study method to derive the framework by analyzing 40 publicly funded and supported business model experiments that address pressing city challenges under one initiative. This research contributes to the scholarly discourse on business model innovation in the context of smart cities

The Nature of Digital Technologies - Development of a Multi-Layer Taxonomy

Digitalization disrupts business models and smartifies products and services. Based on the ever-faster emergence and adoption of digital technologies such as the Internet of Things, blockchain, or augmented reality, digitalization irreversibly changes our private lives and organizational routines from all industries on a global scale. Thereby, digitalization develops unlimited potential in terms of innovation, connectivity, efficiency and productivity improvements. However, research and practice still lack a fundamental understanding of the nature of digital technologies. To address this gap, we developed a multi-layer taxonomy of digital technologies that includes eight dimensions structured along the layers of established modular architectures, i.e. service, content, network, and device. Based on our taxonomy, we also identified seven archetypes of digital technologies by means of a cluster analysis. To revise and evaluate our artefacts, we classified 45 digital technologies from the Gartner Hype Cycle of Emerging Technologies and conducted evaluation rounds with other researchers. Our results contribute to the descriptive knowledge on digital technologies. They enable researchers and practitioners to classify digital technologies on two levels of aggregation and to make informed decisions about their adoption

Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article

RTST Trend Report: lead theme Contextualisation

Specht, M., Börner, D., Tabuenca, B., Ternier, S., De Vries, F., Kalz, M., Drachsler, H., & Schmitz, B. (2012). RTST Trend Report: lead theme Contextualisation. Deliverable 1.7 of STELLAR network of excellence. Heerlen, The Netherlands.In summary this trend-scouting report highlights different design dimensions of contextualizing learning. On the one hand designing educational context: the components and constituents of the educational setting, which also have to be orchestrated in an instructional design or the process of orchestration (Luckin, 2010, Specht, 2009) on the other hand bridging and linking learning contexts for seamless learning support: Wong et al. define design dimensions of seamless learning experiences and which gaps they identify and what challenges must be tackled to create seamless learning experiences (Wong, 2011).STELLAR Network of Excellence, Grant 23191