Service Discovery Research Articles

An Evaluation of Ubiquitous Service Discovery and Remote Management in Ad-Hoc Networks

Due to the rise of ubiquitous computing, issues around the discovery of new services have become a hot topic in the academic community. This is because futuristic ubiquitous computing frameworks as well as unsupervised Ad hoc systems will require service discovery. Automatically discovering network services and their characteristics, as well as dynamically advertising their persistence, are both possible with service discovery. Multiple service discovery technologies, including Service Discovery Protocols (SDP) runners, Universal Plug and Play (UPnP), Service Location Protocol (SLP), and Jini, have been suggested. In this paper, we detail the process of service discovery and control using Ubiquitous Remote Manager (URM), a system for remote and autonomous management of home gateways that allows for more system simplicity and finer grained command of the underlying network in a ubiquitous computing setting. Ubiquitous Service Discovery (USD) is a key component of the infrastructure of the smart homes of the future. USD's goal is to locate the most suitable service, given the user's preferences and needs, in a pervasive and extensive setting.

System Statuses in Academic Libraries: Increasing Transparency and Improving the User Experience

"is facebook down" trended on Google on October 4, 2021, when Facebook became temporarily globally unavailable. Web users proactively seek information when they encounter issues online. System status dashboards are one way libraries can leverage this behavior to share changes to resources or services and improve transparency, reference services, and the user experience. The University at Albany Libraries Discovery Services Librarian and User Experience Librarian implemented Springshare’s System Status Management tool to share system statuses with library staff and the campus community. The institutional context, implementation, and impact are described in this article. The authors reviewed Association of Research Libraries members’ library websites to investigate if and how system statuses are being used to communicate with patrons. Results indicate that the majority of libraries reviewed do not employ a dedicated system status dashboard to share information with their constituents, but may use other types of alerts instead.

An Improved Web Service Recommendation Method based on Decomposition Machine Model

With the increasing number of Web services with similar functions on the Internet, traditional collaborative filtering service recommendation methods may encounter problems such as data sparseness, cold start, and poor scalability. To solve the above problems, this project proposes a new Web service recommendation method based on the decomposition machine model. The method decomposes the user trust relationship matrix and the product rating matrix while adding the geographic location information of the service, and transforms the correlation matrix of the calculated user feature vector and item feature vector into the same latent factor space by means of a decomposition machine. Optimize training model parameters to provide users with accurate prediction scores. The ultimate purpose of QoS prediction is to recommend high-quality services to users, improve the efficiency of users' discovery and selection of high-quality services, and ultimately promote the utilization of network Web services and promote service providers to release higher-quality services. Scientific significance, but also has better application value.

A New Decision-Making Method for Service Discovery and Selection in the Internet of Things Using Flower Pollination Algorithm

A New Decision-Making Method for Service Discovery and Selection in the Internet of Things Using Flower Pollination Algorithm

NHS knowledge and library services in England in the digital age.

National Health Service (NHS) knowledge and library services in England are integrating digital advances into their systems and services. Health Education England (HEE) leads on the development of NHS library services. A key workstream focuses on (1) improving the infrastructure to enable discovery and management of digital knowledge resources; (2) collaborating with local teams to establish regional library management systems that are integrated with the new national discovery service for healthcare staff and learners. This article explores initiatives on resource discovery as well as the need for system-wide partnership working to ensure that biomedical knowledge in computable form is findable, accessible, interoperable and reusable. Low levels of health and digital literacy pose a significant barrier to using health information and accessing health services. A range of interventions are aimed at enhancing citizens' digital and health literacy skills. The education and life-long learning needs of the knowledge and library services workforce are considered. Working with CILIP and higher education institutions, HEE delivers a range of educational offers through its Learning Academy. As Artificial Intelligence and automation are implemented in health systems, knowledge and library staff form a crucial bridge between technology and those who use it.

Testing Two Discovery Systems: A Usability Study Comparing Student Perceptions of EDS and Primo

Library discovery systems are a primary tool for research and discovery of local collections in many academic libraries. Usability studies on these systems is an important practice to identify technical issues, determine local customization, and inform continuity in library instruction. Most usability studies, however, are completed on a single discovery system. This study is unique in that it compares EBSCO’s EBSCO Discovery Service (EDS) with Ex Libris’s Primo discovery systems. To date, this is the only published usability study conducted on the two systems. Nineteen undergraduate and graduate participants were led through a series of nine usability tasks in both systems. The quantitative and qualitative results of the study indicate that discovery systems are powerful tools when managed consistently and holistically by addressing the discovery search needs of students and faculty through information literacy instruction and oversight by a discovery working group.

A Fog Computing Orchestrator Architecture With Service Model Awareness

Fog Computing can facilitate the adoption of the Everything-as-a-Service paradigm in infrastructure segments that are located closer to the end user, or to the data source, compared to typical Cloud solutions. This enables combining the advantages of flexible service deployment models with the need to cope with the strict requirements–especially in terms of latency–of emerging applications in softwarized networks. Along comes the need to consider aspects of service orchestration specific to the Fog environment and its intrinsically dynamic nature. In this paper we propose an architecture for flexible Fog Computing service orchestration, with a particular focus on the awareness of service deployment models. We discuss the design choices and describe the components and operations of the proposed orchestration system. We then present a complete working implementation of such architecture, including insights on its ability to handle critical orchestration functions such as service discovery and resource monitoring. We also report on the experimental validation of the system and the performance evaluation on real-world equipment, proving the feasibility and the effectiveness of the approach on a dynamic Fog infrastructure. We complement the work by presenting the results of a combinatorial analysis, validated by simulation, of the service model-aware resource selection process. As a result of our investigation, we show that Fog services can be effectively deployed in a matter of a few seconds, or even in less than one second when suitable Fog nodes are available, taking advantage of the awareness of the available service models.

A Network-Embedding-Based Approach for Scalable Network Navigability in Content-Centric Social IoT

Social Internet of Things (SIoT) boosts the Internet of Things (IoT) by integrating the concept of social networking to advance the discovery of content and service. Another common and promising solution to IoT’s improvement is adopting the emerging multiaccess edge computing (MEC) paradigm by bringing computational and storage capacity at the edge. Concerning the MEC-enabled SIoT, this article aims to propose a network-embedding-based solution for scalable network navigation via leveraging the social similarity of SIoT. Different from the traditional SIoT, we characterize the social relationship from the contents point of view since MEC enables the edge with cache storage. Then, a novel heuristic embedding algorithm termed HeurEmb is proposed to embed the SIoT into the hyperbolic space to facilitate coordinate-based navigability. HeurEmb is a decentralized approach that leverages the social similarity to compute coordinates, bypassing the heavy computation of numerically maximizing the likelihood. Moreover, HeurEmb infers the virtual coordinates on multiple-level hyperbolic disks, which enabled the efficiently vertical and horizontal search for the destination. We analyze the complexity of HeurEmb and then evaluate its performance via the simulation study. The simulation results show that HeurEmb is efficient, achieving a decrease of up to two orders of magnitude of running time compared with the latest work, and effective, improving the success ratio and navigation path length. Finally, we apply HeurEmb in a content retrieval scenario of SIoT using a real-world trace. We make only a minor modification to HeurEmb-based forwarding, and the resulting strategy can achieve similar performance as the best benchmark.

Proposition of Web Services Discovery and Composition Approach: Application in E-learning Platform

The rapid penetration of Web technologies and strong economic pressure have led to an irresistible proliferation of Web services. In this paper, we are interested in the discovery and composition of Web services. In this sense, we propose a conceptual architecture and technical tools to carry out our approach. The originality of the proposed solution lies in the use of mixed tools ranging from multi-agents systems to Business Process Management (BPM) techniques, including Matchmaking algorithms and semantic models.

The least-used key selection method for information retrieval in large-scale Cloud-based service repositories

As the number of devices connected to the Internet of Things (IoT) increases significantly, it leads to an exponential growth in the number of services that need to be processed and stored in the large-scale Cloud-based service repositories. An efficient service indexing model is critical for service retrieval and management of large-scale Cloud-based service repositories. The multilevel index model is the state-of-art service indexing model in recent years to improve service discovery and combination. This paper aims to optimize the model to consider the impact of unequal appearing probability of service retrieval request parameters and service input parameters on service retrieval and service addition operations. The least-used key selection method has been proposed to narrow the search scope of service retrieval and reduce its time. The experimental results show that the proposed least-used key selection method improves the service retrieval efficiency significantly compared with the designated key selection method in the case of the unequal appearing probability of parameters in service retrieval requests under three indexing models.

Bioinformatic workflow fragment discovery leveraging the social-aware knowledge graph.

Constructing a novel bioinformatic workflow by reusing and repurposing fragments crossing workflows is regarded as an error-avoiding and effort-saving strategy. Traditional techniques have been proposed to discover scientific workflow fragments leveraging their profiles and historical usages of their activities (or services). However, social relations of workflows, including relations between services and their developers have not been explored extensively. In fact, current techniques describe invoking relations between services, mostly, and they can hardly reveal implicit relations between services. To address this challenge, we propose a social-aware scientific workflow knowledge graph (S 2 KG) to capture common types of entities and various types of relations by analyzing relevant information about bioinformatic workflows and their developers recorded in repositories. Using attributes of entities such as credit and creation time, the union impact of several positive and negative links in S 2 KG is identified, to evaluate the feasibility of workflow fragment construction. To facilitate the discovery of single services, a service invoking network is extracted form S 2 KG, and service communities are constructed accordingly. A bioinformatic workflow fragment discovery mechanism based on Yen’s method is developed to discover appropriate fragments with respect to certain user’s requirements. Extensive experiments are conducted, where bioinformatic workflows publicly accessible at the myExperiment repository are adopted. Evaluation results show that our technique performs better than the state-of-the-art techniques in terms of the precision, recall, and F1.

UNITE Species Hypotheses Matching Analysis

UNITE (User-friendly Nordic ITS Ectomycorrhizal Database) (Nilsson et al. 2018) is a theoretical and practical platform for calculating, identifying and communicating DNA-based species hypotheses that may not have been described as formal species yet. UNITE species hypotheses (SH) matching analysis is a digital service for the global species discovery from environmental DNA (eDNA). SH matching service is based on UNITE datasets hosted in PlutoF. The tool places a user's unknown DNA sequences in FASTA format into existing UNITE species hypotheses and forms SHs not yet present in the system. Its output in a series of CSV and HTML files (Fig. 1) includes information about what species are present in eDNA samples, whether they are potentially undescribed new species, where they are found in other studies, whether they are alien or threatened species, etc. The service is very useful for understanding species distribution patterns, host range, etc. Analysis output will provide Digital Object Identifier (DOI)-based stable identifiers for communicating species hypotheses found in eDNA. DOIs are connected to the taxonomic backbone of PlutoF and GBIF (Global Biodiversity Information Facility). In this way every DOI is accompanied by a taxon name, which is still widely used for the communication of species. In the case of undescribed species, DOIs will be issued by the PlutoF system. All UNITE services are focused on fungi but cover all Eukarya by using publicly available rDNA ITS (ribosomal DNA Internal Transcribed Spacer) marker sequences accompanied by sample metadata. SH matching analysis results can be published in GBIF as DNA-derived occurrence data where occurrences are linked to SH identifiers present in GBIF backbone taxonomy. Source code of the SH matching analysis tool is available in GitHub with an implementation accessible online on the PlutoF platform for registered users. PlutoF (Abarenkov et al. 2010) is an open data management platform for biology and related disciplines. Users can manage their bodiversity datasets through a full data life cycle—from uploading to publishing and archiving in FAIR (Findable, Accessible, Interoperable, Reusable) way. PlutoF also features an analysis module by providing analytical services for molecular sequence identification and species discovery from eDNA samples. There are more than 8 000 registrered users in PlutoF with more than 500 users who have used its analysis module (as of August, 2022).

Research and Application of the Internet of Things Service Platform Based on Semantic Network

In order to study the Internet of Things service platform based on semantic Network, a service-oriented clustering labeling method is proposed, firstly, a semantic Web service similarity computing method is proposed to solve the shortcomings of the existing methods. This method describes the designed semantic Web service annotation clustering algorithm based on AP algorithm. The distance matrix and related parameters of the algorithm are calculated and assigned according to the results of similarity calculation. The correctness of preference parameter selection will be compared and verified in the service discovery system. The greater the value of damping coefficient, the more iterations, and the slower the convergence rate. However, when the value of damping coefficient is 0.7, the number of iterations is moderate and the convergence rate is the best, which proves that this method has a better effect on service function matching.

Mobile learning: research context, methodologies and future works towards middle-aged adults - a systematic literature review.

Over the past several years, mobile learning concepts have changed the way people perceived on mobile devices and technology in the learning environment. In earlier days, mobile devices were used mainly for communication purposes. Later, with many new advanced features of mobile devices, they have opened the opportunity for individuals to use them as mediated technology in learning. The traditional way of teaching and learning has shifted into a new learning dimension, where an individual can execute learning and teaching everywhere and anytime. Mobile learning has encouraged lifelong learning, in which everyone can have the opportunity to use mobile learning applications to gain knowledge. However, many of the previous studies on mobile learning have focused on the young and older adults, and less intention on middle-aged adults. In this research, it is targeted for the middle-aged adults which are described as those who are between the ages of 40 to 60. Middle-aged adults typically lead very active lives while at the same time are also very engaged in self-development programs aimed at enhancing their spiritual, emotional, and physical well-being. In this paper, we investigate the methodology used by researchers based on the research context namely, acceptance, adoption, effectiveness, impact, intention of use, readiness, and usability of mobile learning. The research context was coded to the identified methodologies found in the literature. This will help one to understand how mobile learning can be effectively implemented for middle-aged adults in future work. A systematic review was performed using EBSCO Discovery Service, Science Direct, Google Scholar, Scopus, IEEE and ACM databases to identify articles related to mobile learning adoption. A total of 65 journal articles were selected from the years 2016 to 2021 based on Kitchenham systematic review methodology. The result shows there is a need to strengthen research in the field of mobile learning with middle-aged adults.

Meta-Context Ontology for Self-Adaptive Mobile Web Service Discovery in Smart Systems

Self-adaptive mobile web service (MWS) discovery in Smart Systems depends on heterogeneous context-sensitive attributes that are composed dynamically to satisfy the user’s MWS request despite the constantly changing environment. Several ontologies are developed to deal with the issues of heterogeneous knowledge representation in smart systems. Unfortunately, these ontologies are mostly inexpressive, unextendible, constricted, and slightly fragmented due to a lack of socially focused ontology development procedures. In this paper, a context ontology for self-adaptive MWS discovery in smart systems is proposed to provide a better representation of the heterogeneous context for smart systems and facilitate the delivery of MWS. The lightweight unified process for ontology building (UPON Lite) is adopted for ontology development. The ontology is experimentally evaluated in protégé using real-world smart systems from the healthcare and agriculture domain. Consequently, the ontology was found to be more expressive, extensible, and support self-adaptive MWS discovery in Smart Systems.

A semantic matching approach addressing multidimensional representations for web service discovery

In recent years, discovering appropriate web services has become increasingly difficult as the number of services has grown rapidly. With the goal of improving discovery performance through accurate text matching, this study developed a service discovery method that constructs a neural matching network based on multidimensional service representations. Specifically, we performed data processing and adopted three methods called term frequency-inverse document frequency, Word2Vec, and ELMo to generate multidimensional representations for capturing the word frequency, static context features, and dynamic context features of each keyword. Based on these features, we calculated the cosine similarity of pairs of keywords to construct a multidimensional similarity matrix. We then implemented convolution, pooling, and optimization operations to construct a neural matching network that has a direct impact on the accuracy of service discovery. Finally, for a given query, target services are retrieved by ranking candidate services according to the scores predicted by the matching network. The proposed method was evaluated through multiple comparisons and the experimental results demonstrate the effectiveness of optimal web service retrieval.

Service Discovery Method Based on Knowledge Graph and Word2vec

Mashup is a new type of application that integrates multiple Web APIs. For mashup application development, the quality of the selected APIs is particularly important. However, with the rapid development of Internet technology, the number of Web APIs is increasing rapidly. It is unrealistic for mashup developers to manually select appropriate APIs from a large number of services. For existing methods, there is a problem of data sparsity, because one mashup is related to a few APIs, and another problem of over-reliance on semantic information. To solve these problems in current service discovery approaches, we propose a service discovery approach based on a knowledge map (SDKG). We embed service-related information into the knowledge graph, alleviating the impact of data sparsity and mining deep relationships between services, which improves the accuracy of service discovery. Experimental results show that our approach has obvious advantages in accuracy compared with the existing mainstream service discovery approaches.

A thorough analysis on women’s disaster adaption techniques in climate change

Climate change has posed a serious threat to humanity, particularly to women, who are among the most vulnerable. Adaptation as a measure of disaster mitigation has received a lot of attention in the wake of climate change, but nearly all of it has disregarded women's contribution to disaster adaptation as a change agent. This research aims to review the available literature on women's adaptation methods in the face of climate-related disasters. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) technique was used to guide this research. With a thorough screening method, 19 articles out of 695 were selected utilizing two databases: Scopus and EBSCo discovery service. The findings are presented in a thematic way titled after the various adaption tactics employed by women across the world. The findings suggest that women have demonstrated their ability to use beneficial adaptation techniques in the face of several societal constraints, including reproductive responsibility. As a result of the findings, certain policy recommendations have been made, emphasizing the relevance of women's capacity building in disaster impact mitigation

Efficacy of Pilates in Functional Body Composition: A Systematic Review

Background: The aim of this review was to collect and systematize results of studies from the last 5 years concerning the influence of the Pilates method (PM) on functional body composition (FBC). Methods: The criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) were used in this review. We conducted research in three scientific databases: (i) Web of Science Core Collection, (ii) SCOPUS, and (iii) search directory of the library catalog of the Faculty of Sport and Physical Education of the University of Coimbra—EBSCO Discovery Services. We found 334 articles, covering the period between 1 January 2017 and 31 December 2021. After the selection process, we found 33 eligible articles. Results: The main results seem to point to a tendency to get a better body weight and body fat percentage (BFP), hip circumference (HC), waist, and skinfolds from the chest, abdomen, triceps, and supra-iliac in mostly female samples. Available evidence indicates that, in body composition (BC) and FBC, Pilates practice tends to be effective in reducing obesity as a multifactorial condition. Conclusions: It is concluded that there is a marked trend in the benefits of Pilates in FBC, which is in line with other systematic reviews at the BC level (which includes body weight (BW) and body mass index (BMI)) and in the reduction of the percentage of fat mass (FM). It is also concluded that there is a vast lack of studies on the male population, preventing further scientific development in this area. The limitations of this systematic review can be overcome with studies that bring together multidisciplinary aspects of FBC, better designed and methodologically more robust, which will allow more reliable analyses for the implementation of the Pilates method in terms of FBC. In addition, further studies with a male sample or mixed samples (men vs. women) could confirm the trend of no gender differences in the benefits of Pilates practice.

MATEM

The paradigms of Cloud Computing have risen at a very rapid rate. The Cloud Computing demands that a trustworthy or reliable service should be availed by its potential user. However, it is always a challenge for any cloud user to look for service that is suitable as well as reliable in every aspect. For a trustworthy discovery and delivery of cloud services, a multi-layered model i.e., Multi Agent based Trust Evaluation Model (MATEM) has been proposed. It is a multi-agent trust model that will make use of multiple agents to perform and evaluate the credibility of trust through trust evaluation system. Also, the performance validation has proven that the final calculated values of trust will be helpful in providing reliable cloud services to its users.