An extensive Review of Web-Based Multi Granularity Service Composition

The paper reviews the efforts to compose SOAP, non-SOAP and non-web services. Traditionally efforts were made for composite SOAP services, however, these efforts did not include the RESTful and non-web services. A SOAP service uses structured exchange methodology for dealing with web services while a non-SOAP follows different approach. The research paper reviews the invoking and composing a combination of SOAP, non-SOAP, and non-web services into a composite process to execute complex tasks on various devices. It also shows the systematic integration of the SOAP, non-SOAP and non-web services describing the composition of heterogeneous services than the ones conventionally used from the perspective of resource consumption. The paper further compares and reviews different layout model for the discovery of services, selection of services and composition of services in Cloud computing. Recent research trends in service composition are identified and then research about microservices are evaluated and shown in the form of table and graphs.

Opportunities and Challenges in Code Search Tools

Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique used to build code search tools, and classified existing tools into focusing on supporting seven different search tasks. Based on our findings, we identified a set of outstanding challenges in existing studies and a research roadmap for future code search research.

Analyzing publication trend on microcredit research using bibliometrics approach

This paper aims to apply the bibliometrics method to investigate the research trends in the study of microcredit, specifically analyzing the quality of the research productions by the citation status and the mutual influence of these publications. This study has used the bibliometric method for an overview analysis of all 382 publications on the topic of microcredit indexed in the Scopus database for the period of 1994 to 2021. The results of the study provide an overview of the publication trend on the microcredit topic, such as (i) the most influential publications; (ii) the most cited authors; (iii) the most cited journals by publications on the microcredit topic; (iv) the main research keywords that network linked among publications on the microcredit topic; (v) the most productive institutions; (vi) the trend of research collaboration among countries in publications on the microcredit topic. The article provides an objective overview of the microcredit topic development and is a valuable reference for scholars studying finance, economics, and business management.

Coffee wilt disease

In the early 20th century, coffee wilt disease (CWD) caused by the vascular wilt pathogen, Fusarium xylarioides, spread across Africa destroying coffee trees, reducing yields and significantly impacting producer livelihoods. Through systematic sanitation and establishment of breeding programmes in affected countries, CWD appeared to decline. However, the disease re-emerged and increased to epidemic proportions in the 1990s affecting robusta coffee in DRC, Uganda and Tanzania and arabica coffee in Ethiopia. In 1999, 14.5 million robusta coffee trees were estimated to have been destroyed in Uganda alone. This chapter discusses the history, impact, symptoms, cause and spread of CWD. A summary of the Regional Coffee Wilt Programme (RCWP) which examined many aspects of the disease and its management is also provided. . Future research trends include host specificity, underlying resistance mechanisms and the role of alternative hosts. Investigation of pathogen ecology is needed to allow greater focus on agroecological management practices.

Research trends in tourism and hospitality from 1991 to 2020: an integrated approach of corpus linguistics and bibliometrics

PurposeThis study aims to track the historical development in tourism and hospitality research over the past 30 years by applying a novel interdisciplinary approach, combining both corpus linguistics and bibliometric analysis.Design/methodology/approachMost frequently discussed topics and newly emerging topics were identified by investigating 18,266 abstracts from 18 leading tourism and hospitality journals with corpus linguistics toolkit AntConc and natural language processing (NLP) tool spaCy. Trend analysis and bibliometric methods were used to determine the longitudinal changes of research topics, most highly-cited publications and authors' production.FindingsThis study revealed the evolution patterns of the identified 576 most frequently discussed topics across the four subperiods (1991–2000, 2001–2010, 2011–2015 and 2016–2020). Specifically, results showed that information technology-related topics account for the largest proportion of the identified 38 newly emerging topics from 2011. Besides, researchers are increasingly focusing on the use of more sophisticated and advanced statistical methodologies.Practical implicationsThis study helps researchers make sensible decisions on what research topics to explore; it also helps practitioners and stakeholders make the shift and track opportunities in the field.Originality/valueNo other studies have employed the novel interdisciplinary approach, combining corpus linguistic tools in linguistics, NLP techniques in computer science and bibliometric analysis in library and information science, for exploring research trends in tourism and hospitality.

Research trends of blended language learning: A bibliometric synthesis of SSCI-indexed journal articles during 2000–2019

This study aims to synthesize research trends of blended language learning studies over the past two decades, from 2000 to 2019. Data were collected from the Web of Science, and a total of 60 SSCI-indexed journal articles were retrieved for bibliometric synthesis. Drawing on the revised technology-based learning model, participants, learning strategies, research methods, research foci, adopted technologies, and application effectiveness, advantages, and challenges were addressed. The findings demonstrated that publications were increasing rapidly, and that most articles were published in computer-assisted language learning, educational technology, and applied linguistic journals. The most common target language was English as a foreign language, and the most common learners were college students. In most studies, technologies were mainly used for the purposes of practice or exercises. Mixed, quantitative, and qualitative methods were frequently adopted, with a particular eye on the experiment design, questionnaires, and other specific methods in the second decade. Productive language skills, along with autonomy, satisfaction, and motivation, were major research foci. Language management systems and computer and web-based applications were frequently adopted technologies. Findings of application effectiveness, advantages, and challenges were summarized.

Evaluating Research Trends from Journal Paper Metadata, Considering the Research Publication Latency

Investigating the research trends within a scientific domain by analyzing semantic information extracted from scientific journals has been a topic of interest in the natural language processing (NLP) field. A research trend evaluation is generally based on the time evolution of the term occurrence or the term topic, but it neglects an important aspect—research publication latency. The average time lag between the research and its publication may vary from one month to more than one year, and it is a characteristic that may have significant impact when assessing research trends, mainly for rapidly evolving scientific areas. To cope with this problem, the present paper is the first work that explicitly considers research publication latency as a parameter in the trend evaluation process. Consequently, we provide a new trend detection methodology that mixes auto-ARIMA prediction with Mann–Kendall trend evaluations. The experimental results in an electronic design automation case study prove the viability of our approach.