Purpose The purpose of this paper is to discuss the emerging and innovative technologies which integrate together to form smart libraries. Smart libraries are the new generation libraries, which work with the amalgamation of smart technologies, smart users and smart services. Design/methodology/approach An extensive review of literature on “smart libraries” was carried to ascertain the emerging technologies in the smart library domain. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored initially to ascertain the extent of literature published on Smart Libraries and their varied aspects. Literature was searched against various keywords like smart libraries, smart technologies, Internet of Things (IoT), Electronic resource management (ERM), Data mining, Artificial intelligence (AI), Ambient intelligence, Blockchain Technology and Augmented Reality. Later on, the works citing the literature on Smart Libraries were also explored to visualize a broad spectrum of emerging concepts about this growing trend in libraries. Findings The study confirms that smart libraries are becoming smarter with the emerging smart technologies, which enhances their working capabilities and satisfies the users associated with them. Implementing the smart technologies in the libraries has bridged the gap between the services offered by the libraries and the rapidly changing and competing needs of the humans. Practical implications The paper highlights the emerging smart technologies in smart libraries and how they influence the efficiency of libraries in terms of users, services and technological integration. Originality/value The paper tries to highlight the current technologies in the smart library set-ups for the efficient working of library set-ups.

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Purpose Data mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining; big data mining; big data mining and Internet of Things (IoT); and educational data mining. This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data. Design/methodology/approach An extensive review of literature on data mining and its allied techniques was carried to ascertain the emerging procedures and techniques in the domain of data mining. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored to discover the extent of literature published on Data Mining and its varied facets. Literature was searched against various keywords such as data mining; information mining; big data; big data and IoT; and educational data mining. Further, the works citing the literature on data mining were also explored to visualize a broad gamut of emerging techniques about this growing field. Findings The study validates that knowledge discovery in databases has rendered data mining as an emerging field; the data present in these databases paves the way for data mining techniques and analytics. This paper provides a unique view about the usage of data, and logical patterns derived from it, how new procedures, algorithms and mining techniques are being continuously upgraded for their multipurpose use for the betterment of human life and experiences. Practical implications The paper highlights different aspects of data mining, its different technological approaches, and how these emerging data technologies are used to derive logical insights from data and make data more meaningful. Originality/value The paper tries to highlight the current trends and facets of data mining.

The paper investigates changing levels of online concern about the Kashmiri Pandit migration of the 1990s on Twitter. Although decades old, this movement of people is an ongoing issue in India, with no current resolution. Analysing changing reactions to it on social media may shed light on trends in public attitudes to the event. Tweets were downloaded from Twitter using the academic version of its applications programming interface (API) using the free social media analytics software Mozdeh. A set of 1000 tweets was selected for a content analysis with a random number generator in Mozdeh. The results show that the number of tweets about the issue has increased over time, mainly from India, and predominantly driven by the release of films like Shikara and The Kashmir Files. The tweets show apparent universal sup­port for the Pandits but often express strong emotions or criticize the actions of politicians, showing that the migration is an ongoing source of anguish and frustration that needs resolution. The results also show that social media analysis can give insights even into primarily offline political issues that predate the popularity of the web, and can easily incorporate international perspectives necessary to understand complex migration issues.

The year 2020 brought a big concern for the global community because of COVID-19, which affected every sector of society, and tourism is no exception. Researchers across the globe are publishing their studies related to different dimensions of tourism in the context of COVID-19, and images have formed an essential component of their research. In tourism, images related to COVID-19 can open new dimensions for scholars. The main aim of the research is to measure the retrieval effectiveness of three image search engines (ISEs), that is, Bing Images, Google Images and Yahoo Image Search, concerning images related to COVID-19 and tourism. The study attempts to identify the capability of the ISEs to retrieve the desired and actual images related to COVID-19 and tourism. The PubMed Central (PMC) Database was consulted to retrieve the desired images and develop a testbed. The advanced search feature of PMC Database was explored by typing the search terms ‘COVID-19’ and ‘Tourism’ using ‘AND’ operator to make the search more comprehensive. Both the terms were searched against the ‘Figure/Table’ caption to retrieve papers carrying images related to COVID-19 and tourism. Queries were executed across the select ISEs, that is, Bing Images, Google Images and Yahoo Image Search. Retrieved images were individually analysed against the original image from the articles to determine the Precision, Relative Recall, F-Measure and Fallout Ratio. The format of the images in JPG/JPEG, besides checking the original image rank in the retrieved lot, was also ascertained. Bing Images scores more in terms of Mean Precision, followed by Google Images and Yahoo Image Search. For the Relative Recall measure, Google Images scores high, followed by Bing Images and Yahoo Image Search, respectively. Regarding F-Measure and Fallout Ratio, Bing Images outperforms Google Images and Yahoo Image Search. In retrieving the sought format of JPG/JPEG, Google Images performs best, followed by Yahoo Image Search and Bing Images. Google Images produces the original image at the first rank on more than one occasion. In contrast, Bing Images retrieves the original image at the first rank in two instances. Yahoo Images performs poorly over this metric as it does not retrieve any original image at the first rank on any other instance. The study cannot be generalised as the scope is only limited to the images indexed by PMC. Furthermore, the retrieval effectiveness of only three ISEs is measured. The study is the first to measure the retrieval effectiveness of ISEs in retrieving images related to the COVID-19 pandemic and tourism. The study can be extended across other image-indexing databases pertinent to tourism studies, and the retrieval effectiveness of other ISEs can also be considered.