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The cornovirus disease (COVID-19) pandemic has had a severe impact on our daily lives. As a result, there has been an increasing demand for technological solutions to overcome such challenges. The Internet of Things (IoT) has recently emerged to improve many aspects of human’s day-to-day activities and routines. IoT makes it easier to follow the safety guidelines and precautions provided by the World Health Organization (WHO). Prior reports have shown that the world nowadays may need more IoT facilities than ever before. However, little is known about the reaction of the IoT community towards defeating the COVID-19 pandemic, technologies being used, solutions being provided, and how our societies perceive the IoT means available to them. In this paper, we conduct an empirical study to investigate the IoT response to the COVID-19 pandemic. In particular, we study the characteristics of the IoT solutions hosted on a large online IoT community (i.e., Hackster.io) throughout the year of 2020. The study: (a) explores the proportion, types, and nations of IoT solutions/engineers that contributed to defeating COVID-19, (b) characterizes the complexity of COVID-19 IoT solutions, and (c) identifies how IoT solutions are perceived by the surrounding community. Our results indicate that IoT engineers have been actively working towards providing solutions to help their societies, especially in the most affected nations. Our findings (i) provide insights into the aspects IoT practitioners need to pay more attention to when developing IoT solutions for COVID-19 and to (ii) outlines the common IoT solutions and technologies available to humans to deal with the current challenges.

Semantic web is a knowledge graph formed around semantic languages to enable computers and software to understand contents on the web. The content is explicitly annotated with semantic metadata using Resource Description Framework (RDF) language. However, the main issue is how to efficiently retrieve the RDF data taking into account a wide variety semantic and syntax nature and large-scale of such data. This paper aims to introduce a novel mechanism based on K-medoids algorithm for narrowing down the contents of the Web to clusters pertaining subset of information. We integrated sequence alignment algorithms with linguistic similarity measures to build a distance matrix which is used later in K-medoids clustering algorithm. The experimental outcomes showed a promised result for accuracy and quality of clustering.

Text classification is still an area growing as text sizes grow rapidly with the information and internet revolution. The majority of conventional text classification approaches are hierarchical training, where the classification system explicitly differentiates between groups. However, this is different from the nature of language processing which suffers from ambiguity and lack of clarity. That is, the instance could be of more than one class. This paper handles the problem of text classification by applying a novel classification method based on sequence alignment with simple fuzzy concepts. The experiments showed expected performance compared to other conventional classifications of natural languages.

Managing temporal data gets increased demand for many significant areas such as scientific and financial applications. This paper proposes a new index (TOIX), which is designed in order to provide more efficient evaluation of temporal queries on XML documents. The index lies on mapping the twigs into temporal objects and then using these objects instead for answering the query. An improvement of the naive algorithm using a B-tree as well. Finally, a set of conducted experiments were performed and showed that our proposed algorithm outperforms the state of the art indexing algorithms in certain cases.

Different models have been proposed recently for representing temporal data, tracking historical information and retrieving temporal queries results efficiently. We consider the problem of indexing temporal XML documents. In particular, we propose an indexing scheme that uses a summary structure and a matrix that captures the structural relationships as well as time intervals inside a temporal XML document. We introduce an algorithm to efficiently process all types of temporal queries with any depth using our newly proposed index. We show that our proposed index out-performs the state of the art indices in terms of both query processing time and support for different temporal query types.

Inspired by the great success of information retrieval (IR) style keyword search on the web, keyword search on XML has emerged recently. Existing methods cannot resolve challenges addressed by using keyword search in Temporal XML documents. We propose a way to evaluate temporal keyword search queries over Temporal XML documents. Moreover, we propose a new ranking method based on the time-aware IR ranking methods to rank temporal keyword search queries results. Extensive experiments have been conducted to show the effectiveness of our approach.