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A survey of Japan's new religions (1) shows “ancestor worship” (2) to have played an important part in various religious bodies (3), as a means for attaining happiness for the living. Generally speaking, new religions advocate that the adequate veneration of ancestors leads them to enter a “blissful state”, as a result of which they can bring happiness to their living descendants (4). At the same time, the new religions also teach that lack of proper consolation for ancestors results in misfortune for the living (5). This tendency to put importance on ancestor worship is a particular feature of the Reiyu‐kai group of new religions which denotes those religious bodies which were established by schism within Reiyu‐kai (lit. the Spirit Friends' Society) and by the subsequent re‐splitting of those bodies (6) which resulted. The parent organization, Reiyu‐kai, is a large‐scale new religion of lay Nichiren origin (7), which was founded by Kakutaro Kubo and Kimi Kotani in 1925, and its doctrine of memorial rites for ancestors, concentrating on the combination of ancestor worship and faith as found in the Lotus Sutra, draws upon that of the Bussho Gonen movement founded by Toshizo Nishida (8).

Ethics for Indigenous peoples come from ancestral knowledge, from the ways we exist in creation and the ways that we are urged to respond to the whole of creation. As people grown from ancestors who we believe are always with us, we look to our own teachings to strengthen our ethics of living, being and undertaking research. These teachings still speak to us through chants, songs, stories and many other forms of belief. This chapter outlines Māori beliefs and the power of Māori belief by examining how ancestors continue to inform and speak. Research ethics are assumed to reside within a living human world, but Māori ethics includes both the seen and unseen worlds. These beliefs create a powerful challenge to the notion of sovereignty and offer powerful counterhegemonic views to racism and exploitative regimes of power. They also inform the ways that we understand ethical approaches to all our relations in the world.

The purpose of this paper is to provide an efficient algorithm for Extensible Markup Language (XML) twig query evaluation.

A single‐phase holistic twig pattern matching method based on the TwigStack algorithm is proposed. The method applies a novel stack structure to preserve the holisticity of the twig matches. Twig matches rooted at elements that are currently in the root stack are output directly.

Without generating individual path matches as intermediate results, the method is able to avoid the storage and output/input of the individual path matches, and totally eliminate the potentially time‐consuming merging operation. Experimental results demonstrate the applicability and advantages of our approach.

The paper proposes an efficient XML twig query evaluation algorithm, which by both theoretical analyses and empirical studies demonstrates its advantages over the current state‐of‐the‐art algorithm TwigStack.

The purpose of this paper is to propose efficient algorithms for structural grouping over Extensible Markup Language (XML) data, called TOPOLOGICAL ROLLUP (T‐ROLLUP), which are to compute aggregation functions based on XML data with multiple hierarchical levels. They play important roles in the online analytical processing of XML data, called XML‐OLAP, with which complex analysis over XML can be performed to discover valuable information from XML.

Several variations of algorithms are proposed for efficient T‐ROLLUP computation. First, two basic algorithms, top‐down algorithm (TDA) and bottom‐up algorithm (BUA), are presented in which the well‐known structural‐join algorithms are used. The paper then proposes more efficient algorithms, called single‐scan by preorder number and single‐scan by postorder number (SSC‐Pre/Post), which are also based on structural joins, but have been modified from the basic algorithms so that multiple levels of grouping are computed with a single scan over node lists. In addition, the paper attempts to adopt the algorithm for parallel execution in multi‐core environments.

Several experiments are conducted with XMark and synthetic XML data to show the effectiveness of the proposed algorithms. The experiments show that proposed algorithms perform much better than the naïve implementation. In particular, the proposed SSC‐Pre and SSC‐Post perform better than TDA and BUA for all cases. Beyond that, the experiment using the parallel single scan algorithm also shows better performance than the ordinary basic algorithm.

This paper focuses on the T‐ROLLUP operation for XML data analysis. For this reason, other operations related to XML‐OLAP, such as CUBE, WINDOWING, and RANKING should also be investigated.

The paper presents an extended version of one of the award winning papers at iiWAS2008.

Purpose – The purpose of this chapter is to bring data to suggest that group processes have a biological base, lodged in human neurology as it evolved over the last 7 million years.

Design/methodology/approach – The method for discovering the neurological basis of group processes is labelled evolutionary sociology, and this method revolves around: (1) cladistic analysis of traits of distant ancestors to humans and the great apes, with whom humans share a very high proportion of genes, (2) comparative neurology between the great apes and humans that can inform us about how the brains of humans were rewired from the structures shared by the last common ancestor to humans and apes, and (3) ecological analysis of the habitats and niches that generated selection pressures on the neurology of apes and hominins.

Findings – A key finding is that most of the interpersonal processes that drive group processes are neurologically based and evolved before the brain among hominins was sufficiently large to generate systems of symbols organized in cultural texts remotely near the human measure. There is, then, good reason to study the neurological basis of behavior because neurology explains more about the dynamics of interpersonal behavior than does culture, which was a very late arrival to the hominin line.

Research implications – One implication of these findings is that social scientific analysis of interpersonal processes and group dynamics can no longer assume that groups are solely a constructed process, mediated by culture and social structure. There were powerful selection pressures during the course of hominin evolution to increase hominin sociality and especially group formation, which required considerable rewiring of the basic ape brain. Since groups are not “natural” to apes in general and even to an evolved ape-like humans, it is important to discover how humans ever became group-organizing animals. The answer resides in the dramatic enhancing of emotions in hominins and humans, which shifts attention away from the neocortex to the older subcortical areas of the brain. Once this shift is made, theorizing and research, as well as public views on human sociality, need to be recast as, first, an evolved biological trait and, only second, as a most tenuous and fragile of a big-brained animal using language and culture to construct its social world.

Originality/value – The value of this kind of analysis is to liberate sociology and the social sciences in general from simplistic views that, because humans have language and can use language to construct culture and social structures, the underlying biology and neurology of human action is not relevant to understanding the social world. Indeed, just the opposite is the case: to the extent that social scientists insist upon a social constructionists research agenda, they will fail to conceptualize and perform research on more fundamental forces in the social world, including group dynamics.

The purpose of this study is to exploit the lowest common ancestor technique in an m-ary data aggregation tree in the fog computing-enhanced IoT to assist in contact tracing in COVID-19. One of the promising characteristics of the Internet of Things (IoT) that can be used to save the world from the current crisis of COVID-19 pandemic is data aggregation. As the number of patients infected by the disease is already huge, the data related to the different attributes of patients such as patient thermal image record and the previous health record of the patient is going to be gigantic. The authors used the technique of data aggregation to efficiently aggregate the sensed data from the patients and analyse it. Among the various inferences drawn from the aggregated data, one of the most important is contact tracing. Contact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease.

The authors propose to exploit the technique of lowest common ancestor in an m-ary data aggregation tree in the Fog-Computing enhanced IoT to help the health-care experts in contact tracing in a particular region or community. In this research, the authors argue the current scenario of COVID-19 pandemic, finding the person or a group of persons who has/have infected a group of people is of extreme importance. Finding the individuals who have been infected or are infecting others can stop the pandemic from worsening by stopping the community transfer. In a community where the outbreak has spiked, the samples from either all the persons or the patients showing the symptoms are collected and stored in an m-ary tree-based structure sorted over time.

Contact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease. The authors exploited the technique of lowest common ancestor in an m-ary data aggregation tree in the fog-computing-enhanced IoT to help the health-care experts in contact tracing in a particular region or community. The simulations were carried randomly on a set of individuals. The proposed algorithm given in Algorithm 1 is executed on the samples collected at level-0 of the simulation model, and to aggregate the data and transmit the data, the authors implement Algorithm 2 at the level-1. It is found from the results that a carrier can be easily identified from the samples collected using the approach designed in the paper.

The work presented in the paper can aid the health-care experts fighting the COVID-19 pandemic by reducing the community transfer with efficient contact tracing mechanism proposed in the paper.

Fighting COVID-19 efficiently and saving the humans from the pandemic has huge social implications in the current times of crisis.

To the best of the authors’ knowledge, the lowest common ancestor technique in m-ary data aggregation tree in the fog computing-enhanced IoT to contact trace the individuals who have infected or were infected during the transmission of COVID-19 is first of its kind proposed. Creating a graph or an m-ary tree based on the interactions/connections between the people in a particular community like location, friends and time, the authors can attempt to traverse it to find out who infected any two persons or a group of persons or was infected by exploiting the technique of finding out the lowest common ancestor in a m-ary tree.

The increasing popularity of XML has generated a lot of interest in query processing over graph‐structured data. To support efficient evaluation of path expressions structured indexes have been proposed. Extending the proposed indexes to work with large XML graphs and to support intra‐ or inter‐document links requires a lot of computing power for the creation process and a lot of space to store the indexes. Moreover, the efficient evaluation of ancestors‐descendants queries over arbitrary graphs with long paths is a severe problem. This paper aims to propose a scalable path index which is based on the concept of 2‐hop covers as introduced by Cohen et al.

The problem of efficiently managing and querying XML documents poses interesting challenges on database research. The proposed algorithm for index creation scales down the original graph size substantially. As a result a directed acyclic graph with a smaller number of nodes and edges will emerge. This reduces the number of computing steps required for building the index. Thus, computing time and space will be reduced as well. The index also permits ancestors‐descendants relationships to be efficiently evaluated. Moreover, the proposed index has a nice property in comparison to most other work; it is optimized for descendants‐or‐self queries on arbitrary graphs with link relationships.

In this paper, a scalable path index is proposed. It can efficiently address the problem of querying large XML documents that contain links and have cycles. Cycles in the graph stress path‐indexing algorithms. An overview about 2‐hop cover and the algorithms that used to build the index are given.

This paper works on the updating problem. Since the construction of the index is quite complex its construction make sense for some time. However, this means it is currently dealing with the problem of updating XML‐documents.

This paper presents an efficient path index that can test the reachability between two nodes and evaluate ancestors‐descendants queries over arbitrary graphs with long paths.

Nowadays there are a large number of XML documents on the web. This means that information retrieval techniques for searching XML documents are very important and necessary for internet users. Moreover, it is often said that users of search engines want to browse only relevant content in each document. Therefore, an effective XML element search aims to produce only the relevant elements or portions of an XML document. Based on the demand by users, the purpose of this paper is to propose and evaluate a method for obtaining more accurate search results in XML search.

The existing approaches generate a ranked list in descending order of each XML element's relevance to a search query; however, these approaches often extract irrelevant XML elements and overlook more relevant elements. To address these problems, the authors' approach extracts the relevant XML elements by considering the size of the elements and the relationships between the elements. Next, the authors score the XML elements to generate a refined ranked list. For scoring, the authors rank high the XML elements that are the most relevant to the user's information needs. In particular, each XML element is scored using the statistics of its descendant and ancestor XML elements.

The experimental evaluations show that the proposed method outperforms BM25E, a conventional approach, which neither reconstructs XML elements nor uses descendant and ancestor statistics. As a result, the authors found that the accuracy of an XML element search can be improved by reconstructing the XML elements and emphasizing the informative ones by applying the statistics of the descendant XML elements.

This work focused on the effectiveness of XML element search and the authors did not consider the search efficiency in this paper. One of the authors' next challenges is to reduce search time.

The paper proposes a method for improving the effectiveness of XML element search.

This paper focuses on the citizens’ movement for promoting shizensou that has arisen since the early 1990s in Japan. It ascertains the social backgrounds of the movement, which originally began as an attempt to preserve nature, and the proponents’ view of the next world and their views on nature. This analysis includes social factors such as demographic trends toward the nuclear family. Furthermore, based on written newsletter reports on shizensou, this paper will examine views of the next world, and indicate that their views are animistic. Finally, I will argue that present-day shizensou is a revitalization of the custom of scattering of the ashes, which was performed in ancient Japan, but it also involved other new elements.