Eugene Ch'ng

Visual Heritage in the Digital Age

This book presents methods for capturing data, modeling and engaging with heritage through digital interfaces, plus case studies of sites in Europe, North and Central America and collections relating to ancient Middle Eastern and North African civilizations.

Enhancing virtual reality with artificial life: reconstructing a flooded European mesolithic landscape

The fusion of Virtual Reality and Artificial Life technologies has opened up a valuable and effective technique for research in the field of dynamic archaeological reconstruction. This paper describes early evaluations of simulated vegetation and environmental models using decentralized Artificial Life entities. The results demonstrate a strong feasibility for the application of integrated VR and Artificial Life in solving a 10,000 year old mystery shrouding a submerged landscape in the Southern North Sea, off the east coast of the United Kingdom. Three experimental scenarios with dynamic, “artificial” vegetation are observed to grow, reproduce, and react to virtual environmental parameters in a way that mimics their physical counterparts. Through further experimentation and refinement of the Artificial Life rules, plus the integration of additional knowledge from subject matter experts in related scientific fields, a credible reconstruction of the ancient and, today, inaccessible landscape may be within our reach.

Predicting online product sales via online reviews, sentiments, and promotion strategies

Purpose – The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales. Design/methodology/approach – The authors designed a big data architecture and deployed Node.js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales. Findings – This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with ...

The bottom-up formation and maintenance of a Twitter community: Analysis of the #FreeJahar Twitter community

Purpose – The article explores the formation, maintenance and disintegration of a fringe Twitter community in order to understand if offline community structure applies to online communities Design/methodology/approach – The research adopted Big Data methodological approaches in tracking user-generated contents over a series of months and mapped online Twitter interactions as a multimodal, longitudinal ‘social information landscape’. Centrality measures were employed to gauge the importance of particular user nodes within the complete network and time-series analysis were used to track ego centralities in order to see if this particular online communities were maintained by specific egos. Findings – The case study shows that communities with distinct boundaries and memberships can form and exist within Twitter’s limited user content and sequential policies, which unlike other social media services, do not support formal groups, demonstrating the resilience of desperate online users when their ideology overcome social media limitations. Analysis in this article using social networks approaches also reveals that communities are formed and maintained from the bottom-up. Research limitations/implications – The research data is based on a particular dataset which occurred within a specific time and space. However, due to the rapid, polarising group behaviour, growth, disintegration and decline of the online community, the dataset presents a ‘laboratory’ case from which many other online community can be compared with. It is highly possible that the case can be generalised to a broader range of communities and from which online community theories can be proved/disproved. Practical implications – The article showed that particular group of egos with high activities, if removed, could entirely break the cohesiveness of the community. Conversely, strengthening such egos will reinforce the community strength. The questions mooted within the paper and the methodology outlined can potentially be applied in a variety of social science research areas. The contribution to the understanding of a complex social and political arena, as outlined in the paper, is a key example of such an application within an increasingly strategic research area - and this will surely be applied and developed further by the computer science and security community. Originality/value – The majority of researches that cover these domains have not focused on communities that are multimodal and longitudinal. This is mainly due to the challenges associated with the collection and analysis of continuous datasets that have high volume and velocity. Such datasets are therefore unexploited with regards to cyber-community research.

New Ways of Accessing Information Spaces Using 3D Multitouch Tables

Multitouch-Multiuser Table Computing is the new paradigm for accessing rich information in public spaces. As revolutionary natural user interfaces provide new ways of interacting with virtual information spaces, visual information developers must rethink their methodologies in the design of new applications that augments multiple user interaction and collaboration. This paper addresses a specific aspect of the multitouch-multiuser paradigm for virtual artefacts. Particularly, the paper addresses pilot developments of 3D multitouch-multiuser table applications in the presentation of heritage information in order to begin to qualitatively understand group behavior and user interactions on 3D information spaces using touch. The qualitative observations here pave the way for more structured quantitative usability studies.

3D Archaeological Reconstruction and Visualisation: An Artificial Life Model for Determining Vegetation Dispersal Patterns in Ancient Landscapes

This paper describes a methodology and software engine for generating dynamic vegetation models for archaeological reconstruction and interactive visualisation, integrating the disciplines of artificial life (Alife) and virtual reality. The engine, based on the concept of emergence (a phenomenon in complex Alife systems), uses real botanical parameters, channelled through simple rules, in order to synthesise the dispersal patterns of natural vegetation communities as they grow, reproduce, and compete for resources. The foci for the development and evaluation of the Alife engine described relate to different scenarios in nature as may have existed during the Mesolithic period. Results from the study showed evidence of correlations between the artificial vegetation and their natural counterparts, demonstrating the feasibility of using such models in historical landscape reconstructions

Predicting online e-marketplace sales performances

Confirming the predictive power of product review volume and rating on sales.Examining product type, answers, discount and information usefulness as moderators.Using big data architecture to collect data for model testing. To manage supply chain efficiently, e-business organizations need to understand their sales effectively. Previous research has shown that product review plays an important role in influencing sales performance, especially review volume and rating. However, limited attention has been paid to understand how other factors moderate the effect of product review on online sales. This study aims to confirm the importance of review volume and rating on improving sales performance, and further examine the moderating roles of product category, answered questions, discount and review usefulness in such relationships. By analyzing 2939 records of data extracted from Amazon.com using a big data architecture, it is found that review volume and rating have stronger influence on sales rank for search product than for experience product. Also, review usefulness significantly moderates the effects of review volume and rating on product sales rank. In addition, the relationship between review volume and sales rank is significantly moderated by both answered questions and discount. However, answered questions and discount do not have significant moderation effect on the relationship between review rating and sales rank. The findings expand previous literature by confirming important interactions between customer review features and other factors, and the findings provide practical guidelines to manage e-businesses. This study also explains a big data architecture and illustrates the use of big data technologies in testing theoretical framework.

A Photogrammetric Analysis of Cuneiform Tablets for the Purpose of Digital Reconstruction

Despite the advances made in the recording and cataloguing of cuneiform tablets, there is still much work to be done in the field of cuneiform reconstruction. The processes employed to rebuild cuneiform fragments still rely on glue and putty, with manual matching of fragments from catalogues or individual collections. The reconstruction process is hindered by inadequate information about the size and shape of fragments, and the inaccessibility of the original fragments makes finding information difficult in some collections. Most catalogue data associated with cuneiform tablets concerns the content of the text, and not the physical appearance of complete or fragmented tablets. This paper shows how photogrammetric analysis of cuneiform tablets can be used to retrieve physical information directly from source materials without the risk of human error. An initial scan of 8000 images from the CDLI database has already revealed interesting new information about the tablets held in cuneiform archives, and offer...

Social information landscapes: Automated mapping of large multimodal, longitudinal social networks

Purpose – This article presents a Big Data solution as a methodological approach to the automated collection, cleaning, collation and mapping of multimodal, longitudinal datasets from social media. The article constructs Social Information Landscapes. Design/methodology/approach – The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages and other forms of media, so long as a formal data structure proposed here can be constructed. Findings – The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on Web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g., centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating and mapping such datasets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem. Research limitations/implications – The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this article is to demonstrate the feasibility of using automated approaches for acquiring massive, connected datasets for academic inquiry in the social sciences. Practical implications – The methods presented in this article, and the Big Data architecture presented here have great practical values to individuals and institutions which have low budgets. The software-hardward integrated architecture uses open source software, and the social information landscapes mapping algorithms are not difficult to implement. Originality/value – The majority of research in the literatures uses traditional approach for collecting social networks data. The traditional approach is slow, tedious and does not yield a large enough sample for the data to be significant for analysis. Whilst traditional approach collects only a small percentage of data, the original methods presented could possibility collect entire datasets in social media due to its scalability and automated mapping techniques.

Crowd behavior mining with virtual environments

This article explores ways in which virtual environments can be used for crowdsourcing and behavior mining for filling gaps within the information space of topical research. Behavior mining in this article refers to the act of harvesting the latent or instinctive behavior of participants, usually a crowd, and injecting the population behavior into a preset context, such as within a virtual environment so that the subjective behaviors and the contexts are merged. The experimental approach combines various modalities centered upon virtual environments so as to induce presence in order to bring participants into the context. This approach is new and not well studied; however, it has real potential in research dealing with behaviors and culture in reconstructed virtual environments. Two virtual environments case studies at the 2012 and 2015 Royal Society Summer Science Exhibition are presented, which demonstrate that the unique crowdsourcing activity is able to fill gaps within the information space so that answers to research questions can be more complete. Thus, by reconstructing and replicating a lost landscape, and by injecting harvested human behavior into the context of the landscape, we may be able to gather much more information than conventional methods will allow.