Digital heritage research re-theorised: ontologies and epistemologies in a world of big data

ABSTRACT

This article provides the first theoretical treatment of the ontologies and epistemologies of digital heritage research at the time of the interconnected and social web, based on extensive empirical and analytical investigation. We draw on observations and concepts developed while conducting the first study of public experiences of the past that utilised big data – over 1.4 million Facebook posts, comments and replies – to revisit or generate new theory from the ground up. We expect that this will help scholars from a range of fields in the humanities, social and computing sciences who are interested in undertaking digital heritage research to understand the deeper implications of their work, the complexities and limitations of the knowledge they create, and its value in exposing the processes via which heritage is made and assessed.     

Discovering the Language of Data: Personal Pattern Languages and the Social Construction of Meaning from Big Data

Abstract

This paper attempts to address two issues relevant to the sense-making of Big Data. First, it presents a case study for how a large dataset can be transformed into both a visual language and, in effect, a ‘text’ that can be read and interpreted by human beings. The case study comes from direct observation of graduate students at the IIT Institute of Design who investigated task-switching behaviours, as documented by productivity software on a single user’s laptop and a smart phone. Through a series of experiments with the resulting dataset, the team effects a transformation of that data into a catalogue of visual primitives — a kind of iconic alphabet — that allow others to ‘read’ the data as a corpus and, more provocatively, suggest the formation of a personal pattern language. Second, this paper offers a model for human-technical collaboration in the sense-making of data, as demonstrated by this and other teams in the class. Current sense-making models tend to be data- and technology-centric, and increasingly presume data visualization as a primary point of entry of humans into Big Data systems. This alternative model proposes that meaningful interpretation of data emerges from a more elaborate interplay between algorithms, data and human beings.     

Using Airborne LiDAR Survey to explore Historic-era archaeological landscapes of Montserrat in the Eastern Caribbean

Abstract

This article describes what appears to be the first archaeological application of airborne LiDAR survey to historic-era landscapes in the Caribbean archipelago, on the island of Montserrat. LiDAR is proving invaluable in extending the reach of traditional pedestrian survey into less favorable areas, such as those covered by dense neotropical forest and by ashfall from the past two decades of active eruptions by the Soufrière Hills volcano, and to sites in localities that are inaccessible on account of volcanic dangers. Emphasis is placed on two aspects of the research: first, the importance of ongoing, real-time interaction between the LiDAR analyst and the archaeological team in the field; and second, the advantages of exploiting the full potential of the three-dimensional LiDAR point cloud data for purposes of the visualization of archaeological sites and features.     

The importance of a reliable grouping - Neutron activation analysis (NAA) data of Mycenaean pottery sherds re-evaluated with the Bonn filter method

Using the Bonn statistical procedure to form groups of samples of similar composition and hence origin, published NAA concentration data of Mycenaean pottery sherds from Mycenae and Tiryns measured in Vienna have been newly grouped and, after minor interlaboratory adjustments of a few elemental concentration values, proven to agree with well known Mycenaean patterns measured in Bonn and other laboratories. This demonstrates the importance of a good grouping procedure and also, that data from different laboratories can be used in provenance studies providing a larger reference data base.     

Baseline Models for Two‐Mode Social Network Data

Baseline models have been used in the analysis of social networks as a way to understand how empirical networks differ from “random” ones. For the purposes of social network analysis, a “random” network is one chosen—at random—from a population of possible graphs derived from a given generating function. Although these principled hypothesis tests have a long history, many of their properties and extensions to multiple data structures—here, specifically two‐mode data—have been overlooked. This article focused on applications of different baseline models to two data sets: donations and voting of the 111th U.S. Congress, and organizations involved in forums on watershed policy in San Francisco, USA. Tests using each data set, but with different baseline reference distributions, will illustrate the range of possible questions baseline models can address and the differences between them. The ability to apply different models and generate a constellation of results provides a deeper understanding of the structure of the system.     

Terrestrial laser scanning intensity data applied to damage detection for historical buildings

This paper presents a methodology to combine the technology of the terrestrial laser scanner with the techniques of digital image processing in order to study damages on stony materials that constitute historical buildings. This methodology has been tested using intensity data from three terrestrial laser scanners with different technical specifications. The unsupervised classification algorithms were used for the classification of 2D intensity images derived from the 3D information obtained from laser scanner equipment. The results obtained show the potential of the use of intensity data from terrestrial laser scanner for the recognition and characterization of certain pathologies in building materials that constitute historical buildings.     

Non-destructive analysis of amber artefacts from the prehistoric Cioclovina hoard (Romania)

Amber beads from the Cioclovina hoard (Hunedoara County, Romania), dating from the transitional period between the Late Bronze Age and the Iron Age, were analysed by Fourier transform infrared spectroscopy – variable angle reflectance (FTIR-VAR) and Fourier transform Raman spectroscopy (FT-Raman). The aim was to determine if the raw material came from the shores of the Baltic Sea, on the Amber Route which crosses Central Europe, or if it originated from the area of the main amber occurrences in Romania, in Buz?u County, around the village of Col?i. All applied analyses strongly suggest that a large part of the amber from the Cioclovina deposit has a Romanian origin, and thus no connection with the Amber Route.     

Algorithmic War: Everyday Geographies of the War on Terror

Abstract:  Technologies that deploy algorithmic calculation are becoming ubiquitous to the homeland securitization of the war on terror. From the surveillance networks of the city subway to the biometric identifiers of new forms of border control, the possibility to identify "association rules" between people, places, objects and events has brought the logic of pre-emption into the most mundane and prosaic spaces. Yet, it is not the case that the turn to algorithmic calculation simply militarizes society, nor even that we are witnessing strictly a commercialization of security. Rather, algorithmic war is one form of Foucault's sense of a "continuation of war by other means", where the war-like architectures of self/other, here/there, safe/risky, normal/suspicious are played out in the politics of daily life. This paper explores the situated interplay of algorithmic practices across commercial, security, and military spheres, revealing the violent geographies that are concealed in the glossy techno-science of algorithmic calculation.