Patricia Martin-Rodilla

Extending an Abstract Reference Model for Transdisciplinary Work in Cultural Heritage

Obtaining models of cultural heritage that guarantee information interoperability and, at the same time, maintain a high degree of fitness to the problem at hand is not a trivial quest. This paper proposes a two-step approach to attain this, where particular models for each problem at hand are derived from a common, standardised Cultural Heritage Abstract Reference Model (CHARM) by using specific rules that guarantee abstract interoperability while allowing for as much specificity as necessary. This is illustrated through a case study involving three different communities, each with a different conceptual model of cultural heritage, which still generate a seamless object model.

Automatic process model discovery from textual methodologies

Process mining has been successfully used in automatic knowledge discovery and in providing guidance or support. The known process mining approaches rely on processes being executed with the help of information systems thus enabling the automatic capture of process traces as event logs. However, there are many other fields such as Humanities, Social Sciences and Medicine where workers follow processes and log their execution manually in textual forms instead. The problem we tackle in this paper is mining process instance models from unstructured, text-based process traces. Using natural language processing with a focus on the verb semantics, we created a novel unsupervised technique TextProcessMiner that discovers process instance models in two steps: 1.ActivityMiner mines the process activities; 2.ActivityRelationshipMiner mines the sequence, parallelism and mutual exclusion relationships between activities. We employed technical action research through which we validated and preliminarily evaluated our proposed technique in an Archaeology case. The results are very satisfactory with 88% correctly discovered activities in the log and a process instance model that adequately reflected the original process. Moreover, the technique we created emerged as domain independent.

The role of software in cultural heritage issues: Types, user needs and design guidelines based on principles of interaction

In most cases, the studied software in the cultural heritage domain has been designed from the perspective of other disciplines, such as forestry engineering, geography or documentation. In the Institute of Heritage Sciences, the cultural heritage is studied as a research topic, with methodologies to study the cultural heritage activities and considering the processing of data derived from these processes like a way to add value and knowledge in these contexts [4]. From this perspective, this paper shows a process of requirement elicitation with cultural heritage professionals and the needs identified by them. It mainly focuses on the identification of interaction human-computer (IHC) needs.

User interface design guidelines for rich applications in the context of cultural heritage data

Advanced interaction techniques are necessary to explore the potential of large data-volume systems. In this context, rich internet application patterns were defined, but usually reduced to the development of social web applications. However, other types of applications, such as data-analysis applications, require also advanced interaction solutions to assist users in making decisions and data-analysis. This paper identifies a set of problems emerged in the interaction between humans and data-analysis applications. We propose a set of guidelines for rich applications as a solution for these problems. As illustrative example of a real data-analysis environment, the paper focuses on a case study in the cultural heritage domain, highlighting the existing interaction problems and how they can be solved through the design guidelines proposed. The set of design guidelines allows to specify interfaces abstractly, creating a repository to solve interaction problems. These guidelines aim to serve as a basis for a future identification of new rich applications design patterns.

Assessing data analysis performance in research contexts: An experiment on accuracy, efficiency, productivity and researchers’ satisfaction

Abstract Any knowledge generation process involves raw data comprehension, evaluation and inferential reasoning. These practices, common to different disciplines, are known as data analysis, and represent the most important set of activities in research contexts. Researchers use data analysis software methods and tools for generating new knowledge in their daily data analysis. In recent years, data analysis software has been incorporating explicit references in modelling of cognitive processes, in order to improve the assistance offered in data analysis tasks. However, data analysis software commercial suites are still resisting this inclusion, and there is little empirical work done in knowing more about how cognitive aspects inclusion in software helps researchers in analyzing data. In this paper, we evaluate the impact produced by the explicit inclusion of cognitive processes in the assistance logic of software tools design and development. We conducted an empirical experiment comparing data analysis performance using traditional software versus data analysis performance using software-assistance tools which incorporate cognitive processes in their design. The experiment is designed in terms of accuracy, efficiency, productivity and user satisfaction during the data analysis made by researchers. It allowed us to find some clear benefits of the cognitive inclusion in the software designed for research contexts, with statistically significant differences in terms of accuracy, productivity and researchers satisfaction in support of this explicit inclusion, although some efficiency weaknesses are detected. We also discuss the implications of these results for the priority of cognitive inclusion in the software tools design for research contexts data analysis.

Prior Research Design

The main objective of the research carried out here is determining and showing how it is possible for software

Dealing with Archaeological Particularities

Now that we have contextualized and motivated why it is important a co-research approach to assist via software knowledge generation processes, we are ready to focus on the archaeological domain and their particularities. As we have showed in previous chapters, archaeological data sets (and in general the conceptualization of the archaeological entities and processes) present some characteristics that, without represent unique situation because most of them are common in other humanistic disciplines, their treatment is essential for an effective approach to the topic.

Prior Empirical Results

In the previous chapter, an in-depth exploration of the problem (how to use software to assist to the generation of knowledge in archaeology) was given, along with a description of existing studies in related areas.

Presentation and Interaction Mechanisms

In recent decades, the strategic importance of software data interaction and presentation techniques for the analysis of large volumes of data (Big Data), along with their use in decision making, has grown considerably with the appearance of emerging disciplines [148], professions [66] and techniques which assist human beings in the handling and interpretation of data.

Existing Techniques and Tools

Dealing with abstraction processes that takes part in the human mind is not an easy task. As other kind of research areas.