Clear Visual Separation of Temporal Event Sequences

Extracting and visualizing informative insights from temporal event sequences becomes increasingly difficult when data volume and variety increase. Besides dealing with high event type cardinality and many distinct sequences, it can be difficult to tell whether it is appropriate to combine multiple events into one or utilize additional information about event attributes. Existing approaches often make use of frequent sequential patterns extracted from the dataset, however, these patterns are limited in terms of interpretability and utility. In addition, it is difficult to assess the role of absolute and relative time when using pattern mining techniques. In this paper, we present methods that addresses these challenges by automatically learning composite events which enables better aggregation of multiple event sequences. By leveraging event sequence outcomes, we present appropriate linked visualizations that allow domain experts to identify critical flows, to assess validity and to understand the role of time. Furthermore, we explore information gain and visual complexity metrics to identify the most relevant visual patterns. We compare composite event learning with two approaches for extracting event patterns using real world company event data from an ongoing project with the Danish Business Authority.Comment: In Proceedings of the 3rd IEEE Symposium on Visualization in Data Science (VDS), 201

Should I Bug You? Identifying Domain Experts in Software Projects Using Code Complexity Metrics

In any sufficiently complex software system there are experts, having a deeper understanding of parts of the system than others. However, it is not always clear who these experts are and which particular parts of the system they can provide help with. We propose a framework to elicit the expertise of developers and recommend experts by analyzing complexity measures over time. Furthermore, teams can detect those parts of the software for which currently no, or only few experts exist and take preventive actions to keep the collective code knowledge and ownership high. We employed the developed approach at a medium-sized company. The results were evaluated with a survey, comparing the perceived and the computed expertise of developers. We show that aggregated code metrics can be used to identify experts for different software components. The identified experts were rated as acceptable candidates by developers in over 90% of all cases

Taxonomy for Humans or Computers? Cognitive Pragmatics for Big Data

Criticism of big data has focused on showing that more is not necessarily better, in the sense that data may lose their value when taken out of context and aggregated together. The next step is to incorporate an awareness of pitfalls for aggregation into the design of data infrastructure and institutions. A common strategy minimizes aggregation errors by increasing the precision of our conventions for identifying and classifying data. As a counterpoint, we argue that there are pragmatic trade-offs between precision and ambiguity that are key to designing effective solutions for generating big data about biodiversity. We focus on the importance of theory-dependence as a source of ambiguity in taxonomic nomenclature and hence a persistent challenge for implementing a single, long-term solution to storing and accessing meaningful sets of biological specimens. We argue that ambiguity does have a positive role to play in scientific progress as a tool for efficiently symbolizing multiple aspects of taxa and mediating between conflicting hypotheses about their nature. Pursuing a deeper understanding of the trade-offs and synthesis of precision and ambiguity as virtues of scientific language and communication systems then offers a productive next step for realizing sound, big biodiversity data services

British research in accounting and finance (2001–2007): the 2008 research assessment exercise

No abstract available

Understandings and Misunderstandings of Multidimensional Poverty Measurement

Multidimensional measures provide an alternative lens through which poverty may be viewed and understood. In recent work we have attempted to offer a practical approach to identifying the poor and measuring aggregate poverty (Alkire and Foster 2011). As this is quite a departure from traditional unidimensional and multidimensional poverty measurement – particularly with respect to the identification step – further elaboration may be warranted. In this paper we elucidate the strengths, limitations, and misunderstandings of multidimensional poverty measurement in order to clarify the debate and catalyse further research. We begin with general definitions of unidimensional and multidimensional methodologies for measuring poverty. We provide an intuitive description of our measurement approach, including a ‘dual cutoff’ identification step that views poverty as the state of being multiply deprived, and an aggregation step based on the traditional Foster Greer and Thorbecke (FGT) measures. We briefly discuss five characteristics of our methodology that are easily overlooked or mistaken and conclude with some brief remarks on the way forward.