Incremental Discovery of Prominent Situational Facts

We study the novel problem of finding new, prominent situational facts, which are emerging statements about objects that stand out within certain contexts. Many such facts are newsworthy---e.g., an athlete's outstanding performance in a game, or a viral video's impressive popularity. Effective and efficient identification of these facts assists journalists in reporting, one of the main goals of computational journalism. Technically, we consider an ever-growing table of objects with dimension and measure attributes. A situational fact is a "contextual" skyline tuple that stands out against historical tuples in a context, specified by a conjunctive constraint involving dimension attributes, when a set of measure attributes are compared. New tuples are constantly added to the table, reflecting events happening in the real world. Our goal is to discover constraint-measure pairs that qualify a new tuple as a contextual skyline tuple, and discover them quickly before the event becomes yesterday's news. A brute-force approach requires exhaustive comparison with every tuple, under every constraint, and in every measure subspace. We design algorithms in response to these challenges using three corresponding ideas---tuple reduction, constraint pruning, and sharing computation across measure subspaces. We also adopt a simple prominence measure to rank the discovered facts when they are numerous. Experiments over two real datasets validate the effectiveness and efficiency of our techniques

A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

Doing business with animals : moral entrepreneurship and ethical room for manoeuvre in livestock related sector

The overall objective of this dissertation is to study moral entrepreneurship within animal and business ethics in relation to moral change. In particular the current capability in bringing about moral change and its potential to do so

Improving human interaction research through ecological grounding

In psychology, we tend to follow the general logic of falsificationism: we separate the ‘context of discovery’ (how we come up with theories) from the ‘context of justification’ (how we test them). However, when studying human interaction, separating these contexts can lead to theories with low ecological validity that do not generalize well to life outside the lab. We propose borrowing research procedures from well-established inductive methodologies in interaction research during the process of discovering new regularities and analyzing natural data without being led by theory. We introduce research procedures including the use of naturalistic study settings, analytic transcription, collections of cases, and data analysis sessions, and illustrate these with examples from a successful cross-disciplinary study. We argue that if these procedures are used systematically and transparently throughout a research cycle, they will lead to more robust and ecologically valid theories about interaction within psychology and, with some adaptation, can enhance the reproducibility of research across many other areas of psychological science

Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

A TETRAD-based Approach for Theory Development in Information Systems Research

Theory development and theory testing are two primary processes in social science research. Statistical methods and tools are used in various stages of these processes. Information systems researchers have employed many statistical methods and tools for theory testing. However, very few statistical approaches are known to help researchers with theory development. In this paper, we introduce TETRAD as a powerful approach to aid researchers in developing and discovering new theoretical relationships. We illustrate the TETRAD approach by re-analyzing data from two articles published in premier information systems journals. The results from the previous examples demonstrate that TETRAD is a useful tool for uncovering potential theoretical relationships, especially when prior knowledge of underlying theory bases is lacking. We demonstrate that TETRAD is an effective and powerful statistical tool that can assist researchers in the iterative process of theory development