Exploring ‘events’ as an information systems research methodology

This paper builds upon existing research and commentary from a variety of disciplinary sources including Information Systems, Organisational and Management Studies, and the Social Sciences that focus upon the meaning, significance and impact of ‘events’ in both an organisational and a social sense. The aim of this paper is to define how the examination of the event is an appropriate, viable and useful Information Systems methodology. Our argument is that focusing on the ‘event’ enables the researcher to more clearly observe and capture the complexity, multiplicity and mundaneity of everyday lived experience. The use and notion of ‘event’ has the potential to reduce the methodological dilemmas associated with the micromanagement of the research process – an inherent danger of traditional and ‘virtual' ethnographic approaches. Similarly, this paper addresses the over-emphasis upon managerialist, structured and time-fixated praxis that is currently symptomatic of Information Systems research. All of these concerns are pivotal points of critique found within eventoriented literature. An examination of event-related theory within interpretative disciplines directs the focus of this paper towards the more specific realm of the ‘event scene’. The notion of the ‘event scene’ originated in the action based (and anti-academy) imperatives of the Situationists and emerged in an academic sense as critical situational analysis. Event scenes are a focus for contemporary critical theory where they are utilised as a means of representing theoried inquiry in order to loosen the restrictions that historical and temporally bound analysis imposes upon most interpretative approaches. The use of event scenes as the framework for critiquing established conceptual assumptions is exemplified by their use in CTheory. In this journal's version and articulation of the event scene poetry, commentary, multi-vocal narrative and other techniques are legitimated as academic forms. These various forms of multi-dimensional expression are drawn upon to enrich the understandings of the ‘event’, to extricate its meaning and to provide a sense of the moment from which the point of analysis stems. The objective of this paper is to advocate how Information Systems research can (or should) utilize an event scene oriented methodology

A Graph-based Technique for Higher Order Topological Data Structure Visualisation

Esta publicação foi agraciada com o prémio GISRUK 2005 “Whittles Publishing” Best Paper Award.Interpretation and analysis of spatial phenomena is a highly time consuming and laborious task in several fietds of the Geomatics world (Anders et al., 1999). That is why the automation of those tasks is especially needed in areas such as Geographical Information Science (GlScience). Carrying out these tasks in the context of an urban scene is particulariy challenging given its complexity: relatively small component elements and itt"it g"nrially complei spatial pattern (Eyton, 1993, and Barr & Barnsley, 1996, both cited in Barnsley and Barr, 1997). Topology is a particularly important research area in the field of GlScience, for it is a central àefining feature of a geographical information system (GIS). But, as far as topological relàtionships between spatial objects are concerned, "generally speaking .ottt.Àporary desktop bIS packages do not support further information beyond the first level oi adjâcency" (Theobald, 2001). Therefore, this research project focused on scene analysis bi buiiding up a technique for the better understanding of topological relationships between vector-based GIS objects, beyond the fnst level of adjacency. Another initial interest was to investigate the possible use of graph theory for this purpose. To date, this mathematical framework has been used in different applications in a wide range of fields to represent connections and relationships between spatial entities. Several u,rtùo6 (including Laurini and Thompson, 1992) have maintained that "this particular tool is extremely valuable and efficient in storing and describing the spatial structure of geographicil entities and their spatial arrangement". Theobald (2001) added that "concepts àf gruptt theory allow us to extend the standard notion of adjacency". The aim of retrieving structured information translated into more meaningful homogeneous regions, for instancJ fro* an initial unstructured data set, may be achieved by identifuing mJaningful structures within the initial random collection of objects and by understanding the spatial arrangement between them. We believe that applying graph theory and carrying out graph analysis may accomplish this

On the complexity of collaborative cyber crime investigations

This article considers the challenges faced by digital evidence specialists when collaborating with other specialists and agencies in other jurisdictions when investigating cyber crime. The opportunities, operational environment and modus operandi of a cyber criminal are considered, with a view to developing the skills and procedural support that investigators might usefully consider in order to respond more effectively to the investigation of cyber crimes across State boundaries

Using dempster-shafer theory to fuse multiple information sources in region-based segmentation

This paper presents a new method for segmentation of images into large regions that reflect the real world objects present in a scene. It explores the feasibility of utilizing spatial configuration of regions and their geometric properties (the so-called Syntactic Visual Features [1]) for improving the correspondence of segmentation results produced by the well-known Recursive Shortest Spanning Tree (RSST) algorithm [2] to semantic objects present in the scene. The main contribution of this paper is a novel framework for integration of evidence from multiple sources with the region merging process based on the Dempster-Shafer (DS) theory [3] that allows integration of sources providing evidence with different accuracy and reliability. Extensive experiments indicate that the proposed solution limits formation of regions spanning more than one semantic object

Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy

In this paper we shall consider the problem of deploying attention to subsets of the video streams for collating the most relevant data and information of interest related to a given task. We formalize this monitoring problem as a foraging problem. We propose a probabilistic framework to model observer's attentive behavior as the behavior of a forager. The forager, moment to moment, focuses its attention on the most informative stream/camera, detects interesting objects or activities, or switches to a more profitable stream. The approach proposed here is suitable to be exploited for multi-stream video summarization. Meanwhile, it can serve as a preliminary step for more sophisticated video surveillance, e.g. activity and behavior analysis. Experimental results achieved on the UCR Videoweb Activities Dataset, a publicly available dataset, are presented to illustrate the utility of the proposed technique.Comment: Accepted to IEEE Transactions on Image Processin

Probabilistic ToF and Stereo Data Fusion Based on Mixed Pixel Measurement Models

This paper proposes a method for fusing data acquired by a ToF camera and a stereo pair based on a model for depth measurement by ToF cameras which accounts also for depth discontinuity artifacts due to the mixed pixel effect. Such model is exploited within both a ML and a MAP-MRF frameworks for ToF and stereo data fusion. The proposed MAP-MRF framework is characterized by site-dependent range values, a rather important feature since it can be used both to improve the accuracy and to decrease the computational complexity of standard MAP-MRF approaches. This paper, in order to optimize the site dependent global cost function characteristic of the proposed MAP-MRF approach, also introduces an extension to Loopy Belief Propagation which can be used in other contexts. Experimental data validate the proposed ToF measurements model and the effectiveness of the proposed fusion techniques

Socially Constrained Structural Learning for Groups Detection in Crowd

Modern crowd theories agree that collective behavior is the result of the underlying interactions among small groups of individuals. In this work, we propose a novel algorithm for detecting social groups in crowds by means of a Correlation Clustering procedure on people trajectories. The affinity between crowd members is learned through an online formulation of the Structural SVM framework and a set of specifically designed features characterizing both their physical and social identity, inspired by Proxemic theory, Granger causality, DTW and Heat-maps. To adhere to sociological observations, we introduce a loss function (G-MITRE) able to deal with the complexity of evaluating group detection performances. We show our algorithm achieves state-of-the-art results when relying on both ground truth trajectories and tracklets previously extracted by available detector/tracker systems