Abstract. Computational Grids are distributed systems that provide access to computational resources in a transparent fashion. Collecting and providing information about the status of the Grid itself is called Grid monitoring. We describe R-GMA (Relational Grid Monitoring Architecture) as a solution to the Grid monitoring problem. It uses a local as view approach to information integration and will be a component of the European Union’s DataGrid. The R-GMA architecture and mechanisms are general and could be used in other areas where there is a need for publishing and querying information in a distributed fashion. 1

Data streams appear in many recent applications, where data are constantly changing or take the form of continuously arriving streams. We focus on data streams generated by sensors for monitoring users in immersive environments. To recognize users ' interactions, we need to analyze the aggregation of several sensor data streams and match the result to a set of known actions. In addition, we need to separate a continuous series of actions into recognizable atomic actions. Hence, we first propose a distance metric, weighted-sum Singular Value Decomposition (SVD), suitable for similarity measurement of immersive data sequences. Subsequently, we propose a mutual information based heuristic for separation of the action sequences. Finally, we perform several empirical experiments using real-world virtual-reality devices to verify the effectiveness of our approach. 1

Abstract. Feature subset selection (FSS) is one of the techniques to preprocess the data before performing any data mining tasks, e.g., classification and clustering. FSS provides both cost-effective predictors and a better understanding of the underlying process that generated data. We propose a novel method of FSS for Multivariate Time Series (MTS) based on Common Principal Component Analysis, termed CLeVer. Traditional FSS techniques, such as Recursive Feature Elimination (RFE) and Fisher Criterion (FC), have been applied to MTS datasets, e.g., Electro Encephalogram (EEG) datasets. However, these techniques may lose the correlation information among features, while our proposed technique utilizes the properties of the principal component analysis to retain that information. In order to evaluate the effectiveness of our selected subset of features, we employ classification as the target data mining task. Our exhaustive sets of experiments show that CLeVer outperforms RFE and FC by up to 100 % in terms of classification accuracy, while requiring significantly less processing time (up to 2 orders of magnitude) than RFE and FC.

Abstract. Grids are distributed systems that provide access to computational resources in a transparent fashion. Providing information about the status of the Grid itself is called Grid monitoring. As an approach to this problem, we present the Relational Grid Monitoring Architecture (R-GMA), which tackles Grid monitoring as an information integration problem. A novel feature of R-GMA is its support for integrating stream data via a simple “local as view ” approach. We describe the infrastructure that R-GMA provides for publishing and querying monitoring data. In this context, we discuss the semantics of continuous queries, provide characterisations of query plans, and present an algorithm for computing such plans. The concepts and mechanisms offered by R-GMA are general and can be applied in other areas where there is a need for publishing and querying information in a distributed fashion. 1

We present a continuous and unobtrusive approach to analyze and reason about users ’ personal experiences of interacting with virtual and game environments. Focusing on an immersive educational game environment that we are developing, this is achieved through the capture and storage of user’s movements and events that occur as a result of interactions with and within immersive environments. Termed immersidata, we then query and analyze immersidata to make sense of user behavior. Two example approaches are described. The first describes an application ISIS (Immersidata analySIS) that provides a tool for analysis of user behavior/experience through the indexing of immersidata with video clips of students’ gaming sessions. This approach is described by way of an example to identify the causes of interruptions or breaks in interactions/focus of attention to facilitate the identification of problematic design. In our second example we describe our work towards classifying students ’ performance through immersidata. To this aim, we describe one example of transforming immersidata into multivariate time series and then by applying feature subset selection techniques we identify the features that differentiate students. We describe the application of this approach to identify novice and expert players with 90 % accuracy. One proposal is to use this to customize the game environment appropriate to the students’ ability. Finally, we present future directions for the continuation of the work presented herein and also, the application of the immersidata system to capture, store and analyze personal behavior/experiences and provide appropriate feedback in our work and home environments.

Visual Sensor Networks (VSNs) represent a qualitative leap in functionality over existing sensornets. Cooperating networks of cameras could reconstruct features in three dimensions, produce images from novel viewpoints, match trajectories or objects against known patterns, or combine these tasks to provide a flexible monitoring system. With high data rates and precise calibration requirements, VSNs present challenges not faced by today’s sensornets. The power and bandwidth required to transmit video data from hundreds or thousands of cameras to a central location for processing would be enormous. A network of smart cameras will process video data in real time, extracting features and 3D geometry from the raw images of cooperating cameras. These results will be stored and processed in the network, near their origin. New content-routing techniques will allow cameras to find common features—critical for calibration, search, and tracking. A novel query mechanism will mediate access to this distributed datastore, allowing high-level features to be described as compositions in space-time of simpler features. 1.