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Data- and model-driven computer simulations are increasingly critical in many application domains. These simulations may track 10s or 100s of parameters, affected by complex inter-dependent dynamic processes. Moreover, decision makers usually need to run large simulation ensembles, containing 1000s of simulations. In this paper, we rely on a tensor-based framework to represent and analyze patterns in large simulation ensemble data sets to obtain a high-level understanding of the dynamic processes implied by a given ensemble of simulations.We, further, note that the inherent sparsity of the simulation ensembles (relative to the space of potential simulations one can run) constitutes a significant problem in discovering these underlying patterns. To address this challenge, we propose a partition-stitch sampling scheme, which divides the parameter space into subspaces to collect several lower modal ensembles, and complement this with a novel Multi-Task Tensor Decomposition (M2TD), technique which helps effectively and efficiently stitch these subensembles back. Experiments showed that, for a given budget of simulations, the proposed structured sampling scheme leads to significantly better overall accuracy relative to traditional sampling approaches, even when the user does not have perfect information to help guide the structured partitioning process.

The problem of finding interesting tuples in a data set, more commonly known as the skyline problem, has been extensively studied in scenarios where the data is static. More recently, skyline research has moved towards data streaming environments, where tuples arrive/expire in a continuous manner. Several algorithms have been developed to track skyline changes over sliding windows; however, existing methods focus on skyline analysis in which all required skyline attributes belong to a single incoming data stream. This constraint renders current algorithms unsuitable for applications that require a real-time “join” operation to be carried out between multiple incoming data streams, arriving from different sources, before the skyline query can be answered. Based on this motivation, in this paper, we address the problem of computing skyline-window-join (SWJ) queries over pairs of data streams, considering sliding windows that take into account only the most recent tuples. In particular, we propose a Layered Skyline-window-Join (LSJ) operator that (a) partitions the overall process into processing layers and (b) maintains skyline-join results in an incremental manner by continuously monitoring the changes in all layers of the process. We combine the advantages of existing skyline methods (including those that efficiently maintain skyline results over a single stream, and those that compute the skyline of pairs of static data sets) to develop a novel iteration-fabric skyline-window-join processing structure. Using the iteration-fabric, LSJ eliminates redundant work across consecutive windows by leveraging shared data across all iteration layers of the windowed skyline-join processing. To the best of our knowledge, this is the first paper that addresses join-based skyline queries over sliding windows. Extensive experimental evaluations over real and simulated data show that LSJ provides large gains over naive extensions of existing schemes which are not designed to eliminate redundant work across multiple processing layers.

Media-rich ubiquitous distributed media processing workflow systems continuously sense users' needs, status, and the context, filter and fuse a multitude of real-time media data, and react by adapting the environment to the user. One challenge facing these systems is that they need to process real-time data arriving continuously from the sensors and data rates and qualities may vary dramatically. Thus, reducing the amount of unqualified data objects that need to be processed within the underlying media processing workflows can enhance the performance significantly. In this paper, we first focus on the prediction of the output qualities at the actuator, especially in the presence of fusion operators in the workflow. We then present quality-aware early object elimination schemes to enable informed resource savings in continuous real-time media processing workflow middleware.

In situ wireless sensor networks, not only have to route sensed data from sources to destinations, but also have to filter and fuse observations to eliminate potentially irrelevant data. If data arrive faster to such fusion nodes than the speed with which they can consume the inputs, this will result in an overflow of input buffers. In this paper, we develop load shedding mechanisms which take into consideration both data quality and expensive nature of fusion operators. In particular, we present quality assessment models for objects and fusion operators and we highlight that such quality assessments may impose partial orders on objects.

Basic message processing tasks, such as well-formedness checking and grammar validation, can be off-loaded from the service providers' own infrastructures. To enable effective off-loading of processing tasks, we introduce the Prefix Automata SyStem - PASS, a middleware architecture which distributively processes XML payloads of Web service SOAP messages during their routing towards Web servers. PASS is based on a network of automata, where PASS-nodes independently but cooperatively process parts of the SOAP message XML payload.

ARIA, ARchitecture for Interactive Arts, is a middleware to process, filter, and fuse sensory inputs and actuate responses in real-time. An ARIA media processing workflow describes how the data sensed through media will be processed and what audio-visual responses will be actuated. Each object streamed between ARIA processing components is subject to transformations, as described by a media workflow graph. The media capture and processing components, such as media filters and fusion operators, are programmable and adaptable; i.e, the delay, size, frequency, and quality/precision characteristics of individual operators can be controlled via a number of parameters. In this paper, we present the underlying model which captures the dynamic nature of the ARIA media processing workflows. This model enables design time verification, optimization, and runtime adaptation.

The capability of extracting critical information from a live video signal and presenting it to end-users in an easy-to-grasp form is essential in many application domains, including GIS, intelligence, surveillance, and manufacturing. We outline on the development of methods and algorithms that are necessary for real-time analysis of video data, both in compressed or uncompressed domains, the generation of visual icons that embody the characteristics of the objects of interest (say enemy craft, main actor, star player, etc.) and presentation of the panoramic spatio-temporal view of the entire scene in the form of video mosaics.