An XML Framework for Integrating Continuous Queries, Composite Event Detection, and Database Condition Monitoring for Multiple Data Streams

With advancements in technology over the last ten years, data management issues have evolved from a stored persistent form to also include streaming data generated from sensors and other software monitoring tools. Furthermore, distributed, event-based systems are becoming more prevalent, with a need to develop applications that can dynamically respond to information extracted from data streams. This research is investigating the integration of stream processing and event processing techniques, with expressive filtering capabilities that include queries over persistent databases to provide application context to the filtering process. Distributed Event Processing Agents (DEPAs) continuously filter events from multiple data streams of different formats that provide XML views. Composite events for data streams are expressed using the Composite Event Detection Language (CEDL) and mapped to Composite XQuery (CXQ) for implementation. CXQ is a language that extends XQuery with features from CEDL, including operators for expressing sequence, disjunction, conjunction, repetition, aggregation, and time windows for events. Continuous queries and composite event filters are integrated with techniques for materialized view maintenance and incremental evaluation in condition monitoring to provide efficient ways of enhancing stream filters with database queries. The filtering and event detection load is distributed among multiple DEPAs, with CXQ expressions decomposed to allocate subcomponents of the expression to DEPAs that efficiently communicate in the global detection of composite events. A unique aspect of our research is that it extends XQuery with temporal, composite event features to combine techniques for continuous queries in stream processing, incremental evaluation in condition monitoring, and detection and filtering of composite events, creating an expressive environment for the extraction of meaningful events from multiple data streams with XML views

On Detection of Black Hole Quasi-Normal Ringdowns: Detection Efficiency and Waveform Parameter Determination in Matched Filtering

Gravitational radiation from a slightly distorted black hole with ringdown waveform is well understood in general relativity. It provides a probe for direct observation of black holes and determination of their physical parameters, masses and angular momenta (Kerr parameters). For ringdown searches using data of gravitational wave detectors, matched filtering technique is useful. In this paper, we describe studies on problems in matched filtering analysis in realistic gravitational wave searches using observational data. Above all, we focus on template constructions, matches or signal-to-noise ratios (SNRs), detection probabilities for Galactic events, and accuracies in evaluation of waveform parameters or black hole hairs. We have performed matched filtering analysis for artificial ringdown signals which are generated with Monte-Carlo technique and injected into the TAMA300 observational data. It is shown that with TAMA300 sensitivity, the detection probability for Galactic ringdown events is about 50% for black holes of masses greater than $20 M_{\odot}$ with SNR $> 10$. The accuracies in waveform parameter estimations are found to be consistent with the template spacings, and resolutions for black hole masses and the Kerr parameters are evaluated as a few % and $\sim 40$, respectively. They can be improved up to $< 0.9$ and $< 24$ for events of ${\rm SNR} \ge 10$ by using fine-meshed template bank in the hierarchical search strategy.Comment: 10 pages, 10 figure

A Survey on IT-Techniques for a Dynamic Emergency Management in Large Infrastructures

This deliverable is a survey on the IT techniques that are relevant to the three use cases of the project EMILI. It describes the state-of-the-art in four complementary IT areas: Data cleansing, supervisory control and data acquisition, wireless sensor networks and complex event processing. Even though the deliverable’s authors have tried to avoid a too technical language and have tried to explain every concept referred to, the deliverable might seem rather technical to readers so far little familiar with the techniques it describes

A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal

Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured "noises". As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in performing efficient signal separation. We address here the problem of multiple reflections, caused by wave-field bouncing between layers. Since only approximate models of these phenomena are available, we propose a flexible framework for time-varying adaptive filtering of seismic signals, using sparse representations, based on inaccurate templates. We recast the joint estimation of adaptive filters and primaries in a new convex variational formulation. This approach allows us to incorporate plausible knowledge about noise statistics, data sparsity and slow filter variation in parsimony-promoting wavelet frames. The designed primal-dual algorithm solves a constrained minimization problem that alleviates standard regularization issues in finding hyperparameters. The approach demonstrates significantly good performance in low signal-to-noise ratio conditions, both for simulated and real field seismic data

Synthetic retina for AER systems development

Neuromorphic engineering tries to mimic biology in information processing. Address-Event Representation (AER) is a neuromorphic communication protocol for spiking neurons between different layers. AER bio-inspired image sensor are called “retina”. This kind of sensors measure visual information not based on frames from real life and generates corresponding events. In this paper we provide an alternative, based on cheap FPGA, to this image sensors that takes images provided by an analog video source (video composite signal), digitalizes it and generates AER streams for testing purposes.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-0

A Monitoring Language for Run Time and Post-Mortem Behavior Analysis and Visualization

UFO is a new implementation of FORMAN, a declarative monitoring language, in which rules are compiled into execution monitors that run on a virtual machine supported by the Alamo monitor architecture.Comment: In M. Ronsse, K. De Bosschere (eds), proceedings of the Fifth International Workshop on Automated Debugging (AADEBUG 2003), September 2003, Ghent. cs.SE/030902

Design of a wireless passive sensing system for impact detection of aerospace composite structures

In this paper, the design and implementation of a novel on-board wireless passive sensing system for impact detection of composite airframe is presented for the first time. Several modules, including filtering, impact detection, local processing and wireless transmission are designed and evaluated for detecting rare, random and transitory impact events. An event-triggered mechanism with high responsiveness is adopted to reduce the system power dissipation and to maintain the detection effectiveness. This design allows the system to be adaptive, energy-efficient and highly responsive to impacts. The whole system was implemented in an experimental study, and the effectiveness was evaluated and illustrated. The system was woken up by impact events in around 12 µs, and the impact data were recorded at 200 kHz (up to 5.33 MHz). This work provides a guideline for low-power, high-responsiveness passive on-board sensing system design. This system can also be adapted to other sensing applications in aerospace engineering