General Boolean Expressions in Publish-Subscribe Systems

The increasing amount of electronically available information in society today is undeniable. Examples include the numbers of general web pages, scientific publications, and items in online auctions. From a user's perspective, this trend will lead to information overflow. Moreover, information publishers are compromised by this situation, as users have greater difficulty in identifying useful information. Publish-subscribe systems can be applied to cope with the reality of information overflow. In these systems, users specify their information interests as subscriptions and, subsequently, only matching information (event messages) is delivered; uninteresting information is filtered out before reaching users. In this dissertation, we consider content-based publish-subscribe systems, a sophisticated example of these systems. They perform the information-filtering task based on the content of provided information. In order to deal with high numbers of subscriptions and frequencies of event messages, publish-subscribe systems are realized as distributed systems. Advertisements---publisher specifications of potential future event messages---are optionally applied in these systems to reduce the internal distribution of subscriptions. Existing work on content-based publish-subscribe concepts mainly focuses on subscriptions and advertisements as pure conjunctive expressions. Therefore, subscriptions or advertisements using operators other than conjunction need to be canonically converted to disjunctive normal form by these systems. Each conjunctive component is then treated as individual subscription or advertisement. Unfortunately, the size of converted expressions is exponential in the worst case. In this dissertation, we show that the direct support of general Boolean subscriptions and advertisements improves the time and space efficiency of general-purpose content-based publish-subscribe systems. For this purpose, we develop suitable approaches for the filtering and routing of general Boolean expressions in these systems. Our approaches represent solutions to exactly those components of content-based publish-subscribe systems that currently restrict subscriptions and advertisements to conjunctive expressions. On the subscription side, we present an effective generic filtering algorithm, and a novel approach to optimize event routing tables, which we call subscription pruning. To support advertisements, we show how to calculate the overlap between subscriptions and advertisements, and introduce the first designated subscription routing optimization, which we refer to as advertisement pruning. We integrate these approaches into our prototype BoP (BOolean Publish-subscribe) which allows for the full support of general Boolean expressions in its filtering and routing components. In the evaluation part of this dissertation, we empirically analyze our prototypical implementation BoP and compare its algorithms to existing conjunctive solutions. We firstly show that our general-purpose Boolean filtering algorithm is more space- and time-efficient than a general-purpose conjunctive filtering algorithm. Secondly, we illustrate the effectiveness of the subscription pruning routing optimization and compare it to the existing covering optimization approach. Finally, we demonstrate the optimization effect of advertisement pruning while maintaining the existing overlapping relationships in the system

The PyCBC search for gravitational waves from compact binary coalescence

We describe the PyCBC search for gravitational waves from compact-object binary coalescences in advanced gravitational-wave detector data. The search was used in the first Advanced LIGO observing run and unambiguously identified two black hole binary mergers, GW150914 and GW151226. At its core, the PyCBC search performs a matched-filter search for binary merger signals using a bank of gravitational-wave template waveforms. We provide a complete description of the search pipeline including the steps used to mitigate the effects of noise transients in the data, identify candidate events and measure their statistical significance. The analysis is able to measure false-alarm rates as low as one per million years, required for confident detection of signals. Using data from initial LIGO's sixth science run, we show that the new analysis reduces the background noise in the search, giving a 30% increase in sensitive volume for binary neutron star systems over previous searches.Comment: 29 pages, 7 figures, accepted by Classical and Quantum Gravit

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

The instanton method and its numerical implementation in fluid mechanics

A precise characterization of structures occurring in turbulent fluid flows at high Reynolds numbers is one of the last open problems of classical physics. In this review we discuss recent developments related to the application of instanton methods to turbulence. Instantons are saddle point configurations of the underlying path integrals. They are equivalent to minimizers of the related Freidlin-Wentzell action and known to be able to characterize rare events in such systems. While there is an impressive body of work concerning their analytical description, this review focuses on the question on how to compute these minimizers numerically. In a short introduction we present the relevant mathematical and physical background before we discuss the stochastic Burgers equation in detail. We present algorithms to compute instantons numerically by an efficient solution of the corresponding Euler-Lagrange equations. A second focus is the discussion of a recently developed numerical filtering technique that allows to extract instantons from direct numerical simulations. In the following we present modifications of the algorithms to make them efficient when applied to two- or three-dimensional fluid dynamical problems. We illustrate these ideas using the two-dimensional Burgers equation and the three-dimensional Navier-Stokes equations

Dimension-Based Subscription Pruning for Publish/Subscribe Systems

Subscription pruning has been proven as valuable routing optimization for Boolean subscriptions in publish/ subscribe systems. It aims at optimizing subscriptions independently of each other and is thus applicable for all kinds of subscriptions regardless of their individual and collective structures. The original subscription pruning approach tries to optimize the event routing process based on the expected increase in network load. However, a closer look at pruning-based routing reveals its further applicability to optimizations in respect to other dimensions. In this paper, we introduce and investigate subscription pruning based on three dimensions of optimization: network load, memory usage, and system throughput. We present the algorithms to perform prunings based on these dimensions and discuss the results of a series of practical experiments. Our analysis reveals the advantages and disadvantages of the different dimensions of optimization and allows conclusions about the suitability of dimension-based pruning for different application requirements