Monitoring of user-defined constraints on time-varying data is a fundamental functionality in various sensor-based real-time applications such as environmental monitoring, process control, location-based surveillance, etc. In general, these applications track real-world objects and constantly evaluate the constraints over the object trace to take a timely reaction upon their violation or satisfaction. While it is ideal that all the constraints are evaluated accurately in real-time, data streams often contain incomplete and delayed information, rendering the evaluation results of the constraints uncertain to some degree. In this dissertation, we provide a comprehensive approach to the problem of monitoring constraint-based queries over data streams for which the data or timestamp values are inherently uncertain. First, we propose a generic framework, namely Ptmon, for monitoring timing constraints and detecting their violation early, based on the notion of probabilistic violation time. In doing so, we provide a systemic approach for deriving a set of necessary timing constraints at compilation time. Our work is innovative in that the framework is formulated to be modular with respect to the probability distributions on timestamp values. We demonstrate the applicability of the framework for different timestamp models. Second, we present a probabilistic timing join operator, namely Ptjoin, as an extended functionality of Ptmon, which performs stream join operations based on temporal proximity as well as temporal uncertainty. To efficiently check the Ptjoin condition upon event arrivals, we introduce the stream-partitioning technique that delimits the probing range tightly. Third, we address the problem of monitoring value-based constraints that are in the form of range predicates on uncertain data values with confidence thresholds. A new monitoring scheme Spmon that can reduce the amount of data transmission and thus expedite the processing of uncertain data streams is introduced. The similarity concept that was originally intended for real-time databases is extended for our probabilistic data stream model where each data value is given by a probability distribution. In particular, for uniform and gaussian distributions, we show how we derive a set of constraints on distribution parameters as a metric of similarity distances, exploiting the semantics of probabilistic queries being monitored. The derived constraints enable us to formulate the probabilistic similarity region that suppresses unnecessary data transmission in a monitoring system. / text

Graduate

Thesis (M.S.)--Ohio University, November, 2002. / Title from PDF t.p. Includes bibliographical references (leaves 101-103).

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2008. / Includes bibliographical references (leaves 48-52). Also available in electronic version.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Thesis (M.Sc.) - Carleton University, 2002. / Includes bibliographical references (p. 98-101). Also available in electronic format on the Internet.

Thesis (Master)--İzmir Institute of Technology, İzmir, 2004 / Includes bibliographical references (leaves. 55).

The purpose of this research is to develop an optimal kernel which would be used in a real-time engineering and communications system. Since the application is a real-time system, relevant real-time issues are studied in conjunction with kernel related issues. The emphasis of the research is the development of a kernel which would not only adhere to the criteria of a real-time environment, namely determinism and performance, but also provide the flexibility and portability associated with non-real-time environments. The essence of the research is to study how the features found in non-real-time systems could be applied to the real-time system in order to generate an optimal kernel which would provide flexibility and architecture independence while maintaining the performance needed by most of the engineering applications. Traditionally, development of real-time kernels has been done using assembly language. By utilizing the powerful constructs of the C language, a real-time kernel was developed which addressed the goals of flexibility and portability while still meeting the real-time criteria. The implementation of the kernel is carried out using the powerful 68010/20/30/40 microprocessor based systems.

A Thesis Submitted to the Faculty 0/ Engineering, University 0/ Lite Witwatersrand, Johannesburg in fulfilment 0/ the requirements /01' the Degree 0/ Doctor 0/ Philosophy. Johannesburg 1990. / This thesis studies fault tolerant strategies for real-time distributed computer control systems so as to propose an error recovery technique that renders individual processors on the network resistant to soft failures. The technique is effective for soft failures which have as certain maximum duration, and does not require the use of specialised hardware. Attention is focused on achieving resistance to soft failures in environments which have demanding time constraints such as those found in computer systems for process control, materials handling and automated manufacturing. [Abbreviated Abstract. Open document to view full version]

Fleissner Sebastian. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 91-95). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Legend of Hercules --- p.1 / Chapter 1.2 --- Background --- p.2 / Chapter 1.3 --- Research Purpose --- p.2 / Chapter 1.4 --- Research Overview --- p.3 / Chapter 1.5 --- Thesis Organization --- p.4 / Chapter 2 --- Background and Definitions --- p.6 / Chapter 2.1 --- General Purpose GPU Computing --- p.6 / Chapter 2.1.1 --- Four Generations of GPU Hardware --- p.6 / Chapter 2.1.2 --- GPU Architecture & Terms --- p.7 / Chapter 2.1.3 --- General Purpose GPU Programming --- p.9 / Chapter 2.1.4 --- Shader Programming Languages --- p.12 / Chapter 2.2 --- Cryptography Overview --- p.13 / Chapter 2.2.1 --- "Alice, Bob, and Friends" --- p.14 / Chapter 2.2.2 --- Cryptographic Hash Functions --- p.14 / Chapter 2.2.3 --- Secret Key Ciphers --- p.15 / Chapter 2.2.4 --- Public Key Encryption --- p.16 / Chapter 2.2.5 --- Digital Signatures --- p.17 / Chapter 2.3 --- The Montgomery Method --- p.18 / Chapter 2.3.1 --- Pre-computation Step --- p.19 / Chapter 2.3.2 --- Obtaining the Montgomery Representation --- p.19 / Chapter 2.3.3 --- Calculating the Montgomery Product(s) --- p.19 / Chapter 2.3.4 --- Calculating final result --- p.20 / Chapter 2.3.5 --- The Montgomery Exponentiation Algorithm . . --- p.20 / Chapter 2.4 --- Elliptic Curve Cryptography --- p.21 / Chapter 2.4.1 --- Introduction --- p.21 / Chapter 2.4.2 --- Recommended Elliptic Curves --- p.22 / Chapter 2.4.3 --- Coordinate Systems --- p.23 / Chapter 2.4.4 --- Point Doubling --- p.23 / Chapter 2.4.5 --- Point Addition --- p.24 / Chapter 2.4.6 --- Double and Add --- p.25 / Chapter 2.4.7 --- Elliptic Curve Encryption --- p.26 / Chapter 2.5 --- Related Research --- p.28 / Chapter 2.5.1 --- Secret Key Cryptography on GPUs --- p.28 / Chapter 2.5.2 --- Remotely Keyed Cryptographics --- p.29 / Chapter 3 --- Proposed Algorithms --- p.30 / Chapter 3.1 --- Introduction --- p.30 / Chapter 3.2 --- Chapter Organization --- p.31 / Chapter 3.3 --- Algorithm Design Issues --- p.31 / Chapter 3.3.1 --- Arithmetic Density and GPU Memory Access . --- p.31 / Chapter 3.3.2 --- Encoding Large Integers with Floating Point Numbers --- p.33 / Chapter 3.4 --- GPU Montgomery Algorithms --- p.34 / Chapter 3.4.1 --- Introduction --- p.34 / Chapter 3.4.2 --- GPU-FlexM-Prod Specification --- p.37 / Chapter 3.4.3 --- GPU-FlexM-Mul Specification --- p.43 / Chapter 3.4.4 --- GPU-FlexM-Exp Specification --- p.45 / Chapter 3.4.5 --- GPU-FixM-Prod Specification --- p.46 / Chapter 3.4.6 --- GPU-FixM-Mul Specification --- p.50 / Chapter 3.4.7 --- GPU-FixM-Exp Specification --- p.52 / Chapter 3.5 --- GPU Elliptic Curve Algorithms --- p.54 / Chapter 3.5.1 --- GPU-EC-Double Specification --- p.55 / Chapter 3.5.2 --- GPU-EC-Add Specification --- p.59 / Chapter 3.5.3 --- GPU-EC-DoubleAdd Specification --- p.64 / Chapter 4 --- Analysis of Proposed Algorithms --- p.67 / Chapter 4.1 --- Performance Analysis --- p.67 / Chapter 4.1.1 --- GPU-FlexM Algorithms --- p.69 / Chapter 4.1.2 --- GPU-FixM Algorithms --- p.72 / Chapter 4.1.3 --- GPU-EC Algorithms --- p.77 / Chapter 4.1.4 --- Summary --- p.82 / Chapter 4.2 --- Usability of Proposed Algorithms --- p.83 / Chapter 4.2.1 --- Signcryption --- p.84 / Chapter 4.2.2 --- Pure Asymmetric Encryption and Decryption --- p.85 / Chapter 4.2.3 --- Simultaneous Signing of Multiple Messages --- p.86 / Chapter 4.2.4 --- Relieving the Main Processor --- p.87 / Chapter 5 --- Conclusions --- p.88 / Chapter 5.1 --- Research Results --- p.88 / Chapter 5.2 --- Future Research --- p.89 / Bibliography --- p.91