No description available.

No description available.

No description available.

No description available.

The vision of the future Internet of Things is posing new challenges due to gigabytes of data being generated everyday by millions of sensors, actuators, RFID tags, and other devices. As the volume of data is growing dramatically, so is the demand for performance enhancement. When it comes to this big data problem, much attention has been given to cloud computing and virtualization for their almost unlimited resource capacity, flexible resource allocation and management, and distributed processing ability that promise high scalability and availability. On the other hand, the variety of types and nature of data is continuously increasing. Almost without exception, data centers supporting cloud based services are monitored for performance and security and the resulting monitoring data needs to be stored somewhere. Similarly, billions of sensors that are scattered throughout the world are pumping out huge amount of data, which is handled by a database. Typically, the monitoring data consists time series, that is numbers indexed by time. To handle this type of time series data a distributed time series database is needed.   Nowadays, many database systems are available but it is difficult to use them for storing and managing large volumes of time series data. Monitoring large amounts of periodic data would be better done using a database optimized for storing time series data. The traditional and dominant relational database systems have been questioned whether they can still be the best choice for current systems with all the new requirements. Choosing an appropriate database for storing huge amounts of time series data is not trivial as one must take into account different aspects such as manageability, scalability and extensibility. During the last years NoSQL databases have been developed to address the needs of tremendous performance, reliability and horizontal scalability. NoSQL time series databases (TSDBs) have risen to combine valuable NoSQL properties with characteristics of time series data from a variety of use-cases.   In the same way that performance has been central to systems evaluation, energy-efficiency is quickly growing in importance for minimizing IT costs. In this thesis, we compared the performance of two NoSQL distributed time series databases, OpenTSDB and InfluxDB, based on the energy consumed by them in different scenarios, using the same set of machines and the same data. We evaluated the amount of energy consumed by each database on single host and multiple hosts, as the databases compared are distributed time series databases. Individual analysis and comparative analysis is done between the databases. In this report we present the results of this study and the performance of these databases based on energy consumption.

by Man-Cheung Hau. / Bibliography: leaves 110-112 / Thesis (M.Ph.)--Chinese University of Hong Kong, 1984

by Chan, Kin Pong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 100-105). / Abstracts in English and Chinese. / Acknowledgments --- p.ii / Abstract --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Wavelet Transform --- p.4 / Chapter 1.2 --- Time Warping --- p.5 / Chapter 1.3 --- Outline of the Thesis --- p.6 / Chapter 2 --- Related Work --- p.8 / Chapter 2.1 --- Similarity Models for Time Series --- p.8 / Chapter 2.2 --- Dimensionality Reduction --- p.11 / Chapter 2.3 --- Wavelet Transform --- p.15 / Chapter 2.4 --- Similarity Search under Time Warping --- p.16 / Chapter 3 --- Dimension Reduction by Wavelets --- p.21 / Chapter 3.1 --- The Proposed Approach --- p.21 / Chapter 3.1.1 --- Haar Wavelets --- p.23 / Chapter 3.1.2 --- DFT versus Haar Transform --- p.27 / Chapter 3.1.3 --- Guarantee of no False Dismissal --- p.29 / Chapter 3.2 --- The Overall Strategy --- p.34 / Chapter 3.2.1 --- Pre-processing --- p.35 / Chapter 3.2.2 --- Range Query --- p.35 / Chapter 3.2.3 --- Nearest Neighbor Query --- p.36 / Chapter 3.3 --- Performance Evaluation --- p.39 / Chapter 3.3.1 --- Stock Data --- p.39 / Chapter 3.3.2 --- Synthetic Random Walk Data --- p.45 / Chapter 3.3.3 --- Scalability Test --- p.51 / Chapter 3.3.4 --- Other Wavelets --- p.52 / Chapter 4 --- Time Warping --- p.55 / Chapter 4.1 --- Similarity Search based on K-L Transform --- p.60 / Chapter 4.2 --- Low Resolution Time Warping --- p.63 / Chapter 4.2.1 --- Resolution Reduction of Sequences --- p.63 / Chapter 4.2.2 --- Distance Compensation --- p.67 / Chapter 4.2.3 --- Time Complexity --- p.73 / Chapter 4.3 --- Adaptive Time Warping --- p.77 / Chapter 4.3.1 --- Time Complexity --- p.79 / Chapter 4.4 --- Performance Evaluation --- p.80 / Chapter 4.4.1 --- Accuracy versus Runtime --- p.80 / Chapter 4.4.2 --- Precision versus Recall --- p.85 / Chapter 4.4.3 --- Overall Runtime --- p.91 / Chapter 4.4.4 --- Starting Up Evaluation --- p.93 / Chapter 5 --- Conclusion and Future Work --- p.95 / Chapter 5.1 --- Conclusion --- p.95 / Chapter 5.2 --- Future Work --- p.96 / Chapter 5.2.1 --- Application of Wavelets on Biomedical Signals --- p.96 / Chapter 5.2.2 --- Moving Average Similarity --- p.98 / Chapter 5.2.3 --- Clusters-based Matching in Time Warping --- p.98 / Bibliography --- p.99

Leung Tat Wing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 94-98). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Unusual Pattern Discovery --- p.3 / Chapter 1.2 --- Trend Prediction --- p.4 / Chapter 1.3 --- Thesis Organization --- p.5 / Chapter 2 --- Unusual Pattern Discovery --- p.6 / Chapter 2.1 --- Introduction --- p.6 / Chapter 2.2 --- Related Work --- p.7 / Chapter 2.2.1 --- Time Series Discords --- p.7 / Chapter 2.2.2 --- Brute Force Algorithm --- p.8 / Chapter 2.2.3 --- Keogh et al.'s Algorithm --- p.10 / Chapter 2.2.4 --- Performance Analysis --- p.14 / Chapter 2.3 --- Proposed Approach --- p.18 / Chapter 2.3.1 --- Haar Transform --- p.20 / Chapter 2.3.2 --- Discretization --- p.22 / Chapter 2.3.3 --- Augmented Trie --- p.24 / Chapter 2.3.4 --- Approximating the Magic Outer Loop --- p.27 / Chapter 2.3.5 --- Approximating the Magic Inner Loop --- p.28 / Chapter 2.3.6 --- Experimental Result --- p.28 / Chapter 2.4 --- More on discord length --- p.42 / Chapter 2.4.1 --- Modified Haar Transform --- p.42 / Chapter 2.4.2 --- Fast Haar Transform Algorithm --- p.43 / Chapter 2.4.3 --- Relation between discord length and discord location --- p.45 / Chapter 2.5 --- Further Optimization --- p.47 / Chapter 2.5.1 --- Improved Inner Loop Heuristic --- p.50 / Chapter 2.5.2 --- Experimental Result --- p.52 / Chapter 2.6 --- Top K discords --- p.53 / Chapter 2.6.1 --- Utility of top K discords --- p.53 / Chapter 2.6.2 --- Algorithm --- p.58 / Chapter 2.6.3 --- Experimental Result --- p.62 / Chapter 2.7 --- Conclusion --- p.64 / Chapter 3 --- Trend Prediction --- p.69 / Chapter 3.1 --- Introduction --- p.69 / Chapter 3.2 --- Technical Analysis --- p.70 / Chapter 3.2.1 --- Relative Strength Index --- p.70 / Chapter 3.2.2 --- Chart Analysis --- p.70 / Chapter 3.2.3 --- Dow Theory --- p.71 / Chapter 3.2.4 --- Moving Average --- p.72 / Chapter 3.3 --- Proposed Algorithm --- p.79 / Chapter 3.3.1 --- Piecewise Linear Representation --- p.80 / Chapter 3.3.2 --- Prediction Tree --- p.82 / Chapter 3.3.3 --- Trend Prediction --- p.84 / Chapter 3.4 --- Experimental Results --- p.86 / Chapter 3.4.1 --- Experimental setup --- p.86 / Chapter 3.4.2 --- Experiment on accuracy --- p.87 / Chapter 3.4.3 --- Experiment on performance --- p.88 / Chapter 3.5 --- Conclusion --- p.90 / Chapter 4 --- Conclusion --- p.92 / Bibliography --- p.94

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Ip Ching-Tak. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 68-70). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Importance of Time Series Analysis with Outliers --- p.1 / Chapter 2 --- Outlier Analysis in Time Series --- p.4 / Chapter 2.1 --- Basic Idea --- p.4 / Chapter 2.2 --- Outliers in Time Series --- p.6 / Chapter 2.2.1 --- One Outlier Case --- p.6 / Chapter 2.2.2 --- Multiple Outliers Case --- p.8 / Chapter 2.3 --- Outlier Identification --- p.9 / Chapter 2.3.1 --- Outlier Detection of One Outlier Case --- p.9 / Chapter 2.3.2 --- Case of Unknown Model Parameters --- p.10 / Chapter 2.3.3 --- Iterative Identification Procedure --- p.10 / Chapter 3 --- ARMA Model Forecasting --- p.13 / Chapter 3.1 --- Unknown Model Problem --- p.13 / Chapter 3.1.1 --- AR Approximation --- p.14 / Chapter 3.1.2 --- ARMA Approximation --- p.15 / Chapter 3.1.3 --- "Comparison of AIC, AICC and BIC" --- p.16 / Chapter 3.2 --- A Simulation Study --- p.19 / Chapter 3.2.1 --- Results for One-Step-Ahead Forecast --- p.20 / Chapter 3.2.2 --- Results for the Mean of Multiple Forecasts --- p.22 / Chapter 4 --- ARIMA Model Forecasting --- p.24 / Chapter 4.1 --- Effect of Differencing on Time Series --- p.24 / Chapter 4.1.1 --- Outlier Free Model --- p.24 / Chapter 4.1.2 --- Outlier Model --- p.25 / Chapter 4.2 --- Unknown Model Problem --- p.28 / Chapter 4.2.1 --- AR Approximation --- p.28 / Chapter 4.2.2 --- ARMA Approximation --- p.28 / Chapter 4.3 --- Unknown Differencing Case --- p.29 / Chapter 4.4 --- A Simulation Study --- p.29 / Chapter 4.4.1 --- Results for One-Step-Ahead Forecast --- p.30 / Chapter 4.4.2 --- Results for the Mean of Multiple Forecasts --- p.32 / Chapter 5 --- Illustrative Examples --- p.34 / Chapter 5.1 --- Examples of Stationary Time Series --- p.34 / Chapter 5.1.1 --- Example 1 --- p.34 / Chapter 5.1.2 --- Example 2 --- p.36 / Chapter 5.2 --- Examples of Nonstationary Time Series --- p.37 / Chapter 5.2.1 --- Example 3 --- p.37 / Chapter 5.2.2 --- Example 4 --- p.38 / Chapter 6 --- Conclusion --- p.40 / Chapter A --- "Comparison of AIC, AICC and BIC" --- p.42 / Chapter A.1 --- AR Approximation Results --- p.42 / Chapter A.2 --- ARMA Approximation Results --- p.45 / Chapter B --- Simulation Results for ARMA Models --- p.47 / Chapter C --- Simulation Results for ARIMA Models --- p.56 / Chapter D --- SACF and SPACF of Examples --- p.65 / Bibliography --- p.68