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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.

It will be our purpose to study a generalized definition of sum of a series and the restrictions which must be placed upon it in order that it shall satisfy the generally accepted requirements of any generalized definition of sum of a series. We shall then proceed to investigate the possibilities of further generalizing this process.

In this report we study the Aizawa field by first computing a Taylor series expansion for the solution of an initial value problem. We then look for singularities (equilibrium points) of the field and plot the set of solutions which lie in the linear subspace spanned by the eigenvectors. Finally, we use the Parameterization Method to compute one and two dimensional stable and unstable manifolds of equilibria for the system. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

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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This study investigates and identifies the factors that affect mistrust and uncertainty when Korean industrial multinational enterprises (MNEs) generate new ideas in the front-end phase of new product development (NPD) processes. Specifically, this study focuses on the process of applying research data to support the generation and development of new products. Current research indicates that many companies frequently experience difficulties in this regard. This study comprises a large body of empirical study-centred research. It collates multiple interviews with research-based teams (consisting of planners as well as marketers and lifestyle researchers) and practice-based teams (i.e. designers and engineers) of leading multinational companies in the smart electronics and automobile industries. A primary outcome of the research is the specification of key constructs relating to mistrust and uncertainty during the application of research data in the process of selecting new ideas within the front-end of the NPD process, which appear to be linked to three factors: (i) a lack of common language (perspectives and approaches related to data and information when obtaining insights) between diverse expertise groups, (ii) a lack of appropriate communication channels between different functional groups, and (iii) a lack of productive tactics in using internal information. This research aims to enrich NPD studies by presenting the validity of the existing theory with detailed practical examples and to find distinctive new knowledge by identifying emerging issues from recent NPD processes in the industry. Furthermore, this study establishes an idea generation framework that will potentially enable MNEs to use their research data more effectively when developing real products and to better perform cross-functional tasks.

We extend Urban's construction of eigenvarieties for reductive groups G such that G(R) has discrete series to include characteristic p points at the boundary of weight space. In order to perform this construction, we define a notion of "locally analytic" functions and distributions on a locally Q_p-analytic manifold taking values in a complete Tate Z_p-algebra in which p is not necessarily invertible. Our definition agrees with the definition of locally analytic distributions on p-adic Lie groups given by Johansson and Newton.