The seven compositions that comprise this dissertation are represented by the following files: text file (pdf), seven video performances (mp4), and corresponding zipped files of custom software and affiliated files (various file types). / This Digital Portfolio Dissertation centers on a collection of seven digital videos of performances of original electroacoustic compositions that feature data-driven instruments. The dissertation also includes a copy of the original software and affiliated files used in performing the portfolio of music, and a text document that analyzes and describes the following for each of the seven compositions: (1) the design and implementation of each of the seven complete data-driven instruments; (2) the musical challenges and opportunities provided by data-driven instruments; (3) the performance techniques employed; (4) the compositional structure; (5) the sound synthesis techniques used, and (6) the data-mapping strategies used. The seven compositions demonstrate a variety of electroacoustic and performance techniques and employ a range of interface devices as front-ends to the data-driven instruments. The seven interfaces that I chose to use for my compositions include the Wacom Tablet, the Leap Motion device for hand and finger detection, the Blue Air infrared sensor device for distance measurements, the Nintendo Wii Remote wireless game controller, the Gametrak three-dimensional, position tracking system, the eMotion™ Wireless Sensor System, and a custom sensor-based interface that I designed and fabricated. The title of this dissertation derives from the extra-musical impulses that drove the creative impulses of the seven original electroacoustic compositions for data-driven instruments. Of the seven compositions, six of the pieces have connections to literature. Despite the fact there is a literary sheen to these musical works, the primary impulses of these compositions arise from the notion of absolute music – music for music’s sake, music that is focused on sound and the emotional and intellectual stimulus such sound can produce when humans experience it. Thus, I simultaneously work both sides of the musical street with my compositions containing both extra-musical and absolute musical substance.

Over the past years, the value of data and DDDM have increased significantly as technological advancements have made it possible to store and analyze large amounts of data at a reasonable cost. This has resulted in completely new business models that has disrupt whole industries. DDDM allows businesses to rely their decisions on data, as opposed to on gut feeling. Up until this point, literature is eligible to provide a general view of what are the major challenges corporations encounter when implementing a DDDM strategy. However, as the field is still rather new, the challenges identified are yet very general and many corporations, especially B2B MNCs selling consumer goods, seem to struggle with this implementation. Hence, a single-case study on such a corporation, named Alpha, was carried out with the purpose to explore what are their major challenges in this process. Semi-structured interviews revealed evidence of four major findings, whereas, execution and organizational culture were supported in existing literature, however, two additional findings associated with organizational structure and consumer behavior data were discovered in the case of Alpha. Based on this, the conclusions drawn were that B2B MNCs selling consumer goods encounter the challenges of identifying local markets as frontrunners for strategies such as the one to become more data-driven, as well as the need to find a way to retrieve consumer behavior data. With these two main challenges identified, it can provide a starting point for managers when implementing DDDM strategies in B2B MNCs selling consumer goods in the future.

In a networked system, operational data collected by sensors or extracted from system logs can be used for target performance prediction, anomaly detection, etc. However, the number of metrics collected from a networked system is very large and usually can reach about 106 for a medium-sized system. This project aims to analyze and compare different unsupervised machine learning methods such as Unsupervised Feature Selection, Principle Component Analysis, Autoencoder, which can lead to efficient learning from high-dimensional data. The objective is to reduce the dimensionality of the input space while maintaining the prediction performance when compared with the learning on the full feature space. The data used in this project is collected from a KTH testbed which runs a Video-on-Demand service and a Key-Value store under different types of traffic load. The findings confirm the manifold hypothesis, which states that real-world high-dimensional data lie on lowdimensional manifolds embedded within the high-dimensional space. In addition, this project investigates data visualization of infrastructure measurements through two-dimensional plots. The results show that we can achieve data separation by using different mapping methods. / I ett nätverkssystem kan driftsdata som samlats in av sensorer eller extraherats från systemloggar användas för att förutsäga målprestanda, anomalidetektering etc. Antalet mätvärden som samlats in från ett nätverkssystem är dock mycket stort och kan vanligtvis uppgå till cirka 106 för ett medelstort system. Projektet syftar till att analysera och jämföra olika oövervakade metoder för maskininlärning, till exempel Oövervakad funktionsval, analys av huvudkomponent, autokodare, vilket kan leda till effektivt lärande av högdimensionell data. Målet är att minska ingångsutrymmet och samtidigt bibehålla prediktionsprestanda jämfört med inlärningen på hela funktionen. Uppgifterna som används i detta projekt samlas in från en KTH-testbädd som driver en Video-on-Demand-tjänst och en Key-Value-butik under olika typer av trafikbelastning. Resultaten bekräftar mångfaldshypotesen, som säger att verkliga högdimensionella data ligger på lågdimensionella grenrören inbäddade i det högdimensionella rymden. Dessutom undersöker detta projekt datavisualisering av infrastrukturmätningar genom tvådimensionella tomter. Resultaten visar att vi kan uppnå dataseparering genom att använda olika kartläggningsmetoder.

In the modern era of high-stakes accountability, test data have become much more than a simple comparison (Schmoker, 2006; Payne & Miller, 2009). The information provided in modern data reports has become an invaluable tool to drive instruction in classrooms. However, there is a lack of good training for educators to evaluate data and translate findings into solid practices that can improve student learning (Blair, 2006; Dynarski, 2008; Light, Wexler, & Heinze, 2005; Payne & Miller, 2009). Some schools are good at collecting data, but often fall short at what to do next. It is the role of the principal to serve as an instructional leader and guide teachers to the answer the reoccurring question of "now what?" The purpose of this study was to investigate ways in which principals build successful data-driven instructional systems within their schools using a qualitative multi-site case study method. This research utilized a triangulation approach with structured interviews, on-site visits, and document reviews from various middle school supervisors, principals, and teachers. The findings are presented in four common themes and patterns identified as essential components administrators used to implement data-driven instructional systems to improve student achievement. The themes are 1) administrators must clearly define the vision and set the expectation of using data to improve student achievement, 2) administrators must take an active role in the data-driven process, 3) data must be easily accessible to stakeholders, and 4) stakeholders must devote time on a regular basis to the data-driven process. The four themes led to the conclusion of ten common steps administrators can use to acculturate their school or school division with the data-driven instruction process. / Ed. D.

Management of water quality is generally based on physically-based equations or hypotheses describing the behaviour of water bodies. In recent years models built on the basis of the availability of larger amounts of collected data are gaining popularity. This modelling approach can be called data driven modelling. Observational data represent specific knowledge, whereas a hypothesis represents a generalization of this knowledge that implies and characterizes all such observational data. Traditionally deterministic numerical models have been used for predicting flow and water quality processes in inland and coastal basins. These models generally take a long time to run and cannot be used as on-line decision support tools, thereby enabling imminent threats to public health risk and flooding etc. to be predicted. In contrast, Data driven models are data intensive and there are some limitations in this approach. The extrapolation capability of data driven methods are a matter of conjecture. Furthermore, the extensive data required for building a data driven model can be time and resource consuming or for the case predicting the impact of a future development then the data is unlikely to exist. The main objective of the study was to develop an integrated approach for rapid prediction of bathing water quality in estuarine and coastal waters. Faecal Coliforms (FC) were used as a water quality indicator and two of the most popular data mining techniques, namely, Genetic Programming (GP) and Artificial Neural Networks (ANNs) were used to predict the FC levels in a pilot basin. In order to provide enough data for training and testing the neural networks, a calibrated hydrodynamic and water quality model was used to generate input data for the neural networks. A novel non-linear data analysis technique, called the Gamma Test, was used to determine the data noise level and the number of data points required for developing smooth neural network models. Details are given of the data driven models, numerical models and the Gamma Test. Details are also given of a series experiments being undertaken to test data driven model performance for a different number of input parameters and time lags. The response time of the receiving water quality to the input boundary conditions obtained from the hydrodynamic model has been shown to be a useful knowledge for developing accurate and efficient neural networks. It is known that a natural phenomenon like bacterial decay is affected by a whole host of parameters which can not be captured accurately using solely the deterministic models. Therefore, the data-driven approach has been investigated using field survey data collected in Cardiff Bay to investigate the relationship between bacterial decay and other parameters. Both of the GP and ANN models gave similar, if not better, predictions of the field data in comparison with the deterministic model, with the added benefit of almost instant prediction of the bacterial levels for this recreational water body. The models have also been investigated using idealised and controlled laboratory data for the velocity distributions along compound channel reaches with idealised rods have located on the floodplain to replicate large vegetation (such as mangrove trees).

Data Driven Design metodologija plačiai naudojama įvairiose programinėse sistemose. Šios metodologijos tikslas – atskirti bei lygiagretinti programuotojų ir projektuotojų veiklą. Sistemos branduolio funkcionalumas yra įgyvendinamas sąsajomis, o dinamika – scenarijų pagalba. Taip įvedamas abstrakcijos lygmuo, kurio dėka programinis produktas tampa lankstesnis, paprasčiau palaikomas ir tobulinamas, be to šiuos veiksmus galima atlikti lygiagrečiai. Darbo tikslas buvo sukurti automatinį kodo generatorių, kuris transformuotų grafiškai sumodeliuotą scenarijų į programinį kodą. Generuojant programinį kodą automatiškai ženkliai sumažėja sintaksinių bei loginių klaidų tikimybė, viskas priklauso nuo sumodeliuoto scenarijaus. Kodas sugeneruojamas labai greitai ir visiškai nereikalingas programuotojo įsikišimas. Šis tikslas pasiektas iškėlus biznio logikos projektavimą į scenarijaus projektavimą, o kodo generavimo posistemę realizavus žiniatinklio paslaugos principu. Kodas generuojamas neprisirišant prie konkrečios architektūros, technologijos ar taikymo srities panaudojant įskiepių sistemą . Grafiniame scenarijų kūrimo įrankyje sumodeliuojamas scenarijus ir tada transformuojamas į metakalbą , iš kurios ir generuojamas galutinis programinis kodas. Metakalba – tam tikromis taisyklėmis apibrėžta „XML “ kalba. Realizavus eksperimentinę sistemą su didelėmis problemomis nebuvo susidurta. Naujos sistemos modeliavimas projektavimo įrankiu paspartino kūrimo procesą septynis kartus. Tai įrodo... [toliau žr. visą tekstą] / Data Driven Design methodology is widely used in various program systems. This methodology aim is to distinguish and parallel software developer and scenario designer’s work. Core functionality is implemented via interfaces and dynamics via scenario support. This introduces a level of abstraction, which makes software product more flexible easily maintained and improved, in addition these actions can be performed in parallel. The main aim of this work was to create automatic code generator that transforms graphically modeled scenario to software code. Automatically generated software code restricts probability of syntactic and logical errors, all depends on scenario modeling. Code is generated instantly and no need software developer interference. This aim is achieved by moving business logic designing to scenario designing process and code generator service making as a “Web service”. Using cartridge based system code is generated not attached to a specific architecture, technology or application domain. In graphical scenario modeling tool scenario is modeled and transformed to metalanguage, from which software code is generated. Metalanguage – with specific rules defined “XML” language. Experimental system was developed with no major problems. New project modeling with our modeling tool speeded the development process by seven times. This proves modeling tool advantage over manual programming.

Web services have made many kinds of data and computing services available. However, to use web services often requires significant programming efforts and thus limits the people who can take advantage of them to only a small group of skilled programmers. In this dissertation, I will present a tool called Gneiss that extends the spreadsheet model to support four challenging aspects of using web services: programming two-way data communications with web services, creating interactive GUI applications that use web data sources, using hierarchical data, and using live streaming data. Gneiss contributes innovations in spreadsheet languages, spreadsheet user interfaces and interaction techniques to allow programming tasks that currently require writing complex, lengthy code to instead be done using familiar spreadsheet mechanisms. Spreadsheets are arguably the most successful and popular data tools among people of all programming levels. This work advances the use of spreadsheets to new domains and could benefit a wide range of users from professional programmers to end-user programmers.

In smart energy systems, load curve refers to the time series reported by smart meters, which indicate the energy consumption of customers over a certain period of time. The widespread use of load curve (data) in demand side management and demand response programs makes it one of the most important resources. To capture the load behavior or energy consumption patterns, load curve modeling is widely applied to help the utilities and residents make better plans and decisions. In this dissertation, with the help of load curve modeling, we focus on data-driven solutions to three load monitoring problems in different scenarios of smart energy systems, including residential power systems and datacenter power systems and covering the research fields of i) data cleansing, ii) energy disaggregation, and iii) fine-grained power monitoring. First, to improve the data quality for load curve modeling on the supply side, we challenge the regression-based approaches as an efficient way to load curve data cleansing and propose a new approach to analyzing and organizing load curve data. Our approach adopts a new view, termed portrait, on the load curve data by analyzing the inherent periodic patterns and re-organizing the data for ease of analysis. Furthermore, we introduce strategies to build virtual portrait datasets and demonstrate how this technique can be used for outlier detection in load curve. To identify the corrupted load curve data, we propose an appliance-driven approach that particularly takes advantage of information available on the demand side. It identifies corrupted data from the smart meter readings by solving a carefully-designed optimization problem. To solve the problem efficiently, we further develop a sequential local optimization algorithm that tackles the original NP-hard problem by solving an approximate problem in polynomial time. Second, to separate the aggregated energy consumption of a residential house into that of individual appliances, we propose a practical and universal energy disaggregation solution, only referring to the readily available information of appliances. Based on the sparsity of appliances' switching events, we first build a sparse switching event recovering (SSER) model. Then, by making use of the active epochs of switching events, we develop an efficient parallel local optimization algorithm to solve our model and obtain individual appliances' energy consumption. To explore the benefit of introducing low-cost energy meters for energy disaggregation, we propose a semi-intrusive appliance load monitoring (SIALM) approach for large-scale appliances situation. Instead of using only one meter, multiple meters are distributed in the power network to collect the aggregated load data from sub-groups of appliances. The proposed SSER model and parallel optimization algorithm are used for energy disaggregation within each sub-group of appliances. We further provide the sufficient conditions for unambiguous state recovery of multiple appliances, under which a minimum number of meters is obtained via a greedy clique-covering algorithm. Third, to achieve fine-grained power monitoring at server level in legacy datacenters, we present a zero-cost, purely software-based solution. With our solution, no power monitoring hardware is needed any more, leading to much reduced operating cost and hardware complexity. In detail, we establish power mapping functions (PMFs) between the states of servers and their power consumption, and infer the power consumption of each server with the aggregated power of the entire datacenter. We implement and evaluate our solution over a real-world datacenter with 326 servers. The results show that our solution can provide high precision power estimation at both the rack level and the server level. In specific, with PMFs including only two nonlinear terms, our power estimation i) at the rack level has mean relative error of 2.18%, and ii) at the server level has mean relative errors of 9.61% and 7.53% corresponding to the idle and peak power, respectively. / Graduate / 0984 / 0791 / 0800 / tangguo1999@gmail.com

There has been extensive research done in the area of Semiparametric Regression. These techniques deliver substantial improvements over previously developed methods, such as Ordinary Least Squares and Kernel Regression. Two of these hybrid techniques: Model Robust Regression 1 (MRR1) and Model Robust Regression 2 (MRR2) require the choice of an appropriate bandwidth for smoothing and a mixing parameter that allows a portion of a nonparametric fit to be used in fitting a model that may be misspecifed by other regression methods. The current method of choosing the bandwidth and mixing parameter does not guarantee the optimal choices in either case. The immediate objective of the current work is to address this process of choosing the optimal bandwidth and mixing parameter and to examine the behavior of these estimates using 3D plots. The 3D plots allow us to examine how the semiparametric techniques: MRR1 and MRR2, behave for the optimal (AVEMSE) selection process when compared to data-driven selectors, such as PRESS* and PRESS**. It was found that the structure of MRR2 behaved consistently under all conditions. MRR2 displayed a wider range of "acceptable" values for the choice of bandwidth as opposed to a much more limited choice when using MRR1. These results provide general support for earlier fndings by Mays et al. (2000).

This thesis is mostly about supervised visual recognition problems. Based on a general definition of categories, the contents are divided into two parts: one which models categories and one which is not category based. We are interested in data driven solutions for both kinds of problems. In the category-free part, we study novelty detection in temporal and spatial domains as a category-free recognition problem. Using data driven models, we demonstrate that based on a few reference exemplars, our methods are able to detect novelties in ego-motions of people, and changes in the static environments surrounding them. In the category level part, we study object recognition. We consider both object category classification and localization, and propose scalable data driven approaches for both problems. A mixture of parametric classifiers, initialized with a sophisticated clustering of the training data, is demonstrated to adapt to the data better than various baselines such as the same model initialized with less subtly designed procedures. A nonparametric large margin classifier is introduced and demonstrated to have a multitude of advantages in comparison to its competitors: better training and testing time costs, the ability to make use of indefinite/invariant and deformable similarity measures, and adaptive complexity are the main features of the proposed model. We also propose a rather realistic model of recognition problems, which quantifies the interplay between representations, classifiers, and recognition performances. Based on data-describing measures which are aggregates of pairwise similarities of the training data, our model characterizes and describes the distributions of training exemplars. The measures are shown to capture many aspects of the difficulty of categorization problems and correlate significantly to the observed recognition performances. Utilizing these measures, the model predicts the performance of particular classifiers on distributions similar to the training data. These predictions, when compared to the test performance of the classifiers on the test sets, are reasonably accurate. We discuss various aspects of visual recognition problems: what is the interplay between representations and classification tasks, how can different models better adapt to the training data, etc. We describe and analyze the aforementioned methods that are designed to tackle different visual recognition problems, but share one common characteristic: being data driven. / <p>QC 20140604</p>