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Description
With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors that provide real time information of the traffic in the city, a change in these trajectories with time can reveal that the road network has changed. To detect changes, trajectories are modeled with a Hidden Markov Model (HMM). A modified training algorithm, for parameter estimation in HMM, called m-BaumWelch, is used to develop likelihood estimates under assumed changes and used to detect changes in trajectory data with time. Data from vehicles are used to test the method for change detection. Secondly, sequential pattern mining is used to develop a model to detect changes in frequent patterns occurring in trajectory data. The aim is to answer two questions: Are the frequent patterns still frequent in the new data? If they are frequent, has the time interval distribution in the pattern changed? Two different approaches are considered for change detection, frequency-based approach and distribution-based approach. The methods are illustrated with vehicle trajectory data. Finally, a model is developed for clustering and outlier detection in semantic trajectories. A challenge with clustering semantic trajectories is that both numeric and categorical attributes are present. Another problem to be addressed while clustering is that trajectories can be of different lengths and also have missing values. A tree-based ensemble is used to address these problems. The approach is extended to outlier detection in semantic trajectories.
ContributorsKondaveeti, Anirudh (Author) / Runger, George C. (Thesis advisor) / Mirchandani, Pitu (Committee member) / Pan, Rong (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012
Description
In order to cope with the decreasing availability of symphony jobs and collegiate faculty positions, many musicians are starting to pursue less traditional career paths. Also, to combat declining audiences, musicians are exploring ways to cultivate new and enthusiastic listeners through relevant and engaging performances. Due to these challenges, many community-based chamber music ensembles have been formed throughout the United States. These groups not only focus on performing classical music, but serve the needs of their communities as well. The problem, however, is that many musicians have not learned the business skills necessary to create these career opportunities. In this document I discuss the steps ensembles must take to develop sustainable careers. I first analyze how groups build a strong foundation through getting to know their communities and creating core values. I then discuss branding and marketing so ensembles can develop a public image and learn how to publicize themselves. This is followed by an investigation of how ensembles make and organize their money. I then examine the ways groups ensure long-lasting relationships with their communities and within the ensemble. I end by presenting three case studies of professional ensembles to show how groups create and maintain successful careers. Ensembles must develop entrepreneurship skills in addition to cultivating their artistry. These business concepts are crucial to the longevity of chamber groups. Through interviews of successful ensemble members and my own personal experiences in the Tetra String Quartet, I provide a guide for musicians to use when creating a community-based ensemble.
ContributorsDalbey, Jenna (Author) / Landschoot, Thomas (Thesis advisor) / McLin, Katherine (Committee member) / Ryan, Russell (Committee member) / Solis, Theodore (Committee member) / Spring, Robert (Committee member) / Arizona State University (Publisher)
Created2013
Description
American Primitive is a composition written for wind ensemble with an instrumentation of flute, oboe, clarinet, bass clarinet, alto, tenor, and baritone saxophones, trumpet, horn, trombone, euphonium, tuba, piano, and percussion. The piece is approximately twelve minutes in duration and was written September - December 2013. American Primitive is absolute music (i.e. it does not follow a specific narrative) comprising blocks of distinct, contrasting gestures which bookend a central region of delicate textural layering and minimal gestural contrast. Though three gestures (a descending interval followed by a smaller ascending interval, a dynamic swell, and a chordal "chop") were consciously employed throughout, it is the first gesture of the three that creates a sense of unification and overall coherence to the work. Additionally, the work challenges listeners' expectations of traditional wind ensemble music by featuring the trumpet as a quasi-soloist whose material is predominately inspired by transcriptions of jazz solos. This jazz-inspired material is at times mimicked and further developed by the ensemble, also often in a soloistic manner while the trumpet maintains its role throughout. This interplay of dialogue between the "soloists" and the "ensemble" further skews listeners' conceptions of traditional wind ensemble music by featuring almost every instrument in the ensemble. Though the term "American Primitive" is usually associated with the "naïve art" movement, it bears no association to the music presented in this work. Instead, the term refers to the author's own compositional attitudes, education, and aesthetic interests.
ContributorsJandreau, Joshua (Composer) / Rockmaker, Jody D (Thesis advisor) / Rogers, Rodney I (Committee member) / Demars, James R (Committee member) / Arizona State University (Publisher)
Created2014
Description
Multidimensional data have various representations. Thanks to their simplicity in modeling multidimensional data and the availability of various mathematical tools (such as tensor decompositions) that support multi-aspect analysis of such data, tensors are increasingly being used in many application domains including scientific data management, sensor data management, and social network data analysis. Relational model, on the other hand, enables semantic manipulation of data using relational operators, such as projection, selection, Cartesian-product, and set operators. For many multidimensional data applications, tensor operations as well as relational operations need to be supported throughout the data life cycle. In this thesis, we introduce a tensor-based relational data model (TRM), which enables both tensor- based data analysis and relational manipulations of multidimensional data, and define tensor-relational operations on this model. Then we introduce a tensor-relational data management system, so called, TensorDB. TensorDB is based on TRM, which brings together relational algebraic operations (for data manipulation and integration) and tensor algebraic operations (for data analysis). We develop optimization strategies for tensor-relational operations in both in-memory and in-database TensorDB. The goal of the TRM and TensorDB is to serve as a single environment that supports the entire life cycle of data; that is, data can be manipulated, integrated, processed, and analyzed.
ContributorsKim, Mijung (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sundaram, Hari (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2014
Description
US Senate is the venue of political debates where the federal bills are formed and voted. Senators show their support/opposition along the bills with their votes. This information makes it possible to extract the polarity of the senators. Similarly, blogosphere plays an increasingly important role as a forum for public debate. Authors display sentiment toward issues, organizations or people using a natural language.
In this research, given a mixed set of senators/blogs debating on a set of political issues from opposing camps, I use signed bipartite graphs for modeling debates, and I propose an algorithm for partitioning both the opinion holders (senators or blogs) and the issues (bills or topics) comprising the debate into binary opposing camps. Simultaneously, my algorithm scales the entities on a univariate scale. Using this scale, a researcher can identify moderate and extreme senators/blogs within each camp, and polarizing versus unifying issues. Through performance evaluations I show that my proposed algorithm provides an effective solution to the problem, and performs much better than existing baseline algorithms adapted to solve this new problem. In my experiments, I used both real data from political blogosphere and US Congress records, as well as synthetic data which were obtained by varying polarization and degree distribution of the vertices of the graph to show the robustness of my algorithm.
I also applied my algorithm on all the terms of the US Senate to the date for longitudinal analysis and developed a web based interactive user interface www.PartisanScale.com to visualize the analysis.
US politics is most often polarized with respect to the left/right alignment of the entities. However, certain issues do not reflect the polarization due to political parties, but observe a split correlating to the demographics of the senators, or simply receive consensus. I propose a hierarchical clustering algorithm that identifies groups of bills that share the same polarization characteristics. I developed a web based interactive user interface www.ControversyAnalysis.com to visualize the clusters while providing a synopsis through distribution charts, word clouds, and heat maps.
In this research, given a mixed set of senators/blogs debating on a set of political issues from opposing camps, I use signed bipartite graphs for modeling debates, and I propose an algorithm for partitioning both the opinion holders (senators or blogs) and the issues (bills or topics) comprising the debate into binary opposing camps. Simultaneously, my algorithm scales the entities on a univariate scale. Using this scale, a researcher can identify moderate and extreme senators/blogs within each camp, and polarizing versus unifying issues. Through performance evaluations I show that my proposed algorithm provides an effective solution to the problem, and performs much better than existing baseline algorithms adapted to solve this new problem. In my experiments, I used both real data from political blogosphere and US Congress records, as well as synthetic data which were obtained by varying polarization and degree distribution of the vertices of the graph to show the robustness of my algorithm.
I also applied my algorithm on all the terms of the US Senate to the date for longitudinal analysis and developed a web based interactive user interface www.PartisanScale.com to visualize the analysis.
US politics is most often polarized with respect to the left/right alignment of the entities. However, certain issues do not reflect the polarization due to political parties, but observe a split correlating to the demographics of the senators, or simply receive consensus. I propose a hierarchical clustering algorithm that identifies groups of bills that share the same polarization characteristics. I developed a web based interactive user interface www.ControversyAnalysis.com to visualize the clusters while providing a synopsis through distribution charts, word clouds, and heat maps.
ContributorsGokalp, Sedat (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Liu, Huan (Committee member) / Woodward, Mark (Committee member) / Arizona State University (Publisher)
Created2015
Description
This project is a practical annotated bibliography of original works for oboe trio with the specific instrumentation of two oboes and English horn. Presenting descriptions of 116 readily available oboe trios, this project is intended to promote awareness, accessibility, and performance of compositions within this genre.
The annotated bibliography focuses exclusively on original, published works for two oboes and English horn. Unpublished works, arrangements, works that are out of print and not available through interlibrary loan, or works that feature slightly altered instrumentation are not included.
Entries in this annotated bibliography are listed alphabetically by the last name of the composer. Each entry includes the dates of the composer and a brief biography, followed by the title of the work, composition date, commission, and dedication of the piece. Also included are the names of publishers, the length of the entire piece in minutes and seconds, and an incipit of the first one to eight measures for each movement of the work.
In addition to providing a comprehensive and detailed bibliography of oboe trios, this document traces the history of the oboe trio and includes biographical sketches of each composer cited, allowing readers to place the genre of oboe trios and each individual composition into its historical context. Four appendices at the end include a list of trios arranged alphabetically by composer's last name, chronologically by the date of composition, and by country of origin and a list of publications of Ludwig van Beethoven's oboe trios from the 1940s and earlier.
The annotated bibliography focuses exclusively on original, published works for two oboes and English horn. Unpublished works, arrangements, works that are out of print and not available through interlibrary loan, or works that feature slightly altered instrumentation are not included.
Entries in this annotated bibliography are listed alphabetically by the last name of the composer. Each entry includes the dates of the composer and a brief biography, followed by the title of the work, composition date, commission, and dedication of the piece. Also included are the names of publishers, the length of the entire piece in minutes and seconds, and an incipit of the first one to eight measures for each movement of the work.
In addition to providing a comprehensive and detailed bibliography of oboe trios, this document traces the history of the oboe trio and includes biographical sketches of each composer cited, allowing readers to place the genre of oboe trios and each individual composition into its historical context. Four appendices at the end include a list of trios arranged alphabetically by composer's last name, chronologically by the date of composition, and by country of origin and a list of publications of Ludwig van Beethoven's oboe trios from the 1940s and earlier.
ContributorsSassaman, Melissa Ann (Author) / Schuring, Martin (Thesis advisor) / Buck, Elizabeth (Committee member) / Holbrook, Amy (Committee member) / Hill, Gary (Committee member) / Arizona State University (Publisher)
Created2014
Description
Galaxies with strong Lyman-alpha (Lya) emission line (also called Lya galaxies or emitters) offer an unique probe of the epoch of reionization - one of the important phases when most of the neutral hydrogen in the universe was ionized. In addition, Lya galaxies at high redshifts are a powerful tool to study low-mass galaxy formation. Since current observations suggest that the reionization is complete by redshift z~ 6, it is therefore necessary to discover galaxies at z > 6, to use their luminosity function (LF) as a probe of reionization. I found five z = 7.7 candidate Lya galaxies with line fluxes > 7x10-18 erg/s/cm/2 , from three different deep near-infrared (IR) narrowband (NB) imaging surveys in a volume > 4x104Mpc3. From the spectroscopic followup of four candidate galaxies, and with the current spectroscopic sensitivity, the detection of only the brightest candidate galaxy can be ruled out at 5 sigma level. Moreover, these observations successfully demonstrate that the sensitivity necessary for both, the NB imaging as well as the spectroscopic followup of z~ 8 Lya galaxies can be reached with the current instrumentation. While future, more sensitive spectroscopic observations are necessary, the observed Lya LF at z = 7.7 is consistent with z = 6.6 LF, suggesting that the intergalactic medium (IGM) is relatively ionized even at z = 7.7, with neutral fraction xHI≤ 30%. On the theoretical front, while several models of Lya emitters have been developed, the physical nature of Lya emitters is not yet completely known. Moreover, multi-parameter models and their complexities necessitates a simpler model. I have developed a simple, single-parameter model to populate dark mater halos with Lya emitters. The central tenet of this model, different from many of the earlier models, is that the star-formation rate (SFR), and hence the Lya luminosity, is proportional to the mass accretion rate rather than the total halo mass. This simple model is successful in reproducing many observable including LFs, stellar masses, SFRs, and clustering of Lya emitters from z~ 3 to z~ 7. Finally, using this model, I find that the mass accretion, and hence the star-formation in > 30% of Lya emitters at z~ 3 occur through major mergers, and this fraction increases to ~ 50% at z~7.
ContributorsShet Tilvi, Vithal (Author) / Malhotra, Sangeeta (Thesis advisor) / Rhoads, James (Committee member) / Scannapieco, Evan (Committee member) / Young, Patrick (Committee member) / Jansen, Rolf (Committee member) / Arizona State University (Publisher)
Created2011
Description
Query Expansion is a functionality of search engines that suggest a set of related queries for a user issued keyword query. In case of exploratory or ambiguous keyword queries, the main goal of the user would be to identify and select a specific category of query results among different categorical options, in order to narrow down the search and reach the desired result. Typical corpus-driven keyword query expansion approaches return popular words in the results as expanded queries. These empirical methods fail to cover all semantics of categories present in the query results. More importantly these methods do not consider the semantic relationship between the keywords featured in an expanded query. Contrary to a normal keyword search setting, these factors are non-trivial in an exploratory and ambiguous query setting where the user's precise discernment of different categories present in the query results is more important for making subsequent search decisions. In this thesis, I propose a new framework for keyword query expansion: generating a set of queries that correspond to the categorization of original query results, which is referred as Categorizing query expansion. Two approaches of algorithms are proposed, one that performs clustering as pre-processing step and then generates categorizing expanded queries based on the clusters. The other category of algorithms handle the case of generating quality expanded queries in the presence of imperfect clusters.
ContributorsNatarajan, Sivaramakrishnan (Author) / Chen, Yi (Thesis advisor) / Candan, Selcuk (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Most stars form in groups, and these clusters are themselves nestled within larger associations and stellar complexes. It is not yet clear, however, whether stars cluster on preferred size scales within galaxies, or if stellar groupings have a continuous size distribution. I have developed two methods to select stellar groupings across a wide range of size-scales in order to assess trends in the size distribution and other basic properties of stellar groupings. The first method uses visual inspection of color-magnitude and color-color diagrams of clustered stars to assess whether the compact sources within the potential association are coeval, and thus likely to be born from the same parentmolecular cloud. This method was developed using the stellar associations in the M51/NGC 5195 interacting galaxy system. This process is highly effective at selecting single-aged stellar associations, but in order to assess properties of stellar clustering in a larger sample of nearby galaxies, an automated method for selecting stellar groupings is needed. I have developed an automated stellar grouping selection method that is sensitive to stellar clustering on all size scales. Using the Source Extractor software package on Gaussian-blurred images of NGC 4214, and the annular surface brightness to determine the characteristic size of each cluster/association, I eliminate much of the size and density biases intrinsic to other methods. This automated method was tested in the nearby dwarf irregular galaxy NGC 4214, and can detect stellar groupings with sizes ranging from compact clusters to stellar complexes. In future work, the automatic selection method developed in this dissertation will be used to identify stellar groupings in a set of nearby galaxies to determine if the size scales for stellar clustering are uniform in the nearby universe or if it is dependent on local galactic environment. Once the stellar clusters and associations have been identified and age-dated, this information can be used to deduce disruption times from the age distribution as a function of the position of the stellar grouping within the galaxy, the size of the cluster or association, and the morphological type of the galaxy. The implications of these results for galaxy formation and evolution are discussed.
ContributorsKaleida, Catherine (Author) / Scowen, Paul A. (Thesis advisor) / Windhorst, Rogier A. (Thesis advisor) / Jansen, Rolf A. (Committee member) / Timmes, Francis X. (Committee member) / Scannapieco, Evan (Committee member) / Arizona State University (Publisher)
Created2011
Description
The ever-changing economic landscape has forced many companies to re-examine their supply chains. Global resourcing and outsourcing of processes has been a strategy many organizations have adopted to reduce cost and to increase their global footprint. This has, however, resulted in increased process complexity and reduced customer satisfaction. In order to meet and exceed customer expectations, many companies are forced to improve quality and on-time delivery, and have looked towards Lean Six Sigma as an approach to enable process improvement. The Lean Six Sigma literature is rich in deployment strategies; however, there is a general lack of a mathematical approach to deploy Lean Six Sigma in a global enterprise. This includes both project identification and prioritization. The research presented here is two-fold. Firstly, a process characterization framework is presented to evaluate processes based on eight characteristics. An unsupervised learning technique, using clustering algorithms, is then utilized to group processes that are Lean Six Sigma conducive. The approach helps Lean Six Sigma deployment champions to identify key areas within the business to focus a Lean Six Sigma deployment. A case study is presented and 33% of the processes were found to be Lean Six Sigma conducive. Secondly, having identified parts of the business that are lean Six Sigma conducive, the next steps are to formulate and prioritize a portfolio of projects. Very often the deployment champion is faced with the decision of selecting a portfolio of Lean Six Sigma projects that meet multiple objectives which could include: maximizing productivity, customer satisfaction or return on investment, while meeting certain budgetary constraints. A multi-period 0-1 knapsack problem is presented that maximizes the expected net savings of the Lean Six Sigma portfolio over the life cycle of the deployment. Finally, a case study is presented that demonstrates the application of the model in a large multinational company. Traditionally, Lean Six Sigma found its roots in manufacturing. The research presented in this dissertation also emphasizes the applicability of the methodology to the non-manufacturing space. Additionally, a comparison is conducted between manufacturing and non-manufacturing processes to highlight the challenges in deploying the methodology in both spaces.
ContributorsDuarte, Brett Marc (Author) / Fowler, John W (Thesis advisor) / Montgomery, Douglas C. (Thesis advisor) / Shunk, Dan (Committee member) / Borror, Connie (Committee member) / Konopka, John (Committee member) / Arizona State University (Publisher)
Created2011