This dissertation concerns the development of a new interorganizational vehicle platform in the truck industry. The studied project setting was large, and can be referred to as a mega project. I ask the question How are boundaries organized in an interorganizational vehicle platform project setting, and how can we understand the tensions which arise when such organizing is performed? I assume that tensions arise in relation to questions concerning novelty, interdependencies, and differences.  Tensions should therefore not be seen as something bad, tensions are rather a prerequisite for achieving change.   The overall aim is to create insights in how boundaries in an interorganizational platform project setting are organized between: projects and governing actors, projects and permanent organizations, projects and external organizations, projects and projects, and finally inside projects (between different functions).  A secondary aim is to understand the roles which actors, activities and objects play, and the tensions which are experienced, when boundaries are being challenged and organized.   The study was performed during the concept phase, and a practice approach was used in order to capture the inner life of projects. A project setting with three projects was studied for three months, where I performed 68 interviews and observed 32 meetings. I have used a mix of narrative and alternate templates strategies and induced themes which constitute the base for the analysis.   I assume that boundaries are socially constructed and I argue that traditional normative findings in project management studies should be complemented with findings from organizational theory, and therefore use a multidisciplinary theoretical base. I have combined theories relating to; boundary construction, projects, boundary actors, activities, objects, and coordination/integration.   My analysis consists of two parts, in the first part I analyze value-, mandate-, and structural tensions and finds that actors in the setting; organize a commonality balancing area where decisions are affected by a mandates filter and need to be understood in relation to a coopetitive tensions model. In the second part of the analysis I have found that actors in the setting balance tensions and organize boundaries by performing four major Quality improvement loops based on a fragmented value base where boundary activities should be seen as having three dimensions; administrative, sharing, and political.   The creation of the shared platform is simultaneously affected by strategic, operational, and functional efforts.  This fact in combination with the size and uniqueness of the project setting, leads to the insight that technological innovation must be accompanied by organizational innovation. Therefore I have suggested that organizing of boundaries in interorganizational vehicle project settings should be understood as being performed through Concurrent Boundary Enactment.

The aim of the project was to identify biologically relevant novel gene clusters by using combined genomic data instead of using only gene expression data in isolation. The clustering algorithm based on self-organizing maps (Kasturi et al., 2005) was extended and implemented in order to use gene location data together with the gene expression and the motif occurrence data for gene clustering. A distance function was defined to be used with gene location data. The algorithm was also extended in order to use vector angle distance for gene expression data. Arabidopsis thaliana is chosen as a data source to evaluate the developed algorithm. A test data set was created by using 100 Arabidopsis genes that have gene expression data with seven different time points during cold stress condition, motif occurrence data which indicates the occurrence frequency of 614 different motifs and the chromosomal location data of each gene. Gene Ontology (http://www.geneontology.org) and TAIR (http://arabidopsis.org) databases were used to find the molecular function and biological process information of each gene in order to examine the biological accuracy of newly discovered clusters after using combined genomic data. The biological evaluation of the results showed that using combined genomic data to cluster genes resulted in new biologically relevant clusters.

With the growth of computer networks and the advancement of hardware technologies, unprecedented access to data volumes become accessible in a distributed fashion forming heterogeneous data sources. Understanding and combining these data into data warehouses, or merging remote public data into existing databases can significantly enrich the information provided by these data. This problem is called data integration: combining data residing at different sources, and providing the user with a unified view of these data. There are two issues with making use of remote data sources: (1) discovery of relevant data sources, and (2) performing the proper joins between the local data source and the relevant remote databases. Both can be solved if one can effectively identify semantically-related attributes between the local data sources and the available remote data sources. However, performing these tasks manually is time-consuming because of the large data sizes and the unavailability of schema documentation; therefore, an automated tool would be definitely more suitable. Automatically detecting similar entities based on the content is challenging due to three factors. First, because the amount of records is voluminous, it is difficult to perceive or discover information structures or relationships. Second, the schemas of the databases are unfamiliar; therefore, detecting relevant data is difficult. Third, the database entity types are heterogeneous and there is no existing solution for extracting a richer classification result from the processing of two different data types, or at least from textual and numerical data. We propose to utilize self-organizing maps (SOM) to aid the visual exploration of the large data volumes. The unsupervised classification property of SOM facilitates the integration of completely unfamiliar relational database tables and attributes based on the contents. In order to accommodate heterogeneous data types found in relational databases, we extended the term frequency – inverse document frequency (TF-IDF) measure to handle numerical and textual attribute types by unified vectorization processing. The resulting map allows the user to browse the heterogeneously typed database attributes and discover clusters of documents (attributes) having similar content. iii The discovered clusters can significantly aid in manual or automated constructions of data integrity constraints in data cleaning or schema mappings for data integration.

After dissolving of USSR in 1991 and establishing of capitalistic economic model trade volume between Taiwan and post-Soviet countries has been continuously growing throughout recent 20 years. Economy between Taiwan and Russia and others former Soviet countries stimulates close cooperation, including establishing of joint ventures, procurement, negotiations, which means mutual visiting. Businessmen come to Taiwan into two different ways: groups and individual businessmen (according to my experience, 2 people). First type: some tour agencies, which provide tours for tourists groups and organizing trips for different kinds of delegations. Second type: individual businessmen who comes to Taiwan for 3-5 days. Their plan includes: visiting exhibitions/companies/ factories and after that (or during that time) explore Taiwan: get basic consultations about implementing business, visiting night market, shopping etc. Such individual businessmen hire Russian-speaking translator, who lives in Taiwan and accompany them during visit. They have limited budget (because it is only 2 people), prefer not connect with agencies (language barriers, budget and trust), but find translators through recommendation from TAITRA (Taiwan External Trade Development Council - Russian branch), other businessmen, web-sites (Facebook, Twitter). Unfortunately, often they can find only students, who provide low-level service. Collaborations are spontaneous, not well-organized and not systematic. My target audience is the second type. A niche market opportunity exists in organizing systematic tours for individual businessmen. The uniqueness of the project is organizing tours focusing on business prospective, clients’ oriented service.

What happens in the case of immigrant groups who have had such pre-migration experiences as to require specialized assistance in the adaptation process, and yet whose population is not substantial enough to convince governmental sources of funding their demands? The wave of Burmese refugees fleeing the 1988 crackdown in their country is one such example. Drawing from perspectives of Participatory Action Research (PAR), this study has several objectives. First, it explores the current settlement needs of the Burmese population by way of relating it to the pre-migration context. By identifying those characteristics which influence the ability of this group to effectively compete for resources among organized ethno-cultural groups in Canada, this study hopes to highlight barriers to full participation. Second, a related objective is the documentation of the settlement and integration issues faced by the Burmese population, namely through an exploratory study of experiences of Burmese communities in Winnipeg and Toronto. Third, it seeks to explore the question of social capital within the Burmese population and its possible implications for resettlement and integration process. Fourth, it will attempt to contribute to the testing of Participatory Action Research as a methodological tool in improving our understanding of refugee resettlement. And fifth, it seeks to generate recommendations that will improve the settlement and integration of this target population within Canadian society. Broadly, it is hoped that this study might demonstrate how the particular needs of immigrant groups, in this case statistically small ethno-cultural groups arriving with traumatic refugee experiences, require careful consideration in seeking to facilitate integration through enhanced social capital and self-help.

An interactive visualization of self-organizing radio networks is developed. When the size and complexity of today’s radio networks grows, the need of automated network organizing methods increase to cut down on work, money and mistakes. The automation, however, leads the network operators to lose control over their own network and possible trust issues come along. Instead of giving back control to the operators, which would increase costs and work, Ericsson has suggested creating a visualization making clear that their self-organizing methods work as intended and letting the operator to efficiently explore their own network data. In this thesis project a visualization application is developed allowing the network operator to explore the settings and performance of their network organized by Ericsson’s automatic algorithm called Automatic Neighbor Relations (ANR). The user can interact with the visualization by picking, filtering, and more, to find potential patterns in the data, find bad data values, and see how settings affect the performance of the network. The visualization is built around a map where parameter and performance data is presented. Other visualization components come from the visualization framework GeoAnalytics Visualization (GAV), developed at Linköpings universitet, which also stands as a basis for the entire visualization.

What happens in the case of immigrant groups who have had such pre-migration experiences as to require specialized assistance in the adaptation process, and yet whose population is not substantial enough to convince governmental sources of funding their demands? The wave of Burmese refugees fleeing the 1988 crackdown in their country is one such example. Drawing from perspectives of Participatory Action Research (PAR), this study has several objectives. First, it explores the current settlement needs of the Burmese population by way of relating it to the pre-migration context. By identifying those characteristics which influence the ability of this group to effectively compete for resources among organized ethno-cultural groups in Canada, this study hopes to highlight barriers to full participation. Second, a related objective is the documentation of the settlement and integration issues faced by the Burmese population, namely through an exploratory study of experiences of Burmese communities in Winnipeg and Toronto. Third, it seeks to explore the question of social capital within the Burmese population and its possible implications for resettlement and integration process. Fourth, it will attempt to contribute to the testing of Participatory Action Research as a methodological tool in improving our understanding of refugee resettlement. And fifth, it seeks to generate recommendations that will improve the settlement and integration of this target population within Canadian society. Broadly, it is hoped that this study might demonstrate how the particular needs of immigrant groups, in this case statistically small ethno-cultural groups arriving with traumatic refugee experiences, require careful consideration in seeking to facilitate integration through enhanced social capital and self-help.

This dissertation develops a "neighborhood" based neural network model utilizing wavelet analysis and Self-organizing Map (SOM) to predict building baseline energy use. Wavelet analysis was used for feature extraction of the daily weather profiles. The resulting few significant wavelet coefficients represent not only average but also variation of the weather components. A SOM is used for clustering and projecting high-dimensional data into usually a one or two dimensional map to reveal the data structure which is not clear by visual inspection. In this study, neighborhoods that contain days with similar meteorological conditions are classified by a SOM using significant wavelet coefficients; a baseline model is then developed for each neighborhood. In each neighborhood, modeling is more robust without unnecessary compromises that occur in global predictor regression models. This method was applied to the Energy Predictor Shootout II dataset and compared with the winning entries for hourly energy use predictions. A comparison between the "neighborhood" based linear regression model and the change-point model for daily energy use prediction was also performed. We also studied the application of the non-parametric nearest neighborhood points approach in determining the uncertainty of energy use prediction. The uncertainty from "local" system behavior rather than from global statistical indices such as root mean square error and other measures is shown to be more realistic and credible than the statistical approaches currently used. In general, a baseline model developed by local system behavior is more reliable than a global baseline model. The "neighborhood" based neural network model was found to predict building baseline energy use more accurately and achieve more reliable estimation of energy savings as well as the associated uncertainties in energy savings from building retrofits.

<p>An interactive visualization of self-organizing radio networks is developed. When the size and complexity of today’s radio networks grows, the need of automated network organizing methods increase to cut down on work, money and mistakes. The automation, however, leads the network operators to lose control over their own network and possible trust issues come along. Instead of giving back control to the operators, which would increase costs and work, Ericsson has suggested creating a visualization making clear that their self-organizing methods work as intended and letting the operator to efficiently explore their own network data.</p><p>In this thesis project a visualization application is developed allowing the network operator to explore the settings and performance of their network organized by Ericsson’s automatic algorithm called Automatic Neighbor Relations (ANR). The user can interact with the visualization by picking, filtering, and more, to find potential patterns in the data, find bad data values, and see how settings affect the performance of the network.</p><p>The visualization is built around a map where parameter and performance data is presented. Other visualization components come from the visualization framework GeoAnalytics Visualization (GAV), developed at Linköpings universitet, which also stands as a basis for the entire visualization.</p>

Thesis (Ph. D.)--University of Chicago, Dept of Physics, August 2003. / Includes bibliographical references. Also available on the Internet.