Very large sets of multi-source, multi-dimensional and time varying data are generated during the execution of construction projects, especially large-scale infrastructure projects. Emphasized in this thesis is how data visualization can provide important insights during the planning, implementation and post project analysis phases of linear projects in an urban environment, which are attended by a complex working environment and multiple stakeholders. These insights can lead to enhanced communication and better decision making. Thesis objectives are four fold: (i) examine how the representation of a schedule using linear planning charts can assist with assessing the quality of a schedule in terms of the construction strategy, communicate schedule intent to projects participants, and assist with telling the as-built story; (ii) explore images useful for representing multi source, multi-dimensional, time varying as-built construction data in support of management functions specifically with regards to communication and decision making; (iii) demonstrate the ability of visual representations of construction data to assist in telling the as-built story of a project in a manner that provides useful insights to project participants; and, (iv) critique the images presented in light of the data visualization principles and interaction tools identified, and suggest improvements as appropriate and possibly other images, including properties desired. In addressing these objectives, the methodology involved a review of computer science and construction literature as it pertains to data visualization and a case study of a past project which reflected the scale and complexity of planning and executing linear projects in an urban environment. The planned and as-built story were captured from the available data depicting the contractor’s perspective in the project’s product, process, and as-built views which were replicated in a research software system called REPCON software. This system supports selected data visualization capabilities, which were examined and critiqued as part of this thesis. It is demonstrated that data visualization is a powerful paradigm for gaining insights into the quality of a project’s plan and for understanding a project’s as-built performance. Greater benefits could be achieved by exploiting cutting edge visualization tools and by designing and implementing a more comprehensive set of images.

With the ever increasing popularity of emails, it is very common nowadays that people discuss specific issues, events or tasks among a group of people by emails. Those discussions can be viewed as conversations via emails and are valuable for the user as a personal information repository. For instance, in 10 minutes before a meeting, a user may want to quickly go through a previous discussion via emails that is going to be discussed in the meeting soon. In this case, rather than reading each individual email one by one, it is preferable to read a concise summary of the previous discussion with major information summarized. In this thesis, we study the problem of discovering and summarizing email conversations. We believe that our work can greatly support users with their email folders. However, the characteristics of email conversations, e.g., lack of synchronization, conversational structure and informal writing style, make this task particularly challenging. In this thesis, we tackle this task by considering the following aspects: discovering emails in one conversation, capturing the conversation structure and summarizing the email conversation. We first study how to discover all emails belonging to one conversation. Specifically, we study the hidden email problem, which is important for email summarization and other applications but has not been studied before. We propose a framework to discover and regenerate hidden emails. The empirical evaluation shows that this framework is accurate and scalable to large folders. Second, we build a fragment quotation graph to capture email conversations. The hidden emails belonging to each conversation are also included into the corresponding graph. Based on the quotation graph, we develop a novel email conversation summarizer, ClueWordSummarizer. The comparison with a state-of-the-art email summarizer as well as with a popular multi-document summarizer shows that ClueWordSummarizer obtains a higher accuracy in most cases. Furthermore, to address the characteristics of email conversations, we study several ways to improve the ClueWordSummarizer by considering more lexical features. The experiments show that many of those improvements can significantly increase the accuracy especially the subjective words and phrases.

Cancer is a dangerous disease causing the most deaths in the world today and around 550,000 deaths in America per year (American Cancer Society). Larynx cancer data was recorded by Kardaun (1983). The data was collected at a Dutch hospital during 1970 – 1978. Ninety male adults with cancer of larynx were involved into the study. Each patient was divided into one of four groups depending on his or her illness condition. The data also recorded their age, lifetime, and year of entering the research. These are common factors as factors of other cancer data. The purpose of this thesis is to apply proportional hazard regression model, additive hazard regression model, censored quantiles regression model, and censored linear regression model to analyze the above larynx cancer data and find the best regression model of data by using each method. Comparison and suggestion for which method should be used in specific situation are also made. Some related topics are also mentioned so we can have resource for future study. Key words: right censoring, proportional risk model, additive risk model, quantiles regression model, linear regression model.

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Netgi tobuliausiai suplanuotame tyrime atsiranda įvairių rūšių klaidų, dėl kurių gali būti gauti nepatikimi ar nepakankamai tikslūs tyrimo rezultatai, taigi labai svarbu kiek įmanoma labiau sumažinti tų klaidų įtaką tyrimo rezultatams – sumų, vidurkių, santykių įvertiniams. Vienas iš galimų statistinio tyrimo klaidų tipų yra klaidos dėl neatsakymo į apklausą. Jos atsiranda tuomet, kai atsakytojas neatsako į vieną ar kelis klausimyno klausimus. Neatsakymai tyrimuose pasitaiko dėl įvairių priežasčių. Jie iššaukia standartinių įvertinių, kuriuose neatsižvelgiama į neatsakymus, nuokrypį nuo tikrųjų mus dominančių reikšmių, o taip pat šių įvertinių dispersijos padidėjimą. Dabartinėje praktikoje neatsakymai į apklausą nagrinėjami dviem požiūriais: visų pirma bandoma išvengti arba sumažinti neatsakymų lygį. Yra nemažai literatūros ir metodologinės medžiagos tyrinėjančios neatsakymų priežastis bei pateikiančios rekomendacijas kaip sumažinti neatsakymų lygį, tačiau, kai tyrime jau yra neatsakymų, dominančius įvertinius reikia sukonstruoti taip, kad tyrimo rezultatai būtų kuo tikslesni. Neatsakymų sukeliamiems tyrimo rezultatų nuokrypiams sumažinti naudojami įvairūs būdai. Vienas tokių metodų yra praleistų reikšmių įrašymas. Įrašymas – tai trūkstamų duomenų užpildymo būdas, kuris yra labai naudingas analizuojant nepilnas duomenų sekas. Jis išsprendžia duomenų trūkumo problemą duomenų analizės pradžioje. Praleistų reikšmių įrašymo metodika šiuo metu sparčiai vystosi, galima rasti... [toliau žr. visą tekstą] / Nonresponse has been a matter of concern for several decades in survey theory and practice. The problem can be viewed from two different angles: the prevention or avoidance of nonresponse before it occurs, and the special estimation techniques when nonresponse has occurred. The objective of this work is to describe main methods of estimation when nonresponse occurs. Special attention is drawn on one nonresponse estimation method – imputation. Imputation is the procedure when missing values for one or more study variables are “filled in” with substitutes constructed according to some rules, or observed values for elements other than nonrespondents. In this work imputation methods based on some of the more commonly used statistical rules are considered. Some of them are tested on data set having the same distribution as the data of the real survey taken in Statistics Lithuania. The imputation methods are compared with each other and the best imputation method for this data set is picked up. Special attention is paid on regression imputation.

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In the absence of clinical markers for Chronic Fatigue Syndrome (CFS), research to find a biological basis for it is still open. Many data-mining techniques have been widely employed to analyze biomedical data describing different aspects of CFS. However, the inconsistency of the results of these studies reflect the uncertainty in regards to the real basis of this disease. In this thesis, we show that CFS has a biological basis that is detectable in gene expression data better than blood profile and Single Nucleotide Polymorphism (SNP) data. Using random forests, the analysis of gene expression data achieves a prediction accuracy of approximately 89%. We also identify sets of differentially expressed candidate genes that might contribute to CFS. We show that the integration of data spanning multiple levels of the biological scale might reveal further insights into the understanding of CFS. Using integrated data, we achieve a prediction accuracy of approximately 91%. We find that Singular Value Decomposition (SVD) is a useful technique to visualize the performance of random forests. / Thesis (Master, Computing) -- Queen's University, 2007-12-11 12:15:40.096