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.

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.

Master of Science / Department of Computing and Information Sciences / William H. Hsu / In this report, I describe a query-driven system which helps in deciding which restaurant to invest in or which area is good to open a new restaurant in a specific place. Analysis is performed on already existing businesses in every state. This is based on certain factors such as the average star rating, the total number of reviews associated with a specific restaurant, the price range of the restaurant etc. The results will give an idea of successful restaurants in a city, which helps you decide where to invest and what are the things to be kept in mind while starting a new business. The main scope of the project is to concentrate on Analytics and Data Visualization.

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. / Applied Science, Faculty of / Civil Engineering, Department of / Graduate

Indiana University-Purdue University Indianapolis (IUPUI) / For individuals in the tourism industry and other businesses, the department of tourism in the government, or the individuals who are planning a travel, the data of tourist population movement can be a valuable resource that can uncover insights that could bring more profit and more tourists, or make the trip more enjoyable. As visualization is an effective way of conveying information with multiple dimensions, we would like to visualize the geo-temporal floating population data of tourists and residents in Jeju island in the Republic of Korea in two-dimensional space. In this study, we introduce the two methods we have implemented for visualizing the geo-temporal data using color curves as the representation of time dimension. We use the dots as the markers of floating population, and each color of dots represents the 24 hours of a day. In the first method, we plot the colored dots directly on the map, thereby coloring the area the data represents. In the second method, we plot the same dots inside a semi-transparent circle divided into arcs that represent each month of a year. The user can compare the population of tourists and residents between the different times of a day, the different months and the weather conditions to analyze the floating population in the given area.

In today's information age, data collection is not the ultimate goal; it is simply the first step in extracting knowledge-rich information to shape future decisions. In this thesis, we present ChartVisio - a simple web-based visual data-mining system that lets users quickly explore databases and transform raw data into processed visuals. It is highly interactive, easy to use and hides the underlying complexity of querying from its users. Data from tables is internally mapped into charts using aggregate functions across tables. The tool thus integrates querying and charting into a single general-purpose application. ChartVisio has been designed as a component of the Benchmark data engine, being developed at the Computer Science department, University of New Orleans. The data engine is an intelligent website generator and users who create websites using the Data Engine are the site owners. Using ChartVisio, owners may generate new charts and save them as XML templates for prospective website surfers. Everyday Internet users may view saved charts with the touch of a button and get real-time data, since charts are generated dynamically. Website surfers may also generate new charts, but may not save them as templates. As a result, even non-technical users can design and generate charts with minimal time and effort.

Indiana University-Purdue University Indianapolis (IUPUI) / Benefited from the booming social network, reading posts from other users over the internet is becoming one of commonest ways for people to intake information. One may also have noticed that sometimes we tend to focus on users provide well-founded analysis, rather than those merely who vent their emotions. This thesis aims at finding a simple and efficient way to recognize reliable information sources among countless internet users by examining the sentiments from their past posts. To achieve this goal, the research utilized a dataset of tweets about Apple's stock price retrieved from Twitter. Key features we studied include post-date, user name, number of followers of that user, and the sentiment of that tweet. Prior to making further use of the dataset, tweets from users who do not have sufficient posts are filtered out. To compare user sentiments and the derivative of Apple's stock price, we use Pearson correlation between them to describe how well each user performs. Then we iteratively increase the weight of reliable users and lower the weight of untrustworthy users, the correlation between overall sentiment and the derivative of stock price will finally converge. The final correlations for individual users are their performance scores. Due to the chaos of real-world data, manual segmentation via data visualization is also proposed as a denoise method to improve performance. Besides our method, other metrics can also be considered as user trust index, such as numbers of followers of each user. Experiments are conducted to prove that our method outperforms others. With simple input, this method can be applied to a wide range of topics including election, economy, and the job market.

Why is the term information in English an uncountable noun, whereas in information theory it is a well-defined quantity? Since the amount of information can be quantified, what is the meaning of a fraction of that amount? This dissertation introduces a quasi-entropy matrix which developed from Claude Shannon's information measure as an analytical tool for behavioral studies. Such matrix emphasizes the role of relative characteristics of individual level data across different collections. The real challenge in the big data era is never the size of the dataset, but how data lead scientists to individuals rather than arbitrarily divided statistical groups. This proposed matrix, when combining with other statistical measures, provides a new and easy-to-do method for identifying pattern in a well-defined system because it is built on the idea that uneven probability distributions lead to decrease in system entropy. Although the matrix is not superior to classical correlation techniques, it allows an interpretation not available with traditional standard statistics. Finally, this matrix connects heterogeneous datasets because it is a frequency-based method and it works on the modes of data rather than the means of values. It also visualizes clustering in data although this type of clustering is not measured by the squared Euclidean distance of the numerical attributes.

This thesis describes two problem solving environments that integrate visualization and computational tools into a high level user interface. The objective of a problem solving environment is to provide scientists with a complete, usable, and integrated set of high level facilities for solving problems in a specific domain. Integrating visualization tools with computation tools encourages scientists to think in terms of the overall task of solving a problem, not simply using the visualization to view the results of the computation. This increases their productivity by allowing them to focus on the problem at hand rather than on general computation issues. Two problem solving environments based on this philosophy, but intended for different problem domains, are presented: VizCraft and WBCSim. VizCraft provides an integrated environment for aircraft designers working with multidimensional design spaces. The design problem currently being faced by aircraft designers, some approaches that have been taken in the past towards solving it, and how VizCraft provides a unique approach in helping the designer visualize the problem, are presented. WBCSim provides a Web-based framework for wood scientists conducting research on wood-based composite materials. It integrates legacy simulation codes with a graphical front end, an optimization tool, and a visualization tool. WBCSim serves as a prototype for the design, construction, and evaluation of larger scale problem solving (computing) environments. Several different wood-based composite material simulations are supported. / Master of Science

" More and more researchers are focusing on the management, querying and pattern mining of streaming data. The visualization of streaming data, however, is still a very new topic. Streaming data is very similar to time-series data since each datapoint has a time dimension. Although the latter has been well studied in the area of information visualization, a key characteristic of streaming data, unbounded and large-scale input, is rarely investigated. Moreover, most techniques for visualizing time-series data focus on univariate data and seldom convey multidimensional relationships, which is an important requirement in many application areas. Therefore, it is necessary to develop appropriate techniques for streaming data instead of directly applying time-series visualization techniques to it. As one of the main contributions of this dissertation, I introduce a user-driven approach for the visual analytics of multivariate data streams based on effective visualizations via a combination of windowing and sampling strategies. To help users identify and track how data patterns change over time, not only the current sliding window content but also abstractions of past data in which users are interested are displayed. Sampling is applied within each single time window to help reduce visual clutter as well as preserve data patterns. Sampling ratios scheduled for different windows reflect the degree of user interest in the content. A degree of interest (DOI) function is used to represent a user's interest in different windows of the data. Users can apply two types of pre-defined DOI functions, namely RC (recent change) and PP (periodic phenomena) functions. The developed tool also allows users to interactively adjust DOI functions, in a manner similar to transfer functions in volume visualization, to enable a trial-and-error exploration process. In order to visually convey the change of multidimensional correlations, four layout strategies were designed. User studies showed that three of these are effective techniques for conveying data pattern changes compared to traditional time-series data visualization techniques. Based on this evaluation, a guide for the selection of appropriate layout strategies was derived, considering the characteristics of the targeted datasets and data analysis tasks. Case studies were used to show the effectiveness of DOI functions and the various visualization techniques. A second contribution of this dissertation is a data-driven framework to merge and thus condense time windows having small or no changes and distort the time axis. Only significant changes are shown to users. Pattern vectors are introduced as a compact format for representing the discovered data model. Three views, juxtaposed views, pattern vector views, and pattern change views, were developed for conveying data pattern changes. The first shows more details of the data but needs more canvas space; the last two need much less canvas space via conveying only the pattern parameters, but lose many data details. The experiments showed that the proposed merge algorithms preserves more change information than an intuitive pattern-blind averaging. A user study was also conducted to confirm that the proposed techniques can help users find pattern changes more quickly than via a non-distorted time axis. A third contribution of this dissertation is the history views with related interaction techniques were developed to work under two modes: non-merge and merge. In the former mode, the framework can use natural hierarchical time units or one defined by domain experts to represent timelines. This can help users navigate across long time periods. Grid or virtual calendar views were designed to provide a compact overview for the history data. In addition, MDS pattern starfields, distance maps, and pattern brushes were developed to enable users to quickly investigate the degree of pattern similarity among different time periods. For the merge mode, merge algorithms were applied to selected time windows to generate a merge-based hierarchy. The contiguous time windows having similar patterns are merged first. Users can choose different levels of merging with the tradeoff between more details in the data and less visual clutter in the visualizations. The usability evaluation demonstrated that most participants could understand the concepts of the history views correctly and finished assigned tasks with a high accuracy and relatively fast response time. "