Browsing by Subject "Data"
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Item Aligning Online Privacy Protection with Reasonable Expectations of Privacy: How Joffe Can Be Used to Modernize the Wiretap Act(Minnesota Journal of Law, Science and Technology, 2014-05) Mason, MatthewBetween May 2007 and 2010, as part of its popular Street View project, Google collected an enormous amount of Wi-Fi data transmitted from unencrypted networks throughout the United States and over thirty countries worldwide. After initially denying the collection of any payload data, Google publicly acknowledged that fragmented samples of payload data were collected from open Wi-Fi networks due to a code mistakenly included in its Street View software. Several months later, however, Google admitted that the data collected was not just fragmentary in nature; in some instances the full content of e-mails, URL searches, passwords, and financial transactions were collected. In response to what has been called a “big brother-like . . . invasion of privacy,” investigations have been launched in the United States and abroad. In a private action against Google, the Northern District of California denied Google’s motion to dismiss a claim alleging that Google’s collection of payload data from unencrypted Wi-Fi networks violated the Wiretap Act. The Ninth Circuit affirmed, holding that Wi-Fi communications do not constitute an “electronic communication . . . readily accessible to the general public” under the Wiretap Act, and thus are not exempt from liability. The Ninth Circuit’s ruling in Joffe v. Google, Inc. raises a number of important issues that may have significant implications on privacy protections for Internet and other electronic communication. Joffe exposed our current privacy protection framework as inadequate for new technologies and advancements in communication. Such inadequacy raises the question as to what extent, and in what way, Congress must update the Wiretap Act to accommodate a changing communication landscape since the enactment of the Electronic Communications Privacy Act (ECPA) in 1986. Furthermore, it becomes necessary to consider whether users of unsecured Wi-Fi networks have a reasonable expectation of privacy in their transmitted electronic communications. As a corollary, it is important to examine how offline Fourth Amendment principles may be applied to an increasingly online society to protect an individual’s electronic and Internet communications. This Comment seeks to examine how Congress, and the courts, might use Joffe as a springboard to bring privacy protections up to date with technological and communication advances. This Comment analyzes the reasoning and holding advanced by the Joffe court, placing Joffe in context with the current state of the law, and argues that Congress and courts should use Joffe to align the reality of users’ knowledge of Wi-Fi technology and reasonable expectations of privacy with the Wiretap Act. This Comment concludes that Congress should amend the ECPA to expressly protect both encrypted and unencrypted Wi-Fi transmissions, and that courts should adapt offline Fourth Amendment principles to protect online and other electronic communications.Item Assessing Metadata Quality and Terminology Coverage of a Federally Sponsored Health Data Repository(2016-02) Marc, DavidThe Open Government Initiative began an era of information sharing by publishing data that is accessible to the public. HealthData.gov is a data portal that was developed by the U.S. Federal Government to publish metadata to disseminate information about healthcare datasets to the American people. Despite the growth in the number of datasets published, there has been limited public participation in the use of the data, which has been attributed to the currently implemented methods for data storage and retrieval. An automated assessment of the HealthData.gov metadata was conducted to assess completeness, accuracy, and consistency of metadata published from 2012 to 2014. Also, a method for indexing the datasets using Medical Subject Headings (MeSH) was evaluated using a term coverage study. The results of these studies demonstrated that metadata published in earlier years were less complete, lower quality, and less consistent. Also, metadata that underwent modifications following their original creation were of higher quality. MeSH offered adequate coverage of the metadata concepts, thereby lending support for the adoption of the terminology for indexing purposes. The results suggested that greater standardization is needed when publishing metadata. This research contributed to the development of automated metrics for assessing metadata quality, design recommendations for a framework to supports high quality metadata, and recommendations for expanding MeSH to offer greater coverage of concepts from HealthData.gov.Item Best Types of Commodity Flow Data for Freight, Railroad, and Ports and Waterways Studies(Minnesota Department of Transportation, 2023-02) Fonseca, Camila; Zeerak, Raihana; Napoline, Kimberly; Zhao, JerryThe understanding of freight movement is critical to economic development and competitiveness and to make decisions regarding the transportation system. Despite the increased interest in freight planning and modeling, freight data are limited in availability and granularity, and the existing sources are incomplete or outdated. This research analyzes various types of public and proprietary freight databases to determine which are most helpful for planning, programming, and designing future infrastructure on the truck, rail, air, and waterway networks within Minnesota and surrounding states. There are some comprehensive multimodal freight databases that provide different levels of data granularity. These are typically complemented with other data sources that are specific to a transportation mode. We also interview stakeholders involved in freight planning in Minnesota to identify data gaps and capture current and future data needs. Important needs include (i) mode specific freight data, especially for waterways and ports and air freight; (ii) equity considerations in freight transportation; and (iii) understanding the relationship between freight transportation and climate change. Additional freight data are much needed overall to inform economic development and funding prioritization, as well as to evaluate and minimize supply chain disruptions.Item Data to Policy Change: Creating an Interactive Dashboard to Voice Youth Perspectives(2024-05-01) Gjedrum, Maria; Glass, Allison; Sanchez, Antonio; Wilson, AlexandraThe Dashboard Safety Index Project, in alignment with the 2022 YMB Annual Report and YCB’s mission, aims to provide a continual platform empowering youth to express their perspectives for policymaking. This interactive tool fosters inclusivity and responsiveness by integrating youth voices into community safety and policy discussions. The project employs a comprehensive methodology, including multiple stakeholder perspectives, regional breakdowns, and alignment with UNICEF goal areas. Challenges such as missing data and inconsistencies in data collection are addressed, while recommendations for dashboard design and data collection prioritize user engagement and youth involvement. The project not only reflects a significant step towards inclusivity but also enhances the relevance and impact of public policies concerning youth safety, ultimately serving as a practical, user-centric tool for policy-makers and the community.Item Data-Driven Fault Diagnostic Methods of Mechanical Systems Based on Koopman Operator(2021-08) Nichifor, AlexandruThe Koopman operator is a linear evolution operator that can represent non-linear dynamical systems. Traditionally, there are several methods of analyzing linear dynamical systems. However, when introducing the non-linearity, the analysis of that system becomes exponentially more problematic. This method of linear representations of non-linear dynamical systems is becoming more pertinent as data-driven systems are becoming more abundant. The novel use of the Koopman operator in this paper is to extract and classify significant parameters of a mechanical non-linear dynamic system. This thesis proposes two distinct methods of using the Koopman operator for fault diagnosis. The first method proposes a model to extract key features from a dynamic system and through a neural network is able to classify the existence of a fault. The second method uses parameters derived from the Koopman operator to create a prediction model. This prediction model is used to reconstruct the original system dynamics for a desired time evolution. The two methods are then tested via two separate case studies and the results are discussed.Item Dataset: U.S. Public University Responses to Public Records Requests for Structured Data(2021-03-10) Anderson, Jonathan; Wiley, Sarah K.; and08164@umn.edu; Anderson, JonathanThis dataset is the product of a study that assessed how public universities in the United States respond to public records requests of varying complexity for structured data. When a university provided a substantive response, the following variables were coded: 1. Nature of response: Whether the university produced responsive data, produced or offered different data than what we requested, asserted there were no records, required prepayment before processing, required in-person inspection, or denied the request. 2. Response time: The number of business days (i.e., omitting weekends and holidays) from the day after a request was filed to the day a substantive response was received. 3. Format: The format that data were released: Excel, CSV, PDF, or web page. 4. New record: Whether the university expressly asserted that it is not obligated to create a new record in response to a public records request. 5. Fee estimate: The amount of money a university estimated it would cost to process the request.Item Development and Field Demonstration of DSRC-Based V2I Traffic Information System for the Work Zone(2010-12) Maitipe, Buddhika; Hayee, M. ImranThis report describes the architecture, functionality and the field demonstration of a newly developed dedicated short-range communication (DSRC)-based Vehicle to Infrastructure (V2I) communication system for improving traffic efficiency and safety in the work-zone related congestion buildup on US roadways. The goal was to develop a portable system that can be easily deployed at a work zone site to acquire and communicate important travel information, e.g., travel time (TT) and start of congestion (SoC) location to the driver. By providing this information, i.e., SoC location and TT, drivers can make informed decisions on route choice and be prepared for upcoming congestion. The system is composed of a portable road-side unit (RSU) that can engage the on board units (OBUs) of the traveling vehicles using DSRC technology to acquire necessary traffic data (speed, time, and location). From the acquired data, the RSU periodically estimates the SoC location and TT that are broadcast to all vehicles in its coverage range. An OBU receiving the broadcast message calculates the distance to the SoC location. The distance to the SoC location and TT are then relayed to the driver, who can make smart decisions regarding whether to seek an alternate route and when to expect a sudden speed reduction. Results from the field demonstration have shown that the developed system can adapt to changing work-zone environments smoothly under various congestion patterns on the road.Item Effects of a collaborative intervention on the quality of preservice teachers' data based decision making(2013-10) Wilson, Jennifer A.Effective teaching practices, including the ability to make data-based decisions, are necessary to close achievement gaps. The purpose of this study was to investigate the effects of a collaborative, data-based decision making (DBDM) intervention on the quality of preservice teachers' DBDM. Participants were 45 preservice general educators enrolled in a teacher education course required for elementary teacher licensure. An experimental group design was used to investigate the effects of the intervention on the quality of preservice general educators' data-based decisions. Participants were randomly assigned to either the experimental condition, which was a 75-min, small-group, collaborative intervention on DBDM, or the control condition. Data were analyzed using a Mann Whitney U Test. The results showed that the intervention influenced the quality of preservice teachers' DBDM and that this collaboration influenced the confidence levels of preservice teachers with regard to DBDM.Item Energy feedback at the city-wide scale(2014-05) Carter, Richard AllanClimate change is a growing concern throughout the world. In the United States, leadership has so far failed to establish targeted reductions and agreement on mitigation strategies. Despite this, many large cities are taking on the challenge of measuring their emissions, establishing targeted reductions, and defining strategies for mitigation in the form of Climate Action Plans. Reporting of greenhouse gas (GHG) emissions by these cities is usually based on a one-time, annual calculation. Many studies have been conducted on the impact of providing energy use data or feedback to households, and in some cases, institutional or commercial businesses. In most of those studies, the act of providing feedback has resulted in a reduction of energy use, ranging from 2% to 15%, depending upon the features of the feedback. Many of these studies included only electric use. Studies where all energy use was reported are more accurate representations of GHG emissions. GHG emissions and energy use are not the same, depending on the fuel source and in the case of this paper, the focus is on reducing energy use.This research documents the characteristics of the feedback provided in those studies in order to determine which are most effective and should be considered for application to the community-wide scale. Eleven studies, including five primary and six secondary research papers, were reviewed and analyzed for the features of the feedback. Trends were established and evaluated with respect to their effectiveness and potential for use at the community-wide scale.This paper concludes that additional research is required to determine if the use of energy feedback at the city scale could result in savings similar to those observed at the household scale. This additional research could take advantage of the features assessed here in order to be more effective and to implement the features that are best able to scale up. Further research is needed to determine whether combining city-wide feedback with feedback for individual energy users within the city, both residential and commercial, has an even greater impact on reducing energy use and lowering GHG emissions.Item Framework for Measuring Sustainable Regional Development for the Twin Cities Region(Center for Transportation Studies, 2010-01) Kirk, Kaydee; Tableporter, Jody; Senn, Andrew; Day, Jennifer; Cao, Jason; Fan, Yingling; Schively Slotterback, Carissa; Goetz, Edward; McGinnis, LauriePatterns of growth and development impact our environmental, social, economic, and cultural quality of life. In order to take steps toward sustainable development that will have a positive impact on these effects, this project, sponsored by the McKnight Foundation, identified a framework for an indicator system to measure sustainable regional development in the Twin Cities metropolitan region. The proposed framework includes a set of sustainability principles, indicators, measures, and accompanying data sources. It is anticipated that the McKnight Foundation will use this sustainability framework for internal organizational purposes with the possibility of the system being considered by other local geographies in the future. This framework could also serve as a tool to compare sustainability between the Twin Cities seven-county region and other comparable regions. The report provides a summary of the research, presents a final recommended set of performance measures for the indicators, makes recommendations for the selection of tier 1 and tier 2 indicators, and recommends a plan for next steps.Item Kite: A Scalable Microblogs Data Management System(2017-06) Ahmed, AmrDevelopers, researchers, and practitioners have been building a myriad of applications to analyze microblogs data, e.g., tweets, online reviews, and user comments. Examples of such applications include citizen journalism, events detection and analysis, geo-targeted advertising, medical research, and studying social influences in social sciences. Building such applications require data management infrastructure to deal with microblogs, including data digestion, indexing, and main-memory management. The lack of such infrastructure hinders the scalability and the widespread of such applications especially among users who are not computer scientists. This thesis proposes Kite; an end-to-end system that is able to manage microblogs data at a large scale. Using Kite, developers and practitioners can simply write SQL-like queries without worrying about the internal data management issues. Internally, Kite is equipped with scalable indexing and main-memory management techniques to support top-k temporal, spatial, keyword, and trending queries on both very recent data and historical data. Kite indexer supports scalable digestion and retrieval for incoming fast data in real time. Recent data are digested in efficient main-memory index structures. Kite in-memory index structure are able to scale up a single machine indexing capabilities to handle the overwhelming amount of data in real time. Meanwhile, Kite memory manager is monitoring the memory contents and smartly decides on which data is regularly moved to disk. This is accomplished through effective memory flushing policies that are designed for top-k query workloads, which are popular on microblogs data. Both in-memory and in-disk data are queried seamlessly through efficient retrieval techniques that are encapsulated in Kite query processor. The query processor exploits the top-k ranking function to early prune the search space and reduce the query latency significantly. Kite is open-sourced and available to the community to build on (http://kite.cs.umn.edu). Extensive experimentation on different Kite components show the efficiency and the effectiveness of the proposed techniques to manage microblogs data at scale.Item Modelling hedonic residential rents for land use and transport simulation while considering spatial effects(Journal of Transport and Land Use, 2010) Löchl, Michael; Axhausen, KayThe application of UrbanSim requires land or real estate price data for the study area. These can be difficult to obtain, particularly when tax assessor data and data from commercial sources are unavailable. The article discusses an alternative method of data acquisition and applies hedonic modeling techniques in order to generate the required data. Many studies have highlighted that ordinary least square (OLS) regression approaches lack the ability to consider spatial dependency and spatial heterogeneity, consequently leading to biased and inefficient estimations. Therefore, a comprehensive data set is used for modeling residential asking rents by applying and comparing OLS, spatial autoregressive, and geographically weighted regression (GWR) techniques. The latter technique performed best with regard to model fit, but the issue of correlated coefficients favored a spatial simultaneous autoregressive model. Overall, the article reveals that when housing markets are a particular concern in UrbanSim applications, significant efforts are needed for the price data generation and modeling. The study concludes with further development potentials for UrbanSim.Item Sensor Integration Software(2014-07-23) Murch, AustinItem SmarTrAC: A Smartphone Solution for Context-Aware Travel and Activity Capturing(2015-02) Fan, Yingling; Wolfson, Julian; Adomavicius, Gediminas; Vardhan Das, Kirti; Khandelwal, Yash; Kang, JieThe use of mobile phones in collecting travel behavior data has rapidly increased, especially after GPS tracking technology became widely available in commercial smartphones. Existing smartphone-based tools in the field have generally focused on capturing the “when”, “where”, and “how” of travel, i.e., using the smartphone’s automatic sensing functionality to detect travel mode and to collect position and route data. Although locations and modes of transportation derived from sensing data represent important travel behavior information, travel behavior has many other important dimensions—such as trip purpose, travel experience, and travel companionship (i.e., the “why”, “how”, and “who” of travel)—all of which are critical for understanding people’s travel choices. Some of these dimensions may be inferable from pure sensory data, but reliable inference will generally require long-term use data from a very large number of subjects. Other dimensions are simply inaccessible to passive sensing tools. In contrast, traditional travel diary methods and some first-generation smartphone-based travel survey tools enable the collection of multi-dimensional data through high-intensity sampling and qualitative survey techniques. However, these methods are often burdensome to study subjects and impractical for use in a diverse, mobile, and increasingly time-stressed population.Item Using data to increase student achievement:a case study of success in a sanctioned school.(2011-05) Fischer, Brenda ElaineThe No Child Left Behind Act of 2002 fundamentally changed the ways in which schools are held accountable for the academic achievement of all students. Each year, millions of tests are given to students in the United States to comply with the federal accountability mandates set forth by this unprecedented federal legislation. Since these tests are so plentiful and prevalent and so much time and energy is invested in gathering results, it seems it might be possible for this multitude of data to be used for purposes other than external accountability. Might school leaders be able to utilize the data from mandated standardized tests to strategically enable schools to move toward increased student achievement across curricular goals? This qualitative case study tells the story of how teachers and administrators at one Minnesota elementary school, that was labeled in need of improvement, used a variety of data available to them to increase student academic achievement scores. Findings from this study include discussions of the factors and combination of factors that led to increased academic success. This study also includes suggestions for teachers, principals, policy makers, and institutions of higher learning, based on information gained during interviews and from the literature, for creating the conditions under which data can be used as an essential component in the ongoing challenge to increase academic achievement for all students.