Measuring the Antitrust Revolution

Although antitrust always evolved with the economics of its time, economic analysis was not central to the antitrust enterprise until Continental T.V. Inc. v. GTE Sylvania. In doing so, the Court abandoned the multiple goals of the prior era to embrace a singular economic goal. With a singular goal, antitrust had become revolutionary. How to measure the antitrust revolution has been difficult. In this article, we focus on published case law, which provides a broad set of observations that includes government enforcement actions and private antitrust suits. We use the Caselaw Access Project database and its associated “Historical Trends” tool to track the usage of certain words and phrases in judicial opinions. This article is the first to measure antitrust terms in court cases that combine big data with data visualization techniques to better understand, based on the usage of common antitrust terms, the impact economics has had on decided cases.

Download Full-text

Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

Sustainability ◽

10.3390/su12145595 ◽

2020 ◽

Vol 12 (14) ◽

pp. 5595 ◽

Cited By ~ 3

Author(s):

Ana Lavalle ◽

Miguel A. Teruel ◽

Alejandro Maté ◽

Juan Trujillo

Keyword(s):

Big Data ◽

Smart City ◽

Smart Cities ◽

Fire Department ◽

Heterogeneous Data ◽

Fire Extinguishing ◽

Heterogeneous Data Sources ◽

Iot Devices ◽

Visualization Techniques ◽

The Impact

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.

Download Full-text

Visualization of Big Data Sets Using Computer Graphics

Advances in Data Mining and Database Management - Handbook of Research on Big Data Storage and Visualization Techniques ◽

10.4018/978-1-5225-3142-5.ch020 ◽

2018 ◽

pp. 578-603

Author(s):

Anna Ursyn ◽

Edoardo L'Astorina

Keyword(s):

Big Data ◽

Data Visualization ◽

Visual Analysis ◽

Data Sets ◽

Arrival Times ◽

Learning And Teaching ◽

Knowledge Domains ◽

Visualization Of Data ◽

And Mathematics ◽

Visualization Techniques

This chapter discusses some possible ways of how professionals, researchers and users representing various knowledge domains are collecting and visualizing big data sets. First it describes communication through senses as a basis for visualization techniques, computational solutions for enhancing senses and ways of enhancing senses by technology. The next part discusses ideas behind visualization of data sets and ponders what is and what not visualization is. Further discussion relates to data visualization through art as visual solutions of science and mathematics related problems, documentation objects and events, and a testimony to thoughts, knowledge and meaning. Learning and teaching through data visualization is the concluding theme of the chapter. Edoardo L'Astorina provides visual analysis of best practices in visualization: An overlay of Google Maps that showed all the arrival times - in real time - of all the buses in your area based on your location and visual representation of all the Tweets in the world about TfL (Transport for London) tube lines to predict disruptions.

Download Full-text

Big Data and Visuality

Oxford Research Encyclopedia of Criminology and Criminal Justice ◽

10.1093/acrefore/9780190264079.013.128 ◽

2017 ◽

Author(s):

Janet Chan

Keyword(s):

Big Data ◽

Criminal Justice ◽

Data Storage ◽

Data Visualization ◽

Crime Control ◽

Big Data Analytics ◽

Emotional Impact ◽

Predictive Tool ◽

The Impact ◽

Big Data Technology

Internet and telecommunications, ubiquitous sensing devices, and advances in data storage and analytic capacities have heralded the age of Big Data, where the volume, velocity, and variety of data not only promise new opportunities for the harvesting of information, but also threaten to overload existing resources for making sense of this information. The use of Big Data technology for criminal justice and crime control is a relatively new development. Big Data technology has overlapped with criminology in two main areas: (a) Big Data is used as a type of data in criminological research, and (b) Big Data analytics is employed as a predictive tool to guide criminal justice decisions and strategies. Much of the debate about Big Data in criminology is concerned with legitimacy, including privacy, accountability, transparency, and fairness. Big Data is often made accessible through data visualization. Big Data visualization is a performance that simultaneously masks the power of commercial and governmental surveillance and renders information political. The production of visuality operates in an economy of attention. In crime control enterprises, future uncertainties can be masked by affective triggers that create an atmosphere of risk and suspicion. There have also been efforts to mobilize data to expose harms and injustices and garner support for resistance. While Big Data and visuality can perform affective modulation in the race for attention, the impact of data visualization is not always predictable. By removing the visibility of real people or events and by aestheticizing representations of tragedies, data visualization may achieve further distancing and deadening of conscience in situations where graphic photographic images might at least garner initial emotional impact.

Download Full-text

Interactive Data Visualization: New Directions for Accounting Information Systems Research

Journal of Information Systems ◽

10.2308/jis.2010.24.2.1 ◽

2010 ◽

Vol 24 (2) ◽

pp. 1-37 ◽

Cited By ~ 52

Author(s):

William Dilla ◽

Diane J. Janvrin ◽

Robyn Raschke

Keyword(s):

Data Visualization ◽

Enterprise Resource Planning ◽

Balanced Scorecard ◽

Accounting Information ◽

Resource Planning ◽

Systems Research ◽

Interactive Data ◽

Visualization Techniques ◽

The Impact ◽

The Relationship

ABSTRACT: Many companies today utilize interactive data visualization to present accounting information to external users on their investor relations websites and to internal users in applications such as enterprise resource planning, Balanced Scorecard, network security, and fraud detection systems. We develop a taxonomy for examining the current state of interactive data visualization research related to accounting decision making. We organize our review around three themes: the relationship between task characteristics and interactive data visualization techniques, the relationship between decision maker characteristics and interactive data visualization techniques, and the impact of interactive data visualization techniques on decision processes and outcomes. The review categorizes relevant research, describes the research questions addressed, and suggests avenues for further research.

Download Full-text

DATA VISUALIZATION TECHNIQUES: A REVIEW

August-2020 - Global Journal of Engineering Science and Researches ◽

10.29121/gjesr.v7.i8.2020.3 ◽

2020 ◽

Vol 7 (8) ◽

pp. 18-26

Keyword(s):

Decision Making ◽

Big Data ◽

Data Visualization ◽

Fast Rate ◽

Time Data ◽

Big Data Visualization ◽

Basic Concepts ◽

Visualization Techniques ◽

Pictorial Form

Data visualization involves representing data and information in a graphical or pictorial form so that it can be easily understandable. At Present time, data is increasing at a very fast rate so, it is important to visualize and analyze the massive amount of data by using various visualization techniques. Data Visualization techniques are very helpful to visualize and understand outliers, trends, and patterns in data and thus helpful in decision making. This paper presents a review of the basic concepts of data visualization and various techniques and tools used for visualizing data. Some big data visualization techniques, which are the need of the hour, are also being discussed.

Download Full-text

Data Visualization

Employee Surveys and Sensing ◽

10.1093/oso/9780190939717.003.0019 ◽

2020 ◽

pp. 306-323

Author(s):

Evan F. Sinar

Keyword(s):

Survey Data ◽

Data Visualization ◽

Repeated Administration ◽

Quantitative Information ◽

Evidence Based ◽

Data Sets ◽

High Quality ◽

Text Information ◽

Visualization Techniques ◽

The Impact

Data visualization—a set of approaches for applying graphical principles to represent quantitative information—is extremely well matched to the nature of survey data but often underleveraged for this purpose. Surveys produce data sets that are highly structured and comparative across groups and geographies, that often blend numerical and open-text information, and that are designed for repeated administration and analysis. Each of these characteristics aligns well with specific visualization types, use of which has the potential to—when paired with foundational, evidence-based tenets of high-quality graphical representations—substantially increase the impact and influence of data presentations given by survey researchers. This chapter recommends and provides guidance on data visualization techniques fit to purpose for survey researchers, while also describing key risks and missteps associated with these approaches.

Download Full-text