Carsten Görg

Bio: Carsten Görg is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: Visual analytics & Information visualization. The author has an hindex of 24, co-authored 53 publications receiving 3267 citations. Previous affiliations of Carsten Görg include Saarland University & Georgia Institute of Technology.

Visual Analytics: Definition, Process, and Challenges

Abstract: We are living in a world which faces a rapidly increasing amount of data to be dealt with on a daily basis. In the last decade, the steady improvement of data storage devices and means to create and collect data along the way influenced our way of dealing with information: Most of the time, data is stored without filtering and refinement for later use. Virtually every branch of industry or business, and any political or personal activity nowadays generate vast amounts of data. Making matters worse, the possibilities to collect and store data increase at a faster rate than our ability to use it for making decisions. However, in most applications, raw data has no value in itself; instead we want to extract the information contained in it.

Jigsaw: supporting investigative analysis through interactive visualization

Abstract: Investigative analysts who work with collections of text documents connect embedded threads of evidence in order to formulate hypotheses about plans and activities of potential interest. As the number of documents and the corresponding number of concepts and entities within the documents grow larger, sense-making processes become more and more difficult for the analysts. We have developed a visual analytic system called Jigsaw that represents documents and their entities visually in order to help analysts examine them more efficiently and develop theories about potential actions more quickly. Jigsaw provides multiple coordinated views of document entities with a special emphasis on visually illustrating connections between entities across the different documents.

Jigsaw: Supporting Investigative Analysis through Interactive Visualization

Abstract: Investigative analysts who work with collections of text documents connect embedded threads of evidence in order to formulate hypotheses about plans and activities of potential interest. As the number of documents and the corresponding number of concepts and entities within the documents grow larger, sense-making processes become more and more difficult for the analysts. We have developed a visual analytic system called Jigsaw that represents documents and their entities visually in order to help analysts examine reports more efficiently and develop theories about potential actions more quickly. Jigsaw provides multiple coordinated views of document entities with a special emphasis on visually illustrating connections between entities across the different documents.

Graphs, They Are Changing

Abstract: In this paper we present a generic algorithm for drawing sequences of graphs. This algorithm works for different layout algorithms and related metrics and adjustment strategies. It differs from previous work on dynamic graph drawing in that it considers all graphs in the sequence (offline) instead of just the previous ones (online) when computing the layout for each graph of the sequence. We introduce several general adjustment strategies and give examples of these strategies in the context of force-directed graph layout. Finally some results from our first prototype implementation are discussed.

How important is the Mental map?: an empirical investigation of a dynamic graph layout algorithm

Abstract: While some research has been performed on the human understanding of static graph layout algorithms, dynamic graph layout algorithms have only recently been developed sufficiently to enable similar investigations. This paper presents the first empirical analysis of a dynamic graph layout algorithm, focusing on the assumption that maintaining the "mental map" between time-slices assists with the comprehension of the evolving graph. The results confirm this assumption with respect to some categories of tasks.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

“Bioinformatics” 특집을 내면서

Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

Toyota production system : beyond large-scale production

Abstract: * Starting from Need* Evolution of the Toyota Production System* Further Development* Genealogy of the Toyota Production System* The True Intention of the Ford System* Surviving the Low-Growth Period