The Experts below are selected from a list of 30120 Experts worldwide ranked by ideXlab platform
David S. Ebert - One of the best experts on this subject based on the ideXlab platform.
-
multi aspect visual Analytics on large scale high dimensional cyber security data
2015Co-Authors: Victor Yingjie Chen, Ahmad M Razip, Cheryl Zhenyu Qian, David S. EbertAbstract:In this article, we present a visual Analytics System, SemanticPrism, which aims to analyze large-scale high-dimensional cyber security datasets containing logs of a million computers. SemanticPrism visualizes the data from three different perspectives: spatiotemporal distribution, overall temporal trends, and pixel-based IP (Internet Protocol) address blocks. With each perspective, we use semantic zooming to present more detailed information. The interlinked visualizations and multiple levels of detail allow us to detect unexpected changes taking place in different dimensions of the data and to identify potential anomalies in the network. After comparing our approach to other submissions, we outline potential paths for future improvement.
-
a visual Analytics process for maritime response resource allocation and risk assessment
2014Co-Authors: Abish Malik, Ross Maciejewski, Ben Maule, Yun Jang, Silvia Oliveros, Yang Yang, Matthew White, David S. EbertAbstract:In this paper, we present our collaborative work with the U.S. Coast Guard’s Ninth District and Atlantic Area Commands, in which we develop a visual Analytics System to analyze historic response operations and assess the potential risks in the maritime environment associated with the hypothetical allocation of Coast Guard resources. The System includes linked views and interactive displays that enable the analysis of trends, patterns, and anomalies among the U.S. Coast Guard search and rescue (SAR) operations and their associated sorties. Our System allows users to determine the change in risks associated with closing certain stations in terms of response time and potential lives and property lost. It also allows users to determine which stations are best suited to assuming control of the operations previously handled by the closed station. We provide maritime risk assessment tools that allow analysts to explore Coast Guard coverage for SAR operations and identify regions of high risk. The System also ena...
-
applied visual Analytics for exploring the national health and nutrition examination survey
2012Co-Authors: Silvia Oliveros Torres, David S. Ebert, Heather A Eichermiller, Carol J Boushey, Ross MaciejewskiAbstract:The National Health and Nutrition Examination Survey (NHANES) is a research program to assess the health and nutritional status of the population in the United States. In this work, we present a visual Analytics System designed to help researchers explore patterns and form hypotheses within the NHANES dataset. The visualization component of the environment is an extension of traditional scatter plot matrices. Since the upper portion of the scatter plot matrix is a redundant encoding, we utilize this space, to show the projected N-dimensional clustering of points. The rows and columns of the matrix are automatically ordered using information about the cluster projection in each space as a means of showing the most meaningful dimensions. A comparison module has also been included that allows the user to compare groupings of people to the 2010 Dietary Guidelines for Americans. This tool enhances the analysis work by aiding discovery and hypothesis formation.
Sa-kwang Song - One of the best experts on this subject based on the ideXlab platform.
-
design of marketing scenario planning based on business big data analysis
2015Co-Authors: Sa-kwang Song, Seungkyun Hong, Sungho Shin, Youngmin Kim, Choongnyoung SeonAbstract:As the amount and the type of data for business decision making are rapidly increasing, the importance of big data Analytics is gradually critical for making effective business strategy. However, big data Analytics based decision making Systems basically requires distributed parallel computing capability in order to make timely business strategy recommendation via processing huge amount unstructured as well as structured business data. We introduce a big data Analytics System for automatic marketing scenario planning based on big data platform software such as Hadoop and HBase. The Analytics methodology for scenario planning is based on prescriptive Analytics which is the most advance methodology consisting of generation of business scenarios and their optimization, among the three Analytics of descriptive, predictive, and prescriptive Analytics. Additionally, we developed a prototype of marketing scenario planning System and its graphical user interface, as well as the System architecture based on Hadoop eco-System based distributed parallel computing platform.
-
prescriptive Analytics System for improving research power
2013Co-Authors: Sa-kwang Song, Hanmin Jung, Donald J Kim, Myunggwon Hwang, Jangwon Kim, Doheon Jeong, Seungwoo Lee, Wonkyung SungAbstract:We introduce a prescriptive Analytics System, InSciTe advisory, to provide researchers with advice of their future research direction and strategy. The System analyzes several thousands of heterogeneous types of data sources such as papers, patents, reports, Web news, Web magazines, and collective intelligence data. It consists of two main parts of descriptive Analytics and prescriptive Analytics. Once given a researcher, the descriptive Analytics part provides results from activity history and research power w.r.t the designated researcher. Then, prescriptive Analytics part suggests a group of role model researchers to the researcher, as well as how to be like the role model researchers. The prescription for the researcher is provided according to 5W1H questions and their corresponding answers. All of the analytical results and their explanations about the given researcher are automatically generated and saved to a report. This researcher-centric prescriptive Analytics has not been introduced before and it is useful tool to understand the designated researcher in the perspective of prescriptive as well as descriptive Analytics.
Meihui Zhang - One of the best experts on this subject based on the ideXlab platform.
-
cdas a crowdsourcing data Analytics System
2012Co-Authors: Meiyu Lu, Sai Wu, Yanyan Shen, Meihui ZhangAbstract:Some complex problems, such as image tagging and natural language processing, are very challenging for computers, where even state-of-the-art technology is yet able to provide satisfactory accuracy. Therefore, rather than relying solely on developing new and better algorithms to handle such tasks, we look to the crowdsourcing solution -- employing human participation -- to make good the shortfall in current technology. Crowdsourcing is a good supplement to many computer tasks. A complex job may be divided into computer-oriented tasks and human-oriented tasks, which are then assigned to machines and humans respectively. To leverage the power of crowdsourcing, we design and implement a Crowdsourcing Data Analytics System, CDAS. CDAS is a framework designed to support the deployment of various crowdsourcing applications. The core part of CDAS is a quality-sensitive answering model, which guides the crowdsourcing engine to process and monitor the human tasks. In this paper, we introduce the principles of our quality-sensitive model. To satisfy user required accuracy, the model guides the crowdsourcing query engine for the design and processing of the corresponding crowdsourcing jobs. It provides an estimated accuracy for each generated result based on the human workers' historical performances. When verifying the quality of the result, the model employs an online strategy to reduce waiting time. To show the effectiveness of the model, we implement and deploy two Analytics jobs on CDAS, a twitter sentiment Analytics job and an image tagging job. We use real Twitter and Flickr data as our queries respectively. We compare our approaches with state-of-the-art classification and image annotation techniques. The results show that the human-assisted methods can indeed achieve a much higher accuracy. By embedding the quality-sensitive model into crowdsourcing query engine, we effectively reduce the processing cost while maintaining the required query answer quality.
-
CDAS: A crowdsourcing data Analytics System
2012Co-Authors: Xuan Liu, Meiyu Lu, Sai Wu, Yanyan Shen, Beng Chin Ooi, Meihui ZhangAbstract:Some complex problems, such as image tagging and natural language processing, are very challenging for computers, where even state-of-the-art technology is yet able to provide satisfactory accuracy. Therefore, rather than relying solely on developing new and better algorithms to handle such tasks, we look to the crowdsourcing solution -- employing human participation -- to make good the shortfall in current technology. Crowdsourcing is a good supplement to many computer tasks. A complex job may be divided into computer-oriented tasks and human-oriented tasks, which are then assigned to machines and humans respectively. To leverage the power of crowdsourcing, we design and implement a Crowdsourcing Data Analytics System, CDAS. CDAS is a framework designed to support the deployment of various crowdsourcing applications. The core part of CDAS is a quality-sensitive answering model, which guides the crowdsourcing engine to process and monitor the human tasks. In this paper, we introduce the principles of our quality-sensitive model. To satisfy user required accuracy, the model guides the crowdsourcing query engine for the design and processing of the corresponding crowdsourcing jobs. It provides an estimated accuracy for each generated result based on the human workers' historical performances. When verifying the quality of the result, the model employs an online strategy to reduce waiting time. To show the effectiveness of the model, we implement and deploy two Analytics jobs on CDAS, a twitter sentiment Analytics job and an image tagging job. We use real Twitter and Flickr data as our queries respectively. We compare our approaches with state-of-the-art classification and image annotation techniques. The results show that the human-assisted methods can indeed achieve a much higher accuracy. By embedding the quality-sensitive model into crowdsourcing query engine, we effectiv...[truncated].
Victor Yingjie Chen - One of the best experts on this subject based on the ideXlab platform.
-
a visual Analytics approach to detecting server redirections and data exfiltration
2015Co-Authors: Weijie Wang, Baijian Yang, Victor Yingjie ChenAbstract:How to better find potential cyberattacks is a challenging question for security researchers and practitioners. In recent years, visualization has been applied in the field of analyzing cybersecurity issues, but most work has not been able to provide better than non-visualization based techniques. In this paper, we innovatively designed a visual Analytics System to allow analysts to overview network traffic and identify such suspicious such activities as server redirection attack and data exfiltration. Because of the nature of the problem, the overview design must be scalable, accurate, and fast. Through aggregating traffic data along the two dimensions of duration and payload, the System reveals key network traffic characteristics for the analyst to identify security events. The System is evaluated with the test data sets from VAST 2013 mini-challenge 3. The results are very encouraging and shed a more positive light on applying visual Analytics in information security.
-
multi aspect visual Analytics on large scale high dimensional cyber security data
2015Co-Authors: Victor Yingjie Chen, Ahmad M Razip, Cheryl Zhenyu Qian, David S. EbertAbstract:In this article, we present a visual Analytics System, SemanticPrism, which aims to analyze large-scale high-dimensional cyber security datasets containing logs of a million computers. SemanticPrism visualizes the data from three different perspectives: spatiotemporal distribution, overall temporal trends, and pixel-based IP (Internet Protocol) address blocks. With each perspective, we use semantic zooming to present more detailed information. The interlinked visualizations and multiple levels of detail allow us to detect unexpected changes taking place in different dimensions of the data and to identify potential anomalies in the network. After comparing our approach to other submissions, we outline potential paths for future improvement.
Niklas Elmqvist - One of the best experts on this subject based on the ideXlab platform.
-
datasite proactive visual data exploration with computation of insight based recommendations
2018Co-Authors: Sriram Karthik Badam, Adil M Yalcin, Niklas ElmqvistAbstract:Effective data analysis ideally requires the analyst to have high expertise as well as high knowledge of the data. Even with such familiarity, manually pursuing all potential hypotheses and exploring all possible views is impractical. We present DataSite, a proactive visual Analytics System where the burden of selecting and executing appropriate computations is shared by an automatic server-side computation engine. Salient features identified by these automatic background processes are surfaced as notifications in a feed view, akin to posts in a social media feed. DataSite effectively turns data analysis into a conversation between analyst and computer, thereby reducing the cognitive load and domain knowledge requirements. We validate the System with a user study comparing it to a recent visualization recommendation System, yielding significant improvement particularly for complex analyses that existing Analytics Systems do not support well.
-
ConceptVector: Text Visual Analytics via Interactive Lexicon Building Using Word Embedding
2018Co-Authors: Deokgun Park, Seungyeon Kim, Jurim Lee, Nicholas Diakopoulos, Jaegul Choo, Niklas ElmqvistAbstract:Central to many text analysis methods is the notion of a concept: a set of semantically related keywords characterizing a specific object, phenomenon, or theme. Advances in word embedding allow building a concept from a small set of seed terms. However, naive application of such techniques may result in false positive errors because of the polysemy of natural language. To mitigate this problem, we present a visual Analytics System called ConceptVector that guides a user in building such concepts and then using them to analyze documents. Document-analysis case studies with real-world datasets demonstrate the fine-grained analysis provided by ConceptVector. To support the elaborate modeling of concepts, we introduce a bipolar concept model and support for specifying irrelevant words. We validate the interactive lexicon building interface by a user study and expert reviews. Quantitative evaluation shows that the bipolar lexicon generated with our methods is comparable to human-generated ones.
