Dong Hyun Jeong

On Managing Very Large Sensor-Network Data Using Bigtable

Recent advances and innovations in smart sensor technologies, energy storage, data communications, and distributed computing paradigms are enabling technological breakthroughs in very large sensor networks. There is an emerging surge of next-generation sensor-rich computers in consumer mobile devices as well as tailor-made field platforms wirelessly connected to the Internet. Billions of such sensor computers are posing both challenges and opportunities in relation to scalable and reliable management of the peta- and exa-scale time series being generated over time. This paper presents a Cloud-computing approach to this issue based on the two well-known data storage and processing paradigms: Bigtable and MapReduce.

A survey of cloud-based network intrusion detection analysis

As network traffic grows and attacks become more prevalent and complex, we must find creative new ways to enhance intrusion detection systems (IDSes). Recently, researchers have begun to harness both machine learning and cloud computing technology to better identify threats and speed up computation times. This paper explores current research at the intersection of these two fields by examining cloud-based network intrusion detection approaches that utilize machine learning algorithms (MLAs). Specifically, we consider clustering and classification MLAs, their applicability to modern intrusion detection, and feature selection algorithms, in order to underline prominent implementations from recent research. We offer a current overview of this growing body of research, highlighting successes, challenges, and future directions for MLA-usage in cloud-based network intrusion detection approaches.

A multi-level intrusion detection method for abnormal network behaviors

Abnormal network traffic analysis has become an increasingly important research topic to protect computing infrastructures from intruders. Yet, it is challenging to accurately discover threats due to the high volume of network traffic. To have better knowledge about network intrusions, this paper focuses on designing a multi-level network detection method. Mainly, it is composed of three steps as (1) understanding hidden underlying patterns from network traffic data by creating reliable rules to identify network abnormality, (2) generating a predictive model to determine exact attack categories, and (3) integrating a visual analytics tool to conduct an interactive visual analysis and validate the identified intrusions with transparent reasons.To verify our approach, a broadly known intrusion dataset (i.e. NSL-KDD) is used. We found that the generated rules maintain a high performance rate and provide clear explanations. The proposed predictive model resulted about 96% of accuracy in detecting exact attack categories. With the interactive visual analysis, a significant difference among the attack categories was discovered by visually representing attacks in separated clusters. Overall, our multi-level detection method is well-suited for identifying hidden underlying patterns and attack categories by revealing the relationship among the features of network traffic data.

Using Personality Factors to Predict Interface Learning Performance

This current study explored the impact of individual differences in personality factors on interface interaction and learning performance in both an interactive visualization and a menu-driven web application. Participants were administered 6 psychometric measures designed to assess trait anxiety, locus of control, and other personality traits. Participants were then asked to complete 3 procedural tasks and 3 inferential tasks in each interface. Results demonstrated that participants with an external locus of control completed inferential tasks more quickly than those with an internal locus. Factor analysis of items from the 6 psychometric scales isolated a 9-item short measure, which showed trending with procedural scores. Additionally, data demonstrated that the visualization interface was more effective and efficient for the completion of the inferential tasks. Participants also preferred the visualization to the web interface for both types of task. Implications and future directions of this research are also discussed.

Evaluating the relationship between user interaction and financial visual analysis

It has been widely accepted that interactive visualization techniques enable users to more effectively form hypotheses and identify areas for more detailed investigation. There have been numerous empirical user studies testing the effectiveness of specific visual analytical tools. However, there has been limited effort in connecting a userpsilas interaction with his reasoning for the purpose of extracting the relationship between the two. In this paper, we present an approach for capturing and analyzing user interactions in a financial visual analytical tool and describe an exploratory user study that examines these interaction strategies. To achieve this goal, we created two visual tools to analyze raw interaction data captured during the user session. The results of this study demonstrate one possible strategy for understanding the relationship between interaction and reasoning both operationally and strategically.

A Bigtable/MapReduce-Based Cloud Infrastructure for Effectively and Efficiently Managing Large-Scale Sensor Networks

This paper proposes a novel approach for effectively and efficiently managing large-scale sensor networks defining a Cloud infrastructure that makes use of Bigtable at the data layer and MapReduce at the processing layer. We provide principles and architecture of our proposed infrastructure along with its experimental evaluation on a real-life computational platform. Experiments clearly confirm the effectiveness and the efficiency of the proposed research.

Comparing different levels of interaction constraints for deriving visual problem isomorphs

Interaction and manual manipulation have been shown in the cognitive science literature to play a critical role in problem solving. Given different types of interactions or constraints on interactions, a problem can appear to have different degrees of difficulty. While this relationship between interaction and problem solving has been well studied in the cognitive science literatures, the visual analytics community has yet to exploit this understanding for analytical problem solving. In this paper, we hypothesize that constraints on interactions and constraints encoded in visual representations can lead to strategies of varying effectiveness during problem solving. To test our hypothesis, we conducted a user study in which participants were given different levels of interaction constraints when solving a simple math game called Number Scrabble. Number Scrabble is known to have an optimal visual problem isomorph, and the goal of this study is to learn if and how the participants could derive the isomorph and to analyze the strategies that the participants utilize in solving the problem. Our results indicate that constraints on interactions do affect problem solving, and that while the optimal visual isomorph is difficult to derive, certain interaction constraints can lead to a higher chance of deriving the isomorph.

Interactive visual analysis of time-series microarray data

Estimating dynamic regulatory pathways using DNA microarray time-series can provide invaluable information about the dynamic interactions among genes and result in new methods of rational drug design. Even though several purely computational methods have been introduced for DNA pathway analysis, most of these techniques do not provide a fully interactive method to explore and analyze these dynamic interactions in detail, which is necessary to obtain a full understanding. In this paper, we present a unified modeling and visual approach focusing on visual analysis of gene regulatory pathways over time. As a preliminary step in analyzing the gene interactions, the method applies two different techniques, a clustering algorithm and an auto regressive (AR) model. This approach provides a successful prediction of the dynamic pathways involved in the biological process under study. At this level, these pure computational techniques lack the transparency required for analysis and understanding of the gene interactions. To overcome the limitations, we have designed a visual analysis method that applies several visualization techniques, including pixel-based gene representation, animation, and multi-dimensional scaling (MDS), in a new way. This visual analysis framework allows the user to quickly and thoroughly search for and find the dynamic interactions among genes, highlight interesting gene information, show the detailed annotations of the selected genes, compare regulatory behaviors for different genes, and support gene sequence analysis for the interesting genes. In order to enhance these analysis capabilities, several methods are enabled, providing a simple graph display, a pixel-based gene visualization technique, and a relation-displaying technique among gene expressions and gene regulatory pathways.

Two Visualization Tools for Analyzing Agent-Based Simulations in Political Science

Agent-based modeling has become a key technique for modeling and simulating dynamic, complicated behaviors in the social and political sciences. Although many robust toolkits for developing and running these simulations exist, systems that support analysis of their results are few and tend to be overly general. So, social scientists have had difficulty interpreting the results of their increasingly complex simulations. To help bridge this gap between data generation and interpretation, researchers collaborated with political science analysts to design two tools for interactive data exploration and domain-specific data analysis. Testing by the analysts validated that these tools provided an efficient framework to explore individual trajectories and the relationships between variables. The tools also supported hypothesis generation by enabling analysts to group simulations according to multidimensional similarity and drill down to investigate further.

User experimentation: an evaluation of velocity control techniques in immersive virtual environments

While many of the existing velocity control techniques are well designed, the techniques are often application-specific, making it difficult to compare their effectiveness. In this paper, we evaluate five known velocity control techniques using the same experimental settings. We compare the techniques based on the assumption that a good travel technique should be easy to learn and easy to use, should cause the user to have few collisions with the VE, should allow the user to complete tasks faster, and should promote better recollection of the environment afterwards. In our experiments, we ask twenty users to use each velocity control technique to navigate through virtual corridors while performing information-gathering tasks. In all cases, the users use pointing to indicate the direction of travel. We then measure the users’ ability to recollect the information they see in the VE, as well as how much time they spend in the VE and how often they collide with the virtual walls. After each test, we use questionnaires to evaluate the ease of learning and ease of use of the velocity control technique, and the users’ sense of presence in the environment. Each of the travel techniques is then evaluated based on the users’ performances in the VE and the results of their questionnaires.