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Due to the increasing popularity of cooking-recipe sharing sites and the success of complex network science, attention has recently been devoted to developing an effective networkbased method of analyzing the characteristics of ingredient combinations used in recipes. Unlike previous approaches dealing with static properties, we aim at analyzing the dynamical changes in ingredient pairs jointly used...
Data Jacket (DJ) is a technique for sharing information about data and for considering the potential value of datasets, with the data itself hidden, by describing the summary of data in natural language. In DJs, variables are described by variable labels (VLs), which are the names/meanings of variables. In the previous study, the matrix-based method for inferring VLs in DJs whose VLs are unknown,...
This article is a comprehensive literature review of student-facing learning analytics reporting systems that track learning analytics data and report it directly to students. This literature review builds on four previously conducted literature reviews in similar domains. Out of the 945 articles retrieved from databases and journals, 93 articles were included in the analysis. Articles were coded...
Most of our current understanding of how programmers perform various software maintenance and evolution tasks is based on controlled studies or interviews, which are inherently limited in size, scope, and realism. Replicating controlled studies in the field can both explore the findings of these studies in wider contexts and study new factors that have not been previously encountered in the laboratory...
In this paper, we addressed in social network visualization for people's interest in music. This visualization system designed for interest in music on social networks provides several capabilities: (A) visualization for friends who share the same interest in music, (B) to group people who share the same interest in music into categories, and (C) to recommend songs function for an increase in the...
There is an increasing need to quickly understand the contents log data. A wide range of patterns can be computed and provide valuable information: for example existence of repeated sequences of events or periodic behaviors. However patternminingtechniquesoftenproducemanypatternsthathave to be examined one by one, which is time consuming for experts. On the other hand, visualization techniques are...
It is very challenging to generate a high dynamic range (HDR)-like low dynamic range (LDR) image, called wide dynamic range (WDR) image, without the ghost artifact from multiple differently exposed LDR images for dynamic scenes. Since the ghost artifact is caused by the moving objects in the input LDR images, it is required to properly detect the motion region to prevent the ghost artifact in the...
In many areas of science, scientists need to find distinct common characterizations of the same objects and, vice versa, identify sets of objects that admit multiple shared descriptions. For example, a biologist might want to find a set of bioclimatic conditions and a set of species, such that this bioclimatic profile adequately characterizes the areas inhabited by these fauna. In data analysis, the...
Provenance captured from E-Science experimentation is often large and complex, for instance, from agent-based simulations that have tens of thousands of heterogeneous components interacting over extended time periods. The subject of study of my dissertation is the use of E-Science provenance at scale. My initial research studied the visualization of large provenance graphs and proposed an abstract...
In modern social media, massive visual and textual data are collected and uploaded to social websites everyday. How to extract useful knowledge from such multiple modality data and organize it in an efficient way remains an important problem. The goal of this dissertation is to investigate joint visual and textual mining for social media data. My dissertation aims at contributing to our theoretical...
The trends in a research field, especially changes in the features over the years, are subjects of interest for many researchers. This paper reports an exploratory analysis of the changes of research topics in an academic field. The target data of the analysis are the author-keywords included in papers presented at a series of academic conferences, IEEE International Conference on Data Mining (ICDM)...
POLAR is an experimental test-bed visualisation tool for Patterns of Life analysis, developed on the basis of knowledge elicitation with stakeholders. It uses multiple and coordinated views for exploring geo-temporal datasets. The system has three modes of interaction for addressing different kinds of PoL questions. It supports the exploration of movement patterns with resolutions ranging from intercontinental...
Due to the progressing growth of temporal data, data analysis and exploration become more and more difficult task; particularly in the medical domain. For this reason, it seems important to use Decision Support System (DSS) based on the decisional tool Knowledge Discovery in Data (KDD) and the visualization techniques for assisting user to get and understand information. Our research is based on the...
We present a scalable, interactive graph visualization system to support multi-resolution exploration of million-node graphs in real time. By adapting a state-of-the-art graph algorithm, called Slash & Burn, our prototype system generates a multi-resolution view of graphs with up to 69 million edges under a few seconds. We are experimenting with interaction techniques that help users interactively...
Mining multivariate time series data by clustering is an important research topic. Time series can be clustered by standard approaches like k-means, or by advanced methods such as subspace clustering and triclustering. A problem with these new methods is the lack of a general evaluation scheme that can be used by researchers to understand and compare the algorithms, publications on new algorithms...
This paper will show how machine learning and data visualization techniques are being used to execute real television ad buys. We present an innovative data visualization tool which allows users to filter, histogram, and sort so as to identify the television inventory with highest value per dollar. Using the application users have been able to identify media that performs 50% better than previous...
We propose TopGraphVisualizer, a tool to support the discovery of relevant topological patterns in attributed graphs. It relies on a new pattern detection method that crucially needs for sophisticated post processing and visualization. A topological pattern is defined as a set of vertex attributes and topological properties (i.e., properties that characterize the role of a vertex within a graph) that...
Important term extraction from the current web page is a common process in many client-based web applications such as query-free search, search query recommendation, information extraction, user profiling, and personalization. However, the tuning cost is large for deciding parameters when updating modules or dictionaries, or when porting an application for other languages or devices. We propose an...
This paper presents the main features of ConAn, a tool supporting an approach to find scattered and tangled class members in OO systems and to group them in concerns. The recovered information is useful for refactoring/migration tasks, such as towards Aspect Oriented Programming (AOP).
A bibliographic database houses vital information pertinent to the research community. By extracting implicit hidden information in such data collections, social networks can be used to represent the various data inter-relationships.In this paper, we propose a new method for the discovery and visualization of research communities from bibliography information. We utilize the hierarchical overlapping...
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