Fatma Bouali

VizAssist: an interactive user assistant for visual data mining

We study in this work how a user can be guided to find a relevant visualization in the context of visual data mining. We present a state of the art on the user assistance in visual and interactive methods. We propose a user assistant called VizAssist, which aims at improving the existing approaches along three directions: it uses simpler computational models of the visualizations and the visual perception guidelines, in order to facilitate the integration of new visualizations and the definition of a mapping heuristic. VizAssist allows the user to provide feedback in a visual and interactive way, with the aim of improving the data to visualization mapping. This step is performed with an interactive genetic algorithm. Finally, VizAssist aims at proposing a free on-line tool (www.vizassist.fr) that respects the privacy of the user data. This assistant can be viewed as a global interface between the user and some of the many visualizations that are implemented with D3js.

On studying a 3D user interface for OLAP

In this paper, a new visual and interactive user interface for OLAP is presented, and its strengths and weaknesses examined. A survey on 3D interfaces for OLAP is detailed, which shows that only one interface that uses Virtual Reality has been proposed. Then we present our approach: it consists of a 3D representation of OLAP cubes where many OLAP operators have been integrated and where several measures can be visualized. A 3D stereoscopic screen can be used in conjunction with a 3D mouse. Finally a user study is reported that compares standard dynamic cross-tables with our interface on different tasks. We conclude that 3D with stereoscopy is not as promising as expected even with recent 3D devices.

A User Assistant for the Selection and Parameterization of the Visualizations in Visual Data Mining

We deal in this paper with the problem of automating the process of choosing an appropriate visualization and its parameters in the context of visual data mining (VDM). To solve this problem, we develop a user assistant that performs 2 steps: the system starts by suggesting to users different mappings between their data and possible visualizations. This is performed with a simple but generic heuristic that can be applied to any visualization. Then, the user selects a visualization among those proposed by our assistant, and he may further improve the parameters set that defines the mapping between the visual attributes and the data attributes. For this purpose, we use an interactive genetic algorithm (IGA), which allows users to visually evaluate and adjust the mappings. We present a user evaluation that confirms the interest of our system in two tasks.

An Approximate Proximity Graph Incremental Construction for Large Image Collections Indexing

This paper addresses the problem of the incremental construction of an indexing structure, namely a proximity graph, for large image collections. To this purpose, a local update strategy is examined. Considering an existing graph G and a new node q, how only a relevant sub-graph of G can be updated following the insertion of q? For a given proximity graph, we study the most recent algorithm of the literature and highlight its limitations. Then, a method that leverages an edge-based neighbourhood local update strategy to yield an approximate graph is proposed. Using real-world and synthetic data, the proposed algorithm is tested to assess the accuracy of the approximate graphs. The scalability is verified with large image collections, up to one million images.

Technical Section: EXOD: A tool for building and exploring a large graph of open datasets

We present in this paper a tool called EXOD (EXploration of Open Datasets) for the visual analysis of a large collection of open datasets. EXOD aims at helping the users to find datasets of interest. EXOD starts with the download of a large collection of datasets from an open data web site. For each dataset, it extracts its meta-data and its content. To describe each dataset in a vector space, EXOD extracts features by using text mining techniques. It considers both the metadata and the content of each dataset. Using this feature space, EXOD establishes a proximity graph by computing the Relative Neighborhood Graph. Considering the size of the collection, EXOD uses a GPU-based implementation for building this graph. We visualize the graph using the Tulip software and provide a visual and interactive global map of the collection. We developed a specific plug-in for Tulip to download and open the datasets in an interactive way. All of the presented results concern the French Open Data. EXOD was able to process 293,000 datasets, and half of this collection was visualized in Tulip. We show how clusters and other information can be discovered and how the created links can be used for local and content-based exploration.

Visual mining of time series using a tubular visualization

In this paper, we study the visual mining of time series, and we contribute to the study and evaluation of 3D tubular visualizations. We describe the state of the art in the visual mining of time-dependent data, and we concentrate on visualizations that use a tubular shape to represent data. After analyzing the motivations for studying such a representation, we present an extended tubular visualization. We propose new visual encodings of the time and data, new interactions for knowledge discovery, and the use of rearrangement clustering. We show how this visualization can be used in several real-world domains and that it can address large datasets. We present a comparative user study. We conclude with the advantages and the drawbacks of our method (especially the tubular shape).

Visual Analysis System for Features and Distances Qualitative Assessment: Application to Word Image Matching

In this paper, a visual analysis system to qualitatively assess the features and distance functions that are used for calculating dissimilarity between two word images is presented. Computation of dissimilarity between two images is the prerequisite for image matching, indexing and retrieval problems. First, the features are extracted from the word images and a distance between each image to others is computed and represented in a matrix form. Then, based on this distance matrix, a proximity graph is built to structure the set of word images and highlight their topology. The proposed visual analysis system is a web based platform that allows visualisation and interactions on the obtained graph. This interactive visualisation tool inherently helps users to quickly analyse and understand the relevance and robustness of selected features and corresponding distance function in a unsupervised way, i.e. without any ground truth. Experiments are performed on a handwritten dataset of segmented words. Three types of features and four distance functions are considered to describe and compare the word images. Theses material are leveraged to evaluate the relevance of the built graph, and the usefulness of the platform.

On visualizing large multidimensional datasets with a multi-threaded radial approach

In this paper, we study how to visualize large amounts of multidimensional data with a radial visualization. For such a visualization, we study a multi-threaded implementation on the CPU and the GPU. We start by reviewing the approaches that have visualized the largest multidimensional datasets and we focus on the approaches that have used CPU or GPU parallelization. We consider the radial visualizations and we describe our approach (called POIViz) that uses points of interest to determine a layout of a large dataset. We detail its parallelization on the CPU and the GPU. We study the efficiency of this approach with different configurations and for large datasets. We show that it can visualize, in less than one second, millions of data with tens of dimensions, and that it can support “real-time” interactions even for large datasets. We conclude on the advantages and limits of the proposed visualization.

Open Data Integration

Since several years, even some decades, a major problem in computer sciences is directly linked with data integration. When it becomes necessary to process information coming from different data sources, several problems may appear turning the process of integration more difficult to be accomplished. Nowadays, more and more information is flowing and is provided into the Web - Data Integration became even more important. The emerging trend of Open Data (OD) is an active actor for this event. Integrating data coming from public entities can be a difficult process. Large quantities of datasets are made available. However, an important level of heterogeneity may also exist: Datasets can be on different formats, forms and shapes. If the fact of being possible to access this information can be an important asset, it also can be completely useless if it is not possible to interpret so it can be used. Information Visualization may be an important tool to help the OD integration process. This paper presents problems and barriers which can be encountered on the OD integration process. The paper also describes how Information Visualization can be used to facilitate the integration of OD and turn the procedure more effective, friendlier, and faster.

Using Information Visualization to Support Open Data Integration

Data integration has always been a major problem in computer sciences. The more heterogeneous, large and distributed the data sources become, the more difficult the data integration process is. Nowadays, more and more information is being made available on the Web. This is especially the case in the Open Data (OD) movement. Large quantities of datasets are published and accessible. Besides size, heterogeneity grows as well: Datasets exist e.g. in different formats and shapes (tabular files, plain-text files and so on). The ability to efficiently interpret and integrate such datasets is of paramount importance for their potential users. Information Visualization may be an important tool to support this OD integration process. This article presents problems which can be encountered in the data integration process, and, more specifically, in the OD integration process. It also describes how Information Visualization can support OD integration process and make it more effective, friendlier, and faster.