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About 7,743 papers for “interactive data exploration”

Interactive Data Exploration using Gestures

Jiang, Lilong · OhioLink ETD Center (Ohio Library and Information Network) · 2017

Ad-hoc data exploration is an important paradigm for users to analyze data and gain instant insights.In terms of interaction devices, there has been a revolution in the availability of new devices that go beyond the traditional keyboard modality.Tablets and smartphones use capacitive touch as their only mode of interaction.Furthermore, there has been a proliferation of motion capture-based devices in the recent past, including Google Glass, Kinect,

Cited by 0Open access

Interactive Exploration of Multidimensional Data

David A. Rabenhorst · 1994

This paper describes a system called Diamond for interactive exploration of multidimensional data. Diamond takes advantage of human pattern recognition and processing capacity, and puts major emphasis on performance and responsiveness. This creates a highly productive symbiosis between the human and the system. The basic philosophy of Diamond is to depict the data with pictures, and to help the user manipulate the pictures to rapidly gain insight.

Cited by 0

Data Exploration with Interactive Elements

2020

This chapter focuses on the cooking analogy to guide data citizen through the steps for re-creating an example report. The first step in that process is to select a dish to cook. This step involves determining the purpose of the report, who the audience is, and what elements are used to communicate information about the data graphically. The chapter develops a report that provides a greater degree of freedom to explore the data. It explains how to build a “workbench,” which is a Data Studio report built for data exploration with different filtering methods for interactivity. It follows the cooking analogy to build the new report with a shared file as data set. The chapter covers the following Data Studio concepts: workbenches for data exploration, using a shared resource, creating calculated fields, reusing elements, new chart types, interactive chart filtering, and filter controls.

Cited by 0

Interactive Clustering for Data Exploration

Joel R. Brandt, et al. · 2008

Clustering algorithms are widely used in data analysis systems. However, these systems are largely static in nature. There may be interaction with the resulting visualization of the clustering, but there is rarely interaction with the process. Here, we describe a system for visual data exploration using clustering. The system makes the exploration and understanding of moderately-large (500-1000 instances) multidimensional (10-20 dimensions) data sets easier.

Cited by 0

Xmdvtool: quality-aware interactive data exploration

Elke Rundensteiner, Matthew Ward, Zaixian Xie et al. · 2007

In this work, we describe our approach for making the interactive data exploration system, called XmdvTool, qualityaware to assure informed decision-making. XmdvTool Q makes quality or lack thereof explicit for all stages of the data exploration process from raw data, to abstracted data, to the final visual displays, allowing users to query and navigate through data-, structure- and quality-spaces.

Cited by 0

IDEA: Interactive Data Exploration and Analysis

Peter G. Selfridge, Divesh Srivastava, Lynn O. Wilson · 1996

The analysis of business data is often an ill-defined task characterized by large amounts of noisy data. Because of this, business data analysis must combine two kinds of intertwined tasks: exploration and analysis. Exploration is the process of finding the appropriate subset of data to analyze, and analysis is the process of measuring the data to provide the business answer. While there are many tools available both for exploration and for analysis, a single tool or set of tools may not provide full support for these intertwined tasks. We report here on a project that set out to understand a specific business data analysis problem and build an environment to support it. The results of this understanding are, first of all, a detailed list of requirements of this task; second, a set of capabilities that meet these requirements; and third, an implemented client-server solution that addresses many of these requirements and identifies others for future work. Our solution incorporates sever...

Cited by 0

Interactive data exploration using semantic windows

Alexander Kalinin, Uğur Çetintemel, Stan Zdonik · 2014

We present a new interactive data exploration approach, called Semantic Windows (SW), in which users query for multidimensional "windows" of interest via standard DBMS-style queries enhanced with exploration constructs. Users can specify SWs using (i) shape-based properties, e.g., "identify all 3-by-3 windows", as well as (ii) content-based properties, e.g., "identify all windows in which the average brightness of stars exceeds 0.8". This SW approach enables the interactive processing of a host of useful exploratory queries that are difficult to express and optimize using standard DBMS techniques. SW uses a sampling-guided, data-driven search strategy to explore the underlying data set and quickly identify windows of interest. To facilitate human-in-the-loop style interactive processing, SW is optimized to produce online results during query execution. To control the tension between online performance and query completion time, it uses a tunable, adaptive prefetching technique. To enable exploration of big data, the framework supports distributed computation.

Cited by 65

User Learning In Interactive Data Exploration

Sanad Saha, Nischal Aryal, Leilani Battle et al. · 2024

Users explore large, complex datasets to find interesting hypotheses and previously unseen insights. In this process, known as data exploration, users often generate database queries without any precise goals or concrete information need, posing challenges for database systems that assume the user has a clear intent a priori. In response, system developers often model users' exploration strategies over time, which could enable the system to predict and adapt to users' subsequent actions. However, current models generally treat users' exploration behavior as static, whereas in reality, users dynamically change their behavior in response to what they learn during exploration. In this paper, we present an analysis of existing data exploration logs to quantify shifts in users' data exploration strategies over time. Our analysis confirms that users shift their behavior over time, and state-of-the-art learning algorithms struggle to adapt to this evolution, revealing new avenues for building more accurate models of user exploration behavior within data exploration systems.

Cited by 2

xplorerr: Tools for Interactive Data Exploration

Aravind Hebbali · 2019

Tools for interactive data exploration built using 'shiny'. Includes apps for descriptive statistics, visualizing probability distributions, inferential statistics, linear regression, logistic regression and RFM analysis.

Cited by 1Open access

exCon: Interactive Exploration of Contour Data

Bryan A. Hanson · 2014

Interactive tools to explore topographic-like data sets. Such data sets take the form of a matrix in which the rows and columns provide location/frequency information, and the matrix elements contain altitude/response information. Such data is found in cartography, 2D spectroscopy and chemometrics. The functions in this package create interactive web pages showing the contoured data, possibly with slices from the original matrix parallel to each dimension. The interactive behavior is created using the 'D3.js' 'JavaScript' library by Mike Bostock.

Cited by 0Open access

DivIDE: efficient diversification for interactive data exploration

Khan, Hina A., Sharaf, Mohamed A., Albarrak, Abdullah · Queensland's institutional digital repository (The University of Queensland) · 2014

Today, Interactive Data Exploration (IDE) has become a main constituent of many discovery-oriented applications, in which users repeatedly submit exploratory queries to identify interesting subspaces in large data sets. Returning relevant yet diverse results to such queries provides users with quick insights into a rather large data space. Meanwhile, search results diversification adds additional cost to an already computationally expensive exploration process. To address this challenge, in this paper, we propose a novel diversification scheme called DivIDE, which targets the problem of efficiently diversifying the results of queries posed during data exploration sessions. In particular, our scheme exploits the properties of data diversification functions while leveraging the natural overlap occurring between the results of different queries so that to provide significant reductions in processing costs. Our extensive experimental evaluation on both synthetic and real data sets shows the significant benefits provided by our scheme as compared to existing methods.

Cited by 2

Interactive Visual Exploration of Big Spatial Data

Ghosh, Saheli · eScholarship (California Digital Library) · 2020

Visual exploration has become an integral part of big spatial data management. With the increase in volume and number of spatial datasets, several specialized mechanisms have been proposed to speed up the exploration of these datasets. However, the existing techniques have major limitations which make them incapable of providing visual exploration for hundreds of thousands of big datasets on a single machine. These indexes are either data indexes, which index data records, or image indexes, which index partially generated visualizations. Data indexes are fast to build but are only helpful for selecting a few records to visualize. On the other hand, image indexes are capable of visualizing big data in a short time but they are heavy on construction time and storage sizes.I introduced two new index structures, termed AID and AID*, that facilitate the visual exploration of an arbitrarily large number of big spatial datasets on a single machine.The indexes define multi-resolution fixed-size tiles on the input and classify them as image, data, shallow, or empty tiles, based on their processing cost. Then, it uses this classification to build an index with a minimal index size and construction time, while supporting the desired real-time exploration interface. The index is constructed in parallel, using Hadoop or Spark, and is accessible to end users through a standard web interface similar to Google Maps. The small size of the index allows a single-machine server to host arbitrarily many datasets. The experiments, on up-to 1~TB of data and 27~billion records, show that the construction of the proposed index is up-to an order of magnitude faster than the baselines without compromising the end-user interactivity.On the other hand, the growth and rise of geospatial data, in the present world, with the ever increasing need to explore the geospatial data, many big data repositories were launched to host hundreds of thousands of datasets. In particular, UCR-Star focuses mainly on geospatial data and provides an interactive web interface to explore the metadata and contents of big geospatial data using a map interface. The primary target of UCR-Star is the ease of visually exploring these spatial datasets without having to actually download terabytes of data. However, with hundreds of thousands of datasets in these repositories, users get lost and cannot find the datasets that might be useful to them. To address these problems, I worked on the first recommendation system for geospatial data exploration. We first define three recommendation tasks that could be helpful to users, namely, recommending i) a subset within a dataset, ii) a way to visualize the data, and iii) recommending an entire dataset. We then generalize them into one general visualization recommendation problem and we propose solutions based on collaborative filtering, tensor decomposition, and graph convolutional networks. We run experiments on real datasets from UCR-Star and show the effectiveness of the proposed solutions to solve the visualization recommendation problem.

Cited by 0

Interactive visual exploration of large relation data

Li Yang, Mustafa Sanver · 2008

As data become ubiquitous and immense in many activities from business decision making to scientific analysis, effective analysis of such data becomes an important research area. Data analysis requires active involvement of human beings and visualization is a powerful tool to capture profound insights from data. However, interactive visual exploration of massive data poses fundamental technical challenges to both data visualization and database management systems. Millions of billions of data records clutter the screen and existing database management systems are inadequate for overview-and-drill-down data access for interactive data exploration. We have proposed a density-based methodology to address the above challenge. We present multiresolution data aggregation as an intermediate representation of data between visualization tools and databases. Data aggregated at multiple resolutions are stored in internal nodes of a partition-based high dimensional tree index while the individual records are stored in leaf nodes. Such a piggyback ride of aggregated data efficiently supports resolution-based data access patterns of large relational data. Thus, multiresolution data aggregation conveys density-based data input to visualizations. Therefore, many existing visualization techniques which are inefficient in visualizing large data can accept this new data input and participate in analysis of large data. To demonstrate the feasibility and effectiveness of the new representation, a client-server visualization tool is developed. The server, KDBMS, is a kdB-tree variant to organize data in multiple resolutions and the client, mVis, is a dynamic, flexible, and extendible visualization framework. We have extended 3D footprint splatting with grand tour, density-based parallel coordinates, and density-based scatterplot matrices to render data aggregations. The framework provides a data-independent and plug-in-based environment for quick development and integration of new visualization techniques, in addition to the linking mechanism to interconnect its components.

Cited by 0