2013 Proceeding
We have entered the era of big data. Massive datasets, surpassing terabytes and petabytes in size are now commonplace. They arise in numerous settings in science, government, and enterprises, and technology exists by which we can collect and store such massive amounts of information. Yet, making sense of these data remains a fundamental challenge. We lack the means to exploratively analyze databases of this scale. Currently, few technologies allow us to freely "wander" around the data, and make discoveries by following our intuition, or serendipity. While standard data mining aims at finding highly interesting results, it is typically computationally demanding and time consuming, thus may not be well-suited for interactive exploration of large datasets.
Interactive data mining techniques that aptly integrate human intuition, by means of visualization and intuitive human-computer interaction techniques, and machine computation support have been shown to help people gain significant insights into a wide range of problems. However, as datasets are being generated in larger volumes, higher velocity, and greater variety, creating effective interactive data mining techniques becomes a much harder task.
Proceeding Downloads
Interactive visual analytics for high dimensional data
Many modern data sets can be represented in high dimensional vector spaces and have benefited from computational methods that utilize advanced techniques from numerical linear algebra and optimization. Visual analytics approaches have contributed ...
Building blocks for exploratory data analysis tools
Data exploration is largely manual and labor intensive. Although there are various tools and statistical techniques that can be applied to data sets, there is little help to identify what questions to ask of a data set, let alone what domain knowledge ...
Methods for exploring and mining tables on Wikipedia
Knowledge bases extracted automatically from the Web present new opportunities for data mining and exploration. Given a large, heterogeneous set of extracted relations, new tools are needed for searching the knowledge and uncovering relationships of ...
One click mining: interactive local pattern discovery through implicit preference and performance learning
It is known that productive pattern discovery from data has to interactively involve the user as directly as possible. State-of-the-art toolboxes require the specification of sophisticated workflows with an explicit selection of a data mining method, ...
Lytic: synthesizing high-dimensional algorithmic analysis with domain-agnostic, faceted visual analytics
We present Lytic, a domain-independent, faceted visual analytic (VA) system for interactive exploration of large datasets. It combines a flexible UI that adapts to arbitrary character-separated value (CSV) datasets with algorithmic preprocessing to ...
A process-centric data mining and visual analytic tool for exploring complex social networks
Social scientists and observational scientists have a need to analyze complex network data sets. Examples of such exploratory tasks include: finding communities that exist in the data, comparing results from different graph mining algorithms, ...
Zips: mining compressing sequential patterns in streams
We propose a streaming algorithm, based on the minimal description length (MDL) principle, for extracting non-redundant sequential patterns. For static databases, the MDL-based approach that selects patterns based on their capacity to compress data ...
Augmenting MATLAB with semantic objects for an interactive visual environment
Analysis tools such as Matlab, R, and SAS support a myriad of built-in computational functions and various standard visualization techniques. However, most of them provide little interaction from visualizations mainly due to the fact that the tools ...
Online spatial data analysis and visualization system
With the exponential growth of the usage of web map services, the geo data analysis has become more and more popular. This paper develops an online spatial data analysis and visualization system, TerraFly GeoCloud, which facilitates end users to ...
Randomly sampling maximal itemsets
Pattern mining techniques generally enumerate lots of uninteresting and redundant patterns. To obtain less redundant collections, techniques exist that give condensed representations of these collections. However, the proposed techniques often rely on ...
Towards anytime active learning: interrupting experts to reduce annotation costs
Many active learning methods use annotation cost or expert quality as part of their framework to select the best data for annotation. While these methods model expert quality, availability, or expertise, they have no direct influence on any of these ...
Storygraph: extracting patterns from spatio-temporal data
Analysis of spatio-temporal data often involves correlating different events in time and location to uncover relationships between them. It is also desirable to identify different patterns in the data. Visualizing time and space in the same chart is not ...
Index Terms
- Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics
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Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| IDEA '13 | 25 | 11 | 44% |
| Overall | 25 | 11 | 44% |