In recent years there has been a growing interest in developing new methods and systems that allow users to interactively explore large volumes of data, such as document collections, multimedia collections or biomedic...
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ISBN:
(纸本)9781450348935
In recent years there has been a growing interest in developing new methods and systems that allow users to interactively explore large volumes of data, such as document collections, multimedia collections or biomedical datasets. There are various approaches to support users in this interactive environment ranging from the development of new algorithms through visualisation methods to specialised interfaces. The overarching goal of this workshop is to bring together a group of researchers spanning across multiple facets of exploratorysearch and dataanalytics to discuss, and outline research challenges for this novel area. Copyright held by the owner/author(s).
Modern applications in this digital age collect a staggering amount of time series data from economic growth rates to electrical household consumption habits. To make sense of it, domain analysts interactively sift th...
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ISBN:
(纸本)9781450341974
Modern applications in this digital age collect a staggering amount of time series data from economic growth rates to electrical household consumption habits. To make sense of it, domain analysts interactively sift through these time series collections in search of critical relationships between and recurring patterns within these time series. The ONEX (Online Exploration of Time Series) system supports effective exploratory analysis of time series collections composed of heterogeneous, variable-length and misaligned time series using robust alignment dynamic time warping (DTW) methods. To assure real-time responsiveness even for these complex and compute-intensive analytics, ONEX precomputes and then encodes time series relationships based on the inexpensive-to-compute Euclidean distance into the ONEX base. Thereafter, based on a solid formal foundation, ONEX uses DTW-enhanced analytics to correctly extract relevant time series matches on this Euclidean-prepared ONEX base. Our live interactive demonstration shows how our ONEX exploratory tool, supported by a rich array of visual interactions and expressive visualizations, enables efficient mining and interpretation of the MATTERS real data collection composed of economic, social, and education data trends across the fifty American states.
The proceedings contain 6 papers. The topics discussed include: enabling change exploration;interactive exploration of correlated time series;integration and exploration of connected personal digital traces;on achievi...
ISBN:
(纸本)9781450346740
The proceedings contain 6 papers. The topics discussed include: enabling change exploration;interactive exploration of correlated time series;integration and exploration of connected personal digital traces;on achieving diversity in recommender systems;structural query expansion via motifs from Wikipedia;and supporting dynamic quantization for high-dimensional dataanalytics.
Research, especially in the social sciences and humanities, is increasingly reliant on the application of data science methods to analyze large amounts of (often private) data. Secure data enclaves provide a solution ...
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ISBN:
(纸本)9781450350211
Research, especially in the social sciences and humanities, is increasingly reliant on the application of data science methods to analyze large amounts of (often private) data. Secure data enclaves provide a solution for managing and analyzing private data. However, they do not readily support discovery science-a form of exploratory or interactive analysis by which researchers execute a range of analyses in an iterative and collaborative manner. The batch computing model offered by many data enclaves is well suited to executing large compute tasks;however it is far from ideal for day-to-day discovery science as the high latencies inherent in queue-based, batch computing systems hinder interactive analysis. In this paper we describe how we have augmented the Cloud Kotta secure data enclave to support collaborative and interactive analysis of sensitive data. Our model uses Jupyter notebooks as a flexible analysis environment and Python language constructs to support the execution of arbitrary analyses on private data within this secure framework.
An important step in conducting qualitative research on large collections of text is reducing the size of the collection to one that is manageable. While it is common to use a variety of simple sampling methods, the l...
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The proceedings contain 15 papers. The topics discussed include: assisting discovery in public health;what you see is not what you get! detecting Simpson's paradoxes during data exploration;machine learning abstra...
ISBN:
(纸本)9781450350297
The proceedings contain 15 papers. The topics discussed include: assisting discovery in public health;what you see is not what you get! detecting Simpson's paradoxes during data exploration;machine learning abstraction: the *** vision;machine learning explanations for iterative debugging;flipper: a systematic approach to debugging training sets;ProvDB: lifecycle management of collaborative analysis workflows;SOCRAT platform design: a web architecture for interactive visual analytics applications;what users don't expect about exploratorydata analysis on AQP systems;and human-in-the-loop challenges for entity matching: a midterm report.
Similarity searches are at the heart of exploratorydata analysis tasks. Distance metrics are typically used to characterize the similarity between data objects represented as feature vectors. However, when the dimens...
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ISBN:
(纸本)9781450346740
Similarity searches are at the heart of exploratorydata analysis tasks. Distance metrics are typically used to characterize the similarity between data objects represented as feature vectors. However, when the dimensionality of the data increases and the number of features is large, traditional distance metrics fail to distinguish between the closest and furthest data points. Localized distance functions have been proposed as an alternative to traditional distance metrics. These functions only consider dimensions close to query to compute the distance/similarity. Furthermore, in order to enable interactive explorations of high-dimensional data, indexing support for ad-hoc queries is needed. In this work we set up to investigate whether bit-sliced indices can be used for exploratoryanalytics such as similarity searches and data clustering for high-dimensional big-data. We also propose a novel dynamic quantization called Query dependent Equi-Depth (QED) quantization and show its effectiveness on characterizing high-dimensional similarity. When applying QED we observe improvements in kNN classification accuracy over traditional distance functions.
The rapid growth of monitoring applications has led to unprecedented amounts of generated time series data. data analysts typically explore such large volumes of time series data looking for valuable insights. One suc...
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ISBN:
(纸本)9781450346740
The rapid growth of monitoring applications has led to unprecedented amounts of generated time series data. data analysts typically explore such large volumes of time series data looking for valuable insights. One such insight is finding pairs of time series, in which subsequences of values exhibit certain levels of correlation. However, since exploratory queries tend to be initially vague and imprecise, an analyst will typically use the results of one query as a springboard to formulating a new one, in which the correlation specifications are further refined. As such, it is essential to provide analysts with quick initial results to their exploratory queries, which allows for speeding up the refinement process. This goal is challenging when exploring the correlation in a large search space that consists of a big number of long time series. In this work we propose search algorithms that address precisely that challenge. The main idea underlying our work is to design priority-based search algorithms that efficiently navigate the rather large space to quickly find the initial results of an exploratory query. Our experimental results show that our algorithms outperform existing ones and enable high degree of interactivity in exploring large time series data.
The proceedings contain 22 papers. The topics discussed include: a skeleton/cage hybrid paradigm for digital animation;a declarative and classifier gesture recognition method for creating an effective feedback and fee...
The proceedings contain 22 papers. The topics discussed include: a skeleton/cage hybrid paradigm for digital animation;a declarative and classifier gesture recognition method for creating an effective feedback and feedforward system;advanced visual interfaces supporting distributed cloud-based big data analysis;interactivedata visualization for product search;tangibles for graph algorithmic thinking: research questions and work-in-progress;the evolution of a tangible for children's conversations: research questions and progress;developing a n400 brain computer interface based on semantic expectancy;gestural interaction in virtual environments: user studies and applications;effective user interactions for visual analytics tools;semiotic virtual reality framework validation;ChIP: teaching coding in primary schools;the Madeira touch: encouraging visual-spatial exploration using a tactile interactive display;SnAIR drum: a gesture interface for rhythm practice;demonstration of a sensor-based app for self-monitoring of medicine intake;learning system user interface preferences: an exploratory survey;comparison of UX evaluation methods that measures the UX over time;audio guides and human tour guides: measuring children's engagement & learning at a museum setting;UTAssistant: a web platform supporting usability testing in Italian public administrations;advanced interaction paradigms to define smart visit experiences in the internet of things era;does the perception of team collaboration changes with time? study with computer science students;and a multimodal interface for robot-children interaction in autism treatment.
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