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data Quality visual analysis (DQVA) A tool to process and pinspot raw data irregularities

Data Quality Visual Analysis (DQVA) A tool to process and pi...

引用

IEEE Annual Computing and Communication Workshop and conference (CCWC)

作者： Célio Carvalho Rui S. Moreira José Manuel Torres ISUS FCT/UFP Porto Portugal

ISBN: (数字)9781665414906

ISBN: (纸本)9781665430586

This project proposes a machine learning (ML) pipeline for inferring office employee's well-being, from heterogeneous sources of contextual data (cf. physiological, social and workplace environment), which brings several demanding issues. In this paper we focus specifically in raw data collection problems and pre-processing challenges. To start with, context data was collected in real environments, during weeks, in several office organizations and involving employees along theirs daily working routines. Moreover, data collection resort to a wide range of sources (e.g. sensors, questionnaires, apps, etc.) that were subject to potential interferences and noisy conditions. Given the influence of data quality in ML algorithms results and considering the number of instruments used, it was essential to implement a pre-processing stage to automate and improve the quality of collected data. Hence, the usefulness of the proposed DQVA tool, which computes several common statistical measures and provides also graphical and tabular visual insights about the data. For example, it allows to: i) compare data sources from different participants and organizations, on a per sensor/data source basis (through data tables, data distribution histograms, and visualizations); iii) check and pinspot the existence of outliers; iv) visually spot signal gaps; etc. Therefore, we argue that the proposed DQVA tool allows to evaluate, per sensor and per individual, raw data quality, on the integration stage of our classification pipeline. It proved to be an agile, useful and simple to re-use tool for detecting raw data irregularities, thus increasing data quality assurances for the next steps of our classification pipeline.

关键词： visualization data integrity Pipelines Organizations Tools Sensors Classification algorithms

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visual exploration of Time Series Anomalies with Metro-Viz 19

Visual Exploration of Time Series Anomalies with Metro-Viz

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ACM SIGMOD International conference on Management of data (SIGMOD)

作者： Eichmann, Philipp Solleza, Franco Tatbul, Nesime Zdonik, Stan Brown Univ Providence RI 02912 USA Intel Labs Cambridge MA USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781450356435

This demo presents a novel data visualization solution for exploring the results of time series anomaly detection systems. When anomalies are reported, there is a need to reason about the results. We introduce Metro-Viz - a visual tool to assist data scientists in performing this analysis. Metro-Viz offers a rich set of interaction features (e.g., comparative analysis, what-if testing) backed by data management strategies specifically tailored to the workload. We show our tool in action via multiple time series datasets and anomaly detectors.

关键词： time series anomaly detection visual analysis

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VizCertify: A framework for secure visual data exploration 6

VizCertify: A framework for secure visual data exploration

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6th IEEE International conference on data Science and Advanced Analytics (DSAA)

作者： De Stefani, Lorenzo Spiegelberg, Leonhard F. Upfal, Eli Kraska, Tim Brown Univ Dept Comp Sci Providence RI 02912 USA MIT CSAIL 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781728144931

Recently, there have been several proposals to develop visual recommendation systems. The most advanced systems aim to recommend visualizations, which help users to find new correlations or identify an interesting deviation based on the current context of the user's analysis. However, when recommending a visualization to a user, there is an inherent risk to visualize random fluctuations rather than solely true patterns: a problem largely ignored by current techniques. In this paper, we present VizCertify, a novel framework to improve the performance of visual recommendation systems by quantifying the statistical significance of recommended visualizations. The proposed methodology allows to control the probability of misleading visual recommendations using both classical statistical testing procedures and a novel application of the Vapnik Chervonenkis (VC) dimension towards visualization recommendation which results in an effective criterion to decide whether a recommendation corresponds to a true phenomenon or not.

关键词： visualization Recommendation Systems FWER data exploration visual Recommendation

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visual analysis Scenarios for Oceanographic Buoy data 23

Visual Analysis Scenarios for Oceanographic Buoy Data

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23rd International conference in Information visualization (iv) / 16th International conference Computer Graphics, Imaging and visualization (CGiv)

作者： Monteiro, Walbert Cunha Monteiro, Marcela Cunha Miranda, Brunelli Pinto Sampaio Neto, Nelson Cruz Resque dos Santos, Carlos Gustavo Meiguins, Bianchi Serique Fed Univ Para Fac Comp Sci Belem Para Brazil Fed Univ Para Inst Coastal Studies Braganca Para Brazil

ISBN: (纸本)9781728128504

The analysis of oceanographic data is a difficult task due to the large volume and multidimensionality of the data. Often is it necessary to aggregate other datasets to establish relationships and more complex analysis in order to answer environmental questions. Thus, this work aims to present different information visualization techniques (InfoVis) for oceanographic data concerning the most frequently used techniques, such as 2D and 3D Charts, Surface Map, Choropleth Map, and Line Map. For this purpose, a dataset of oceanographic buoys from the National Oceanic and Atmospheric Administration (NOAA) covering a period of 02 years (from 2015 to 2016) was used. This set of data was treated and classified according to the seasonal periods (winter, spring, summer, and autumn). The Rose Wind technique revealed that the wind speed average was between 1012 m/s. The Circle Parking technique showed a predominance of wind and waves from NE. In 2015, highest waves were recorded, mainly during the winter. The obtained results can be considered a useful tool for the assessment of the wind and waves relationship and help to coastal management strategies.

关键词： Information visualization visual analysis Oceanographic Buoy data Temporal Correlation

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Evaluation Models for Luminous Environment Satisfaction in Green Office Buildings Integrating Environmental and Spatial Attributes Based on Massive data Samples

Evaluation Models for Luminous Environment Satisfaction in G...

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Big data & Artificial Intelligence & Software Engineering (ICBASE), International conference on

作者： Liang Ji Mengkun Hu Lina Zhang Yuncan Sun Shanghai Research Institute of Building Sciences Co. Ltd Shanghai China

ISBN: (纸本)9781665427104

The quality of luminous environment is an important aspect of the operational performance of green buildings. A reliable evaluation for occupants’ satisfaction of luminous environment based on not only illuminance but with other more factors is indispensable. Compared to existing research, this study developed an evaluation model for luminous environment satisfaction based on the illuminance, the occupants’ distance from the window and weather conditions by a large- scale field measurement and questionnaire survey. To improve typicality, more than 3000 data samples were obtained from 14 green office buildings located in two different daylight climate zones in China. analysis showed that the satisfaction rate of the luminous environment decreased obviously with the increase of the distance from window and the satisfaction rates tended to be higher on sunny days, especially for the daylight climate zone iv. Based on the above analysis, the luminous environment satisfaction was modelled via binary logistic regression method. Thus, the evaluation models for luminous environment satisfaction in green office buildings located in two daylight climate zones in China were proposed, which can offer a simple way to assess visual comfort and thus support the optimization of indoor environment and software development for green office building management.

关键词： visualization Green buildings Buildings Weather forecasting Predictive models Software data models

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exploration of anomalies in cyclic multivariate industrial time series data for condition monitoring 23

Exploration of anomalies in cyclic multivariate industrial t...

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Workshops of the 23rd International conference on Extending database Technology/23rd International conference on database Theory, EDBT-ICDT-WS 2020

作者： Suschnigg, Josef Mutlu, Belgin Fuchs, Anna Katharina Sabol, Vedran Thalmann, Stefan Schreck, Tobias Pro2Future Avl List Graz Austria Pro2Future Know-Center GmbH Graz Austria Avl List Graz Austria Know-Center GmbH Graz Austria University of Graz Graz Austria Graz University of Technology Graz Austria

Industrial product testing is frequently performed in cycles, resulting in cycle-dependent test data. Monitoring the condition of products under test involves analysis of large and complex test data sets. Main tasks are to detect anomalies and dependencies between observation variables, which appears to be challenging to engineers. In this paper, we present a flexible and extendable visual analytics approach for anomaly detection focusing on cycle-depended data. It is based on a glyph representation to visualize anomaly scores of cycles with respect to interactively selected reference data. Our approach is built on a design study in collaboration with an industrial engineering corporation, and is demonstrated on real data from engines tested on automotive testbeds. Based on findings from evaluation results, we provide a discussion and an outlook for future work. © 2020 Copyright for this paper by its author(s).

关键词： Condition monitoring

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Breaking the Curse of visual data exploration: Improving Analyses by Building Bridges between data World and Real World 10

Breaking the Curse of Visual Data Exploration: Improving Ana...

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14th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 10th International conference on Information visualization Theory and Applications (ivAPP)

作者： Kraus, Matthias Weiler, Niklas Breitkreutz, Thorsten Keim, Daniel A. Stein, Manuel Univ Konstanz Data Anal & Visualizat Constance Germany

ISBN: (纸本)9789897583544

visual data exploration is a useful means to extract relevant information from large sets of data. The visual analytics pipeline processes data recorded from the real world to extract knowledge from gathered data. Subsequently, the resulting knowledge is associated with the real world and applied to it. However, the considered data for the analysis is usually only a small fraction of the actual real-world data and lacks above all in context information. It can easily happen that crucial context information is disregarded, leading to false conclusions about the real world. Therefore, conclusions and reasoning based on the analysis of this data pertain to the world represented by the data, and may not be valid for the real world. The purpose of this paper is to raise awareness of this discrepancy between the data world and the real world which has a high impact on the validity of analysis results in the real world. We propose two strategies which help to identify and remove specific differences between the data world and the real world. The usefulness and applicability of our strategies are demonstrated via several use cases.

关键词： visualization Theory Uncertainty Validation visual Analytics

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Denoising and stability using Independent Component analysis in high dimensions - visual inspection still required 23

Denoising and stability using Independent Component Analysis...

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23rd International conference on the Information visualisation (iv) - Incorporating Biomedical visualization, Learning Analytics and Geometric Modelling and Imaging

作者： Chakrabarty, Subhajit Levkowitz, Haim Univ Massachusetts Lowell Dept Comp Sci Lowell MA 01854 USA

ISBN: (纸本)9781728128382

Independent Component analysis (ICA) has emerged as a useful method for separation of components, such as in removing noise from data. We examine one of the challenges of ICA - instability, particularly in high dimensions, when the independent components vary, each time when ICA is performed. This may be due to various causes including the stochastic nature of the algorithm and the additive noise. The objective of this study is to examine denoising and stability issues of ICA in high dimensions and make a comparative evaluation of select approaches. We take a challenging electrocardiogram dataset which is a high-dimensional time series of multiple sensors. We experiment with a mix of approaches and methods - for resampling, clustering, ICA algorithms and dimensionality. We check the internal validity using the Icasso stability index, the Amari separation performance index and the Minimum Distance (MD) index. The first key contribution of this work is that it finds counter-evidence to the claim that resampling (bootstrapping) tackles the question of stability. The second contribution is that it finds evidence of an important limitation of the Minimum Distance index when dealing with high dimensional data - the index may become highly concentrated and may remain sub-optimal at all dimensions. Selectively removing noise components by visual inspection can improve the Amari, the Icasso or the MD index values. Some automated tools exist, but in high dimensions, visual inspection of the individual components is still required for effective denoising - data driven methods are not good enough.

关键词： independent component analysis time series denoising multiple sensors

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Designing for visual data exploration in Multi-Device Environments

Designing for Visual Data Exploration in Multi-Device Enviro...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Horak, Tom Tech Univ Dresden Interact Media Lab Dresden Germany

ISBN: (纸本)9781450359719

Multi-device environments have an enormous potential to enable more flexible workflows during our daily work. At the same time, visual data exploration is characterized as a fragmented sensemaking process requiring a high degree of flexibility. In my thesis, I am aiming to bring these two worlds into symbiosis, specifically for sensemaking with multivariate data visualizations and graph visualizations. This involves three main objectives: (i) understanding the devices' roles in dynamic device ensembles and their relations to exploration patterns, (ii) identifying mechanisms for adapting visualizations for different devices while preserving a consistent perception and interaction, and, finally, (iii) supporting users and developers in designing such distributed visualization interfaces, e.g., through specific guidelines. As specific contributions, it is planned that (i) and (ii) emerge into a design space, while (iii) leads to a set of heuristics. So far, I was able to extensively work on the first objective as well as to touch on the other two.

关键词： visual data analysis cross-device interaction mobile visualization

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A study on EEG-NIRS testing of drug users' brain function under visual induction 9

A study on EEG-NIRS testing of drug users' brain function un...

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9th International conference on Computing and Pattern Recognition, ICCPR 2020

作者： Yang, Banghua Gu, Xuelin Xu, Ding Fan, Chengcheng School of Mechanical and Electrical Engineering and Automation Shanghai University Shanghai China Shanghai Drug Rehabilitation Administration Bureau Shanghai China

ISBN: (纸本)9781450387835

Long-term drug abuse may change the brain function. In this paper, a new experimental paradigm combining EEG and NIRSIT is designed to explore target stimulus pictures-induced changes in electroencephalograms, as well as the changes in blood oxygen under near-infrared spectroscopy in drug users. The experiment divides into three stages. The first stage denotes the resting state. The task in the second stage includes induction by target stimulation pictures and neutral pictures. And in the third stage, the resting state pictures are all neutral pictures. The electroencephalograms and near-infrared data were collected in the three stages. Then an analysis was made of the ERP potential P300 of the electroencephalograms, the changes in hemoglobin concentration under near-infrared spectroscopy, and the channel time series. An analysis was made of the EEG and NIRSIT data on 120 people who were tested in a rehabilitation center according to the paradigm designed for the experiment. An analysis of the electroencephalograms in the second stage of the experiment shows that target stimulus pictures evoked more obvious P300 volatility in electroencephalograms than neutral pictures. Meanwhile, an analysis of the NIRSIT data shows that the concentration of oxyhemoglobin increased and the concentration of deoxyhemoglobin decreased. exploration of drug rehabilitation studies, laying a foundation for further predicting pathological changes in the human brain, as well as targeted rehabilitation. © 2020 ACM.

关键词： Detoxification

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