Abstract

A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem spurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the prediction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual analytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summarization of the predictive analytics workflow.

Original languageEnglish (US)
Pages (from-to)1-16
Number of pages16
JournalFrontiers of Computer Science
DOIs
StateAccepted/In press - Oct 27 2016

Fingerprint

Visual Analytics
Frustration
Selection Model
Prediction
Model Validation
Summarization
Cleaning
Expertise
Black Box
Feature Selection
Work Flow
Data mining
Learning systems
Feature extraction
Data Mining
Machine Learning
Visualization
Statistics
Closed
Output

Keywords

  • data mining
  • predictive analysis
  • predictive visual analytics
  • visual analytics
  • visualization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Recent progress and trends in predictive visual analytics. / Lu, Junhua; Chen, Wei; Ma, Yuxin; Ke, Junming; Li, Zongzhuang; Zhang, Fan; Maciejewski, Ross.

In: Frontiers of Computer Science, 27.10.2016, p. 1-16.

Research output: Contribution to journalArticle

Lu, Junhua ; Chen, Wei ; Ma, Yuxin ; Ke, Junming ; Li, Zongzhuang ; Zhang, Fan ; Maciejewski, Ross. / Recent progress and trends in predictive visual analytics. In: Frontiers of Computer Science. 2016 ; pp. 1-16.
@article{a88293b1d1794ade9114f568728d3ebc,
title = "Recent progress and trends in predictive visual analytics",
abstract = "A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem spurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the prediction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual analytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summarization of the predictive analytics workflow.",
keywords = "data mining, predictive analysis, predictive visual analytics, visual analytics, visualization",
author = "Junhua Lu and Wei Chen and Yuxin Ma and Junming Ke and Zongzhuang Li and Fan Zhang and Ross Maciejewski",
year = "2016",
month = "10",
day = "27",
doi = "10.1007/s11704-016-6028-y",
language = "English (US)",
pages = "1--16",
journal = "Frontiers of Computer Science",
issn = "2095-2228",
publisher = "Springer Science + Business Media",

}

TY - JOUR

T1 - Recent progress and trends in predictive visual analytics

AU - Lu, Junhua

AU - Chen, Wei

AU - Ma, Yuxin

AU - Ke, Junming

AU - Li, Zongzhuang

AU - Zhang, Fan

AU - Maciejewski, Ross

PY - 2016/10/27

Y1 - 2016/10/27

N2 - A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem spurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the prediction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual analytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summarization of the predictive analytics workflow.

AB - A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem spurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the prediction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual analytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summarization of the predictive analytics workflow.

KW - data mining

KW - predictive analysis

KW - predictive visual analytics

KW - visual analytics

KW - visualization

UR - http://www.scopus.com/inward/record.url?scp=84992390980&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84992390980&partnerID=8YFLogxK

U2 - 10.1007/s11704-016-6028-y

DO - 10.1007/s11704-016-6028-y

M3 - Article

SP - 1

EP - 16

JO - Frontiers of Computer Science

JF - Frontiers of Computer Science

SN - 2095-2228

ER -