Data mining of sensor monitoring time series and knowledge discovery

2011 ◽  
Author(s):  
Shisong Zhu ◽  
Lifang Kong ◽  
Liang Chen
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Monitoring Time ◽  
Sensor Monitoring

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

scholarly journals Knowledge Discovery Through Time Series Applied to Students' Grades

2020 ◽  
Author(s):  
Joaquim Assunção ◽  
Fernando Oliveira ◽  
Claiton Correa
Keyword(s):  
High School ◽  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
School System ◽  
Historical Data ◽  
Traditional Approach ◽  
Time Series Modeling ◽  
Institute Of Technology ◽  
Teaching Learning

Assessment is a constant activity in education, in the school system, and the teaching-learning process. The traditional approach classifies the students learning level through grades. This paper shows an application of knowledge discovery and data mining through classification and clustering via time series modeling on students' grades from high school. We collected historical data from an institute of technology, from this, we created models that can be used to extract patterns to help teachers to understand the profile of the students and provide early warns about possible poor results.

Algorithms of Data Mining and Knowledge Discovery of Correlativity in Two-Dimensional Time Series

2012 ◽  
Vol 263-266 ◽  
pp. 1844-1848
Author(s):  
Shao Lin Hu ◽  
Ye Li ◽  
Wei Zhang
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Stationary Time Series ◽  
Two Dimensional ◽  
Qualitative Knowledge ◽  
Model Set ◽  
Professional Fields ◽  
Mining Algorithms ◽  
New Algorithms

Oriented at dynamic data from complicated process with noise disturbance, it is very difficult to discover knowledge of correlativity and orderliness. Following some analyzing results about the shortcoming of relative coefficients in mining non-stationary time series, a series of new algorithms are built in this paper to mine correlativity in two-dimensional time series. These new algorithms are based on a expansible framework of model set. Based on these new mining algorithms, a making decision table is listed not only to mine correlativity in two-dimensional time series, but also to discover deepening knowledge to transform the qualitative knowledge “nonlinear relativity” as well as “non-relativity” into deeper quantitative knowledge about analytical model. These new approaches given in this paper is exoteric in framework and can be enriched with additional new models. In this way, some professional data mining and knowledge discovery cab be fulfilled to aim at some specific professional fields.

Analisis Data Pembayaran Kredit Nasabah Bank Menggunakan Metode Data Mining

2013 ◽  
Vol 4 (1) ◽  
pp. 18-27
Author(s):  
Ira Melissa ◽  
Raymond S. Oetama
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Database

Data mining adalah analisis atau pengamatan terhadap kumpulan data yang besar dengan tujuan untuk menemukan hubungan tak terduga dan untuk meringkas data dengan cara yang lebih mudah dimengerti dan bermanfaat bagi pemilik data. Data mining merupakan proses inti dalam Knowledge Discovery in Database (KDD). Metode data mining digunakan untuk menganalisis data pembayaran kredit peminjam pembayaran kredit. Berdasarkan pola pembayaran kredit peminjam yang dihasilkan, dapat dilihat parameter-parameter kredit yang memiliki keterkaitan dan paling berpengaruh terhadap pembayaran angsuran kredit. Kata kunci—data mining, outlier, multikolonieritas, Anova

Continuous Glucose Monitoring Time Series and Hypo/Hyperglycemia Prevention: Requirements, Methods, Open Problems

2008 ◽  
Vol 4 (3) ◽  
pp. 181-192 ◽  
Author(s):  
Giovanni Sparacino ◽  
Andrea Facchinetti ◽  
Alberto Maran ◽  
Claudio Cobelli
Keyword(s):  
Time Series ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Open Problems ◽  
Monitoring Time

The AI Delusion

2018 ◽  
Author(s):  
Gary Smith
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Industrial Revolution ◽  
The Real ◽  
Intelligent Machines ◽  
Black Boxes ◽  
Real Danger ◽  
The Way

We live in an incredible period in history. The Computer Revolution may be even more life-changing than the Industrial Revolution. We can do things with computers that could never be done before, and computers can do things for us that could never be done before. But our love of computers should not cloud our thinking about their limitations. We are told that computers are smarter than humans and that data mining can identify previously unknown truths, or make discoveries that will revolutionize our lives. Our lives may well be changed, but not necessarily for the better. Computers are very good at discovering patterns, but are useless in judging whether the unearthed patterns are sensible because computers do not think the way humans think. We fear that super-intelligent machines will decide to protect themselves by enslaving or eliminating humans. But the real danger is not that computers are smarter than us, but that we think computers are smarter than us and, so, trust computers to make important decisions for us. The AI Delusion explains why we should not be intimidated into thinking that computers are infallible, that data-mining is knowledge discovery, and that black boxes should be trusted.

scholarly journals The Upworthy Research Archive, a time series of 32,487 experiments in U.S. media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
J. Nathan Matias ◽  
Kevin Munger ◽  
Marianne Aubin Le Quere ◽  
Charles Ebersole
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Open Access ◽  
Randomized Trial ◽  
Digital Technology ◽  
Behavioral Experiments ◽  
Media Politics ◽  
B Test ◽  
Scientific Questions ◽  
Advance Knowledge

AbstractThe pursuit of audience attention online has led organizations to conduct thousands of behavioral experiments each year in media, politics, activism, and digital technology. One pioneer of A/B tests was Upworthy.com, a U.S. media publisher that conducted a randomized trial for every article they published. Each experiment tested variations in a headline and image “package,” recording how many randomly-assigned viewers selected each variation. While none of these tests were designed to answer scientific questions, scientists can advance knowledge by meta-analyzing and data-mining the tens of thousands of experiments Upworthy conducted. This archive records the stimuli and outcome for every A/B test fielded by Upworthy between January 24, 2013 and April 30, 2015. In total, the archive includes 32,487 experiments, 150,817 experiment arms, and 538,272,878 participant assignments. The open access dataset is organized to support exploratory and confirmatory research, as well as meta-scientific research on ways that scientists make use of the archive.

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