The Possibility of Matrix Decomposition as Anonymization and Evaluation for Time-sequence Data

2018 ◽  
Author(s):  
Tomoaki Mimoto ◽  
Shinsaku Kiyomoto ◽  
Seira Hidano ◽  
Anirban Basu ◽  
Atsuko Miyaji
Keyword(s):  
Sequence Data ◽  
Matrix Decomposition ◽  
Time Sequence

scholarly journals Data mining techniques based on grey system theories for time sequence data

2006 ◽  
Vol 3 (2) ◽  
pp. 73-82 ◽  
Author(s):  
Liu Bin ◽  
Zhang Hui ◽  
Liu Sifeng ◽  
Dang Yaoguo
Keyword(s):  
Data Mining ◽  
Sequence Data ◽  
Status Quo ◽  
Time Sequence ◽  
Grey System ◽  
Science Field ◽  
Data Mining Techniques ◽  
Embedded Knowledge ◽  
The Status ◽  
First Time

Data mining is an interesting focus in computer science field now This paper deals with data mining techniques based on Grey system theories for time sequence data. Firstly, thoughts of data mining with embedded knowledge are expatiated, and the status quo of Data mining techniques is presented briefly. Then, based on the above thoughts and the Grey system theories, data mining techniques based on Grey system theories for time sequence data are proposed for the first time, and the idiographic arithmetic with GM(1,1) as an example is introduced in this paper. Last, it forecasts the total homes in 2002~2005 connecting with Internet in Shang Hai City by the arithmetic.

Hardware Implementation of a Real-Time Data Processing Algorithm in Marine Engineering Data Acquisition

2011 ◽  
Vol 268-270 ◽  
pp. 110-115
Author(s):  
Ling Ma ◽  
Ke Zhu Song ◽  
Jun Feng Yang ◽  
Ping Cao
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Real Time ◽  
Hardware Implementation ◽  
Sequence Data ◽  
Time Sequence ◽  
Time Data ◽  
Real Time Data ◽  
Marine Seismic ◽  
Real Time Data Processing

According to the architecture characteristics of the mass data acquisition system in marine seismic exploration, this paper designed a real-time data processing algorithm which can convert the collected time-sequence data to channel-sequence data. A hardware implementation of the algorithm based on FPGA+DDR SDRAM is developed to complete the whole conversion process. Here, FPGA is used to achieve time sequence data receiving, analyzing, preliminary processing and the interface to DDR SDRAM. Two DDR SDRAM’s are used in ping-pang mode to store time-sequence data and to cooperate with FPGA in realizing time-to-channel sequence data conversion. Test results showed that, after applying the algorithm to the FCI in high-precision marine seismic data acquisition and recording system, this arithmetic could realize caching collected data without redundancy and converting data from time sequence to channel sequence without dead time, besides, this algorithm also greatly improved the efficiency and reliability of data processing.

Measuring the Change in Contingency Tables Using Markov Models — Application to the Effect of Preceding Conception on the Next one

1982 ◽  
Vol 21 (01) ◽  
pp. 31-33
Author(s):  
A. K. Saleh ◽  
M. A. A. Moussa ◽  
H. Hathout ◽  
R. Kasraoui
Keyword(s):  
Linear Models ◽  
Markov Models ◽  
Transition Probabilities ◽  
Sequence Data ◽  
Contingency Tables ◽  
Maternity Hospital ◽  
Time Sequence ◽  
Maximum Likelihood Estimates ◽  
Previous Pregnancy ◽  
Log Linear

A method is described for the analysis of time sequence data viewed as contingency tables. This includes 1.) determining the maximum likelihood estimates of stationary and transition probabilities; 2.) testing stationarity of transition probabilities in first and second order Markov models by fitting the data to the appropriate configuration of log-linear models; 3.) assessing the order of dependence through fitting log-linear models. Data based on a study recently conducted on women delivered in the Principal Maternity Hospital in Kuwait is used for illustration. It is concluded that conception outcome depends on pregnancy order as well as on at least two previous pregnancy outcomes.

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