Sequential pattern mining method for analysis of programming learning history based on the learning process

2014 ◽  
Author(s):  
Shoichi Nakamura ◽  
Kaname Nozaki ◽  
Yasuhiko Morimoto ◽  
Youzou Miyadera
Keyword(s):  
Learning Process ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Mining Method ◽  
Learning History

Detecting Implicit Security Exceptions Using an Improved Variable-Length Sequential Pattern Mining Method

2017 ◽  
Vol 27 (08) ◽  
pp. 1235-1268
Author(s):  
Jinfu Chen ◽  
Saihua Cai ◽  
Dave Towey ◽  
Lili Zhu ◽  
Rubing Huang ◽  
...  
Keyword(s):  
Visual Inspection ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Variable Length ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Mining Method ◽  
Security Testing ◽  
String Searching ◽  
Correct Execution

The process of component security testing can produce massive amounts of monitor logs. Current approaches to detect implicit security exceptions (those which cannot be identified by visual inspection alone) compare correct execution sequences with fixed patterns mined from the execution of sequential patterns in the monitor logs. However, this is not efficient and is not suitable for mining large monitor logs. To enable effective mining of implicit security exceptions from large monitor logs, this paper proposes a method based on improved variable-length sequential pattern mining. The proposed method first mines the variable-length sequential patterns from correct execution sequences and from actual execution sequences, thus reducing the number of patterns. The sequential patterns are then detected using the Sunday string-searching algorithm. We conducted an experimental study based on this method, the results of which show that the proposed method can efficiently detect the implicit security exceptions of components.

scholarly journals Sequential Pattern Mining Model to Identify the Most Important or Difficult Learning Topics via Mobile Technologies

2018 ◽  
Vol 12 (4) ◽  
pp. 109
Author(s):  
Edona Doko ◽  
Lejla Abazi Bexheti ◽  
Mentor Hamiti ◽  
Blerta Prevalla Etemi
Keyword(s):  
Pattern Mining ◽  
Time Series Data ◽  
Theoretical Method ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Mobile Technologies ◽  
Series Data ◽  
Learning History ◽  
History Of ◽  
To Come

The paper aim is to come up with methodology for performing video learning data history of learner’s video watching logs, video segments or time series data in accordance with learning processes via mobile technologies. To reach this goal, it is introduced a theoretical method of sequential pattern mining specialized for learning histories in identifying the most important or difficult learning. Based on this method, it is designed a model for understanding and learning the most difficult topics of students topics. The user will be able to use and access the model through mobile technologies when and where he/she wants. The performed video learning history data of learner’s video watching logs consists of functions that are responsible for collection of stop/replay/backward data activities, generation of sequence from the collected learning histories, extraction of important patterns from a set of sequences, and findings of learner’s most difficult/important topic from the extracted patterns. The paper mainly describes the model for understanding and learning the most difficult topics through the sequential pattern mining method. Implementing the method to use in mobile phones is considered as future aim.

scholarly journals Spatiotemporal Mining of Time-Series Remote Sensing Images Based on Sequential Pattern Mining

2015 ◽  
Vol II-4/W2 ◽  
pp. 111-118
Author(s):  
H. C. Liu ◽  
G. J. He ◽  
X. M. Zhang ◽  
W. Jiang ◽  
S. G. Ling
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Land Cover Change ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Landscape Patterns ◽  
Mining Method ◽  
Study Region

With the continuous development of satellite techniques, it is now possible to acquire a regular series of images concerning a given geographical zone with both high accuracy and low cost. Research on how best to effectively process huge volumes of observational data obtained on different dates for a specific geographical zone, and to exploit the valuable information regarding land cover contained in these images has received increasing interest from the remote sensing community. In contrast to traditional land cover change measures using pair-wise comparisons that emphasize the compositional or configurational changes between dates, this research focuses on the analysis of the temporal sequence of land cover dynamics, which refers to the succession of land cover types for a given area over more than two observational periods. Using a time series of classified Landsat images, ranging from 2006 to 2011, a sequential pattern mining method was extended to this spatiotemporal context to extract sets of connected pixels sharing similar temporal evolutions. The resultant sequential patterns could be selected (or not) based on the range of support values. These selected patterns were used to explore the spatial compositions and temporal evolutions of land cover change within the study region. Experimental results showed that continuous patterns that represent consistent land cover over time appeared as quite homogeneous zones, which agreed with our domain knowledge. Discontinuous patterns that represent land cover change trajectories were dominated by the transition from vegetation to bare land, especially during 2009–2010. This approach quantified land cover changes in terms of the percentage area affected and mapped the spatial distribution of these changes. Sequential pattern mining has been used for string mining or itemset mining in transactions analysis. The expected novel significance of this study is the generalization of the application of the sequential pattern mining method for capturing the spatial variability of landscape patterns, and their trajectories of change, to reveal information regarding process regularities with satellite imagery.

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