Mining Timed Sequential Patterns: The Minits-AllOcc Technique

Tracking #: 734-1714

Responsible editor: 

Richard Mann

Submission Type: 

Research Paper


Sequential pattern mining is one of the data mining tasks used to find the subsequences in a sequence dataset that appear together in order based on time. Sequence data can be collected from devices, such as sensors, GPS, or satellites, and ordered based on timestamps, which are the times when they are generated/collected. Mining patterns in such data can be used to support many applications, including weather forecasting and transportation recommendation systems. Numerous techniques have been proposed to address the problem of how to mine subsequences in a sequence dataset; however, current traditional algorithms ignore the temporal information between the itemset in a sequential pattern. This information is essential in many situations. For example, doctors, even if they know a symptom B will appear after symptom A for a specific disease, must know the time interval of when symptom B is expected to appear to reduce the disease's risk and provide a suitable treatment. Considering temporal relationship information for sequential patterns raises new issues to be solved, such as designing a new data structure to save this information and traversing this structure efficiently to discover patterns without re-scanning the database. In this paper, we propose an algorithm called Minits-AllOcc (MINIng Timed Sequential Pattern for All-time Occurrences) to find sequential patterns and the transition time between itemsets based on all occurrences of a pattern in the database. We also propose a parallel multi-core CPU version of this algorithm, called MMinits-AllOcc (Multi-core for MINIng Timed Sequential Pattern for All-time Occurrences), to deal with Big Data. Extensive experiments on real and synthetic datasets show the advantages of this approach over the brute-force method. Also, the multi-core CPU version of the algorithm is shown to outperform the single-core version on Big Data by 2.5X.



  • Reviewed

Data repository URLs: 

Date of Submission: 

Saturday, November 26, 2022

Date of Decision: 

Friday, April 28, 2023

Nanopublication URLs:



Solicited Reviews:


Review the paper and comment.


• The proposed Minits-AllOcc and MMinits-AllOcc algorithms are capable of mining sequential patterns and the transition time between itemsets based on all occurrences of a pattern in the database. 
• The proposed parallel multi-core CPU version of this algorithm, MMinits-AllOcc, is able to efficiently deal with Big Data.
• Extensive experiments on real and synthetic datasets show the advantages of this approach over the brute-force method, with the multi-core CPU version of the algorithm shown to outperform the single-core version on Big Data by 2.5X.

• Some parts of the proposed algorithm lack clarity and could be better explained. 
• The proposed algorithm could be further improved by incorporating more advanced techniques to optimize the computation time.

Review comment

  1. Structure your abstract as follows- 1) Background 2) Aim/Objective 3) Methodology 4) Results 5) Conclusion. Write 2-4 lines for each and merge everything in one paragraph without any subheading
  2. Abstract must contain the motivation and objective of the article. The Abstract must be very clear and the motive of the paper should be represented in a nutshell.
  3. Introduction should be of 5-7 solid paragraphs and provide structure of work at the end of the Introduction section.
  4. Add more contribution to your study field.
  5. The purpose of study not clear.
  6. Make highlight for objectives
  7. Remove any table or figure which is taken from web. Otherwise you have to get approval from publisher and author in a provided form by springer.
  8. Please avoid to write definitions of terms like Data mining, Sequential pattern mining, Timed sequential patterns, Singe-core and multi-core processor., etc., which are already available over web, try to cite work for such information.
  9. In summary, only provide useful content in your work.

10. These are Title related to your area, you may use.


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  • Sajja, G. S., Mustafa, M., Phasinam, K., Kaliyaperumal, K., Ventayen, R. J. M., & Kassanuk, T. (2021, August). Towards application of machine learning in classification and prediction of heart disease. In 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 1664-1669). IEEE.

Meta-Review by Editor

Your manuscript has been reviewed by two reviewers. Both reviewers found the paper to be interesting and potentially signficant. However, the reviewers also raised concerns about the novelty of the paper, with both noting that it was hard to evaluate the novelty as this wasn't clearly enough described in the manuscript. I consider this to be the major concern that your revision should address. In particular you should clearly compare the algorithm proposed in this manuscript to other existing algorithms, and give some idea of their relative performance, as well as delineating precisely where the new algorithm differs from existing approaches. As noted by reviewer 2, it does not necessarily matter if the computational performance of the new algorithm is slower than other methods, but the manuscript should give some idea of the relative performance in this domain.

Please also note that both reviewers highlighted that 'Not all used and produced data are FAIR and openly available in established data repositories; authors need to fix this'. Making all data FAIR and openly available will be a condition of eventual acceptance.

The reviewers also provided detailed comments on other aspects of the manuscript, and I invite you to consider these carefully in formulating your response and revision of the manuscript.

Richard Mann (

Withdrawn by the authors

This submission was withdrawn upon request by the authors. Thereby it is now marked Rejected instead of Undecided.