SIESTA: A Scalable Infrastructure of Sequential Pattern Analysis

Mavroudopoulos, Ioannis; Gounaris, Anastasios

doi:10.1109/tbdata.2022.3229092

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…As the application fields of information technology become more diverse, research on data analysis, such as clustering [1], classification [2], pattern mining [3], and pattern querying [4], [5], has been actively studied. In addition, the amount of generated data increases rapidly.…”

Section: Introductionmentioning

confidence: 99%

Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic Database

Lee,

Kim,

Cho

et al. 2024

IEEE Access

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Top-k high utility pattern mining, which extracts the highest top-k patterns that the users want to find, has been actively studied. Most previous studies in this domain have focused on static databases, where data insertions do not occur. In the real world, however, various applications continuously generate new data, and existing top-k high utility pattern mining algorithms devised to process static databases cannot handle incremental databases. Although some methods can handle stream data, they have the limitation of processing a portion of the database rather than the entire accumulated database. In this paper, we suggest an efficient incremental mining method that discovers top-k high utility patterns from the entire accumulated database. The proposed approach utilizes a list structure that stores minimal utility information required for the mining process and does not generate candidate itemsets. The suggested algorithm processes the incremental data with a single database scan and restructures the list for efficient mining. Moreover, four efficient threshold raising techniques along with a restoring technique are utilized to calculate the optimal threshold value in an accumulated incremental environment. The results of the experiments on runtime, memory, and scalability show that the suggested method efficiently processes the entire incremental database.INDEX TERMS Data analysis, High utility pattern, Incremental dynamic database, Pattern analysis, Top-k.

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Section: Introductionmentioning

confidence: 99%

Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic Database

Lee,

Kim,

Cho

et al. 2024

IEEE Access

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Advanced incremental erasable pattern mining from the time-sensitive data stream

Kim,

Cho,

Nam

et al. 2024

Knowledge-Based Systems

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Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time Series

Wu,

Wang,

et al. 2024

ACM Trans. Manage. Inf. Syst.

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Recently, order-preserving pattern (OPP) mining has been proposed to discover some patterns, which can be seen as trend changes in time series. Although existing OPP mining algorithms have achieved satisfactory performance, they discover all frequent patterns. However, in some cases, users focus on a particular trend and its associated trends. To efficiently discover trend information related to a specific prefix pattern, this paper addresses the issue of co-occurrence OPP mining (COP) and proposes an algorithm named COP-Miner to discover COPs from historical time series. COP-Miner consists of three parts: extracting keypoints, preparation stage, and iteratively calculating supports and mining frequent COPs. Extracting keypoints is used to obtain local extreme points of patterns and time series. The preparation stage is designed to prepare for the first round of mining, which contains four steps: obtaining the suffix OPP of the keypoint sub-time series, calculating the occurrences of the suffix OPP, verifying the occurrences of the keypoint sub-time series, and calculating the occurrences of all fusion patterns of the keypoint sub-time series. To further improve the efficiency of support calculation, we propose a support calculation method with an ending strategy that uses the occurrences of prefix and suffix patterns to calculate the occurrences of superpatterns. Experimental results indicate that COP-Miner outperforms the other competing algorithms in running time and scalability. Moreover, COPs with keypoint alignment yield better prediction performance.

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SIESTA: A Scalable Infrastructure of Sequential Pattern Analysis

Cited by 3 publications

References 33 publications

Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic Database

Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic Database

Advanced incremental erasable pattern mining from the time-sensitive data stream

Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time Series

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