Maximal Sequential Patterns Research Articles

Visual analysis of oceanic data for marine ecosystems

We present a data visualization approach that allows users to interactively analyse sequential movement patterns and automatically extract rules for marine animal behaviour. This enhances the ability of oceanic experts to understand marine ecosystems. Traditional time-series analysis, basic statistical methods, and single-view visualizations often fail to capture the intricate spatial and temporal dynamics of marine ecosystems. Our visual analytics approach, that leverages sequential pattern mining and rule mining techniques, integrates SPMF's Vertical Mining of Maximal Sequential Patterns (VMSP) algorithm and T-Rule Growth algorithm to efficiently extract and visualize movement patterns and behavioural rules of marine animals. The system also incorporates automated pre-processing to clean and prepare large datasets, reducing the time and effort required for manual handling. The visualization presents oceanic data from detection stations, allowing researchers to discover patterns in the movements of marine species. Our interface also provides geographical visualization of selected patterns or rules and helps users to navigate and find the relevant information. Key principles of our model include multi-view visualizations that provide comprehensive insights by displaying data from various perspectives simultaneously, and geo-spatial time-varying data visualization that captures and represents the dynamic movements of marine species. This approach allows domain experts to interactively explore and analyse sequential patterns and rules, enhancing their ability to make informed decisions. Additionally, feedback received, and insights gained from specialized professionals was used to determine if this approach would be a beneficial complement to existing practice.

DecentralDC: Assessing data contribution under decentralized sharing and exchange blockchain.

The issue of data quality has emerged as a critical concern, as low-quality data can impede data sharing, diminish intrinsic value, and result in economic losses. Current research on data quality assessment primarily focuses on four dimensions: intrinsic, contextual, presentational, and accessibility quality, with intrinsic and presentational quality mainly centered on data content, and contextual quality reflecting data usage scenarios. However, existing approaches lack consideration for the behavior of data within specific application scenarios, which encompasses the degree of participation and support of data within a given scenario, offering valuable insights for optimizing resource deployment and business processes. In response, this paper proposes a data contribution assessment method based on maximal sequential patterns of behavior paradigms (DecentralDC). DecentralDC is composed of three steps: (1) mining the maximal sequential patterns of sharing and exchange behavior paradigms; (2) determining the weights of these paradigms; (3) calculating the contribution of sharing and exchange databases combined with data volume. To validate our approach, two sharing and exchange scenarios of different scales are established. The experimental results in two scenarios validate the effectiveness of our method and demonstrate a significant reduction in cumulative regret and regret rate in data pricing due to the introduction of data contribution. Specifically, compared to the most competitive baseline, the improvements of mean average precision in two scenarios are 6% and 8%. The code and simulation scenarios have been open-sourced and are available at https://github.com/seukgcode/DecentralDC.

NetNMSP: Nonoverlapping maximal sequential pattern mining.

Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms focused on finding all frequent patterns and found lots of redundant short patterns. However, it not only reduces the mining efficiency, but also increases the difficulty in obtaining the demand information. To reduce the frequent patterns and retain its expression ability, this paper focuses on the Nonoverlapping Maximal Sequential Pattern (NMSP) mining which refers to finding frequent patterns whose super-patterns are infrequent. In this paper, we propose an effective mining algorithm, Nettree for NMSP mining (NetNMSP), which has three key steps: calculating the support, generating the candidate patterns, and determining NMSPs. To efficiently calculate the support, NetNMSP employs the backtracking strategy to obtain a nonoverlapping occurrence from the leftmost leaf to its root with the leftmost parent node method in a Nettree. To reduce the candidate patterns, NetNMSP generates candidate patterns by the pattern join strategy. Furthermore, to determine NMSPs, NetNMSP adopts the screening method. Experiments on biological sequence datasets verify that not only does NetNMSP outperform the state-of-the-arts algorithms, but also NMSP mining has better compression performance than closed pattern mining. On sales datasets, we validate that our algorithm guarantees the best scalability on large scale datasets. Moreover, we mine NMSPs and frequent patterns in SARS-CoV-1, SARS-CoV-2 and MERS-CoV. The results show that the three viruses are similar in the short patterns but different in the long patterns. More importantly, NMSP mining is easier to find the differences between the virus sequences.

Sequential pattern mining for ICT risk assessment and management

ICT risk assessment and management relies on the analysis of data on the joint behavior of a target system and its attackers. The tools in the Haruspex suite model intelligent, goal-oriented attackers that reach their goals through sequences of attacks. The tools synthetically generate these sequences through a Monte Carlo method that runs multiple simulations of the attacker behavior. This paper presents a sequential pattern mining analysis of the attack sequence database to extract a high-level and succinct understanding of the attacker strategies against the system to assess. Such an understanding is expressed as a set of sequential patterns that cover, and possibly partition, the attack sequences. This set can be extracted in isolation, or in contrast with the behavior of other attackers. In the latter case, the patterns represent a signature of the behavior of an attacker. The dynamic tools of the suite use this signature to deploy dynamic counter-measures that reduce the security risk. We formally motivate the need for using the class of maximal sequential patterns in covering attack sequences, instead of frequent or closed sequential patterns. When contrasting the behavior of different attackers, we resort to distinguishing sequential patterns. We report an extensive experimentation on a system with 36 nodes, 6 attackers, and 600K attack sequences.

Efficient algorithms for simultaneously mining concise representations of sequential patterns based on extended pruning conditions

The concise representations of sequential patterns, including maximal sequential patterns, closed sequential patterns and sequential generator patterns, play an important role in data mining since they provide several benefits when compared to sequential patterns. One of the most important benefits is that their cardinalities are generally much less than the cardinality of the set of sequential patterns. Therefore, they can be mined more efficiently, use less storage space, and it is easier for users to analyze the information provided by the concise representations. In addition, the set of all maximal sequential patterns can be utilized to recover the complete set of sequential patterns, while closed sequential patterns and sequential generators can be used together to generate non-redundant sequential rules and to quickly recover all sequential patterns and their frequencies. Several algorithms have been proposed to mine the concise representations separately, i.e., each of them has been designed to discover only a type of the concise representation. However, they remain time-consuming and memory intensive tasks. To address this problem, we propose three novel efficient algorithms named FMaxSM, FGenCloSM and MaxGenCloSM to exploit only maximal sequential patterns, to simultaneously mine both the sets of closed sequential patterns and generators, and to discover all three concise representations during the same process. To our knowledge, MaxGenCloSM is the first algorithm for concurrently mining the three concise representations of sequential patterns. The proposed algorithms are based on two novel local pruning strategies called LPMAX and LPMaxGenClo that are designed to prune non-maximal, non-closed and non-generator patterns earlier and more efficiently at two and three successive levels of the prefix tree without subsequence relation checking. Extensive experiments on real-life and synthetic databases show that FMaxSM, FGenCloSM and MaxGenCloSM are up to two orders of magnitude faster than the state-of-the-art algorithms and that the proposed algorithms consume much less memory, especially for low minimum support thresholds and for dense databases.

Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths.

Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.

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This paper propose a method called TMSP to perform squential patten mining. Because maximal patterns compact representations of frequent patterns, so they are used for mining in TMSP. The main idea of TMSP is mining top-k frequent maximal equential patterns of length no less than the minimum length of each pattern (min_l) and no greater than the maximum length of each pattern (max_l) with k is the desired number of maximal sequential patterns to be mined. The proposed method helps user do not need turning specification of a minimum support threshold to perform the mining which is a disadvantage of previous studies. Experimental results on real datasets show that TMSP serves as an efficient solution for mining sequential patterns. The reults also demonstrate that TMSP is better than the maximal sequential pattern mining algorithm (MAXSP) in term memory efficient and easier for users to find the number of required patterns without adjusting minsup.

Distributed Sequential Pattern Mining: A Survey and Future Scope

Distributed sequential pattern mining is the data mining method to discover sequential patterns from large sequential database on distributed environment. It is used in many wide applications including web mining, customer shopping record, biomedical analysis, scientific research, etc. A large research has been done on sequential pattern mining on various distributed environments like Grid, Hadoop, Cluster, Cloud, etc. Different types of sequential pattern mining can be performed are sequential patterns, maximal sequential patterns, closed sequences, constraint based and time interval based sequential patterns. This paper presents a systematic review on work done for sequential pattern mining and advanced sequential pattern mining on distributed environment. This paper finally presents future research directions related to sequential pattern mining in distributed environment. General Terms Association Rule Mining, Sequential Pattern Mining.

Detecting App-DDoS Attacks Based on Maximal Frequent Sequential Pattern Mining

Detecting App-DDoS Attacks Based on Maximal Frequent Sequential Pattern Mining

Parallel mining of maximal sequential patterns using multiple samples

In this paper, we propose a new parallel algorithm, named PMSPX, which mines maximal frequent sequences by using multiple samples to exclude infrequent candidates effectively. A frequent sequence is maximal if none of its supersequences is frequent. Unlike the traditional single-sample methods developed for mining frequent itemsets, PMSPX uses multiple samples. Thus, it can avoid or alleviate some problems inherent in the single-sample methods. We theoretically analyzed how to increase the minimum support level to prevent misestimating infrequent candidates as frequent in the mining of samples. PMSPX is a parallel version of our sequential MSPX algorithm, and it is developed on a cluster of workstations. In PMSPX, each processing node uses MSPX to find a candidate set of local maximal frequent sequences first, independently from other processing nodes. Then, a top-down search is performed, starting with all the candidates, in a synchronous manner to identify real maximal frequent sequences. This asynchronous local mining followed by synchronous global mining approach minimizes the synchronization and communication among the processing nodes. Three database partitioning methods are proposed to distribute the database across the processing nodes, so that their workloads are balanced and the data skewness of the whole database is preserved in the data partition of each node. A comprehensive analysis was performed on PMSPX and existing parallel sequence mining algorithms, and extensive experiments were conducted on PMSPX. PMSPX demonstrates very good speedup and scaleup properties. It also requires less communication and synchronization than other parallel algorithms.

A scalable algorithm for mining maximal frequent sequences using a sample

In this paper, we propose an efficient scalable algorithm for mining Maximal Sequential Patterns using Sampling (MSPS). The MSPS algorithm reduces much more search space than other algorithms because both the subsequence infrequency-based pruning and the supersequence frequency-based pruning are applied. In MSPS, a sampling technique is used to identify long frequent sequences earlier, instead of enumerating all their subsequences. We propose how to adjust the user-specified minimum support level for mining a sample of the database to achieve better overall performance. This method makes sampling more efficient when the minimum support is small. A signature-based method and a hash-based method are developed for the subsequence infrequency-based pruning when the seed set of frequent sequences for the candidate generation is too big to be loaded into memory. A prefix tree structure is developed to count the candidate sequences of different sizes during the database scanning, and it also facilitates the customer sequence trimming. Our experiments showed MSPS has very good performance and better scalability than other algorithms.