Sequential Mining Research Articles

Research Progress in Genome Sequencing and Functional Gene Mining of Cannabis

Research Progress in Genome Sequencing and Functional Gene Mining of Cannabis

Clustering-based multidimensional sequential pattern mining of semantic trajectories

Clustering-based multidimensional sequential pattern mining of semantic trajectories

OntoCommerce: Incorporating Ontology and Sequential Pattern Mining for Personalized E-Commerce Recommendations

OntoCommerce: Incorporating Ontology and Sequential Pattern Mining for Personalized E-Commerce Recommendations

Clustering-based Multidimensional Sequential Pattern Mining of Semantic Trajectories

Clustering-based Multidimensional Sequential Pattern Mining of Semantic Trajectories

Privacy-Enhanced Frequent Sequence Mining and Retrieval for Personalized Behavior Prediction

Privacy-Enhanced Frequent Sequence Mining and Retrieval for Personalized Behavior Prediction

Deep learning and sequence mining for manufacturing process and sequence selection

ABSTRACT Automatic determination of manufacturing process sequences for the physical production of given part designs is key to facilitate on-demand cyber manufacturing. In this work, we propose an integrated framework that (i) identifies manufacturing features from 3D part designs using a Graph Neural Network (GNN), (ii) identifies the manufacturing processes necessary to produce all features in the part using a Convolutional Neural Network (CNN) that considers shape, material properties, and quality information, and (iii) outputs an ordered manufacturing sequence that can produce the designed part with the help of sequence mining. Using these methods, the knowledge required to enable automated manufacturing process selection is easily scalable and updatable without requiring manual population of ad-hoc or rule-based descriptions. We present exemplar implementations of the proposed framework by suggesting manufacturing sequences for discrete parts with multiple features. The suggested manufacturing sequences demonstrate the potential of the proposed framework for use in future on-demand cyber manufacturing applications.

Integrating analytical statistical models, sequential pattern mining, and fuzzy set theory for advanced mobile app reliability assessment

The study presents a new method for evaluating the reliability of mobile applications using the Corcoran model. This model includes several aspects of reliability, including performance, reliability, availability, scalability, security, usability, and testability. The Corcoran model can be applied to evaluate mobile applications by analysing key reliability metrics. Using the model significantly improves the reliability assessment of applications compared to traditional methods, which are primarily focused on desktop and server configurations. The aim of the study is to offer a more optimised approach to evaluating the reliability of mobile applications. The paper examines the problems faced by mobile app developers. This study represents a new application of the Corcoran model in evaluating the reliability of mobile applications. This model is characterised by an emphasis on the use of quantitative statistics and the ability to provide an accurate estimate of the probability of failure without any inaccuracies, which distinguishes this model from other software reliability models. The paper suggests using a combination of analytical statistical models, data extraction methods such as sequential pattern analysis, and fuzzy set theory to implement the Corcoran model. The application of the methodology is demonstrated by studying software error reports and conducting a comprehensive statistical analysis of them. To improve the results of future research, the paper suggests making more extensive use of the Corcoran model in various mobile applications and environments. It is recommended to change the model to take into account the constantly changing characteristics of mobile applications and their increasing complexity. In addition, it is advisable to conduct additional research to improve the data mining methods used in the model and explore the possibility of integrating artificial intelligence for more advanced software reliability analysis. Applying the Corcoran model to the mobile app development process to evaluate reliability can significantly improve the quality of applications, leading to increased customer satisfaction and trust in mobile apps. This model can serve as a guide for developers and companies to evaluate and improve their applications, driving innovation and continuous improvement in the competitive mobile app sector.

Khai thác dữ liệu chuỗi theo mối quan tâm của người dùng

Sequence data mining, also known as sequential pattern mining, is to find all frequent sub-sequences (called sequential patterns) in a sequence database, the threshold of frequency is specified by the user. In recent years, with the explosion growth of information and big data, this problem trends toward mining with constraints to overcome both effectiveness and efficiency challenges since that the constraints represent for the user’s interest. This paper presents a detailed survey of recent studies on mining sequential pattern and the categories of the constraints. Moreover, it also classifies the main methods and analyzes their advantages and limitations, thereby main approaches and strategies to solve sequential pattern mining problems in the future are presented.

Towards utility-driven contiguous sequential patterns in uncertain multi-sequences

In data mining, high-utility sequential pattern mining (HUSPM) focuses more on the specific values of items than on their frequency, making it more practical in real-life scenarios. HUSPM with the contiguous constraint can be used to solve some applications requiring the sequence elements to occur consecutively. Due to device, environment, privacy issues, and other factors, the data is often not accurate, and traditional algorithms for mining high-utility continuous sequence patterns (HUCSPs) do not perform well in handling uncertain data. To address this challenge, this paper presents a new algorithm named uncertain utility-driven contiguous pattern mining (UUCPM), which is able to discover HUCSPs in the uncertain database efficiently and correctly. The algorithm is designed to obtain results from sequence data with uncertain probability set on the item or itemset level. Two tighter upper bounds on utility and corresponding pruning strategies are also proposed, which can effectively process and reduce the number of candidate patterns generated during pattern mining, thereby improving the performance of the mining process. The proposed UUCPM algorithm has been verified for accuracy and performance through extensive experiments, demonstrating its advanced properties. The source code and datasets are available at GitHub https://github.com/DSI-Lab1/UUCPM.

DRIVE: Dockerfile Rule Mining and Violation Detection

A Dockerfile defines a set of instructions to build Docker images, which can then be instantiated to support containerized applications. Recent studies have revealed a considerable amount of quality issues with Dockerfiles. In this article, we propose a novel approach, Dockerfiles Rule mIning and Violation dEtection ( DRIVE ), to mine implicit rules and detect potential violations of such rules in Dockerfiles. DRIVE first parses Dockerfiles and transforms them to an intermediate representation. It then leverages an efficient sequential pattern mining algorithm to extract potential patterns. With heuristic-based reduction and moderate human intervention, potential rules are identified, which can then be utilized to detect potential violations of Dockerfiles. DRIVE identifies 34 semantic rules and 19 syntactic rules including 9 new semantic rules that have not been reported elsewhere. Extensive experiments on real-world Dockerfiles demonstrate the efficacy of our approach.

Steps to implementation: the role of peer feedback inner structure on feedback implementation

Though implementing feedback provided by peers has been an essential step for learning efficiency in peer feedback activities, it remains challenging for students. This study aims to explore the inner structure patterns of students’ peer feedback and how they are related to feedback implementation. Sixty-nine engineering students from a Singapore university participated in the peer feedback activity. Content analysis was conducted to analyse the inner structure of feedback as well as the implementation status of each feedback. Sequential mining was applied to investigate the sequential patterns of the various feedback components. The results show a variation of inner structures in the implemented feedback and unimplemented feedback. The implemented feedback tended to be sequenced with an evaluation or a standpoint before seeking clarification or suggestions for improvement. It was also likely to contain continuous questions seeking clarifications. In comparison, the unimplemented feedback was likely to continuously indicate positive evaluations or agreements. By understanding the inner structure of peer feedback with different implementation statuses, researchers and educators can fine-tune design and instructions to support peer feedback practices.

Outlier based intrusion detection in databases for user behaviour analysis using weighted sequential pattern mining

Outlier based intrusion detection in databases for user behaviour analysis using weighted sequential pattern mining

SingleScan: a comprehensive resource for single-cell sequencing data processing and mining

Single-cell sequencing has shed light on previously inaccessible biological questions from different fields of research, including organism development, immune function, and disease progression. The number of single-cell-based studies increased dramatically over the past decade. Several new methods and tools have been continuously developed, making it extremely tricky to navigate this research landscape and develop an up-to-date workflow to analyze single-cell sequencing data, particularly for researchers seeking to enter this field without computational experience. Moreover, choosing appropriate tools and optimal parameters to meet the demands of researchers represents a major challenge in processing single-cell sequencing data. However, a specific resource for easy access to detailed information on single-cell sequencing methods and data processing pipelines is still lacking. In the present study, an online resource called SingleScan was developed to curate all up-to-date single-cell transcriptome/genome analyzing tools and pipelines. All the available tools were categorized according to their main tasks, and several typical workflows for single-cell data analysis were summarized. In addition, spatial transcriptomics, which is a breakthrough molecular analysis method that enables researchers to measure all gene activity in tissue samples and map the site of activity, was included along with a portion of single-cell and spatial analysis solutions. For each processing step, the available tools and specific parameters used in published articles are provided and how these parameters affect the results is shown in the resource. All information used in the resource was manually extracted from related literature. An interactive website was designed for data retrieval, visualization, and download. By analyzing the included tools and literature, users can gain insights into the trends of single-cell studies and easily grasp the specific usage of a specific tool. SingleScan will facilitate the analysis of single-cell sequencing data and promote the development of new tools to meet the growing and diverse needs of the research community. The SingleScan database is publicly accessible via the website at http://cailab.labshare.cn/SingleScan.

Efficient weighted sequential pattern mining

In real-life applications, data mining task involves extracting valuable but hidden information from massive data. How to effectively find out interesting patterns from large databases is a current topic. Sequential pattern mining is the most popular approach in data mining domain. Traditional sequential pattern mining research generally focuses on discovering frequent sequential patterns. However, the account of occurrence times of patterns does not adequately indicate their importance. For instance, frequent patterns (e.g., pencil and eraser) are not profitable, whereas infrequent patterns (e.g., extreme weather) are high-risk. To extract more useful information, researchers study a weighted sequential pattern mining task. In this paper, an efficient algorithm for weighted sequential pattern mining task, called EWSPM, is proposed. Two new strict upper bounds, namely MWEbound and MSRIWbound, are designed based on the concepts of maximum weight estimation (simplified as MWE) and maximum sumation of remaining item weights (simplified as MSRIW), respectively. These upper bounds achieve better pruning effects and reduce the size of search space during the mining process, which significantly shortens execution time. In addition, a database-projection method is employed to optimize memory usage. It addresses potential memory explosion issues in a certain degree. Finally, we also conducted extensive experiments on nine datasets (including real and synthetic). The experimental results demonstrate that the EWSPM algorithm is capable of mining all interesting patterns efficiently, with the smallest size of search space. Additionally, the novel algorithm also exhibits superior performance in terms of execution time and memory consumption.

Prevention of gas-dynamic phenomena in sequential mining of sylvinite seams KrII and AB at the Upper Kama potassium–magnesium deposit

Prevention of gas-dynamic phenomena in sequential mining of sylvinite seams KrII and AB at the Upper Kama potassium–magnesium deposit

Empirical Mode Decomposition with Imf Using Ant Colony Optimization

The serial remote sensing images allow us to view how a certain area has changed and grown across period. Even though these raw images have fewer opportunities to generate information and additional insight, serial remote sensing images (SRSI) offer a significant opportunity to generate clusters and patterns. The implementation of SRSI aggregation is supported by the developments of patterns in a variety of circumstances, which include urbanization, the spread of native vegetation, and farming. We propose a novel outlook to mining sequence patterns that makes use of Ant Colony Optimization and Empirical Mode Decomposition. The newly created Empirical Mode Decomposition (EMD) and Ant Colony Optimization (ACO) approach is integrated to extract the most important characteristics features of significantly accurate performance from serial remote sensing images based on distinct criteria. The outcomes demonstrate that the accuracy of projected sequence patterns can be greatly improved by combining a new hybrid method with EMD and ACO feature selection. Because of this, the method is appealing for employing a farmland dataset based on serial remote sensing images to create ground-based stream-flow for strategically extracting spatially sequential patterns despite requesting less computational mining time and cost.

A new tree-based approach to mine sequential patterns

Generic sequential pattern mining problem aims to mine the set of sequential patterns from a sequential database that satisfies a minimum support or occurrence threshold constraint. The main challenges that affect the efficiency of a solution lie in reducing the pattern search space, early detecting the infrequent patterns, representing the database in an efficient format, etc. Also, additional challenges get included when the problem environment transitions from static to incremental database leading to not to re-mine but efficiently tracking the effect of the incremental portion over the complete updated database. In this article, we introduce a new tree-based solution to the sequential pattern mining problem, including two sets of novel solutions for static and incremental sequential databases. We propose two new structures, SP-Tree and IncSP-Tree, and design two efficient algorithms, Tree-Miner and IncTree-Miner to mine the complete set of sequential patterns from static and incremental databases respectively. The proposed novel structures provide an efficient manner to store the complete sequential database maintaining “build-once-mine-many” property and giving scope to perform interactive mining. Additionally, we also design a new breath-first based support counting technique to efficiently identify the infrequent patterns at early stages and a new heuristic pruning strategy to reduce pattern search space. We also design a new pattern storage structure BPFSP-Tree to store the frequent patterns during successive iterations in incremental mining to reduce the number of database scans and to remove the infrequent patterns efficiently. A novel structure named Sequence Summarizer is also introduced to efficiently calculate and update the co-occurrence information of the items, especially in an incremental environment. Experimental results from various real-life and synthetic datasets demonstrate the efficiency of our work in comparison with the related state-of-the-art approaches.

Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation

High-utility sequential pattern mining (HUSPM) helps researchers find all subsequences that have high utility in a quantitative sequential database. The HUSPM approach appears to be well suited for resource transformation in DIKWP graphs. However, all the extensions of a high-utility sequential pattern (HUSP) also have a high utility that increases with its length. Therefore, it is difficult to obtain diverse patterns of resources. The patterns that consist of many low-utility items can also be a HUSP. In practice, such a long pattern is difficult to analyze. In addition, the low-utility items do not always reflect the interestingness of association rules. High average-utility pattern mining is considered a solution to extract more significant patterns by considering the lengths of patterns. In this paper, we formulate the problem of top-k high average-utility sequential pattern mining (HAUSPM) and propose a novel algorithm for resource transformation. We adopt a projection mechanism to improve efficiency. We also adopt the sequence average-utility-raising strategy to increase thresholds. We design the prefix extension average utility and the reduced sequence average utility by incorporating the average utility into the utility upper bounds. The results of our comparative experiments demonstrate that the proposed algorithm can achieve sufficiently good performance.

Analysis and Time-series Forecasting of Corporate Stock Price

Fluctuations in the stock market represent the changes in the national economy objectively, stock price prediction predicts the future trend of stocks using the past data, which has been widely focused on. Some machine learning algorithms, such as linear fitting and sequence mining, are used to predict the stock market. However, linear fitting faces the problem of overfitting and black relationships with historical data, while sequence mining is short in efficiency and lacks dynamic adaptations. State-of-the-art methods using attention mechanism in neural networks have shown exceptional performance targeting sequential prediction and classification. In this paper, we propose a combined LSTM-CNN attention model to explore the role of attention mechanism in Long Short-Term Memory (LSTM) network-based stock price movement prediction. Experimental results show that our LSTM-CNN-attention model can provide an accurate prediction and reliable trial on the stock price prediction, and the attention mechanism significantly improves the model performance in the stock market.

MR-AT: Map Reduce based Apriori Technique for Sequential Pattern Mining using Big Data in Hadoop

One of the most well-known and widely implemented data mining methods is Apriori algorithm which is responsible for mining frequent item sets. The effectiveness of the Apriori algorithm has been improved by a number of algorithms that have been introduced on both parallel and distributed platforms in recent years. They are distinct from one another on account of the method of load balancing, memory system, method of data degradation, and data layout that was utilised in their implementation. The majority of the issues that arise with distributed frameworks are associated with the operating costs of handling distributed systems and the absence of high-level parallel programming languages. In addition, when using grid computing, there is constantly a possibility that a node will fail, which will result in the task being re-executed multiple times. The MapReduce approach that was developed by Google can be used to solve these kinds of issues. MapReduce is a programming model that is applied to large-scale distributed processing of data on large clusters of commodity computers. It is effective, scalable, and easy to use. MapReduce is also utilised in cloud computing. This research paper presents an enhanced version of the Apriori algorithm, which is referred to as Improved Parallel and Distributed Apriori (IPDA). It is based on the scalable environment referred as Hadoop MapReduce, which was used to analyse Big Data. Through the generation of split-frequent data regionally and the early elimination of unusual data, the proposed work has its primary objective to reduce the enormous demands placed on available resources as well as the reduction of the overhead communication that occurs whenever frequent data are retrieved. The paper presents the results of tests, which demonstrate that the IPDA performs better than traditional apriori and parallel and distributed apriori in terms of the amount of time required, the number of rules created, and the various minimum support values.