PrefixSpan Algorithm Research Articles

Unlocking the secrets of daily app switching: a comprehensive guide to mastering both intra-app and inter-app search strategies

PurposeThe purpose of this research is to provide insights into the daily search strategies of users, which can inform the enhancement of search experiences across multiple applications. By understanding how users navigate and interact with different apps during their search processes, the study seeks to contribute to the design of more intuitive and user-friendly app systems.Design/methodology/approachThis study employs a mixed-methods approach to analyze users' daily search strategies in a natural cross-app interactive environment. Data collection was conducted using the Critical Incident Technique and the Micro-Moment Time Line, involving 204 participants to capture their real-time search experiences. Open coding techniques were utilized to categorize sequential search tactics, while the PrefixSpan algorithm was applied to identify patterns in frequently applied search strategies.FindingsThe study findings unveil a comprehensive framework that includes a variety of intra-app search tactics and inter-app switching tactics. Five predominant search strategies were identified: Iterative querying, Selective results adoption, Share-related, Recommended browsing, and Organizational results strategies. These strategies reflect the nuanced ways in which users engage with apps to fulfill their information needs.Originality/valueThis research represents a pioneering effort in systematically identifying and categorizing daily search strategies within a natural cross-app interaction context. It offers original contributions to the field by combining intra-app and inter-app tactics, providing a holistic view of user behavior. The implications of these findings are significant for app developers and designers, as they can leverage this knowledge to improve app functionality and user manuals, ultimately enhancing the overall search experience for users.

A Malicious Program Behavior Detection Model Based on API Call Sequences

To address the issue of low accuracy in detecting malicious program behaviors in new power system edge-side applications, we present a detection model based on API call sequences that combines rule matching and deep learning techniques in this paper. We first use the PrefixSpan algorithm to mine frequent API call sequences in different threads of the same program within a malicious program dataset to create a rule base for malicious behavior sequences. The API call sequences to be examined are then matched using the malicious behavior sequence matching model, and those that do not match are fed into the TextCNN deep learning detection model for additional detection. The two models collaborate to accomplish program behavior detection. Experimental results demonstrate that the proposed detection model can effectively identify malicious samples and discern malicious program behaviors.

Application of learning analytics for sequential patterns detection associated with moments of distraction in students in e‐learning platforms

AbstractOwing to the positive impact of technology on education, in recent years, e‐learning platforms have increased in popularity and implementation. However, different authors agree that, despite the benefits of using these platforms, one of the main problems is the distraction during the interaction between the student and the E‐Learning platform, since the students visit web resources as social networks, which is not related to their learning process. to identify patterns associated with moments of distraction that students can present on these platforms. Following the above, this study aims to apply Learning Analytics using the Density‐based spatial clustering of applications with noise (DBSCAN) clustering technique and the PrefixSpan algorithm, to identify frequent sequential patterns of interaction, before a distraction event of the student from their interaction with a virtual learning platform. From this, clustering with a Silhouette coefficient close to 0.75 is obtained, presenting good separation, high cohesion, and compaction in the generated groups. Additionally, a slight characterization of each group is achieved based on the most frequent sequential patterns of interaction. Finally, in this study, a successful integration between DBSCAN and PrefixSpan is achieved, to identify sequential interaction patterns associated with moments of distraction in E‐Learning platforms, which generates a starting point for later studies to make the sequential recommendation of educational resources from these patterns.

Mining Sequential Alarm Pattern Based on the Incremental Causality PrefixSpan Algorithm

Alarm flood is the main reason for the alarm system failure in the modern industry, so suppressing the subsequent alarms caused by anomalous propagation during the alarm flooding is an important issue. An incremental causality prefixSpan malgorithm is proposed to mine the frequent and sequence alarm patterns from the alarm flood, and dynamically update the old alarm mode from the new database. First, the alarm event is re-defined and the detection of alarm flood is formulated. The frequent alarm pattern with causality is expressed with the orderly and time delay features of sequential alarms. Under the new definition of alarm pattern, causality prefixSpan algorithm is proposed by introducing the time constraints, i.e., the causality. Furthermore, a new incremental mining strategy is co-implemented on the improved algorithm. When new alarm flooding data occurs, the old alarm modes are quickly updated without re-scanning the entire changing database. The incremental strategy makes the proposed method easily implemented in the real operation. The effectiveness of the proposed method is verified with the synthetic data sets and Tennessee-Sterman (TE) process.

Feature-based approach and sequential pattern mining to enhance quality of Indonesian automatic text summarization

Indonesian automatic text summarization research is developed rapidly. The quality, especially readability aspect, of text summary can be reached if the meaning of the text can be maintained properly. Therefore, this research aims to enhance the quality of extractive Indonesian automatic text summarization with considering the quality of structured representation of text. This research uses sequential pattern mining (SPM) to produce This research use SPM to produce sequence of words (SoW) as structured text representation using PrefixSpan algorithm. Then, SPM is combined with feature-based approach using sentence scoring method to produce summary. The experiment result using IndoSum dataset shows that even though the combination of SPM and sentence scoring can increase the precision value of recall-oriented understudy for gisting evaluation (ROUGE)-1, ROUGE-2, and ROUGE-L, from 0.68 to 0.76, 0.54 to 0.69, and 0.51 to 0.72. Especially, combination of SPM and Sentence Scoring can enhance precision, recall, and f-measure of ROUGE-L that consider the order of word occurance in measurement. SPM increases ROUGE-L f-measure value of sentence scoring from 0.32 to 0.36. Moreover, combination of sentence scoring and SPM is better than SumBasic that used as feature-based approach in the previous Indonesian text summarization research.

Characteristics, causes, and consequences of technical debt in the automation domain

Technical Debt (TD) is a significant concern in software development, particularly when interdisciplinary teams collaborate and interact. The goal of the study is to investigate TD causal chains and patterns in the industrial automation sector by analyzing 123 mechatronic TD incidents from 47 expert interviews across ten companies. Findings reveal that Requirements, Process, and Test TD are most common, while Build, Versioning, Manufacturing, Code, and Maintenance/Service TD are less frequent. Key causes include ”other priorities”, ”lack of time”, ”historically grown products”, ”lack of market analysis” and ”copy-paste-modify without revising tolerances.” The research identifies correlations between TD subtypes and causes/consequences in relation to company size, experts’ experience, and position, utilizing the Chi-square test and PrefixSpan algorithm. The study also maps the contagious character of TD using Neo4J graphical representation. This first in-depth analysis of TD causal chains in industrial automation contributes qualitatively to understanding TD patterns, helping researchers and practitioners assess TD contagiousness, comprehend its effects, prevent diffusion, and develop repayment strategies To the best of our knowledge, this study’s quantitative analysis approach provides the foundation that will enable future research identifying TD metrics and TD management in multidisciplinary engineering.

Frequent Alarm Pattern Mining of Industrial Alarm Flood Sequences by an Improved PrefixSpan Algorithm

Alarm systems are essential to the process safety and efficiency of complex industrial facilities. However, with the increasing size of plants and the growing complexity of industrial processes, alarm flooding is becoming a serious problem and posing challenges to alarm systems. Extracting alarm patterns from an alarm flood database can assist with an alarm root cause analysis, decision support, and the configuration of an alarm suppression model. However, due to the large size of the alarm database and the problem of sequence ambiguity in the alarm sequence, existing algorithms suffer from excessive computational overhead, incomplete alarm patterns, and redundant outputs. In order to solve these problems, we propose an alarm pattern extraction method based on the improved PrefixSpan algorithm. Firstly, a priority-based pre-matching strategy is proposed to cluster similar sequences in advance. Secondly, we improved PrefixSpan by considering timestamps to tolerate short-term order ambiguity in alarm flood sequences. Thirdly, an alarm pattern compression method is proposed for the further distillation of pattern information in order to output representative alarm patterns. Finally, we evaluated the effectiveness and applicability of the proposed method by using an alarm flood database from a real diesel hydrogenation unit.

Toward Better Structure and Constraint to Mine Negative Sequential Patterns.

Nonoccurring behavior (NOB) studies have attracted the growing attention of scholars as a crucial part of behavioral science. As an effective method to discover both NOB and occurring behaviors (OB), negative sequential pattern (NSP) mining is successfully used in analyzing medical treatment and abnormal behavior patterns. At this time, NSP mining is still an active and challenging research domain. Most of the algorithms are inefficient in practice. Briefly, the key weaknesses of NSP mining are: 1) an inefficient positive sequential pattern (PSP) mining process, 2) a strict constraint of negative containment, and 3) the lack of an effective Negative Sequential Candidate (NSC) generation method. To address these weaknesses, we propose a highly efficient algorithm with improved techniques, named sc-NSP, to mine NSP efficiently. We first propose an improved PrefixSpan algorithm in the PSP mining process, which connects to a bitmap storage structure instead of the original structure. Second, sc-NSP loosens the frequency constraint and exploits the NSC generation method of positive and negative sequential patterns mining (PNSP) (a classic NSP mining method). Furthermore, a novel pruning strategy is designed to reduce the computational complexity of sc-NSP. Finally, sc-NSP obtains the support of NSC by using the most efficient bitwise-based calculation operation. Theoretical analyses show that sc-NSP performs particularly well on data sets with a large number of elements and items in sequence. Comparison and extensive experiments along with case studies on health data show that sc-NSP is 10 times more efficient than other state-of-the-art methods, and the number of NSPs obtained is 5 times greater than other methods.

A hybrid recommender system using topic modeling and prefixspan algorithm in social media

AbstractRoute schema is difficult to plan for tourists, because they demand to pick points of interest (POI) in unknown areas that align with their preferences and limitations. This research proposes a novel personalized method for POI route recommendation that employs contextual data. The proposed approach enhances the existing methods by considering user preferences and multifaceted tourism contexts. Due to the sparsity of the data, the proposed method employs two-level clustering (DBSCAN based on the Manhattan distance) that reduces the time to discover POI. In specific, this approach utilizes the following: first, a topic pattern model is employed to discover the users’ attraction diffusion while improving the user–user similarity model using a novel asymmetric schema. Second, it has used explicit demographic information to alleviate the cold start issue, and third, it proposes a new strategy for assessing user preferences and also combined the context parameters in the form of a vector model with the Term Frequency Inverse Document Frequency technique to find contexts’ similarity. Furthermore, our framework discovers a list of optimal candidate trips by involving personalized POIs in sequential patterns’ mining (SPM); also, it used an adjusted forgotten function to involve the date context of each trip. Based on two datasets (Flickr and Gowalla), our methodology beats other prior approaches in F-score, RMSE, MAP, and NDCG factors in the experimental evaluation.

Semantic Trajectory Frequent Pattern Mining Method with Fuzzy Stay Time Constraint

In the security system, transforming a large number of collected target trajectories into semantic trajectories with a less volume and high quality and mining their frequent patterns are helpful to analyze the target behavior patterns, identify hazard sources, and enhance the internal prevention, and control of the security system. Aiming at the limitation of semantic trace frequent pattern mining method defined by precise stay time in practical application scenarios, a fuzzy semantic trace frequent pattern mining method is proposed. Firstly, the membership function of fuzzy stay time is defined, so the stay time of the target at the stay point is fuzzified, and the fuzzy semantic trajectory is obtained. Then, a fuzzy semantic trajectory frequent pattern mining algorithm FST-FPM (fuzzy semantic trajectory frequent pattern mining) is proposed. The FST-FPM algorithm is experimentally verified on the Geolife public dataset and the self-collected RFID positioning dataset. The experimental results show that FST-FPM algorithm can mine frequent patterns of fuzzy semantic trajectories on Geolife dataset and RFID positioning dataset, and the running time is reduced by more than 10% compared with classical PrefixSpan algorithm, PrefixSpan-x algorithm, and LFFT2 algorithm.

Incremental sequential patterns for multivariate temporal association rules mining

Most temporal association rule mining algorithms can mine the temporal relationship between items, but the sequential relations between successive items remain unknown, while sequential pattern mining algorithms can discover the sequential relationship between successive items but the quantitative time interval of sequential patterns remains unknown. Furthermore, they only focus on mining a historical temporal database, which ignores the fact that temporal databases are continually appended or updated. To address these problems, by integrating the advantages of these two algorithms, we extend the sequential pattern mining algorithm—PrefixSpan by introducing the concept of fuzzy temporal pattern and incremental learning, a new algorithm called Incremental sequential patterns for multivariate temporal association rules mining (ISPTAR) is proposed. First, the temporal transaction dataset obtained by fuzzy discretization of multivariate time series is converted into a temporal sequence dataset. Second, based on the PrefixSpan algorithm, the fuzzy temporal sequential patterns are mined by taking the valid time interval of the sequential pattern and the temporal relationship between items in the sequential pattern into account. Third, fuzzy temporal association rules can be constructed to mine the association between different attributes based on the mined fuzzy temporal sequential patterns. In addition, when new data are added, the proposed algorithm can update the sequential patterns without rescanning the historical database. We compare the ISPTAR algorithm with other sequential patterns mining and association rules mining algorithms on four real datasets from the UCI machine learning data repository. Experimental results demonstrate that ISPTAR, in most cases, outperformed the other algorithms in execution time and the number of generated rules. In addition, the rules generated by ISPTAR contain more temporal information, which is helpful to assist important decision-making.

Design of a prefixspan algorithm based on prefix position form

PrefixSpan algorithm based on sequence pattern is an association algorithm without generating candidate sequences and only needs to scan the original sequence database twice in the whole operation process. Compared with other sequence algorithms, the PrefixSpan algorithm is more efficient and fast. In this paper, PrefixSpan Base on Prefix Position Form, PPFPrefixSpan is proposed by studying the characteristics of the PrefixSpan algorithm and optimizing it based on it. , which solves the problem that the PrefixSpan algorithm produces too many duplicate projection databases in the process of operation, and optimates the PrefixSpan algorithm in space/time.

Multi-Objective Design of Profit Volumes and Closeness Ratings Using MBHS Optimizing Based on the PrefixSpan Mining Approach (PSMA) for Product Layout in Supermarkets

Product layout significantly impacts consumer demand for purchases in supermarkets. Product shelf renovation is a crucial process that can increase supermarket efficiency. The development of a sequential pattern mining algorithm for investigating the correlation patterns of product layouts, solving the numerous problems of shelf design, and the development of an algorithm that considers in-store purchase and shelf profit data with the goal of improving supermarket efficiency, and consequently profitability, were the goals of this research. The authors of this research developed two types of algorithms to enhance efficiency and reach the goals. The first was a PrefixSpan algorithm, which was used to optimize sequential pattern mining, known as the PrefixSpan mining approach. The second was a new multi-objective design that considered the objective functions of profit volumes and closeness rating using the mutation-based harmony search (MBHS) optimization algorithm, which was used to evaluate the performance of the first algorithm based on the PrefixSpan algorithm. The experimental results demonstrated that the PrefixSpan algorithm can determine correlation rules more efficiently and accurately ascertain correlation rules better than any other algorithms used in the study. Additionally, the authors found that MBHS with a new multi-objective design can effectively find the product layout in supermarket solutions. Finally, the proposed product layout algorithm was found to lead to higher profit volumes and closeness ratings than traditional shelf layouts, as well as to be more efficient than other algorithms.

Mining web content usage patterns of electronic commerce transactions for enhanced customer services

AbstractA successful business intelligence solution can help organizations improve the quality and speed of their decision‐making processes by analyzing the consolidated information collected from their websites. Using the current Web server log standard, which indicates only the locations of served Web pages, may lead to inaccurate business analysis for data driven and frequently updated static content Web pages. A properly defined Web content usage data warehouse that captures both dynamic and static contents of web pages provides rich data source for discovering interesting business rules among users' activities. This paper demonstrates the simplicity of data extract, transform and load procedures to import raw Web content usage log to various data models for data analysis, reporting and data mining tools. In this paper, we use two data mining techniques (expected maximization and the prefixspan algorithms) for visitor grouping and path analysis to find interesting patterns in Web content usage log data, an important component of e‐commerce Web sites traffic analysis. Visitor grouping uses data clustering to group visitors/sessions with similar selected attributes' value. Path analysis mines common visiting path sequence. The ultimate goal is to enable the online merchant provide enhanced and differentiated marketing services generally to its existing and potential customers and tell what items customers are interested in instead of ambiguous URLs.

Pattern Mining of Alarm Flood Sequences Using an Improved PrefixSpan Algorithm with Tolerance to Short-Term Order Ambiguity

The alarm system monitors industrial plants in real-time to ensure safe operation. The scale of modern plants is expanding rapidly, processes are becoming increasingly complicated, and the cost of alarm configuration in modern control systems is decreasing. However, alarm systems suffer from low performance. A large number of alarms are often indicated to operators within a short period, known as alarm floods. Analysis and mining of similar patterns among different alarm floods is an efficient approach. Alarm flood analysis facilitates cause analysis of historical flood data, thus locating poorly configured alarms and predicting incoming alarm floods. The strongly correlated alarms almost appear simultaneously, and the order is uncertain. A pattern containing strongly correlated alarms cannot be directly extracted according to the order of occurrence. In this paper, an improved PrefixSpan algorithm is proposed to identify similar patterns with a tolerance to short-term order ambiguity, which treats alarm floods as time-stamped sequences. The effectiveness of the proposed algorithm is verified with Tennessee Eastman process simulations.

Human behavioral pattern analysis-based anomaly detection system in residential space

Increasingly, research has analyzed human behavior in various fields. The fourth industrial revolution technology is very useful for analyzing human behavior. From the viewpoint of the residential space monitoring system, the life patterns in human living spaces vary widely, and it is very difficult to find abnormal situations. Therefore, this study proposes a living space-based monitoring system. The system includes the behavioral analysis of monitored subjects using a deep learning methodology, behavioral pattern derivation using the PrefixSpan algorithm, and the anomaly detection technique using sequence alignment. Objectivity was obtained through behavioral recognition using deep learning rather than subjective behavioral recording, and the time to derive a pattern was shortened using the PrefixSpan algorithm among sequential pattern algorithms. The proposed system provides personalized monitoring services by applying the methodology of other fields to human behavior. Thus, the system can be extended using another methodology or fourth industrial revolution technology.

Abnormal Event Correlation and Detection Based on Network Big Data Analysis

With the continuous development of network technology, various large-scale cyber-attacks continue to emerge. These attacks pose a severe threat to the security of systems, networks, and data. Therefore, how to mine attack patterns from massive data and detect attacks are urgent problems. In this paper, an approach for attack mining and detection is proposed that performs tasks of alarm correlation, false-positive elimination, attack mining, and attack prediction. Based on the idea of CluStream, the proposed approach implements a flow clustering method and a two-step algorithm that guarantees efficient streaming and clustering. The context of an alarm in the attack chain is analyzed and the LightGBM method is used to perform false-positive recognition with high accuracy. To accelerate the search for the filtered alarm sequence data to mine attack patterns, the PrefixSpan algorithm is also updated in the store strategy. The updated PrefixSpan increases the processing efficiency and achieves a better result than the original one in experiments. With Bayesian theory, the transition probability for the sequence pattern string is calculated and the alarm transition probability table constructed to draw the attack graph. Finally, a long-short-term memory network and embedding word-vector method are used to perform online prediction. Results of numerical experiments show that the method proposed in this paper has a strong practical value for attack detection and prediction.

Mining frequent pyramid patterns from time series transaction data with custom constraints

For the problem of mining pyramid scheme patterns, the traditional sequential pattern mining algorithm Prefixspan has many disadvantages such as poor timeliness, uniform threshold, etc. Therefore, we propose a timeliness variable threshold and increment Prefixspan algorithm, named TVI-Prefixspan, for mining the sequential patterns from time series transaction data. To be specific, TVI-Prefixspan aims to mine the patterns that co-occurrence in both an individual sequence and different sequences with high frequency. The most important challenges are how to define the thresholds of frequent one-item and pyramid patterns. We firstly analyze the attributes of the patterns which are hidden in the financial activities between different bank accounts. Secondly, the frequent threshold of each one-item is determined by its different frequency value in normal and pyramid related transaction sequences. We also consider the special relationships in both numerical values and time-series aspects between each pattern’s item. Therefore, TVI-Prefixspan produces the frequent one-item set based on its difference of the normal frequency, and then, mines the pyramid patterns with formulated relation constraints. For describing the correlation, we consider sequential, time interval and one-off constraints simultaneously. The experimental results, in real financial data containing pyramid transactions, show that TVI-Prefixspan algorithm succeeds in mining pyramid scheme patterns quickly and effectively. It is superior to traditional sequential pattern mining algorithms such as Prefixspan in efficiency and mining effect.

Research on Massive News Events Evolution Prediction Based on Improved PrefixSpan Algorithm

The Internet reports massive news event every day. Sequential pattern mining is adopted to study the timing relationship between news event, which can provide reference for the development or prediction of news events. With its advantages in performance and efficiency, PrefixSpan often becomes preferred algorithm in the field of sequence pattern mining. But unfortunately, because of the large amount of news and the long time span between some news events, the event sequences is long and dense, which results many subsequences in the frequent pattern and reduces the algorithm performance. In this paper, we propose an improved PrefixSpan algorithm by integrate subsequences of the supersequences in the frequent pattern and introduce news event weight in the pruning step of the PrefixSpan algorithm. Experiment results show that without affecting expression ability of frequent patterns, the improved PrefixSpan algorithm increases the efficiency by about 20 times as before and eliminates about 10% of the redundant subsequences to get a more concise frequent patterns set.

Mining Evolution Patterns from Complex Trajectory Structures—A Case Study of Mesoscale Eddies in the South China Sea

Real-word phenomena, such as ocean eddies and clouds, tend to split and merge while they are moving around within a space. Their trajectories usually bear one or more branches and are accordingly defined as complex trajectories in this study. The trajectories may show significant spatiotemporal variations in terms of their structures and some of them may be more prominent than the others. The identification of prominent structures in the complex trajectories of such real-world phenomena could better reveal their evolution processes and even shed new light on the driving factors behind them. Methods have been proposed for the extraction of periodic patterns from simple trajectories (i.e., those with linear structure and without any branches) with a focus on mining the related temporal, spatial or semantic information. Unfortunately, it is not appropriate to directly use such methods to examine complex trajectories. This study proposes a novel method to study the periodic patterns of complex trajectories by considering the inherent spatial, temporal and topological information. First, we use a sequence of symbols to represent the various structures of a complex trajectory over its lifespan. We then, on the basis of the PrefixSpan algorithm, propose a periodic pattern mining of structural evolution (PPSE) algorithm and use it to identify the largest and most frequent patterns (LFPs) from the symbol sequence. We also identify potential periodic behaviors. The PPSE method is then used to examine the complex trajectories of the mesoscale eddy in the South China Sea (SCS) from 1993 to 2016. The complex trajectories of ocean eddies in the southeast of Vietnam show are different from other regions in the SCS in terms of their structural evolution processes, as indicated by the LFPs with the longest lifespan, the widest active range, the highest complexity, and the most active behaviors. The LFP in the southeast of Vietnam has the longest lifespan, the widest active range, the highest complexity, and the most active behaviors. Across the SCS, we found seven migration channels. The LFPs of the eddies that migrate through these channels have a temporal cycle of 17–24 years. These channels are also the regions where eddies frequently emerge, as revealed by flow field data.