Path Mining Research Articles

An Interpretable Multi-Model Machine Learning Approach for Spatial Mapping of Deep-Sea Polymetallic Nodule Occurrences

High-resolution mapping of deep-sea polymetallic nodules is needed (a) to understand the reasons behind their patchy distribution, (b) to associate nodule coverage with benthic fauna occurrences, and (c) to enable an accurate resource estimation and mining path planning. This study used an autonomous underwater vehicle to map 37 km2 of a geomorphologically complex site in the Eastern Clarion–Clipperton Fracture Zone. A multibeam echosounder system (MBES) at 400 kHz and a side scan sonar at 230 kHz were used to investigate the nodule backscatter response. More than 30,000 seafloor images were analyzed to obtain the nodule coverage and train five machine learning (ML) algorithms: generalized linear models, generalized additive models, support vector machines, random forests (RFs) and neural networks (NNs). All models ML yielded similar maps of nodule coverage with differences occurring in the range of predicted values, particularly at parts with irregular topography. RFs had the best fit and NNs had the worst spatial transferability. Attention was given to the interpretability of model outputs using variable importance ranking across all models, partial dependence plots and domain knowledge. The nodule coverage is higher on relatively flat seafloor ( < 3°) with eastward-facing slopes. The most important predictor was the MBES backscatter, particularly from incident angles between 25 and 55°. Bathymetry, slope, and slope orientation were important geomorphological predictors. For the first time, at a water depth of 4500 m, orthophoto-mosaics and image-derived digital elevation models with 2-mm and 5-mm spatial resolutions supported the geomorphological analysis, interpretation of polymetallic nodules occurrences, and backscatter response.

A Novel Personalized Learning Path Mining Based on Steiner Tree and Knowledge Graph

Modern society has accumulated great amounts of knowledge since human start learning and communicating. With knowledge’s continuously thriving and getting refined, knowledge itself has developed into a complex system with a huge number of content and structure features. Together with the development of science and technology, the ways of people learning have been changed greatly. Under the pressure of high-paced life and workplace competition, more and more people are in demand of personalized learning paths, in order to achieve their learning target as fast as possible. A personized and scientific learning path is the key to effective learning grogram. However, studies on algorithms of optimal learning path discovery are not comprehensively developed, especially on its performance and application. Therefore, in this article, we propose a new algorithm, Portfolio ST, which is improved from Steiner Tree, for personalized and optimal learning path mining. At first, this paper constructs the knowledge graph of finance, then builds learner models by profiling learners’ owned and aimed knowledge based on the knowledge network. At last, according to the knowledge structure of a person’s learner model, the most personized and shortest learning path is calculated by the Portfolio ST. After the experiment compared with two baseline algorithms, the Portfolio ST algorithm is proved to have the best performance and scalability, by changing owned/aimed/overall knowledge respectively, which is also the best algorithm of learning path mining that can achieve the global optimal among these three algorithms.

MMiKG: a knowledge graph-based platform for path mining of microbiota-mental diseases interactions.

The microbiota-gut-brain axis denotes a two-way system of interactions between the gut and the brain, comprising three key components: (1) gut microbiota, (2) intermediates and (3) mental ailments. These constituents communicate with one another to induce changes in the host's mood, cognition and demeanor. Knowledge concerning the regulation of the host central nervous system by gut microbiota is fragmented and mostly confined to disorganized or semi-structured unrestricted texts. Such a format hinders the exploration and comprehension of unknown territories or the further advancement of artificial intelligence systems. Hence, we collated crucial information by scrutinizing an extensive body of literature, amalgamated the extant knowledge of the microbiota-gut-brain axis and depicted it in the form of a knowledge graph named MMiKG, which can be visualized on the GraphXR platform and the Neo4j database, correspondingly. By merging various associated resources and deducing prospective connections between gut microbiota and the central nervous system through MMiKG, users can acquire a more comprehensive perception of the pathogenesis of mental disorders and generate novel insights for advancing therapeutic measures. As a free and open-source platform, MMiKG can be accessed at http://yangbiolab.cn:8501/ with no login requirement.

Reinforcing path marginalization: revealing the unaccounted labour organization at a mining frontier in Indonesia

Abstract How could we conceptualize path development taking place in regions dominated by extracting industries and subsistence economies? The article expands existing Evolutionary Economic Geography theory by focussing on extractive economies in a peripheral region that has, thus far, barely been covered. It challenges existing conceptualizations by highlighting a tin producing island in Indonesia. The article introduces the concept of path marginalization and its reinforcing mechanisms that explain the dynamics between large and artisanal mining activities. It examines how the mining path is reproduced by the interaction between multiple forms of labour organization that are normally unaccounted for.

Second Life of Post-Mining Facilities: Mines as a Tourist Attraction of Southern Poland

The aim of this article is to present a tourist mining path located in southern Poland. This involves coal, silver, gold, salt, flint, chalk, uranium mines and oil wells. It includes some buildings connected with workers and mine-owners as well. These buildings were previously tied to mining. Currently, they function as museums or service-buildings.

Interaction mechanism of BIM application barriers in prefabricated construction and driving strategies from stakeholders’ perspectives

Although building information modeling application in prefabricated construction (PC-BIM) has become an inevitable trend, current PC-BIM is far from ideal. There are some researches on PC-BIM barriers, however, which stakeholder these barriers come from, what influences they have on each other and how to find the effective transmission paths have not been explored yet which is essential to overcome barriers effectively. Thus, this study aims to make deep analysis of PC-BIM barriers based on stakeholder’s perspectives. After literature review and focus group interviews with 13 authoritative experts, the barrier network based on stakeholders was established and analyzed. With the social network analysis, the critical barriers, stakeholders, together with their interaction relationships and the transmission paths were found and corresponding strategies based on path mining are proposed. This study is of great significance to promote PC-BIM in practice and shed light on the interaction mechanism analysis and driving path mining.

Retracted: Big Data-Driven Vocational Undergraduate Talent Training and Social Needs Adaptability Analysis and Collaborative Path Mining.

[This retracts the article DOI: 10.1155/2022/8476412.].

Big Data-Driven Vocational Undergraduate Talent Training and Social Needs Adaptability Analysis and Collaborative Path Mining.

Aiming at the problem of mismatch between talent cultivation and social demand in the process of undergraduate education, this paper proposes a big data-driven method of adaptability analysis and collaborative path mining between vocational undergraduate talent cultivation and social demand. Starting from the big data-driven vocational undergraduate talent training and social needs, this paper points out the problems existing in the current social needs and puts forward the basic framework of vocational undergraduate talent training mode. Secondly, the clustering model of talent training and social demand is analyzed, and the clustering mining method is proposed. Finally, the big data-driven personnel training and social adaptation mining analysis, in their own ability and social needs adaptability analysis, the basic adaptation accounted for a higher proportion. Professional competence has a higher trust value in cluster analysis. Today's social employment situation is becoming more and more severe, and how to enhance the quality of profession undergraduate students has become one of the theoretical and practical issues worthy of attention in China's colleges at this stage. The talent training model of colleges and universities is closely in route with the demands of society, and the problem of student hire is prominent. Therefore, this paper proposes a student employability training program that combines the elements of student employability through social needs research.

FPIRPQ: Accelerating regular path queries on knowledge graphs

With the growing popularity and application of knowledge-based artificial intelligence, the scale of knowledge graph data is dramatically increasing. As an essential type of query for RDF graphs, Regular Path Queries (RPQs) have attracted increasing research efforts, which explore RDF graphs in a navigational manner. Moreover, path indexes have proven successful for semi-structured data management. However, few techniques can be used effectively in practice for processing RPQ on large-scale knowledge graphs. In this paper, we propose a novel indexing solution named FPIRPQ (Frequent Path Index for Regular Path Queries) by leveraging Frequent Path Mining (FPM). Unlike the existing approaches to RPQs processing, FPIRPQ takes advantage of frequent paths, which are statistically derived from the data to accelerate RPQs. Furthermore, since there is no explicit benchmark targeted for RPQs over RDF graph yet, we design a micro-benchmark including 12 basic queries over synthetic and real-world datasets. The experimental results illustrate that FPIRPQ improves the query efficiency by up to orders of magnitude compared to the state-of-the-art RDF storage engine.

Accident causes data-driven coal and gas outburst accidents prevention: Application of data mining and machine learning in accident path mining and accident case-based deduction

Analyzing the causes of accidents, excavating accident paths, and applying accident prevention are important tasks in safety management. Focusing on coal and gas outburst accidents, this study examined the primary accident path and conducted applied research on the reasoning of the accident case. First, combined with the obtained accident causes, a coupling analysis of the causes of coal and gas outburst accidents was conducted. Second, using the method of data mining coupled with Apriori algorithm, the coupling relationship between each cause module of the coal and gas outburst accident was obtained, and consequently, a path map of the coal and gas outburst accident was drawn. Third, a Bayesian network model for the causes of coal and gas outburst accidents was established based on the accident path map and the probability of occurrence of each cause. Finally, considering the safety concept element (SC1) as an example, the Bayesian network model was used to conduct a sensitivity analysis of accident causes. Thereafter, considering the coal and gas outburst accident of the Sanjia Coal Mine in Guizhou Province as an example, probabilistic reasoning research on the cause of the accident was conducted. The application results showed that (1) under normal conditions, there are approximately 797,280 accident paths for coal and gas outbursts. Following data mining, 188 main accident paths were found. (2) Sensitivity analysis determined 19 factors that were sensitive to safety concept elements (SC1), of which the three most sensitive factors were (i) resource management system procedures (SM7), (ii) safety policy (SM1), and (iii) safety training system procedure (SM8). 13 paths exhibited a sensitivity ≥0.5%, of which 7 exhibited strong sensitivity. (3) The absolute accuracy rate of accident cause reasoning in the Sanjia Coal Mine in Guizhou Province was 71.43%, while the relative accuracy rate was close to 100%. Thus, it was concluded that: (1) the accident path mining method proposed in this paper is feasible for main accident path mining. (2) The Bayesian network model for the causes of coal and gas outburst accidents established in this study can be practically applied for the sensitivity analysis of accident causes and exhibits high reliability in the probabilistic reasoning of accident causes. The results of this study is expected to aid in the prevention of coal and gas outburst accidents, and provide reference and help for the path mining of other accident causes and the probabilistic reasoning of accident causes.

Challenging the set mining path: Agency and diversification in the case of Kiruna

The paper follows the development of the tourism industry in a small, traditional mining town in northern Sweden. It highlights local agency in the diversification process, as well as interpath relations between tourism and mining. Drawing on 21 semi-structured interviews, the paper finds that the two seemingly unrelated paths share the need for a variety of local endowments and in so the dominant mining industry both compete with and support the growing tourism industry. The paper further finds that both private and public actors can exercise change agency, in a region dominated by reproductive agency, and that change agency can widen the room for further actors to exercise change agency.

Learning Dynamic User Interest Sequence in Knowledge Graphs for Click-Through Rate Prediction

Despite that path-based and embedding-based models with knowledge graphs (KGs) achieve better recommendation performance compared with other deep learning based methods, such improvement is limited due to a lack of modeling user's dynamic interest. To address this issue, we explore a principled model to provide semantic understanding of each item in user's historical interest sequence in KGs. Specifically, we propose a multi-granularity dynamic interest sequence learning method, which is based on knowledge-enhanced path mining and interest fluctuation signal discovery, to obtain semantic-enhanced paths. Furthermore, the paths are embedded by the SEP2Vec, and merged through the proposed entropy-aware pooling layer to obtain the user preference representation, which is then used to learn dynamic user interest sequence. Experimental results on two public datasets of movie and music recommendation, and two industrial datasets of personalized local service recommendation in Alipay App have illustrated that the proposed model can achieve significantly better prediction performance compared with other known baselines.

Research and Application of Critical Failure Paths Identification Method for Dam Risk Analysis

Dam is an important part of the national infrastructure, and its safety has been widely concerned. Risk identification of dams plays a significant role in risk assessment and control. Finding out some critical failure paths through adopting timely measures can help reduce the risk occurrence probability effectively. This paper develops an identification method based on the credibility and the interval analytic hierarchy process (IAHP) methods, namely, consistency and difference-based interval analytic hierarchy process (CDB-IAHP) method, to identify the critical failure paths of dams exactly considering the dynamic cognition degree of decision-makers. Based on the fault tree analysis (FTA) method, the framework and analysis for critical failure paths identification of a gravity dam and an Earth-rockfill dam are conducted and made. The results show that the critical failure paths obtained by the proposed method are in line with the statistical data, and the importance of disaster causing factors has some difference with the traditional method. Additionally, some engineering and nonengineering measures are suggested to reduce the impact of potential failure paths. The applications demonstrate that the proposed method shows good applicability for risk analysis and critical failure path mining of dams.

Efficient Path Query Processing Over Massive Trajectories on the Cloud

A path query aims to find trajectories passing a given sequence of connected road segments within a time period. It is very useful in many urban applications: 1) traffic modeling, 2) frequent path mining, 3) intersection coordination, and 4) traffic anomaly detection. Existing solutions for path query processing are implemented based on single machines, which are not efficient for the following tasks: 1) indexing large-scale historical data; 2) handling real-time trajectory updates; and 3) processing concurrent path queries from urban data mining applications. In this paper, we design and implement a cloud-based path query processing framework based on Microsoft Azure. We modify existing suffix tree structure to index trajectories using Azure Table. The proposed system consists of two main parts: 1) back-end processing , which performs pre-processing (i.e., parsing and map-matching) and index building tasks with a distributed computing platform (i.e., Storm) used to efficiently handle massive real-time trajectory updates; and 2) query processing , which answers path queries using Azure Storm to improve efficiency and overcome I/O bottleneck. Extensive experiments are performed based on the real-time taxi trajectories from Guiyang City, the capital of Guizhou Province, China to confirm the system efficiency. We also demonstrate a real deployed traffic analysis system based on our query processing framework.

Social recommendation model based on user interaction in complex social networks.

The user interaction in online social networks can not only reveal the social relationships among users in e-commerce systems, but also imply the social preferences of a target user for recommendation services. However, the current research has rarely explored the impact of social interaction on recommendation performance, especially now that recommender systems face increasing challenges and suffer from poor efficiency due to social data overload. Therefore, applied research on user interaction has become increasingly necessary in the field of social recommendation. In this paper, we develop a novel social recommendation method based on the user interaction in complex social networks, called the SRUI model, to present a basis for improving the efficiency of the recommender systems. Specifically, a weighted social interaction network is first mapped to represent the interactions among social users according to the gathered information about historical user behavior. Thereafter, the complete path set is mined by the complete path mining (CPM) algorithm to find social similar neighbors with tastes similar to those of the target user. Finally, the social similar tendencies of the users on the complete paths are obtained to predict the final ratings of items through the SRUI model. A series of experimental results based on two real public datasets show that our approach performs better than other state-of-the-art methods in terms of recommendation performance.

A Survey on Bio Inspired Algorithms An Efficient Approach for Frequent Path Mining

A Survey on Bio Inspired Algorithms An Efficient Approach for Frequent Path Mining

To make the travel healthier: a new tourism personalized route recommendation algorithm

Travel route planning is an important part of traveler’s tourism planning to make the travel more healthier and to improve the tourists’ satisfying and wellbeing.With the continuous accumulation of network resources, tourists are faced with the problem of information overload when they are planning the travel route. In this work, a new method of travel path mining is proposed, which takes the topic hierarchy of scenic spots and the features of scenic spots into consideration. First of all, the travel text data is captured by the network crawler software, and after the preprocessing of the data, such as word segmenting and denoising, the standardized travel data set is formed. Then, according to the location information of hot scenic spots in the travel notes, carry out the topic stratification of the scenic spots and construct the travel path data set. Finally, the travel route is excavated by taking into account the topic hierarchy and the features of a scenic spot. The experimental results show that the proposed method can effectively extract the travel route from the mass texts of travel notes.

The verification of displacement methane effect caused by hydraulic fracturing in gassy coal seams

During hydraulic fracturing in gassy coal seams, the gas concentration in mining path ways is found to increase significantly. This phenomenon should be the displacement methane effect caused by hydraulic fracturing. Does this effect exist objectively? To this end, laboratory and field verification experiments were carried out. An experimental system integrated with true triaxial hydraulic fracturing, seepage, and displacement gas was developed. The largest sample size was 500 × 500 × 500 mm3. Proper sealing was assured in the experimental system, and the effects of coal bed methane were simulated effectively. Methane at a specific pressure was injected into a sealed coal sample. After pressure stabilization and the methane adsorption reached its equilibrium level, the high-pressure water was injected into the coal sample from the surface. Absorbed methane in the coal sample was displaced from the bottom of the coal by water pressure seepage. After the conduction of deep borehole hydraulic fracturing in a high gassy coal seam, the gas was displaced inward and outward from the main fracture section. The permeability, diffusion, and transfer of the gas resulted in a region of increased methane content in both sides of the main fracture section. And the methane content in the main fracture section was decreased. Along the length of the borehole, the methane content changed significantly. The existence of displaced methane caused by hydraulic fracturing in gassy coal seams was first verified by laboratory experiments and then field tests. The pore-pressure gradient provides power for driving methane by hydraulic fracturing. The amount of desorbed methane resulted from the competitive adsorption of water and methane is more than that of the absorbed methane resulted from increased methane pressure, which provides material guarantee for displacing methane by hydraulic fracturing. The displacement methane caused by hydraulic fracturing in gassy coal seams was also found to be time dependent.

Deformation, Permeability and Acoustic Emission Characteristics of Coal Masses under Mining-Induced Stress Paths

The geomechanical and seepage evolution characteristics of coal masses during mining are the key factors that affect the drainage of coalbed methane and the safety of coal mining. Nevertheless, the influence of mining paths on coal seam permeability is rarely investigated given the complexity of mining-induced stress experiments. To study the effect of mining-induced stress on coal mining, the mechanical properties, acoustic emission characteristics and energy evolution of coal masses were experimentally evaluated through mining-induced stress experiments. Experimental results indicated that at peak intensity, the deviatoric stress and axial strain of coal samples under the stress path of protective coal-seam mining are lower than those of coal samples under the non-pillar stress path. The unloading ratio of confining pressure is large under a stress path of non-pillar mining, and the elastic energy, the absorbed energy, and the dissipated energy of coal mass are low during destruction. The effect of high confining pressure on AE events is pronounced under the non-pillar mining path. The overall b value under high confining pressure is smaller than that under low confining pressure, and AE events generally have high energy. The fracture structure of coal mass is complex, and the fractal size of coal is large under high unloading rates of confining pressure, which induce the increase of permeability after coal destruction.

A Fusion of Multiagent Functionalities for Effective Intrusion Detection System

Provision of high security is one of the active research areas in the network applications. The failure in the centralized system based on the attacks provides less protection. Besides, the lack of update of new attacks arrival leads to the minimum accuracy of detection. The major focus of this paper is to improve the detection performance through the adaptive update of attacking information to the database. We propose an Adaptive Rule-Based Multiagent Intrusion Detection System (ARMA-IDS) to detect the anomalies in the real-time datasets such as KDD and SCADA. Besides, the feedback loop provides the necessary update of attacks in the database that leads to the improvement in the detection accuracy. The combination of the rules and responsibilities for multiagents effectively detects the anomaly behavior, misuse of response, or relay reports of gas/water pipeline data in KDD and SCADA, respectively. The comparative analysis of the proposed ARMA-IDS with the various existing path mining methods, namely, random forest, JRip, a combination of AdaBoost/JRip, and common path mining on the SCADA dataset conveys that the effectiveness of the proposed ARMA-IDS in the real-time fault monitoring. Moreover, the proposed ARMA-IDS offers the higher detection rate in the SCADA and KDD cup 1999 datasets.