Mining Problem Research Articles

Об одной новой схеме применения метода конечных элементов в горных науках

At the moment a large number of research papers are being published in the field of mining sciences, utilizing FEM (finite element method) in one way or another. The vast majority of these papers are devoted to solving various engineering problems in mining using different software products such as ABAQUS, PLAXIS, etc. It has been determined that almost all existing works in this field follow the typical scheme for the application of FEM. Materials and methods. The analysis showed that the typical scheme for the application of FEM in mining sciences contains a fundamental error related to the inadequate consideration of the representative volume of rocks during the construction of a continuous medium model and the formation of a discrete model. This leads to a significant degree of inaccuracy in the modeling results. Results and discussion. To resolve the fundamental error, a new scheme for the application of FEM in mining sciences is proposed. It differs from the typical scheme by introducing a new intermediate model between the rock mass and the continuum model. This model, named ‘rock mass body’, represents a set of geoparticles. The sizes of the geoparticles are determined according to the representative volume of the structural or textural components of the rock mass. Comparative analysis has shown that, unlike the typical scheme for the application of FEM, the use of the proposed scheme allows to get solutions with a high degree of accuracy when researching rocks with coarse-grained and medium-grained structures. Conclusion. An analysis of the typical scheme for the application of FEM identified a fundamental error in its use for rock mass modeling tasks. A new scheme has been proposed to correct this error. Suggestions for practical application and direction of future research. The research results can be used to improve the accuracy of finite element models of rock masses. Further research should focus on improving methods for determining the shape of finite elements in relation to the symmetry of the structural and textural components of the rock mass. At the same time, it is necessary to take into account the fracturing of rocks.

The origin, evolution and impacts of the mining-sceptical movement in Finland

The evolution of the Finnish mining-sceptical movement (MSM) is described and situated within the context of Finnish environmental protest waves (EPWs). This evolution occurred through the uranium debate (UD; since 2005) and mining debate (MD; 2010–ongoing). These stages are described and compared with the characteristics of the Finnish EPWs. The purpose is to determine whether the evolution of the MSM represents a new EPW in Finland.Five EPWs are reported to have occurred in Finland. After the end of the last EPW at the beginning of 2000s, the Finnish environmental movement lost its impetus, followed by a swell/latency. However, the climate change debate returned nuclear energy to the agenda. Together with the rebirth of the Finnish mining industry resulting from activities carried out by foreign companies, uranium exploration restarted in Finland in 2005, which raised opposition. The UD revitalised the environmental movement and opened spaces for local resistance in the countryside. Even though uranium exploration ended in Finland, the UD continued as attention shifted towards other projects associated with uranium. The environmental problems of the Talvivaara mine increased opposition and directed the MD towards projects also focused on other commodities. Because of its duration, impacts, and importance, the evolution of the MSM is suggested to form the sixth Finnish EPW.

An efficient strategy for mining high-efficiency itemsets in quantitative databases

The classic problems in itemset mining involve finding frequent itemsets and high-utility itemsets. However, frequent itemset mining has the disadvantage of not paying attention to the profit of products, while high-utility itemset mining does not address the issue of the cost price of the products. Therefore, neither can locate products with high-efficiency value on investment. To overcome these problems, the high-efficiency itemset mining (HEIM) problem was proposed. Despite its practicality, this issue has received little attention. The algorithms proposed to exploit high-efficiency itemsets (HEI) still use ineffective strategies on dense databases and unstrict upper bounds, requiring a lot of time and memory. To address the current issues with HEIM, the paper proposes tight upper bounds for the early pruning of candidates. Several techniques are also proposed, such as combining similar transactions and saving promising transaction locations, to reduce the cost of database scanning. Finally, the techniques are combined to propose a novel way to implement the MHEI (an efficient strategy for Mining High-Efficiency Itemsets in quantitative databases) to optimize the HEIM process. The experimental process also shows that the proposed algorithm has performance better than the state-of-the-art algorithm in HEIM.

HUSM: High utility subgraph mining in single graph databases

Frequent subgraph mining (FSM) is a crucial research area with diverse applications. However, traditional FSM treats all subgraphs as equally important. In practical applications, some subgraphs may be frequent but not very valuable, while others may be infrequent but highly valuable. To address this issue, we introduce utility pattern mining into single graph mining, enabling the discovery of meaningful patterns based on user's interest rather than relying solely on support. We define a utility function in single graph databases and formally define the problem of high utility subgraph mining (HUSM). In the face of several challenges posed by this new problem, such as the absence of downward closure property and a large number of candidates, we design several utility upper bounds that satisfy the downward closure property. We then develop a HUSM algorithm to efficiently mine all high utility subgraphs in single graph databases. Additionally, we design a lossless concise representation for high utility subgraphs, which has fewer instances than the total number of high utility subgraphs and can derive all of them, making the mining results more representative. The experimental results demonstrate that our method exhibits excellent performance, and the concise representation plays a significant role in simplifying the mining results.

On Reporting Durable Patterns in Temporal Proximity Graphs

Finding patterns in graphs is a fundamental problem in databases and data mining. In many applications, graphs are temporal and evolve over time, so we are interested in finding durable patterns, such as triangles and paths, which persist over a long time. While there has been work on finding durable simple patterns, existing algorithms do not have provable guarantees and run in strictly super-linear time. The paper leverages the observation that many graphs arising in practice are naturally proximity graphs or can be approximated as such, where nodes are embedded as points in some high-dimensional space, and two nodes are connected by an edge if they are close to each other. We work with an implicit representation of the proximity graph, where nodes are additionally annotated by time intervals, and design near-linear-time algorithms for finding (approximately) durable patterns above a given durability threshold. We also consider an interactive setting where a client experiments with different durability thresholds in a sequence of queries; we show how to compute incremental changes to result patterns efficiently in time near-linear to the size of the changes.

Are simple technical trading rules profitable in bitcoin markets?

This paper examines the profitability of simple technical trading rules in bitcoin markets comprehensively, by taking into account realistic investor behaviour and transaction costs, and data mining problems. Realistic investor behaviour is replicated by first employing 75,360 simple technical trading rules, divided over 6 commonly used trading rule classes and daily and intraday frequencies. Next, we select the best performing rules after transaction costs using a multiple hypothesis procedure. Finally, we form portfolios combining the selected rules and analyse their out-of-sample performance. We find that, especially risk–return wise, simple technical trading rules can outperform a buy-and-hold strategy in the bitcoin market out-of-sample.

High utility itemsets mining based on hybrid harris hawk optimization and beluga whale optimization algorithms

The commonly used high utility itemsets mining method for massive data is the intelligent optimization algorithm. In this paper, the WHO (Whale-Hawk Optimization) algorithm is proposed by integrating the harris hawk optimization (HHO) algorithm with the beluga whale optimization (BWO) algorithm. Additionally, a whale initialization strategy based on good point set is proposed. This strategy helps to guide the search in the initial phase and increase the diversity of the population, which in turn improve the convergence speed and algorithm performance. By applying this improved algorithm to the field of high utility itemsets mining, it provides new solutions to optimization problems and data mining problems. To evaluate the performance of the proposed WHO, a large number of experiments are conducted on six datasets, chess, connect, mushroom, accidents, foodmart, and retail, in terms of convergence, recall rates, and runtime. The experimental results show that the convergence of the proposed WHO is optimal in five datasets and has the shortest runtime in all datasets. Compared to PSO, AF, BA, and GA, the average recall rate in the six datasets increased by 32.13%, 49.95%, 12.15%, and 16.24%, respectively.

Analysis of the Application of Storage Resource Pool Technology of Inner Mongolia Power based on the Background of Big Data

With the continuous deepening of the Inner Mongolia Power Company's level of informatization and the continuous expansion of the scale of business and application system construction, the company will also enter the era of big data, and the amount of data will show an explosive growth trend. In order to solve the problems of massive data storage and data value mining faced by the enterprise during its development process, research on the application of storage resource pool technology has been conducted. Ultimately, the unified planning, allocation, and management of storage resources have been achieved, greatly improving the utilization efficiency of storage resources.

Application of support vector machine system introducing multiple submodels in data mining

As the information age develops, the scale and form of data become more diverse and diverse. Therefore, people need to use effective means to process information. For large-scale data mining problems, a clustering-based kernel matrix inner product filtering method is introduced to decompose the original quadratic programming problem into multiple sub-problems to support parallel training. And a Spark-based multiple submodels parallel support vector machine is proposed. By introducing open-source tools such as OpenCV, image feature extraction can be performed on large-scale video data. Finally, combined with the designed parallel support vector machine algorithm, video facial and expression recognition is carried out. These experiments confirmed that the research method achieved a maximum acceleration ratio of 2090 times when processing Covtype datasets. The research model could achieve an accuracy of over 99 %. Under the maximum data scale experiment, the research model improved prediction accuracy by 21 percentage points with an acceptable additional time cost of about 4 min only. Task parallel processing could more fully utilize cluster performance, increasing by approximately 3.5 m/s2 from 30 to 150 cores. The research model had the highest recognition accuracy for facial expressions, further demonstrating the effectiveness and superiority of this method. The research method has improved the efficiency of big data analysis and mining, and is of great significance in parallel analysis of video data.

Short‐term electric load forecasting based on empirical wavelet transform and temporal convolutional network

AbstractShort‐term load forecasting is the basis of power system operation and analysis and is of great significance for the stable operation of power systems. To solve the problems of insufficient mining of the intrinsic information of load data, randomness, and non‐linearity, and to improve the accuracy of load prediction, the authors propose a short‐term power load prediction model based on empirical wavelet transform (EWT) decomposition and the temporal convolutional network (TCN). First, the Pearson coefficient is used to eliminate the less relevant influencing factors to reduce the complexity of the model. Then, the EWT algorithm is used to decompose the original load signal to fully exploit the load data time domain and frequency domain information, while solving the problems of non‐linearity and randomness. Finally, the decomposed subsequences are input to the TCN network for prediction superposition to obtain the final results. The experimental results show that, compared with the single models TCN, gated recurrent units (GRU), and long short‐term memory (LSTM), and the hybrid models EWT‐GRU, EWT‐LSTM, and VMD‐TCN, the R2 of the short‐term power load forecasting model based on EWT‐TCN is improved by 0.2099%, 0.2519%, 0.3453%, 0.0334%, 0.1766%, and 0.1228%, respectively.

Domain-specific knowledge graph rule pattern mining based on generative adversarial networks

Most existing knowledge graphs (KGs) in specific domains suffer from problems of insufficient structural knowledge mining, superficial constraint of rules, incomplete system of rule patterns and higher error rate in the process of automated rule generation. In this paper, we present an adversarial generative approach for rule mining based on generative adversarial networks (GANs). The method firstly extracted a rule set according to a specific rule pattern defined manually, the rule set is then used as the adversarial training dataset for the GAN, That is, the discriminator determines whether a rule is true or not by learning the pattern of the rule set, and the generator tricks the discriminator by forging rules and improves according to the feedback from the generator.Finally, a generator is obtained to generate new rules that conform to the rule pattern, and a discriminator is obtained to determine the confidence of the automatically constructed triples.

Discovery of Actionable Patterns Through Scalable Vertical Data Split Method with Meta Actions and Information Granules

Action Rules are rule based systems that extract actionable patterns which are hidden in big volumes of data. Users need recommendations on actions they can undertake to increase their profit or accomplish their goals, this recommendations are provided by Actionable patterns. In the technological world of big data, massive amounts of data are collected by organizations, including in major domains like financial, medical, social media and Internet of Things(IoT). To analyze and store such a massive amount of data, distributed computing frameworks like Hadoop and Spark are introduced to store the big data in a distributed fashion which manage and analyze them in parallel. The traditional Action Rules extraction models, which analyze the data in a nondistributed fashion, do not perform well when dealing larger datasets. Serious complications of discovering Action Rules with such distributed environments are - data distribution among computing nodes and calculation of major parameters including : support, confidence, utility, and coverage, that represent the whole data. Information granules form basic entities in the world of Granular Computing(GrC), which represents meaningful smaller units derived from a larger complex information system. In this research, we focus on the data distribution phase of the distributed Actionable Pattern Mining problem. To handle the data distribution task by splitting the big data in both horizontal and vertical fashions - we propose partition threshold rho. In this work, we concentrate on using information granules to implement a vertical data splitting strategy with Meta Actions. Hence our results discover valuable Actionable Knowledge with application in Business and Education domains.

An Optimal Transport View for Subspace Clustering and Spectral Clustering

Clustering is one of the most fundamental problems in machine learning and data mining, and many algorithms have been proposed in the past decades. Among them, subspace clustering and spectral clustering are the most famous approaches. In this paper, we provide an explanation for subspace clustering and spectral clustering from the perspective of optimal transport. Optimal transport studies how to move samples from one distribution to another distribution with minimal transport cost, and has shown a powerful ability to extract geometric information. By considering a self optimal transport model with only one group of samples, we observe that both subspace clustering and spectral clustering can be explained in the framework of optimal transport, and the optimal transport matrix bridges the spaces of features and spectral embeddings. Inspired by this connection, we propose a spectral optimal transport barycenter model, which learns spectral embeddings by solving a barycenter problem equipped with an optimal transport discrepancy and guidance of data. Based on our proposed model, we take advantage of optimal transport to exploit both feature and metric information involved in data for learning coupled spectral embeddings and affinity matrix in a unified model. We develop an alternating optimization algorithm to solve the resultant problems, and conduct experiments in different settings to evaluate the performance of our proposed methods.

Spatio-Temporal Pivotal Graph Neural Networks for Traffic Flow Forecasting

Traffic flow forecasting is a classical spatio-temporal data mining problem with many real-world applications. Recently, various methods based on Graph Neural Networks (GNN) have been proposed for the problem and achieved impressive prediction performance. However, we argue that the majority of existing methods disregarding the importance of certain nodes (referred to as pivotal nodes) that naturally exhibit extensive connections with multiple other nodes. Predicting on pivotal nodes poses a challenge due to their complex spatio-temporal dependencies compared to other nodes. In this paper, we propose a novel GNN-based method called Spatio-Temporal Pivotal Graph Neural Networks (STPGNN) to address the above limitation. We introduce a pivotal node identification module for identifying pivotal nodes. We propose a novel pivotal graph convolution module, enabling precise capture of spatio-temporal dependencies centered around pivotal nodes. Moreover, we propose a parallel framework capable of extracting spatio-temporal traffic features on both pivotal and non-pivotal nodes. Experiments on seven real-world traffic datasets verify our proposed method's effectiveness and efficiency compared to state-of-the-art baselines.

Study on effect of magnetization on wetting characteristics of water with different ions to coal dust

Coal dust is a significant problem in underground coal mining, and more economical and effective methods are needed to control coal dust. Magnetized water dust removal technology is considered to be an effective method to solve the problem of high concentration dust. In order to further understand the role of ions in magnetic fields in water, the optimal magnetic field parameters for solutions containing various ions were determined by surface tension and contact angle measurements. The wettability experiments have shown that the optimal magnetization effect can be obtained by placing NaCl solution with the concentration of 0.04 mol/L in the 150mT magnetic field for 120 s at room temperature. Subsequently, the influence of various ions in water on the wettability of coal was investigated using a combination of FTIR testing and Zeta potential experiments. The results showed that the order of the influence intensity of different ions on the wettability of water was Na+ > Mg2+ > Ca2+ when the Cl– content was 0.04 mol/l, and Cl– > CO32– > SO42– when the Na+ content was 0.04 mol/L. Furthermore, Lorentz forces can influence various ions, accelerating their migration and enhancing their accessibility to water clusters. Different ions exert force on the dipoles within water molecules, which can promote or suppress hydrogen bond cleavage. When the number of hydrogen bond breaks rises, it leads to an increase in the wetting properties of the solution. The research findings provide a theoretical foundation for the use of magnetization technology in dust suppression with mist spray in underground coal mines.

DGAMDA: Predicting miRNA-disease association based on dynamic graph attention network.

MiRNA (microRNA)-disease association prediction has essential applications for early disease screening. The process of traditional biological experimental validation is both time-consuming and expensive. However, as artificial intelligence technology continues to advance, computational methods have become efficient tools for predicting miRNA-disease associations. These methods often rely on the combination of multiple sources of association data and require improved feature mining. This study proposes a dynamic graph attention-based association prediction model, DGAMDA, which combines feature mapping and dynamic graph attention mechanisms through feature mining on a single miRNA-disease association network. DGAMDA effectively solves the problems of feature heterogeneity and inadequate feature mining by previous static graph attention mechanisms and achieves high-precision feature mining and association scoring prediction. We conducted a five-fold cross-validation experiment and obtained the mean values of Accuracy, Precision, Recall, and F1-score, which were .8986, .8869, .9115, and .8984, respectively. Our proposed model outperforms other advanced models in terms of experimental results, demonstrating its effectiveness in feature mining and association prediction based on a single association network. In addition, our model can also be used to predict miRNAs associated with unknown diseases.

Know Thy Enemy and Know Yourself – The Role of Operational Data in Managing the Mines and Booby Trap Threat in Vietnam, 1965–73

Victim operated explosive devices (VOEDs) such as mines and booby traps, have been an enduring problem since their large-scale use started in the 1940s. While the overall problem is often known about in general terms, the real complexion of the problem was not necessarily fully appreciated. Eventually the need to understand the problem and the response to it was partially identified and acted upon in Vietnam through the collection and analysis of operational data. This did not solve the problem of mines and booby traps, but it did offer a means to better manage the threat.

Mining the High Dimensional Biological Dataset Using Optimized Colossal Pattern with Dimensionality Reduction

Recent years have seen a lot of attention paid to the mining of enormous item sets from high-dimensional databases. Small and mid-sized data sets take a long time to mine with traditional algorithms since they don’t include the complete and relevant info needed for decision making. Many applications, particularly in bioinformatics, benefit greatly from the extraction of (FCCI) Frequent Colossal Closed Itemsets from a large dataset. In order to extract FCCI from a dataset, present preprocessing strategies fail to remove all extraneous characteristics and rows from the data set completely. In addition, the most current algorithms for this kind are sequential and computationally expensive. A high-dimensional dataset is pruned of all extraneous characteristics and rows using two alternative dimensionality reduction strategies presented in this paper. Then, an optimal feature value is identified by using Equilibrium Optimizer (EO) to identify the threshold value for reduced features. It is designed to discover common items and build association rules if the feature value is smaller than the frequency mining algorithm (IFRS) in conjunction with the Fruit fly Algorithm (FFA). If the feature value exceeds the optimal threshold, then optimized Length restrictions can be used to solve the CP mining problem (LC). Random search is utilized to identify the optimal threshold values of the restrictions and extract the enormous pattern using the Differential Evolutionary Arithmetic Optimization Algorithm. The experiments are carried on twenty biological datasets that us extracted from UCI websites and validated the proposed models in terms of various metrics.

Partial pre-image attack on Proof-of-Work based blockchains

Blockchain is a type of distributed ledger technology that consists of a growing list of records, called blocks, that are securely linked together using cryptography. Each blockchain-based solution deploys a specific consensus algorithm that guarantees the consistency of the ledger over time. The most famous, and yet claimed to be the most secure, is the Proof-of-Work (PoW) consensus algorithm. In this paper, we revisit the fundamental calculations and assumptions of this algorithm, originally presented in the Bitcoin white paper. We break down its claimed calculations in order to better understand the underlying assumptions of the proposal. We also propose a novel formalization model of the PoW mining problem using the Birthday paradox. We utilize this model to formalize and analyze partial pre-image attacks on PoW-based blockchains, with formal analysis that confirms the experimental results and the previously proposed implications. We build on those analyses and propose new concepts for benchmarking the security of PoW-based systems, including Critical Difficulty and Critical Difficulty per given portion. Our calculations result in several important findings, including the profitability of launching partial pre-image attacks on PoW-based blockchains, once the mining puzzle difficulty reaches a given threshold. Specifically, for any compromised portion of the network (q<0.5; honest majority assumption still holds), the attack is formally proven profitable once the PoW mining puzzle difficulty reaches 56 leading zeros.

Choosing allowability boundaries for describing objects in subject areas

Anomaly detection is one of the most promising problems for study and can be used as independent units and preprocessing tools before solving any fundamental data mining problems. This article proposes a method for detecting specific errors with the involvement of experts from subject areas to fill knowledge. The proposed method about outliers hypothesizes that they locate closer to logical boundaries of intervals derived from pair features, and the interval ranges vary in different domains. We construct intervals leveraging pair feature values. While forming knowledge in a specific field, a domain specialist checks the logical allowability of objects based on the range of the intervals. If the objects are logical outliers, the specialist ignores or corrects them. We offer the general algorithm for the formation of the database based on the proposed method in the form of a pseudo-code, and we provide comparison results with existing methods.