A Cardinality Estimator in Complex Database Systems Based on TreeLSTM

Since it is one of the world's most significant financial markets, the foreign exchange (Forex) market has attracted a large number of investors. Accurately anticipating the forex trend has remained a popular but difficult issue to aid Forex traders' trading decisions. It is always a question of how precise a Forex prediction can be because of the market's tremendous complexity. The fast advancement of machine learning in recent decades has allowed artificial neural networks to be effectively adapted to several areas, including the Forex market. As a result, a slew of research articles aimed at improving the accuracy of currency forecasting has been released. The Long Short-Term Memory (LSTM) neural network, which is a special kind of artificial neural network developed exclusively for time series data analysis, is frequently used. Due to its high learning capacity, the LSTM neural network is increasingly being utilized to predict advanced Forex trading based on previous data. This model, on the other hand, can be improved by stacking it. The goal of this study is to choose a dataset using the Hurst exponent, then use a two-layer stacked Long Short-Term Memory (TLS-LSTM) neural network to forecast the trend and conduct a correlation analysis. The Hurst exponent (h) was used to determine the predictability of the Australian Dollar and United States Dollar (AUD/USD) dataset. TLS-LSTM algorithm is presented to improve the accuracy of Forex trend prediction of Australian Dollar and United States Dollar (AUD/USD). A correlation study was performed between the AUD/USD, the Euro and the Australian Dollar (EUR/AUD), and the Australian Dollar and the Japanese Yen (AUD/JPY) to see how AUD/USD movement affects EUR/AUD and AUD/JPY. The model was compared with Single-Layer Long Short-Term (SL-LSTM), Multilayer Perceptron (MLP), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Improved Firefly Algorithm Long Short-Term Memory. Based on the evaluation metrics Mean Square Error (MSE), Root Mean Square Error, and Mean Absolute Error, the suggested TLS-LSTM, whose data selection is based on the Hurst exponent (h) value of 0.6026, outperforms SL-LSTM, MLP, and CEEMDAN-IFALSTM. The correlation analysis conducted shows both positive and negative relations between AUD/USD, EUR/AUD, and AUD/JPY which means that a change in AUD/USD will affect EUR/AUD and AUD/JPY as recorded depending on the magnitude of the correlation coefficient (r).

Cardinality estimation is an important component of query optimization. Its accuracy and efficiency directly decide effect of query optimization. Traditional cardinality estimation strategy is based on original table or sample to collect statistics, then inferring cardinality by collected statistics. It will be low-efficiency when handling big data; Statistics exist update latency and are gotten by inferring, which can not guarantee correctness; Some strategies can get the actual cardinality by executing some subqueries, but they do not keep the result, leading to low efficiency of fetching statistics. Against these problems, this paper proposes a novel cardinality estimation strategy, called cardinality estimation based on query result(CEQR). For keeping correctness of cardinality, CEQR directly gets statistics from query results, which is not related with data size; we build a cardinality table to store the statistics of basic tables and middle results under specific predicates. Cardinality table can provide cardinality services for subsequent queries, and we build a suit of rules to maintain cardinality table; To improve the efficiency of fetching statistics, we introduce the source aware strategy, which hashes cardinality item to appropriate cache. This paper gives the adaptability and deviation analytic of CEQR, and proves that CEQR is more efficient than traditional cardinality estimation strategy by experiments.

Cardinality estimation plays a vital role in query optimizer, the key factors challenge its accuracy are join-crossing correlations between different attributes. Traditional estimation techniques provide poor estimation quality for complex queries with many joins for the lack of correlations. Recently, much work has shown that machine learning based methods overcome the challenge to a certain extent. However, the existing learning-based approaches have two major downsides. First, lack of explicit utilization of the effective and explainable feature: conjunctive predicates between different attributes. Second, the dynamic information associated with the dataset status is not used to encode the static query structure. In this paper, we propose GACE, a cardinality estimator based on Graph Attention Network (GAT) to capture join-crossing correlations between attributes from join graphs constructed on conjunctive predicates. GACE leverages the reliable features Base Table Selectivity and Join Selectivity obtained through traditional techniques to enrich the encoding of query structure and track the status alterations of dataset. Taking the regularity of join graphs into account, a GAT model with batch training supporting is implemented to sufficiently decrease the overhead of training. The results of our empirical evaluation on real-world dataset (IMDb) demonstrate that GACE achieves improvement in quality of cardinality estimation, especially for more joins.

Query optimizer is at the heart of the database systems. Cost-based optimizer studied in this paper is adopted in almost all current database systems. A cost-based optimizer introduces a plan enumeration algorithm to find a (sub)plan, and then uses a cost model to obtain the cost of that plan, and selects the plan with the lowest cost. In the cost model, cardinality, the number of tuples through an operator, plays a crucial role. Due to the inaccuracy in cardinality estimation, errors in cost model, and the huge plan space, the optimizer cannot find the optimal execution plan for a complex query in a reasonable time. In this paper, we first deeply study the causes behind the limitations above. Next, we review the techniques used to improve the quality of the three key components in the cost-based optimizer, cardinality estimation, cost model, and plan enumeration. We also provide our insights on the future directions for each of the above aspects.

Cardinality estimation is a crucial component in query optimizers. After decades of research, employing autoregressive models for cardinality estimation has demonstrated remarkable accuracy. However, when queries involve attributes with large domain sizes, autoregressive model-based estimators struggle to accurately capture the data distribution, leading to poor performance. Furthermore, these models often exhibit significant errors when handling queries with low-selectivity predicates. To address these challenges, we propose a self-adaptive cardinality estimator named AdaCard. Initially, we employ a self-adaptive smoothing factor selection strategy to variably adjust the original data, thereby mitigating the impact of large domain sizes. Secondly, to correct errors stemming from Monte Carlo sampling, we utilize resampling to refine the handling of low-selectivity predicates, thereby improving accuracy. Through evaluation using four real-world benchmarks, we compared AdaCard with mainstream baselines. The final results show that our estimator has the lowest tail estimation error and improves accuracy by nearly 10$\times $ over the second-best method, with similar latency and model size.

The ML-based inferential statistics framework presents a novel solution for database query optimization that addresses critical challenges in statistics maintenance and cardinality estimation. By combining Bayesian learning and reinforcement learning modules, the framework enables continuous adaptation to changing data patterns while minimizing computational overhead. The solution offers improved query performance through better plan selection, reduced resource consumption, and enhanced accuracy in cardinality estimation. The framework's dynamic histogram redistribution mechanism ensures optimal statistics maintenance in high-throughput environments, making it particularly effective for enterprise-scale databases with rapidly evolving data distributions.

As the critical task in query optimization, cardinality estimation is fulfilled by learning the joint probability distribution of table attributes in most of the existing data-driven methods. However, these methods still suffer the efficiency bottleneck, since the exponential combinations of table joins lead to massive probability inferences. To address this issue, we propose the Bayesian Probability Forest (BPF) framework to reduce the calculation of probability inferences. By integrating mutual information and query frequency, we first propose a novel metric to learn the graphical structure of BPF, ensuring that the distance between high-frequency attributes and root node could be minimized. Then, we propose an efficient method for cardinality estimation in BPF. Specifically, the decomposition of cardinality estimation task is proposed to reduce the number of probability inferences, and elimination strategies are given to avoid the redundant calculations w.r.t. low-frequency attributes. Empirical investigations on real-world datasets demonstrate the superiority of our method over other cutting-edge competitors. Our codes are publicly available at https://github.com/CardEstGroup/bpf-ce.git.

Structural Equation/Causal Models (SEMs/SCMs) are widely used in epidemiology and social sciences to identify and analyze the average causal effect (ACE) and conditional ACE (CACE). Traditional causal effect estimation methods such as Inverse Probability Weighting (IPW) and more recently Regression-With-Residuals (RWR) are widely used - as they avoid the challenging task of identifying the SCM parameters - to estimate ACE and CACE. However, much work remains before traditional estimation methods can be used for counterfactual inference, and for the benefit of Personalized Public Policy Analysis (P3A) in the social sciences. While doctors rely on personalized medicine to tailor treatments to patients in laboratory settings (relatively closed systems), P3A draws inspiration from such tailoring but adapts it for open social systems. In this article, we develop a method for counterfactual inference that we name causal-Graphical Normalizing Flow (c-GNF), facilitating P3A. A major advantage of c-GNF is that it suits the open system in which P3A is conducted. First, we show how c-GNF captures the underlying SCM without making any assumption about functional forms. This capturing capability is enabled by the deep neural networks that model the underlying SCM via observational data likelihood maximization using gradient descent. Second, we propose a novel dequantization trick to deal with discrete variables, which is a limitation of normalizing flows in general. Third, we demonstrate in experiments that c-GNF performs on-par with IPW and RWR in terms of bias and variance for estimating the ATE, when the true functional forms are known, and better when they are unknown. Fourth and most importantly, we conduct counterfactual inference with c-GNFs, demonstrating promising empirical performance. Because IPW and RWR, like other traditional methods, lack the capability of counterfactual inference, c-GNFs will likely play a major role in tailoring personalized treatment, facilitating P3A, optimizing social interventions - in contrast to the current `one-size-fits-all' approach of existing methods.

The state of charge (SOC) of a battery is a core component of battery management systems (BMS) and has become a key research focus because of its significant role in the development of clean energy. To address the limitations of traditional SOC estimation methods, this paper proposes a joint SOC estimation algorithm based on FFRLS-AEKF-LSTM. First, a second-order RC equivalent circuit model of the battery is established, using data from hybrid pulse power characterization (HPPC) tests as input. The model parameters are initially identified the forgetting factor recursive least squares (FFRLS) method. Then, the adaptive extended Kalman filter (AEKF) algorithm is employed to update and estimate the SOC iteratively. Finally, the parameters obtained from the AEKF, together with voltage and current data under HPPC conditions, are used to train a long short-term memory (LSTM) neural network to predict the SOC. Experimental results show that the proposed joint algorithm achieves a root mean square error (RMSE) and mean absolute error (MAE) of less than 1%, demonstrating excellent performance.

Accurate cardinality estimation is critically important to high-quality query optimization. It is well known that conventional cardinality estimation based on histograms or similar statistics may produce extremely poor estimates in a variety of situations, for example, queries with complex predicates, correlation among columns, or predicates containing user-defined functions. In this paper, we propose a new, general cardinality estimation technique that combines random sampling and materialized view technology to produce accurate estimates even in these situations. As a major innovation, we exploit feedback information from query execution and process control techniques to assure that estimates remain statistically valid when the underlying data changes. Experimental results based on a prototype implementation in Microsoft SQL Server demonstrate the practicality of the approach and illustrate the dramatic effects improved cardinality estimates may have.

Language models based on Long Short-Term Memory (LSTM) neural networks have been widely applied to automatic speech recognition, natural language processing research. However, current LSTMs are trained by employing the cross-entropy loss function, which only considers the target category without considering the competing categories during the training processes. Thus, current training methods cannot fully exploit the discriminative information provided by the data labels. To tackle this problem, we propose a Large Margin Long Short-Term Memory Neural Network (LMLSTM) model in this paper. Our model employs the large margin discriminative principle as a heuristic term to navigate the convergence process during training. Our model has improved the discriminative ability of the original LSTM while maintaining the capability when generating sequential data. Our proposed large margin term was tested on the Penn Treebank corpus language modelling task. Experimental results demonstrate that the proposed LMLSTM model outperforms current LSTM models in terms of accuracy and perplexity without increasing the depth.

Neural network-based parameter estimation of stochastic differential equations driven by Lévy noise

Economy and carbon emissions optimization of different countries or areas in the world using an improved Attention mechanism based long short term memory neural network

An ensemble deep learning model for the detection of Oral Squamous Cell Carcinoma (OSCC) using histopathological images. To improve the early detection and diagnosis of oral cancer through advanced image analysis techniques. The basic methods of the convolutional neural network (CNN) and long short-term memory (LSTM) neural network are combine the convolutional neural network (CNN) and long short-term memory (LSTM) neural network to design an open oral scoring model based on CNN + LSTM. An experimental environment is then built to preprocess the data, and finally the model built in this study is trained and simulated. Evaluation metrics demonstrate the effectiveness of the model in diagnosing OSCC with high precision, preprocess the data, offering a potential tool for clinical decision support. Final outcome an oral cancer data analysis based cancer detection for cancer or non-cancer classification.

In the realm of water resource management, meticulous monitoring and control methodologies are quintessential to the refinement of wastewater treatment processes. This research elucidates an avant-garde methodology for forecasting the Chemical Oxygen Demand (COD), an instrumental indicator of water quality, by harnessing the capabilities of long short-term memory (LSTM) neural networks in conjunction with Internet of Things (IoT) paradigms. The efficacy of the LSTM model is juxtaposed with that of an advanced Deep Belief Network (DBN), as well as contemporary models like a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) hybrid model and a Transformer-based model, employing data sourced from a wastewater treatment facility located in Changsha. The empirical findings show that notwithstanding the comparable training durations used, the LSTM model exhibits a preeminent error rate of merely 7%, thus surpassing the DBN model (which has an error rate of 35%), the CNN-LSTM model (registering a 22% error rate), and the Transformer-based model (with a 17% error rate) in its predictive precision. This research underscores the potential of integrating an astute wastewater control system with IoT and LSTM models, thereby hinting at prospective enhancements in the sustainability and operational efficacy of wastewater treatment installations.