bluuebunny/crossref_metadata_2025 huggingface dataset:
A Four-Grade Astrocytoma Classification System with Optimal Feature Selection Using Ant Colony Optimization Algorithm
Authors: George Nimmy , Manuel Manju
Published: May 2024

An efficient Four-Grade Astrocytoma brain tumor classification is proposed in this research work. The work highlights the optimal feature selection using a hybrid combination of Ant Colony Optimization (ACO) algorithm and machine learning techniques, leading to better classification accuracy. A diverse set of features including spatial and frequency domain features will provide better discrimination of patterns. ACO provides a robust and efficient mechanism for exploring the vast space of possible feature subsets. A total of 135 features are extracted including Tamura features, Gabor Wavelet Features, Coiflet Wavelet Coefficients, Speeded Up Robust Features, and GLCM features. Ant Colony Optimization is used to find out the optimum set of features. The selected feature set is used for training a Decision Tree classifier, where the Astrocytoma MRI images are categorized into four grades of astrocyoma namely, pilocytic astrocytoma, diffuse astrocytoma, anaplastic astrocytoma and glioblastoma multiforne. Classification is also performed using KNN classifier, SVM classifier and Ensemble Bagged Tree Classifier. Performance metrics like accuracy, sensitivity, specificity, precision and FScore are utilized to evaluate the proposed system’s performance. It is found out that the decision tree classifier outperforms the other classifiers. Thus this integrated approach of ACO with machine learning classifiers results in improved Astrocytoma classification performance aiding clinicians in making accurate treatment decisions.
Homogenous Ensembles of Neuro-Fuzzy Classifiers using Hyperparameter Tuning for Medical Data
Authors: Ouifak Hafsaa , Afkhkhar Zaineb , Manzi Alain Thierry Iliho , Idri Ali
Published: May 2024

Neuro-fuzzy techniques have been widely used in many applications due to their ability to generate interpretable fuzzy rules. Ensemble learning, on the other hand, is an emerging paradigm in artificial intelligence used to improve performance results by combining multiple single learners. This paper aims to develop and evaluate a set of homogeneous ensembles over four medical datasets using hyperparameter tuning of four neuro-fuzzy systems: adaptive neuro-fuzzy inference system (ANFIS), Dynamic evolving neuro-fuzzy system (DENFIS), Hybrid fuzzy inference system (HyFIS), and neuro-fuzzy classifier (NEFCLASS). To address the interpretability challenges and to reduce the complexity of high-dimensional data, the information gain filter was used to identify the most relevant features. After that, the performance of the neuro-fuzzy single learners and ensembles was evaluated using four performance metrics: accuracy, precision, recall, and f1 score. To decide which single learners/ensembles perform better, the Scott-Knott and Borda count techniques were used. The Scott-Knott first groups the models based on the accuracy to find the classifiers appearing in the best cluster, while the Borda count ranks the models based on all the four performance metrics without favoring any of the metrics. Results showed that: (1) The number of the combined single learners positively impacts the performance of the ensembles, (2) Single neuro-fuzzy classifiers demonstrate better or similar performance to the ensembles, but the ensembles still provide better stability of predictions, and (3) Among the ensembles of different models, ANFIS provided the best ensemble results.
An OWA Based MCDM Framework for Analyzing Multidimensional Twitter Data: A Case Study on the Citizen-Government Engagement During COVID-19
Authors: Gupta Ankit , Singh Sarabjeet , Rana Harmesh , Prashar Vinay Kumar , Yadav Rajan
Published: May 2024

In this global pandemic caused by coronavirus, the role of social media is found to be vital for spreading awareness and faultless news about various aspects of the pandemic. Governments across the world are constantly using available social media platforms to communicate crisis information efficiently to the public, which ultimately making citizens aware about the prevailing conditions. This study systematically investigates how Indian government agencies used social media platform-Twitter to disseminate the relevant information, and to reach out to the citizens during COVID 19 crisis. Spread across various parameters over many days, the twitter data was scrapped from the official Twitter accounts of different government officials. To aggregate and summarize this multi-dimensional data and to process it further, a novel multi-criteria decision making based framework that makes the use of Clustering and Ordered weighted operators is being introduced in this study. Many OWA operators have been introduced in the recent past after their introduction in late 90s, and are used successfully for aggregation purposes in many domains. The summarized values received as an output of the framework are then used to analyze the government response towards COVID 19 situation across various parameters.
RESEARCH PROGRESS OF HIGH PERFORMANCE COATINGS PREPARED BY TUNGSTEN INERT GAS (TIG) CLADDING ON TYPICAL METALLIC MATERIALS: A MINI REVIEW
Authors: HAO YE , HAN FENG , LIN NAIMING , YANG YANG , ZENG QUNFENG , NOURI MEISAM , LIU XIUBO
Published: May 2024

With the rapid advancement of modern society, the applications of materials are expanding across various fields. Among these, metallic materials have found wide use in industries such as biomedical, petrochemical, aerospace, orbital bridges, and marine vessels, making significant contributions to the manufacturing sector of the economy. As heavy machinery in industries undergoes rapid updates and iterations, there is a growing demand for metal fittings and structural components. However, these metal materials often experience common forms of failures, such as corrosion and wear, particularly at the surface, highlighting the crucial importance of surface protection. Material surface engineering presents a viable solution that effectively balances internal performance and investment costs, specifically addressing issues related to surface degradation and repair. By controlling the surface composition and structural design, it becomes possible to manage friction, corrosion, and mechanical properties of metal materials, resulting in notable advancements. One notable technique that has gained considerable traction in automotive engineering, aerospace, and other industries is tungsten inert gas (TIG) cladding. This technique offers unique composition properties, a pollution-free coating process, and a strong metallurgical bond between the alloy coating and the substrate. This review aims to provide a concise overview of TIG cladding, discussing parameter selection and the impact of different cladding powders on coating characteristics. Additionally, it summarizes the recent applications and research status of TIG cladding on titanium alloys, stainless steels, and other nonferrous alloys concerning their wear resistance, mechanical properties, and corrosion resistance. By exploring the advancements and findings in these areas, this review aims to contribute to the body of knowledge in the field of surface engineering and facilitate further research and development in TIG cladding technology.
RECENT ADVANCES IN SURFACE MODIFICATION OF ENGINEERING MATERIALS BY TIG CLADDING PROCESS: A REVIEW
Authors: PAUL PRITAM , GHOSH SUBRATA KUMAR , MAJI PABITRA , NATH RAHUL KANTI , KUMAR PRABHAT
Published: May 2024

The surfaces of engineering components experience severe conditions during service life and may lead to catastrophic failure if proper care is not taken. The coating is one of the best possible ways to enhance surface properties of a part. Tungsten inert gas (TIG) cladding has emerged as a new technique which shows promising results in surface cladding domain. This paper deals with the recent developments in TIG cladding process. The processing parameters associated with the TIG cladding process are discussed. The microstructure, microhardness, wear, and corrosion behavior of the TIG-cladded layers are mentioned and correlated with the processing conditions. Finally, this paper concludes with some suggestions for future research.
Three-dimensional numerical simulation on the directly written waveguide formation with 320nm UV laser in lithium niobate
Authors: Zuo Yue , Wu Bo , Shen Yonghang
Published: May 2024

Three-dimensional numerical simulation on the waveguide formation written directly by 320[Formula: see text]nm ultraviolet laser in congruent lithium niobate (LN) crystal is presented. With the recent development of praseodymium (Pr)-doped laser crystal, the 320 nm UV laser has become easily available today. Though the absorption coefficient of this wavelength laser in LN is relatively low at room temperature, which might deter the direct waveguide writing fabrication, the situation will be much different when temperature rises. The absorption coefficient of this laser in LN will increase significantly under elevated temperature. When the temperature-dependent absorption coefficient is taken into consideration, the coupling of temperature and absorbed laser power will bring about interesting absorption characteristics. That is, the penetration depth of the laser will be affected by its power and the focus condition. In this work, we investigate the waveguide formation process in heated LN crystal via the lithium ion thermal-diffusion model. Cases where the laser is focused onto the surface and into the crystal are studied separately. The results show that when extra heated, the LN crystal can strongly absorb the 320[Formula: see text]nm laser focused on the surface and then form surface waveguides. Besides, when focused into the crystal, the laser will penetrate into the crystal and create internal high-temperature area with appropriated power and focusing parameter. However, in this case, only a relatively small refractive index change is obtained, which is not large enough to support efficient waveguide mode.
On metric approximate subgroups
Authors: Hrushovski Ehud , Rodríguez Fanlo Arturo
Published: May 2024

Let [Formula: see text] be a group with a metric invariant under left and right translations, and let [Formula: see text] be the ball of radius [Formula: see text] around the identity. A [Formula: see text]-metric approximate subgroup is a symmetric subset [Formula: see text] of [Formula: see text] such that the pairwise product set [Formula: see text] is covered by at most [Formula: see text] translates of [Formula: see text]. This notion was introduced in [T. Tao, Product set estimates for noncommutative groups, Combinatorica, 28(5) (2008) 547–594, doi:10.1007/s00493-008-2271-7; T. Tao, Metric entropy analogues of sum set theory (2014), https://terrytao.wordpress.com/2014/03/19/metric-entropy-analogues-of-sum-set-theory/] along with the version for discrete groups (approximate subgroups). In [E. Hrushovski, Stable group theory and approximate subgroups, J. Amer. Math. Soc. 25(1) (2012) 189–243, doi:10.1090/S0894-0347-2011-00708-X], it was shown for the discrete case that, at the asymptotic limit of [Formula: see text] finite but large, the “approximateness” (or need for more than one translate) can be attributed to a canonically associated Lie group. Here we prove an analogous result in the metric setting, under a certain finite covering assumption on [Formula: see text] replacing finiteness. In particular, if [Formula: see text] has bounded exponent, we show that any [Formula: see text]-metric approximate subgroup is close to a [Formula: see text]-metric approximate subgroup for an appropriate [Formula: see text].
Null Phase Assumption-Based Technique for Constructing the Target Model of Seismic Irregularity on High-Speed Railways
Authors: Yu Jian , Zhou Wangbao , Jiang Lizhong , Liu Xiang
Published: May 2024

High-speed railways are “lifeline projects” that shoulder the heavy responsibility of transporting relief supplies and medical forces for the first time after earthquakes. To ensure the train’s safety after earthquakes, it is of great urgency to ascertain a post-earthquake speed threshold. To that end, a target model for seismic irregularity emerges as a key parameter. In this paper, a null phase assumption-based technique for the mutual conversion between evolutionary power spectral density and non-stationary signal was proposed. Taking a high-speed railway track-bridge system as the research object, the target model of seismic irregularities was constructed based on the proposed technique. The rationality of the target model of seismic irregularities was verified, and the construction parameter settings were discussed. Moreover, a simplified frequency-domain fitting method for the target model of seismic irregularities was proposed based on the spectral decomposition theory. According to the research findings, the null phase assumption-based technique is capable of performing interconversion between seismic irregularity and its evolutionary power spectral density with satisfactory accuracy. It is recommended to set the minimum number of seismic irregularities, spatial sampling interval, hop size, and length of window function as 50, 0.25[Formula: see text]m, 1, and 40 to 100, respectively.
Generalized Hom–Lie–Rinehart algebras
Authors: Moukala Norbert Mahoungou
Published: May 2024

The first-order Hom-differential operators on a commutative Hom-associative algebra with unit are introduced. We describe Hom–Lie algebra associated with a module of first-order Hom-differential operators. We give the notion of a generalized Hom–Lie–Rinehart algebra and we show the characterization of generalized Hom–Lie–Rinehart algebras in terms of Hom–Gerstenhaber–Jacobi algebras. We prove the necessary and sufficient condition to obtain a generalized Hom–Lie–Rinehart algebra structure on a Hom–Lie algebroid.
Adoption of Electronic Knowledge Repositories: Influencing Factors in the Indian Software Industry
Authors: Chugh Mitali , Upadhayay Rajesh Kumar
Published: May 2024

Purpose: This study intends to examine the factors that impact the intention and actual usage of Electronic Knowledge Repositories (EKR) by knowledge seekers in the Indian software sector. The study examines how perceived utility, perceived output quality, resource availability, and perceived organisational structure support influence the adoption of EKR. Design/methodology/approach: The data were gathered from 505 employees in 27 software engineering companies in the National Capital Region (NCR) of India using a self-administered survey. The study employed structural equation modelling (SEM) to examine the connections among the variables. Findings: The findings suggest that perceived usefulness, perceived output quality, resource availability, and reported organisational structure support have a favourable impact on the desire to adopt EKR. Using EKR favourably influences its utilisation by knowledge seekers in the Indian software sector. Research limitations/implications: The study focussed exclusively on the software sector in the NCR area of India, perhaps restricting the applicability of the results. Future studies should examine more variables that impact EKR adoption and study various organisational contexts. Practical implications: The research indicates that organisations should prioritise improving the perceived utility of EKR, assuring high output quality, allocating sufficient resources for EKR access, and maintaining supporting organisational structures to encourage EKR adoption among knowledge seekers. Originality/value: This study experimentally investigates the characteristics that influence EKR adoption in the Indian software sector, contributing to existing literature. The results offer useful insights for firms looking to enhance their knowledge management strategies by effectively utilising EKR.
Integration of autoencoder and graph convolutional network for predicting breast cancer drug response
Authors: Abinas V. , Abhinav U. , Haneem E. M. , Vishnusankar A. , Nazeer K. A. Abdul
Published: May 2024

Background and objectives: Breast cancer is the most prevalent type of cancer among women. The effectiveness of anticancer pharmacological therapy may get adversely affected by tumor heterogeneity that includes genetic and transcriptomic features. This leads to clinical variability in patient response to therapeutic drugs. Anticancer drug design and cancer understanding require precise identification of cancer drug responses. The performance of drug response prediction models can be improved by integrating multi-omics data and drug structure data. Methods: In this paper, we propose an Autoencoder (AE) and Graph Convolutional Network (AGCN) for drug response prediction, which integrates multi-omics data and drug structure data. Specifically, we first converted the high dimensional representation of each omic data to a lower dimensional representation using an AE for each omic data set. Subsequently, these individual features are combined with drug structure data obtained using a Graph Convolutional Network and given to a Convolutional Neural Network to calculate IC[Formula: see text] values for every combination of cell lines and drugs. Then a threshold IC[Formula: see text] value is obtained for each drug by performing K-means clustering of their known IC[Formula: see text] values. Finally, with the help of this threshold value, cell lines are classified as either sensitive or resistant to each drug. Results: Experimental results indicate that AGCN has an accuracy of 0.82 and performs better than many existing methods. In addition to that, we have done external validation of AGCN using data taken from The Cancer Genome Atlas (TCGA) clinical database, and we got an accuracy of 0.91. Conclusion: According to the results obtained, concatenating multi-omics data with drug structure data using AGCN for drug response prediction tasks greatly improves the accuracy of the prediction task.
Decoherence and the classes of maximally entangled states
Authors: Buniy Roman V. , Feger Robert P. , Kephart Thomas W.
Published: May 2024

Self-interactions and interaction with the environment inevitably push quantum systems towards highly mixed states, which is the definition of decoherence. However, if decoherence is completely suppressed, then any quantum system evolves unitarily through pure states towards maximally entangled configurations. We argue that these maximally entangled states of the systems fall into the well-defined classes that can be uniquely described by the values of certain entanglement invariants. After discussing these ideas, we present examples of maximally entangled states for a number of generic systems, construct compact states in the most entangled classes for tripartite systems, and suggest how they may be constructed for other [Formula: see text]-partite systems. We study random walks through the space of entanglement classes to see how unitary evolution along paths to the maximally entangled states, i.e. entanglement growth, works in practice.
ON THE MODEL OF INTERACTION BETWEEN NORMAL AND TUMOR CELLS DURING TUMOR DEVELOPMENT
Authors: Pankratov E. L.
Published: May 2024

We introduce a model for the prognosis of the population of cells during the development of tumors. Also, we introduce an analytical approach for analysis of the introduced model. The approach gives a possibility to take into account the changing conditions of the considered processes.
Recent Progress in the Applications of UIO-66 Materials: A Review
Authors: Wang Luyu , Song Jia , Yu Chunyang
Published: May 2024

The integration of different disciplines is the core of the application orientation of UIO-66 materials, and it has expanded the boundaries of the application field of UIO-66 materials by exploring infinite space in chemical composition and material properties. Significant advancements have been achieved in utilizing the distinct characteristics of UIO-66 materials, both in basic and practical terms. In this review, our aim is to examine the key achievements in utilizing UIO-66 materials, encompassing areas like gas separation, catalysis, quartz crystal microbalance sensor and energy storage, and offer an essential viewpoint on their progress in practical applications. Finally, we succinctly address the primary obstacles that must be tackled in these domains to lay the groundwork for industrial utilization.
Permanence and extinction for the stochastic plant–pollinator model with degenerate diffusion
Authors: Zhao Yanfei , Li Yongkun
Published: May 2024

This paper investigates dynamical behaviors of a stochastic plant–pollinator model with degenerate diffusion. We derive a sufficient and almost necessary condition for the permanence and extinction for plant–pollinator model, which characterizes the long-time behavior of the dynamic systems. Examples and numerical results are provided to illustrate our findings.
A reaction–diffusion model of major emerging infectious diseases in a spatially heterogeneous environment and case study
Authors: Duan Xuyue , Wu Yan , Wang Kai , Li Yong , Peng Zhihang
Published: May 2024

During the outbreak of major emerging infectious diseases, virus droplets can survive in the environment as aerosols for hours to days, and their impact on human infectious diseases is often overlooked. In addition, the speed of transmission of infectious diseases is often closely related to transportation. Therefore, studying the impact of environmental viruses and transportation on disease development is significant for effective infectious disease prevention and control. We proposed a degenerate reaction–diffusion infectious disease model ([Formula: see text]) considering environmental virus interference and established a well-posedness and threshold system for this model. We have obtained the system solution approaches the disease-free equilibrium ([Formula: see text]) when the basic reproduction number [Formula: see text]. The system has at least one positive steady state solution (PSS) when [Formula: see text]. This paper used a non-standard finite difference method discretization model while data fitting and parameter estimation were performed based on data provided by the Health Commission. Further sensitivity analysis was conducted on [Formula: see text]. At the same time, we also discussed the impact of various parameters in the early stages of the outbreak of major emerging infectious diseases on the development of the disease. Research found that even if the contact rate between people is controlled at a shallow level, the disease may persist. In the early stages of major emerging outbreak of infectious diseases, immediately reducing the use of transportation can effectively reduce the speed of disease spread.
Spectral properties of generalized Paley graphs of (qℓ + 1)th powers and applications
Authors: Podestá Ricardo A. , Videla Denis E.
Published: May 2024

We consider a special class of generalized Paley graphs over finite fields, namely the Cayley graphs with vertex set [Formula: see text] and connection set the nonzero [Formula: see text]th powers in [Formula: see text], as well as their complements. We explicitly compute the spectrum and the energy of these graphs. As a consequence, the graphs turn out to be (with trivial exceptions) simple, connected, non-bipartite, integral and strongly regular, of pseudo or negative Latin square type. Using the spectral information we compute several invariants of these graphs. We exhibit infinitely many pairs of integral equienergetic non-isospectral graphs. As applications, on the one hand we solve Waring’s problem over [Formula: see text] for the exponents [Formula: see text], for each [Formula: see text] and for infinitely many values of [Formula: see text] and [Formula: see text]. We obtain that the Waring number [Formula: see text] or [Formula: see text], depending on [Formula: see text] and [Formula: see text], thus solving some open cases. On the other hand, we construct infinite towers of integral Ramanujan graphs in all characteristics. Finally, we give the Ihara zeta functions of these graphs.
The Nexus Between Energy Demand and Currency Valuation: Evidence from Selected OECD Countries
Authors: Chang Bisharat Hussain , Alzoubi Haitham M. , Salman Asma , Chang Ali Gohar , Uddin Mohammed Ahmar , Khan Jameel Ahmed
Published: May 2024

This study focuses on the unequal relationship between energy consumption and its determinants. Past studies have not examined how minor and substantial currency value changes affect energy consumption in the organization for economic cooperation and development (OECD) countries. This study compares the effects of modest and significant exchange rate (ER) changes on energy demand (ED) in OECD countries, which include Greece, Belgium, Ireland, Denmark, Portugal, Norway and Italy. Our work adds to the literature by distinguishing the effect of small to significant changes in currency fluctuations. We do this with a sophisticated model, an updated multiple threshold nonlinear autoregressive distributed lag (MTNARDL). Next, we compare the model’s outcomes to conventional nonlinear autoregressive distributed lag (NARDL) and autoregressive distributed lag (ARDL) models. According to NARDL and ARDL estimates, co-integration is present in the context of Belgium. However, the MTNARDL model division of series suggests cointegration in all sample countries. It implies that this model is superior to previous ones. We conclude with policy recommendations based on the results of our inquiry.
Reservoir Sampling over Joins
Authors: Dai Binyang , Hu Xiao , Yi Ke
Published: May 2024

Sampling over joins is a fundamental task in large-scale data analytics. Instead of computing the full join results, which could be massive, a uniform sample of the join results would suffice for many purposes, such as answering analytical queries or training machine learning models. In this paper, we study the problem of how to maintain a random sample over joins while the tuples are streaming in. Without the join, this problem can be solved by some simple and classical reservoir sampling algorithms. However, the join operator makes the problem significantly harder, as the join size can be polynomially larger than the input. We present a new algorithm for this problem that achieves a near-linear complexity. The key technical components are a generalized reservoir sampling algorithm that supports a predicate, and a dynamic index for sampling over joins. We also conduct extensive experiments on both graph and relational data over various join queries, and the experimental results demonstrate significant performance improvement over the state of the art.
CAVE: Concurrency-Aware Graph Processing on SSDs
Authors: Papon Tarikul Islam , Chen Taishan , Zhang Shuo , Athanassoulis Manos
Published: May 2024

Large-scale graph analytics has become increasingly common in areas like social networks, physical sciences, transportation networks, and recommendation systems. Since many such practical graphs do not fit in main memory, graph analytics performance depends on efficiently utilizing underlying storage devices. These out-of-core graph processing systems employ sharding and sub-graph partitioning to optimize for storage while relying on efficient sequential access of traditional hard disks. However, today's storage is increasingly based on solid-state drives (SSDs) that exhibit high internal parallelism and efficient random accesses. Yet, state-of-the-art graph processing systems do not explicitly exploit those properties, resulting in subpar performance.

In this paper, we develop CAVE, the first graph processing engine that optimally exploits underlying SSD-based storage by harnessing the available storage device parallelism via carefully selecting which I/Os to graph data can be issued concurrently. Thus, CAVE traverses multiple paths and processes multiple nodes and edges concurrently, achieving parallelization at a granular level. We identify two key ways to parallelize graph traversal algorithms based on the graph structure and algorithm: intra-subgraph and inter-subgraph parallelization. The first identifies subgraphs that contain vertices that can be accessed in parallel, while the latter identifies subgraphs that can be processed in their entirety in parallel. To showcase the benefit of our approach, we build within CAVE parallelized versions of five popular graph algorithms (Breadth-First Search, Depth-First Search, Weakly Connected Components, PageRank, Random Walk) that exploit the full bandwidth of the underlying device. CAVE uses a blocked file format based on adjacency lists and employs a concurrent cache pool that is essential to the parallelization of graph algorithms. By experimenting with different types of graphs on three SSD devices, we demonstrate that CAVE utilizes the available parallelism, and scales to diverse real-world graph datasets. CAVE achieves up to one order of magnitude speedup compared to the popular out-of-core systems Mosaic and GridGraph, and up to three orders of magnitude speedup in runtime compared to GraphChi.
Data Acquisition for Improving Model Confidence
Authors: Li Yifan , Yu Xiaohui , Koudas Nick
Published: May 2024

In recent years, there has been a growing recognition that high-quality training data is crucial for the performance of machine learning models. This awareness has catalyzed both research endeavors and industrial initiatives dedicated to data acquisition to enhance diverse dimensions of model performance. Among these dimensions, model confidence holds paramount importance; however, it has often been overlooked in prior investigations into data acquisition methodologies. To address this gap, our work focuses on improving the data acquisition process with the goal of enhancing the confidence of Machine Learning models. Specifically, we operate within a practical context where limited samples can be obtained from a large data pool. We employ well-established model confidence metrics as our foundation, and we propose two methodologies, Bulk Acquisition (BA) and Sequential Acquisition (SA), each geared towards identifying the sets of samples that yield the most substantial gains in model confidence. Recognizing the complexity of BA and SA, we introduce two efficient approximate methods, namely kNN-BA and kNN-SA, restricting data acquisition to promising subsets within the data pool. To broaden the applicability of our solutions, we introduce a Distribution-based Acquisition approach that makes minimal assumption regarding the data pool and facilitates the data acquisition across various settings. Through extensive experimentation encompassing diverse datasets, models, and parameter configurations, we demonstrate the efficacy of our proposed methods across a range of tasks. Comparative experiments with alternative applicable baselines underscore the superior performance of our proposed approaches.
Efficient and Provable Effective Resistance Computation on Large Graphs: An Index-based Approach
Authors: Liao Meihao , Zhou Junjie , Li Rong-Hua , Dai Qiangqiang , Chen Hongyang , Wang Guoren
Published: May 2024

Effective resistance (ER) is a fundamental metric for measuring node similarities in a graph, and it finds applications in various domains including graph clustering, recommendation systems, link prediction, and graph neural networks. The state-of-the-art algorithm for computing effective resistance relies on a landmark technique, which involves selecting a node that is easy to reach by all the other nodes as a landmark. The performance of this technique heavily depends on the chosen landmark node. However, in many real-life graphs, it is not always possible to find an easily reachable landmark node, which can significantly hinder the algorithm's efficiency. To overcome this problem, we propose a novel multiple landmarks technique which involves selecting a set of landmark nodes V
l
such that the other nodes in the graph can easily reach any one of a landmark node in V
l
. Specifically, we first propose several new formulas to compute ER with multiple landmarks, utilizing the concept of Schur complement. These new formulas allow us to pre-compute and maintain several small-sized matrices related to V
l
as a compact index. With this powerful index technique, we demonstrate that both single-pair and single-source ER queries can be efficiently answered using a newly-developed V
l
-absorbed random walk sampling or V
l
-absorbed push technique. Comprehensive theoretical analysis shows that all proposed index-based algorithms achieve provable performance guarantees for both single-pair and single-source ER queries. Extensive experiments on 5 real-life datasets demonstrate the high efficiency of our multiple landmarks-based index techniques. For instance, our algorithms, with a 1.5 GB index size, can be up to 4 orders of magnitude faster than the state-of-the-art algorithms while achieving the same accuracy on a large road network.
Fault Tolerance Placement in the Internet of Things
Authors: Kozar Anastasiia , Del Monte Bonaventura , Zeuch Steffen , Markl Volker
Published: May 2024

Today's IoT applications exploit the capabilities of three different computation environments: sensors, edge, and cloud. Ensuring fault tolerance at the edge level presents unique challenges due to complex network hierarchies and the presence of resource-constrained computing devices. In contrast to the Cloud, the Edge lacks high availability standards and a persistent upstream backup. To ensure reliability, fault tolerance mechanisms have to be deployed on the edge devices along with processing operators competing for available resources. However, existing operator placement strategies are not aware of fault tolerance resource requirements, and existing fault tolerance approaches are not aware of available resources. This miscommunication in resource-constrained environments like the Edge leads to underprovisioning and failures. In this paper, we present a resource-aware fault-tolerance approach that takes the unique characteristics of the Edge into account to provide reliable stream processing. To this end, we model fault tolerance as an operator placement problem that uses multi-objective optimization to decide where to backup data. As opposed to existing approaches that treat operator placement and fault tolerance as two separate steps, we combine them and showcase that this is especially important for low-end edge devices. Overall, our approach effectively mitigates potential failures and outperforms state-of-the-art fault tolerance approaches by up to an order of magnitude in throughput.
In-depth Analysis of Continuous Subgraph Matching in a Common Delta Query Compilation Framework
Authors: Lee Yukyoung , Kim Kyoungmin , Lee Wonseok , Han Wook-Shin
Published: May 2024

The continuous subgraph matching (CSM) problem aims to continuously detect patterns on a dynamic graph, with real-world applications such as fraud detection. Numerous methods have been proposed to address CSM, yet they lack fair comparisons. Furthermore, an existing unified framework for CSM shows misleading experimental results due to its suboptimal implementations. In this paper, we propose a new framework that generates CSM code from the logical and physical plans of delta queries with stacked views. By expressing each CSM method as a delta query plan, our framework enables fair comparisons of CSM methods. Through our comprehensive experiments, we make cause-and-effect arguments for the divergent performance trends from the previous papers and further analyze the individual impacts of various techniques on overall performance. Specifically, our CSM code for an old method significantly outperforms the most recent CSM method, CaLiG, by up to 48.6 times.
Low-Latency Adaptive Distributed Stream Join System Based on a Flexible Join Model
Authors: Wang Qihang , Zuo Decheng , Zhang Zhan , Shu Yanjun , Liu Xin , He Mingxuan
Published: May 2024

Stream join is a fundamental operation in stream processing and has attracted extensive research due to its large resource consumption and serious impact on system performance. As the theoretical basis of stream join systems, the stream join model greatly affects system performance. State-of-the-art stream join models either consume too much computing resources or too much storage resources, thus resulting in lower throughput or higher latency. In this paper, we propose a new stream join model for processing arbitrary join predicates, called CoModel, which offers a flexible trade-off between memory and computing resource consumption. More importantly, CoModel can achieve the minimum sum of the number of store operations and join operations among all existing join models, and thus can achieve the lowest latency and highest throughput when the overheads associated with the local stream join for each input tuple are approximately constant. We give a trade-off strategy for CoModel and theoretically prove its performance advantages based on queuing theory. Furthermore, we design and implement an adaptive distributed stream join system, CoStream, based on CoModel. CoStream can adaptively adjust its structure according to resource constraints and statistics of input data. We conduct extensive experiments for CoStream to evaluate its performance and adaptivity, and the results show that CoStream has the lowest latency and highest throughput in various scenarios.
Object-oriented Unified Encrypted Memory Management for Heterogeneous Memory Architectures
Authors: Sha Mo , Cai Yifan , Wang Sheng , Phan Linh Thi Xuan , Li Feifei , Tan Kian-Lee
Published: May 2024

In contemporary database applications, the demand for memory resources is intensively high. To enhance adaptability to varying resource needs and improve cost efficiency, the integration of diverse storage technologies within heterogeneous memory architectures emerges as a promising solution. Despite the potential advantages, there exists a significant gap in research related to the security of data within these complex systems. This paper endeavors to fill this void by exploring the intricacies and challenges of ensuring data security in object-oriented heterogeneous memory systems. We introduce the concept of Unified Encrypted Memory (UEM) management, a novel approach that provides unified object references essential for data management platforms, while simultaneously concealing the complexities of physical scheduling from developers. At the heart of UEM lies the seamless and efficient integration of data encryption techniques, which are designed to ensure data integrity and guarantee the freshness of data upon access. Our research meticulously examines the security deficiencies present in existing heterogeneous memory system designs. By advancing centralized security enforcement strategies, we aim to achieve efficient object-centric data protection. Through extensive evaluations conducted across a variety of memory configurations and tasks, our findings highlight the effectiveness of UEM. The security features of UEM introduce low and acceptable overheads, and UEM outperforms conventional security measures in terms of speed and space efficiency.
On Efficient Large Sparse Matrix Chain Multiplication
Authors: Lin Chunxu , Luo Wensheng , Fang Yixiang , Ma Chenhao , Liu Xilin , Ma Yuchi
Published: May 2024

Sparse matrices are often used to model the interactions among different objects and they are prevalent in many areas including e-commerce, social network, and biology. As one of the fundamental matrix operations, the sparse matrix chain multiplication (SMCM) aims to efficiently multiply a chain of sparse matrices, which has found various real-world applications in areas like network analysis, data mining, and machine learning. The efficiency of SMCM largely hinges on the order of multiplying the matrices, which further relies on the accurate estimation of the sparsity values of intermediate matrices. Existing matrix sparsity estimators often struggle with large sparse matrices, because they suffer from the accuracy issue in both theory and practice. To enable efficient SMCM, in this paper we introduce a novel row-wise sparsity estimator (RS-estimator), a straightforward yet effective estimator that leverages matrix structural properties to achieve efficient, accurate, and theoretically guaranteed sparsity estimation. Based on the RS-estimator, we propose a novel ordering algorithm for determining a good order of efficient SMCM. We further develop an efficient parallel SMCM algorithm by effectively utilizing multiple CPU threads. We have conducted experiments by multiplying various chains of large sparse matrices extracted from five real-world large graph datasets, and the results demonstrate the effectiveness and efficiency of our proposed methods. In particular, our SMCM algorithm is up to three orders of magnitude faster than the state-of-the-art algorithms.
Query Compilation Without Regrets
Authors: Grulich Philipp M. , Lepping Aljoscha P. , Nugroho Dwi P. A. , Pandey Varun , Del Monte Bonaventura , Zeuch Steffen , Markl Volker
Published: May 2024

Engineering high-performance query execution engines is a challenging task. Query compilation provides excellent performance, but at the same time introduces significant system complexity, as it makes the engine hard to build, debug, and maintain. To overcome this complexity, we propose Nautilus, a framework that combines the ease of use of query interpretation and the performance of query compilation. On the one hand, Nautilus provides an interpretation-based operator interface that enables engineers to implement operators using imperative C++ code to ensure a familiar developer experience. On the other hand, Nautilus mitigates the performance drawbacks of interpretation by introducing a novel trace-based, multi-backend JIT compiler that translates operators into efficient code. As a result, Nautilus bridges the gap between compilation and interpretation and provides the best of both worlds, achieving high performance without sacrificing the productivity of engineers.
Relational Algorithms for Top-k Query Evaluation
Authors: Wang Qichen , Luo Qiyao , Wang Yilei
Published: May 2024

The evaluation of top-k conjunctive queries, a staple in business analysis, often requires evaluating the conjunctive query prior to filtering the top-k results, leading to a significant computational overhead within Database Management Systems (DBMSs). While efficient algorithms have been proposed, their integration into DBMSs remains arduous. We introduce relational algorithms, a paradigm where each algorithmic step is expressed by a relational operator. This allows the algorithm to be represented as a set of SQL queries, enabling easy deployment across different systems that support SQL. We introduce two novel relational algorithms, level-k and product-k, specifically designed for evaluating top-k conjunctive queries and demonstrate that level-k achieves optimal running time for top-k free-connex queries. Furthermore, these algorithms enable easy translation into an oblivious algorithm for secure query evaluations. The presented algorithms are not only theoretically optimal but also exhibit eminent efficiency in practice. The experiment results show significant improvements, with our rewritten SQL outperforming the baseline by up to 6 orders of magnitude. Moreover, our secure implementations not only achieve substantial speedup compared to the baseline with secure guarantees but even surpass those baselines that have no secure guarantees.
Settling Time vs. Accuracy Tradeoffs for Clustering Big Data
Authors: Draganov Andrew , Saulpic David , Schwiegelshohn Chris
Published: May 2024

We study the theoretical and practical runtime limits of k-means and k-median clustering on large datasets. Since effectively all clustering methods are slower than the time it takes to read the dataset, the fastest approach is to quickly compress the data and perform the clustering on the compressed representation. Unfortunately, there is no universal best choice for compressing the number of points -- while random sampling runs in sublinear time and coresets provide theoretical guarantees, the former does not enforce accuracy while the latter is too slow as the numbers of points and clusters grow. Indeed, it has been conjectured that any sensitivity-based coreset construction requires super-linear time in the datase size. We examine this relationship by first showing that there does exist an algorithm that obtains coresets via sensitivity sampling in effectively linear time -- within log-factors of the time it takes to read the data. Any approach that significantly improves on this must then resort to practical heuristics, leading us to consider the spectrum of sampling strategies across both real and artificial datasets in the static and streaming settings. Through this, we show the conditions in which coresets are necessary for preserving cluster validity as well as the settings in which faster, cruder sampling strategies are sufficient. As a result, we provide a comprehensive theoretical and practical blueprint for effective clustering regardless of data size. Our code is publicly available at https://github.com/Andrew-Draganov/Fast-Coreset-Generation and has scripts to recreate the experiments.
SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement
Authors: Gao Shihong , Li Yiming , Zhang Xin , Shen Yanyan , Shao Yingxia , Chen Lei
Published: May 2024

Dynamic graphs are essential in real-world scenarios like social media and e-commerce for tasks such as predicting links and classifying nodes. Temporal Graph Neural Networks (T-GNNs) stand out as a prime solution for managing dynamic graphs, employing temporal message passing to compute node embeddings at specific timestamps. Nonetheless, the high CPU-GPU data loading overhead has become the bottleneck for efficient training of T-GNNs over large-scale dynamic graphs. In this work, we present SIMPLE, a versatile system designed to address the major efficiency bottleneck in training existing T-GNNs on a large scale. It incorporates a dynamic data placement mechanism, which maintains a small buffer space in available GPU memory and dynamically manages its content during T-GNN training. SIMPLE is also empowered by systematic optimizations towards data processing flow. We compare SIMPLE to the state-of-the-art generic T-GNN training system TGL on four large-scale dynamic graphs with different underlying T-GNN models. Extensive experimental results show that SIMPLE effectively cuts down 80.5% ~ 96.8% data loading cost, and accelerates T-GNN training by 1.8× ~ 3.8× (2.6× on average) compared to TGL.
Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in a Colossal Configuration Space
Authors: Liu Junfeng , Wang Fan , Mo Dingheng , Luo Siqiang
Published: May 2024

Mainstream LSM-tree-based key-value stores face challenges in optimizing performance for point lookup, range lookup, and update operations concurrently due to their constrained configurations. They typically follow fixed patterns to specify the level capacity and the number of sorted runs per-level. This confines their designs to a restricted space, limiting opportunities for broader optimizations.

To address this challenge, we consider a more flexible configuration that enables independent adjustments of the number of runs per-level, size ratio, and Bloom filter settings at each LSM-tree level. By carefully analyzing the cost of each operation based on the new design space, we unveil two critical insights for optimizing the tradeoff among the three operations. Firstly, achieving efficient point lookup requires a large last level. Secondly, there is a specific correlation between the number of runs per level and size ratio that is advantageous for overall update and range lookup performance.

Based on these insights, we introduce Moose, a structure delivering an impressive overall performance for point lookup, range lookup, and update concurrently. Furthermore, we also introduce a new framework, Smoose, to navigate the design space for adapting specific workloads. We implemented Moose and Smoose on top of RocksDB and experimental results demonstrate that our proposed approach outperforms state-of-the-art LSM-tree structures across diverse workloads.