Empirical Performance Comparison Research Articles

A Comparative Analysis of Microsoft Fabric and Snowflake: Architectural Paradigms and Performance Metrics in Modern Cloud Data Platforms

Abstract: This article presents a comprehensive comparative analysis of two leading cloud data platforms: Microsoft Fabric and Snowflake. As organizations increasingly rely on cloud-based solutions for data management and analytics, understanding the strengths and limitations of these platforms becomes crucial. This article examines the architectural foundations, functional capabilities, performance metrics, and cost considerations of both platforms. We explore Microsoft Fabric's integrated service model within the Azure ecosystem and Snowflake's multi-cluster shared data architecture, assessing their approaches to data integration, storage, analytics, and machine learning integration. Through empirical performance comparisons and an evaluation of scalability mechanisms, we provide insights into the operational efficiencies of each platform. Additionally, we analyze the security features, compliance standards, and pricing models to offer a holistic view of the total cost of ownership. Our findings reveal distinct advantages in Microsoft Fabric's end-to-end integration and Snowflake's performance in multicloud environments, while also highlighting areas for potential improvement in both platforms. This article aims to serve as a valuable resource for organizations navigating the complex landscape of cloud data solutions, offering evidence-based criteria for platform selection aligned with specific business needs and technological ecosystems

Storage systems’ impact on order picking time: An empirical economic analysis of flow-rack storage systems

Manual order picking (OP) is still the predominant warehousing system in brick-and-mortar grocery retailing, making human workers an integral part of operations. Besides human capabilities, the storage system design was found to impact performance, quality, and worker well-being as the major outcomes of OP systems. In warehousing practice, full-pallet storage systems and high-density flow-rack storage systems are often used in parallel within the same warehouse. However, empirical performance comparisons simultaneously considering picking and replenishment processes are largely missing for such systems. We aspire to close this research gap by empirically examining the economic impact of a high-density flow-rack storage system in which 28 different items are stored in a storage location with the ground floor size of three full pallets. The flow-rack storage system is designed with a slight slope, such that items automatically roll to the front to ensure a first-in-first-out product flow. We apply a parametric log-logistic accelerated failure time model where log-transformed picking time is the dependent variable. Using real-world data containing 2,357,976 picks performed by 192 order pickers in a German grocery warehouse, we find that the high-density flow-rack storage system accelerates the OP process by up to 4.60%, reducing the important performance indicator of picking time. In terms of replenishments, our findings indicate that replenishment processes are decelerated by 38,65% compared to normal pallet-sized rack storage places. Assuming identical labor costs for all warehouse tasks, we finally derive an optimal economic replenishment quantity per storage location in high-density flow-rack storage.

An EAP-Based Mutual Authentication Protocol for WLAN-Connected IoT Devices

Several symmetric and asymmetric encryption based authentication protocols have been developed for the wireless local area networks (WLANs). However, recent findings reveal that these protocols are either vulnerable to numerous attacks or computationally expensive. Considering the demerits of these protocols and the necessity to provide enhanced security, a lightweight extensible authentication protocol based authentication protocol for WLAN-connected Internet of Things devices is presented. We conduct an informal and formal security analysis to ensure robustness against the attacks. Furthermore, the empirical performance analysis and comparison show that the proposed protocol outperforms its counterparts, reducing computational, communication, storage costs, and energy consumption by up to 99%, 80%, 91.8%, and 98%, respectively. Simulation results of the protocol using the NS3 and its overhead under unknown attacks demonstrate that the proposed protocol performs better in all scenarios. A prototype implementation of the protocol has also been tested to evaluate its feasibility in real-time applications.

Fast Cadzow’s Algorithm and a Gradient Variant

The Cadzow’s algorithm is a signal denoising and recovery method which was designed for signals corresponding to low rank Hankel matrices. In this paper we first introduce a Fast Cadzow’s algorithm which is developed by incorporating a novel subspace projection to reduce the high computational cost of the SVD in the Cadzow’s algorithm. Then a Gradient method and a Fast Gradient method are proposed to address the non-decreasing MSE issue when applying the Cadzow’s or Fast Cadzow’s algorithm for signal denoising. Extensive empirical performance comparisons demonstrate that the proposed algorithms can complete the denoising and recovery tasks more efficiently and effectively.

Paracoherent answer set computation

Answer Set Programming (ASP) is a well-established paradigm for declarative programming and nonmonotonic reasoning. ASP allows for flexible modeling using rules. ASP rules induce a set of intended models called answer sets. Incoherence, the non-existence of answer sets, is therefore a feature of ASP, indicating that the rules admit no intended models. However, this feature can also be problematic in certain circumstances: errors that cause incoherence are notoriously difficult to debug, and query answering will not provide any meaningful answers for incoherent programs. Paracoherent semantics have been suggested as a remedy. They extend the classical notion of answer sets to draw meaningful conclusions also from incoherent programs. However, paracoherent semantics have essentially been inapplicable in practice, due to the lack of efficient algorithms and implementations. In this paper, this lack is addressed, and several different algorithms to compute semi-stable and semi-equilibrium models are proposed and implemented within an answer set solving framework. A key role in the framework is played by syntactic program transformations that allow for characterizing paracoherent semantics in terms of the answer sets of transformed programs. Apart from existing transformations from the literature, a novel transformation is also proposed, which provides an alternative characterization of paracoherent semantics in terms of (extended) externally supported models. Notably, the new transformation is more compact than the existing ones, and brings performance benefits. An extensive empirical performance comparison among the algorithms on benchmarks from ASP competitions and a real-world use case is given as well. It shows not only that the methods developed in this paper lead to practically effective systems, but also show a clear advantage of the methods that rely on (extended) externally supported models.

Sparse Analysis Recovery via Iterative Cosupport Detection Estimation

Cosparse analysis model (CAM) provides a new signal processing paradigm for recovering cosparse signals with respect to a given analysis operator from the undersampled linear measurements in the context of emerging theory of compressed sensing (CS). The sparse analysis recovery/cosparse recovery is a key one brought up by this new paradigm. In this paper, we propose a new family of analysis pursuit algorithms for the sparse analysis recovery problem when the signals obey the cosparse analysis model, termed as iterative cosupport detection estimation (ICDE). ICDE is an algorithmic framework, which alternates between detecting a cosupport set of the unknown true signal and estimating the underlying signal by solving a truncated analysis pursuit problem on the detected cosupport. Further, we propose effective implementations of ICDE equipped with an efficient thresholding strategy for cosupport detection. Empirical performance comparisons show that ICDE is favorable in comparison with the state-of-the-art sparse analysis recovery algorithms. Source code of ICDE has been made publicly available on Github: https://github.com/songhp/ICDE.

Coalition Formation of Members based on Geographical Location by Genetic Algorithm

In the era of globalization, the rapid development of mobile technologies has shown the emergence of new forms of group formation. Groups formed of people connected by certain social relationships can be easily set up by online social networks. Additionally, the geographical locations of online users have become an important feature for the group formation. Accordingly, we proposed an algorithm to search of an optimal group formation based on users' location of latitude and longitude coordinates from the map using a heuristic search algorithm named Genetic algorithm (GA). The main object of the proposed algorithm is to arrange people with dissimilar positions into proper groups to have a mean distance between members within a group as short as possible. Furthermore, an empirical performance comparison of previous greedy and GAbased algorithms by simulation results is presented to verify the efficiency of the proposed algorithm.

Accelerated Structured Alternating Projections for Robust Spectrally Sparse Signal Recovery

Consider a spectrally sparse signal $\boldsymbol{x}$ that consists of $r$ complex sinusoids with or without damping. We study the robust recovery problem for the spectrally sparse signal under the fully observed setting, which is about recovering $\boldsymbol{x}$ and a sparse corruption vector $\boldsymbol{s}$ from their sum $\boldsymbol{z}=\boldsymbol{x}+\boldsymbol{s}$. In this paper, we exploit the low-rank property of the Hankel matrix formed by $\boldsymbol{x}$, and formulate the problem as the robust recovery of a corrupted low-rank Hankel matrix. We develop a highly efficient non-convex algorithm, coined Accelerated Structured Alternating Projections (ASAP). The high computational efficiency and low space complexity of ASAP are achieved by fast computations involving structured matrices, and a subspace projection method for accelerated low-rank approximation. Theoretical recovery guarantee with a linear convergence rate has been established for ASAP, under some mild assumptions on $\boldsymbol{x}$ and $\boldsymbol{s}$. Empirical performance comparisons on both synthetic and real-world data confirm the advantages of ASAP, in terms of computational efficiency and robustness aspects.

On carriers collaboration in hub location problems

This paper considers a hub location problem where several carriers operate on a shared network to satisfy a given demand represented by a set of commodities. Possible cooperative strategies are studied where carriers can share resources or swap their respective commodities to produce tangible cost savings while fully satisfying the existing demand. Three different collaborative policies are introduced and discussed, and mixed integer programming formulations are provided for each of them. Theoretical analyses are developed in order to assess the potential savings of each model with respect to traditional non-collaborative approaches. An empirical performance comparison on state-of-art sets of instances offers a complementary viewpoint. The influence of several diverse problem parameters on the performance is analyzed to identify those operational settings enabling the highest possible savings for the considered collaborative hub location models. The number of carriers and the number of open hubs have shown to play a key role; depending on the collaborative strategy, savings of up to 50% can be obtained as the number of carriers increases or the number of open hubs decreases.

Empirical performance of GARCH, GARCH-M, GJR-GARCH and log-GARCH models for returns volatility

Volatility plays an important role in the field of financial econometrics as one of the risk indicators. Many various models address the problem of modeling the volatilities of financial asset returns. This study provides a new empirical performance comparison of the four different GARCH-type models, namely GARCH, GARCH-M, GJR-GARCH, and log-GARCH models based on simulated data and real data such as the DJIA, S&P 500, and S&P CNX Nifty indices on a daily period from January 2000 to December 2017. We also investigate the estimation results obtained using Solver’Excel and verify those results against the results obtained using a Markov chain Monte Carlo method. The simulation study showed that the GARCH model is outperformed by other models. Meanwhile, the empirical study provides evidence that the GJR-GARCH model provides the best fitting, followed by the GARCH-M, GARCH, and log-GARCH models. Furthermore, this study recommends the use of Excel’s Solver in practice when the parameter estimates for GARCH-type model do not close to zero.

Spectral Compressed Sensing via Projected Gradient Descent

Let $x\in\mathbb{C}^n$ be a spectrally sparse signal consisting of $r$ complex sinusoids with or without damping. We consider the spectral compressed sensing problem, which is about reconstructing $x$ from its partial revealed entries. By utilizing the low rank structure of the Hankel matrix corresponding to $x$, we develop a computationally efficient algorithm for this problem. The algorithm starts from an initial guess computed via one-step hard thresholding followed by projection, and then proceeds by applying projected gradient descent iterations to a non-convex functional. Based on the sampling with replacement model, we prove that $O(r^2\log(n))$ observed entries are sufficient for our algorithm to achieve the successful recovery of a spectrally sparse signal. Moreover, extensive empirical performance comparisons show that our algorithm is competitive with other state-of-the-art spectral compressed sensing algorithms in terms of phase transitions and overall computational time.

Fast and provable algorithms for spectrally sparse signal reconstruction via low-rank Hankel matrix completion

A spectrally sparse signal of order $r$ is a mixture of $r$ damped or undamped complex sinusoids. This paper investigates the problem of reconstructing spectrally sparse signals from a random subset of $n$ regular time domain samples, which can be reformulated as a low rank Hankel matrix completion problem. We introduce an iterative hard thresholding (IHT) algorithm and a fast iterative hard thresholding (FIHT) algorithm for efficient reconstruction of spectrally sparse signals via low rank Hankel matrix completion. Theoretical recovery guarantees have been established for FIHT, showing that $O(r^2\log^2(n))$ number of samples are sufficient for exact recovery with high probability. Empirical performance comparisons establish significant computational advantages for IHT and FIHT. In particular, numerical simulations on $3$D arrays demonstrate the capability of FIHT on handling large and high-dimensional real data.

On the Computation of Paracoherent Answer Sets

Answer Set Programming (ASP) is a well-established formalism for nonmonotonic reasoning. An ASP program can have no answer set due to cyclic default negation. In this case, it is not possible to draw any conclusion, even if this is not intended. Recently, several paracoherent semantics have been proposed that address this issue,and several potential applications for these semantics have been identified. However, paracoherent semantics have essentially been inapplicable in practice, due to the lack of efficient algorithms and implementations. In this paper, this lack is addressed, and several different algorithms to compute semi-stable and semi-equilibrium models are proposed and implemented into an answer set solving framework. An empirical performance comparison among the new algorithms on benchmarks from ASP competitions is given as well.

CGIHT: conjugate gradient iterative hard thresholding for compressed sensing and matrix completion

We introduce the conjugate gradient iterative hard thresholding (CGIHT) family of algorithms for the efficient solution of constrained underdetermined linear systems of equations arising in compressed sensing, row-sparse approximation and matrix completion. CGIHT is designed to balance the low per iteration complexity of simple hard thresholding algorithms with the fast asymptotic convergence rate of employing the conjugate gradient method. We establish provable recovery guarantees and stability to noise for variants of CGIHT with sufficient conditions in terms of the restricted isometry constants of the sensing operators. Extensive empirical performance comparisons establish significant computational advantages for CGIHT both in terms of the size of problems, which can be accurately approximated and in terms of overall computation time.

Solving systems of phaseless equations via Kaczmarz methods: a proof of concept study

We study the Kaczmarz methods for solving systems of phaseless equations, i.e., the generalized phase retrieval problem. The methods extend the Kaczmarz methods for solving systems of linear equations by integrating a phase selection heuristic in each iteration and overall have the same per iteration computational complexity. Extensive empirical performance comparisons establish the computational advantages of the Kaczmarz methods over other state-of-the-art phase retrieval algorithms both in terms of the number of measurements needed for successful recovery and in terms of computation time. Preliminary convergence analysis is presented for the randomized Kaczmarz methods.

Performance Comparison of Routing Protocols for Cognitive Radio Networks

Cognitive radio networks (CRNs) have emerged as a promising solution to the ever-growing demand for additional spectrum resources and more efficient spectrum utilization. A large number of routing protocols for CRNs have been proposed recently, each based on different design goals, and evaluated in different scenarios, under different assumptions. However, little is known about the relative performance of all these protocols, let alone the tradeoffs among their different design goals. In this paper, we conduct the first detailed, empirical performance comparison of three representative routing protocols for CRNs, under the same realistic set of assumptions. Our extensive simulation study shows that the performance of routing protocols in CRNs is affected by a number of factors, in addition to PU activity, some of which have been largely ignored by the majority of previous works. We find that different protocols perform well under different scenarios, and investigate the causes of the observed performance. Furthermore, we present a generic software architecture for the experimental evaluation of CRN routing protocols on a testbed based on the USRP2 platform, and compare the performance of two protocols on a 6 node testbed. The testbed results confirm the findings of our simulation study.

Optimal weighted nearest neighbour classifiers

We derive an asymptotic expansion for the excess risk (regret) of a weighted nearest-neighbour classifier. This allows us to find the asymptotically optimal vector of nonnegative weights, which has a rather simple form. We show that the ratio of the regret of this classifier to that of an unweighted k-nearest neighbour classifier depends asymptotically only on the dimension d of the feature vectors, and not on the underlying populations. The improvement is greatest when d=4, but thereafter decreases as $d\rightarrow\infty$. The popular bagged nearest neighbour classifier can also be regarded as a weighted nearest neighbour classifier, and we show that its corresponding weights are somewhat suboptimal when d is small (in particular, worse than those of the unweighted k-nearest neighbour classifier when d=1), but are close to optimal when d is large. Finally, we argue that improvements in the rate of convergence are possible under stronger smoothness assumptions, provided we allow negative weights. Our findings are supported by an empirical performance comparison on both simulated and real data sets.

The landscape adaptive particle swarm optimizer

Several modified particle swarm optimizers are proposed in this paper. In DVPSO, a distribution vector is used in the update of velocity. This vector is adjusted automatically according to the distribution of particles in each dimension. In COPSO, the probabilistic use of a ‘crossing over’ update is introduced to escape from local minima. The landscape adaptive particle swarm optimizer (LAPSO) combines these two schemes with the aim of achieving more robust and efficient search. Empirical performance comparisons between these new modified PSO methods, and also the inertia weight PSO (IFPSO), the constriction factor PSO (CFPSO) and a covariance matrix adaptation evolution strategy (CMAES) are presented on several benchmark problems. All the experimental results show that LAPSO is an efficient method to escape from convergence to local optima and approaches the global optimum rapidly on the problems used.

Large‐scale local search heuristics for the capacitated vertex p‐center problem

AbstractThis article investigates the application of very large neighborhood search techniques for solving the capacitated vertex p‐center problem. We characterize a local search neighborhood in terms of path and cyclic exchanges of customers among facilities, and exploit principles borrowed from network optimization theory to efficiently detect cost‐decreasing solutions in such a neighborhood. We complement the multiexchange methodology with a relocation mechanism specifically designed to perform facility location adjustments. The validity of the proposed approach is supported by empirical investigation and performance comparisons with the commercial code CPLEX. © 2004 Wiley Periodicals, Inc.

Empirical performance comparison of selective and constructive induction

The paper provides the results of a performance comparison study of two symbolic learning programs, both based on the AQ15c learning algorithm. The first program uses a single representation space, while the second one utilizes constructive induction, which changes the representation space. The performance of the compared systems was analyzed using three empirical error rates, including the overall, commission and omission error rates. These were determined by applying the hold-out, 10-fold, and leave-one-out sampling methods. Both systems' performance was calculated for individual stages in a multi-stage knowledge-acquisition process. Learning curves and their envelopes were prepared. The study was conducted using a set of 384 optimal designs of wind bracing in steel skeleton structures of tall buildings. The research methodology and the two learning systems used in the experiments are described, all numerical results are provided, and the conclusions of the research are given.