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Our results confirm that the former is several times faster than the latter under certain conditions.
These results are much faster than previously published results.
This is a 100x improvement over the original single threaded implementation and 12x faster than an optimized cloud server-based implementation using 16 threads.
Proceedings ArticleDOI
Jenq-Shiou Leu, Yun-Sun Yee, Wa-Lin Chen 
06 Sep 2010
17 Citations
In our studies, Hadoop can complete the same query faster than a SQL Server.
Experimental results based on an implementation in Microsoft SQL Server show outstanding performance and scalability.
As our results show, the new plan shapes and execution strategies yield significant performance improvements across the targeted workloads as compared to earlier versions of Microsoft SQL Server.
Since the integrated MonetDB/FastBit system provides the full SQL functionality, the overhead of supporting SQL is not the main reason for the observed performance differences.
We implemented the scenarios in Microsoft SQL Server engine and present the first end-to-end study to demonstrate benefits of remote memory for a variety of micro-benchmarks and industry-standard benchmarks.

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FEM of complex corrosion defect in pipelines ?
5 answers
Finite Element Method (FEM) has been extensively applied to predict failure pressures in pipelines with complex corrosion defects. Studies have shown that interaction effects between defects can significantly impact burst capacities under combined loads, with circumferentially-aligned defects experiencing marked interactions. The mechano-electrochemical (M-E) interaction at corrosion defects can lead to stress concentration and increased corrosion rates, particularly in suspended pipelines, affecting failure pressures. Utilizing both 2D and 3D FEM models for corrosion assessment under M-E interaction reveals differences in stress, strain, and corrosion potential predictions, with the 3D model being more conservative. Understanding these interactions and utilizing accurate FEM models are crucial for the integrity assessment and management of pipelines with complex corrosion defects.
What is the best value of Hits@K for VQA?
5 answers
The concept of Hits@K is not directly related to Visual Question Answering (VQA) as per the provided contexts. Hits@K is primarily discussed in the context of multi-criteria decision making, chemical hazard evaluation, virtual screening, and spatial databases. In the context of multi-criteria decision making, a new type of query called k-hit queries was introduced, focusing on maximizing the probability of selecting a user's favorite item from a set. Hits@K is also mentioned in the context of evaluating chemicals for carcinogenic hazard, where automation using kc-hits software led to process efficiency improvements. Hits@K is further discussed in the context of virtual screening, emphasizing the importance of hit identification criteria and ligand efficiency values. However, the optimal value of Hits@K for VQA specifically is not addressed in the provided contexts.
What are the most popular free SQL courses available online?
4 answers
Some of the most popular free SQL courses available online include those that have been converted into an all-digital remote format delivered via YouTube, focusing on live interaction with database systems and SQL probe queries. Additionally, online SQL environments like SQLzoo.net, MySQL Query Browser, and Teradata SQL Assistant/Web Edition have been used effectively in formal classes for learning SQL, each with its own set of features, strengths, and limitations. Moreover, relational database management systems like MySQL and PostgreSQL are widely available for free on the internet, offering students the opportunity to gain experience with industrial-strength SQL-based client-server DBMS, with PostgreSQL providing advanced features such as object-relational capabilities, transaction support, and foreign keys. These resources collectively provide a comprehensive and accessible way for individuals to learn SQL online at no cost.
What is the relationship between memory bandwidth and the efficiency of data processing in modern computing?
5 answers
The relationship between memory bandwidth and the efficiency of data processing in modern computing systems is critically interdependent, as highlighted by a body of research. Modern computing systems, designed predominantly to move data towards computation, face significant performance, scalability, and energy bottlenecks due to this architecture. These challenges are exacerbated by the increasing data-intensiveness of applications, where data access from memory emerges as a key bottleneck because memory bandwidth and energy do not scale well with demand. The dichotomy between computation and data storage/movement, where a substantial portion of system energy is consumed and performance is lost in moving data, underscores the inefficiency of traditional computing paradigms. To address these inefficiencies, research has explored processing-in-memory (PIM) technologies, which aim to reduce or eliminate data movement by placing computation mechanisms closer to or inside memory storage. This approach leverages the analog operational properties of DRAM and 3D-stacked memory technology to perform massively parallel operations in memory, thereby potentially alleviating the memory bandwidth bottleneck. Furthermore, the importance of memory bandwidth is not limited to traditional computing systems but extends to specialized computing platforms like FPGAs. Efficient use of memory bandwidth is essential for fully utilizing the processing capabilities of these platforms, as demonstrated by FPGA-based implementations of two-dimensional fast Fourier transform (2D-FFT), which address the memory bandwidth bottleneck through algorithm and data path design. In addition, strategies for increasing global cache reuse and optimizing data access patterns have been proposed to mitigate bandwidth limitations and improve data processing efficiency. These strategies include fusing computations on the same data and grouping data used by the same computation to enable contiguous memory access. In summary, the relationship between memory bandwidth and data processing efficiency is a central concern in modern computing, driving innovations in memory system architecture, algorithm design, and processing paradigms to overcome inherent limitations.
What are factors affect the determaination of the optimal degree of parallelism?
5 answers
Determining the optimal degree of parallelism (DOP) is influenced by a variety of factors, each contributing to the overall efficiency and performance of parallel computing systems. One primary factor is the nature of the computational problem itself, including its inherent parallelizability and the specific characteristics of the tasks involved, such as whether the matrices involved are rectangular or square and their density, which affects the computation and parallelization strategy. The application of machine learning techniques to automate DOP tuning highlights the complexity of predicting optimal parallelism levels, suggesting that the characteristics of the query and its execution paths significantly impact the determination of DOP. The execution environment also plays a crucial role, especially when considering workflow processes that involve activities with uncertain execution times and shared resources. This complexity necessitates a sophisticated approach to analyze and compute the DOP for concurrent processes, taking into account the variability and interdependencies of tasks. The performance and real-time loading conditions of the storage device or the computing platform further influence the optimal DOP, necessitating dynamic adjustments to achieve the best I/O efficiency. Integration measures in design and process planning, aiming at efficiency and reduced time-to-market, also affect the optimal DOP by requiring a balance between parallel and integrated procedures. The architecture of the computer system, particularly for systems with MIMD architecture, dictates the optimal number of parallel branches to minimize total time costs. Task dependence graph scheduling schemes that utilize global information and employ task merging and processors pre-allocation techniques further illustrate the impact of program structure and resource allocation strategies on determining the optimal DOP. Automated techniques for finding the optimal number of processors based on efficiency thresholds or cost-benefit ratios demonstrate the importance of application-specific criteria and the potential for real-time adaptation. The process of simulating task assignments to determine an optimal number of tasks for processing requests underscores the relevance of workload characteristics. Lastly, in data-intensive Web service combinations, the balance between service execution efficiency and cost, underpinned by the handling capacity and exercise price of the service, is crucial for determining the parallelism degree. Collectively, these factors underscore the multifaceted nature of determining the optimal DOP, which must account for problem characteristics, execution environment, system architecture, and specific application requirements.
How do AutoML methodologies consider association between models in a pipeline?
5 answers
AutoML methodologies address the association between models in a pipeline by focusing on optimizing the entire Machine Learning pipeline, including preprocessing, model selection, and hyperparameter optimization. Existing approaches often fail to explore deep interactions between pipeline components. To overcome this limitation, novel neural architectures are proposed to capture these interactions, embedding pipelines into latent representations for optimization using techniques like deep-kernel Gaussian Process surrogates within Bayesian Optimization setups. Additionally, a data-centric approach based on meta-features is suggested for constructing flexible and dataset-specific ML pipelines efficiently, considering the impact of complex pipeline structures containing multiple preprocessing and classification algorithms. These methodologies aim to automate the construction of ML pipelines while ensuring optimal performance by integrating meta-learning mechanisms and handling imbalanced datasets effectively.
How to set up an sql server?
5 answers
To set up an SQL Server, you need to follow specific steps. Firstly, ensure you have the necessary installation and configuration knowledge, as highlighted in the book by Buck Woody. Secondly, consider the security aspects by setting up administrator passwords to control access permissions, as detailed in the method provided by Gu Weitao. Additionally, understanding the basics of relational databases, client-server technology, LANs, and SQL Server's architecture, features, and operation is crucial, as discussed in the book by Aloke Nath. Lastly, familiarize yourself with SQL Server 2008's capabilities, such as creating tables, securing data, using Transact-SQL for programming, and setting up backup and recovery processes, as outlined by Robin Dewson. By combining insights from these sources, you can effectively set up an SQL Server for your needs.
How to asses search performance in transactional search intents?
4 answers
To assess search performance in transactional search intents, one can utilize various techniques proposed in the research papers. Understanding query intents is crucial for improving search quality. Differentiating transactional pages from informational ones is key, as transactional intent is common in web searches. Search engine advertising performance is influenced by search intentions, including transactional intent, and the presence of organic results. User interactions reveal underlying intents like transactional, which can significantly impact search engine performance. By classifying search missions into transactional, navigational, or informational categories based on user interactions, search performance can be assessed effectively. Leveraging features like Clicked-URL (CURL) and user behavior signals can enhance the accuracy of identifying transactional queries and evaluating search performance in this specific intent category.
How does pressure relate in gas?
5 answers
Gas pressure is crucial in various gas-related applications, as evidenced by the inventions discussed in the provided contexts. Gas pressure is managed through innovative devices like gas pressure valves, gas pressure regulators, and gas pressure sensors. These devices ensure proper pressure control, sealing performance, stability, and reliability in gas systems. Additionally, gas pressure measuring devices are employed for accurate pressure monitoring in specific environments like coal seams. The pressure pipelines in gas engineering are designed with high connecting strength to withstand pressure variations effectively. Overall, the data from the contexts highlight the significance of maintaining and measuring gas pressure accurately for safe and efficient operations in various gas-related systems.
What are dimensions for search performance in search engines?
5 answers
Dimensions for evaluating search engine performance include traditional measures like recall and precision, as well as newer metrics to assess stability and quality from the user's perspective. These dimensions are crucial for distinguishing search engine performance effectively. In the context of factual searches in full-text databases, measures such as recall, precision, term overlap, and efficiency are considered, with a focus on the Process/Outcome factor, Improvement in Term Overlap, and Efficiency. Additionally, query dimensions can be identified by analyzing significant lists in the top retrieved documents, providing valuable insights into the underlying facets of a query. Furthermore, query performance prediction involves incorporating the time dimension into ranking models, utilizing various predictors like linear regression and neural networks for enhanced performance.
What is the meaning of the word utilization?
5 answers
Utilization refers to the effective use or application of something, such as information systems, computer programs, or materials, to achieve a specific purpose or goal. It involves making the most out of available resources or features. In the context of information systems, utilization is crucial for enhancing performance. For computer programs, increasing utilization involves analyzing user behavior to identify unused or underutilized features and then guiding users towards utilizing them effectively. Designers also focus on utilization by creating products that encourage users to explore and make the most of the available features and materials. In the realm of technology, utilization can involve optimizing memory systems to efficiently access and utilize data for computational tasks.