Explore Microsoft's innovative approach to query optimization in production environments, addressing challenges with general-purpose optimization and introducing specialized cloud-based optimizers. Learn about the implementation details, experiments conducted, and the solution proposed. Discover how

2 views • 27 slides

Optimization in process engineering involves obtaining the best possible solution for a given process by minimizing or maximizing a specific performance criterion while considering various constraints. This process is crucial for achieving improved yields, reducing pollutants, energy consumption, an

10 views • 52 slides

Studying relational models, SQL, database system architecture, operator implementations, data layouts, and query optimization laid the foundation for advanced topics like Concurrency Control and Recovery. Discover how transactions group related actions, ACID properties ensure data integrity, and the

0 views • 57 slides

Swarm Intelligence (SI) is an artificial intelligence technique inspired by collective behavior in nature, where decentralized agents interact to achieve goals. Swarms are loosely structured groups of interacting agents that exhibit collective behavior. Examples include ant colonies, flocking birds,

2 views • 88 slides

Accessing and navigating the Self-Service Banner 9.0 for budget queries can be simplified by following a step-by-step guide. From initiating a new finance query to selecting relevant columns and submitting the query, this process ensures accuracy and efficiency in tracking budget status by account.

0 views • 19 slides

This content covers the fundamentals of query optimization in Database Management Systems (DBMS), including steps involved, required information for evaluating queries, cost-based query sub-system, and the role of various components like query parser, optimizer, plan generator, and cost estimator. I

0 views • 51 slides

Active Learning (AL) is a subset of machine learning where a learning algorithm interacts with a user to label data for desired outputs. It aims to minimize the labeling bottleneck by achieving high accuracy with minimal labeled instances, thus reducing the cost of obtaining labeled data. Techniques

0 views • 17 slides

The DNN Inference Optimization Challenge, organized by Liya Yuan from ZTE, focuses on optimizing deep neural network (DNN) models for efficient inference on-device, at the edge, and in the cloud. The challenge addresses the need for high accuracy while minimizing data center consumption and inferenc

0 views • 13 slides

Spatial data management research focuses on designing robust spatial data structures, inventing new models, constructing query languages, and optimizing query processing. This study explores the estimation of query performance and selectivity, specifically in R-trees, for efficient access planning.

1 views • 32 slides

Discrete Optimization is a field of applied mathematics that uses techniques from combinatorics, graph theory, linear programming, and algorithms to solve optimization problems over discrete structures. This involves creating mathematical models, defining objective functions, decision variables, and

0 views • 12 slides

This study delves into the generalization of Empirical Risk Minimization (ERM) in stochastic convex optimization, focusing on minimizing true objective functions while considering generalization errors. It explores the application of ERM in machine learning and statistics, particularly in supervised

0 views • 11 slides

Explore the world of optimization through convex and general problems, understanding the concepts, constraints, and the difference between convex and non-convex optimization. Discover the significance of local and global optima in solving complex optimization challenges.

0 views • 24 slides

Explore the basic components of optimization problems, such as objective functions, constraints, and global vs. local optima. Learn about single vs. multiple objective functions and constrained vs. unconstrained optimization problems. Dive into the statement of optimization problems and the concept

0 views • 96 slides

Explore the innovative research on Shifting Bloom Filters conducted at Peking University, China, featuring evaluations, conclusions, background information, and insights on membership, association, and multiplicity queries. The study delves into hash functions, theoretical results, and the Shifting

1 views • 25 slides

The study delves into how to assess the completeness of query answers when dealing with partially complete databases. By analyzing data from a telecommunication company’s data warehouse, the query results are examined to determine if all warnings generated by maintenance objects with hardware team

0 views • 23 slides

Explore the relationships between query complexity measures, including quantum query complexity, adversary bounds, and spectral sensitivity, in the context of symmetric functions. Analysis includes sensitivity graphs, the quantum query model, and approximate counting methods. Results cover spectral

0 views • 19 slides

In this lecture, Mohammad Hammoud discusses the importance of relational query languages (QLs) in manipulating and retrieving data in databases. He covers the strong formal foundation of QLs, their distinction from programming languages, and their effectiveness for accessing large datasets. The sess

0 views • 39 slides

This lecture outlines the structure and query process of Priority Search Trees (PST) in computational geometry. It covers heap-based point queries, range trees for windowing queries, handling query ranges in 1D and 2D spaces, and using heaps to efficiently handle query ranges. The content discusses

1 views • 18 slides

Recent research highlights advancements in solving sampling problems in convex optimization, exemplified by works by Yin Tat Lee and Santosh Vempala. The complexity of convex problems, such as the Minimum Cost Flow Problem and Submodular Minimization, are being unraveled through innovative formulas

1 views • 47 slides

Query optimization plays a crucial role in improving database performance. This paper discusses techniques for optimizing join enumeration in transformation-based query optimizers, focusing on avoiding cross-products in join orders. It explores efficient algorithms for generating cross-product-free

0 views • 18 slides

The study explores incorporating contrastive learning into abstractive summarization systems to improve discernment between salient and non-salient content in summaries, aiming for higher relevance to the query. By designing a contrastive learning framework and utilizing segment scores, the system c

0 views • 16 slides

This piece discusses approximation algorithms for stochastic optimization problems, focusing on modeling uncertainty in inputs, adapting to stochastic predictions, and exploring different optimization themes. It covers topics such as weakening the adversary in online stochastic optimization, two-sta

0 views • 33 slides

These notes explore practical tools for corpus search including regular expressions and the corpus query language (CQL/CQP). They provide an introduction to using corpora effectively for pattern identification, with examples and explanations. The guide includes information on levels of annotation an

0 views • 47 slides

BlinkDB is a framework built on Apache Hive, designed to support interactive SQL-like aggregate queries over massive datasets. It creates and maintains samples from data for fast, approximate query answers, supporting various aggregate functions with error bounds. The architecture includes modules f

0 views • 26 slides

Investigation into query completeness over incomplete databases, highlighting the importance of data completeness for accurate query answering. Examples and reasoning provided to illustrate the challenges and considerations in ensuring query completeness.

0 views • 31 slides

Designing a scalable query system for evaluating complex game environments involves key elements like defining required features, structuring query elements, and understanding function models for optimal performance. The system must be customizable, support debugging, and allow runtime parameter adj

0 views • 41 slides

Jena SPARQL for Mac is a powerful tool for querying RDF graphs using SPARQL. Learn about RDF graphs, models, triples, and how SPARQL queries work. Explore ARQ, a query engine that supports the SPARQL RDF Query language and features multiple query languages. Discover how to install ARQ and execute SP

0 views • 25 slides

BlinkDB is a framework built on Hive and Spark that creates and maintains offline samples for fast, approximate query processing. It provides error bars for queries executed on the same data and ensures correctness. The paper introduces innovations like sample creation techniques, error latency prof

0 views • 8 slides

Real-world design problems often require multi-objective optimization, and the Flower Pollination Algorithm (FPA) developed by Xin-She Yang in 2012 mimics the pollination process of flowering plants to efficiently solve such optimization tasks. FPA has shown promising results in extending to multi-o

0 views • 15 slides

This study presents an approach for unsupervised relation detection by aligning query patterns extracted from knowledge graphs and query click logs. The process involves automatic alignment of query patterns to determine relations in a knowledge graph, aiding in tasks like spoken language understand

0 views • 29 slides

Revealing the intricacies of parallel query processing on big data, this content explores various computation models such as MapReduce, MUD, and MRC. It delves into the MPC model in detail, showcasing the tradeoffs between space exponent and computation rounds. The study uncovers lower bounds on spa

0 views • 25 slides

This research presents a hybrid optimization heuristic approach for efficient instruction scheduling in programmable accelerator codesign. It discusses Google's TPU architecture, problem-solving strategies, and computation graph mapping, routing, and timing optimizations. The technique overview high

0 views • 33 slides

This presentation discusses the application of machine learning for optimizing EBIS beam intensity and RHIC luminosity. It covers topics such as motivation, EBIS beam intensity optimization, luminosity optimization, and outlines the plan and summary of the project. Collaborators from MSU, LBNL, and

0 views • 23 slides

Bayesian optimization is being used at LCLS to tune the Free Electron Laser (FEL) pulse energy efficiently. The current approach involves a tradeoff between human optimization and numerical optimization methods, with Gaussian processes providing a probabilistic model for tuning strategies. Prior mea

0 views • 16 slides

The research paper presents a dichotomy on the complexity of consistent query answering for atoms with simple keys. It discusses repairs for uncertain instances in a schema with key constraints, as well as the concept of consistent query answering. The document addresses the problem statement of cer

0 views • 26 slides

The evolution of metalearning in the machine learning community is traced from the initial workshop in 1998 to recent developments in hyper-parameter optimization. Challenges in classifier selection and the validity of hyper-parameter optimization claims are discussed, urging the exploration of spec

1 views • 32 slides

Discussing the connection between Artificial Intelligence (AI)/Machine Learning (ML) and Wireless LAN networks, this document explores how AI/ML can improve IEEE 802.11 features, enhance Wi-Fi performance through optimized data sharing, and enable network slicing for diverse application requirements

0 views • 11 slides

Explore the use of tree automata for reasoning, querying databases using logic languages, optimizing queries through relation algebra, and core problems in query optimization. Learn about data exchange on the web, inference of information from incomplete data, and the semantics of Datalog programs f

0 views • 44 slides

Fast Bayesian Optimization optimizes hyperparameters for machine learning on large datasets efficiently. It involves black-box optimization using Gaussian Processes and acquisition functions. Regular Bayesian Optimization faces challenges with large datasets, but FABOLAS introduces an innovative app

0 views • 12 slides

This presentation delves into the concept of price optimization in the auto insurance industry, covering actuarial, economic, and regulatory aspects. It addresses the controversy surrounding price optimization, various state definitions, concerns, and the use of sophisticated tools to quantify busin

5 views • 29 slides