SQL tuning An execution plan is the output of the optimizer and is presented to the execution engine for
implementation. It instructs the execution engine about the operations that it must perform for
most efficiently retrieving the data required by a query.
The EXPLAIN PLAN statement gathers execution plans chosen by the Oracle optimizer for
the SELECT, UPDATE, INSERT, and DELETE statements. The steps of the execution plan are
not performed in the order in which they are numbered. There is a parent-child relationship
between steps. The row source tree is the core of the execution plan. It shows the following
information: • An ordering of the tables referenced by the statement
• An access method for each table mentioned in the statement
• A join method for tables affected by join operations in the statement
• Data operations, such as filter, sort, or aggregation
In addition to the row source tree (or data flow tree for parallel operations), the plan table
contains information about the following:
• Optimization, such as the cost and cardinality of each operation
• Partitioning, such as the set of accessed partitions
• Parallel execution, such as the distribution method of join inputs
The EXPLAIN PLAN results help you determine whether the optimizer selects a particular When you tune a SQL statement in an OLTP environment, the goal is to drive from the table
that has the most selective filter. This means that there are fewer rows passed to the next
step. If the next step is a join, this means fewer rows are joined. Check to see whether the
access paths are optimal. When you examine the optimizer execution plan, check to confirm
the following:
• The plan is such that the driving table has the best filter.
• The join order in each step means that the fewest number of rows are returned to the
next step (that is, the join order should reflect going to the best not-yet-used filters).
• The join method is appropriate for the number of rows being returned. For example,
nested loops joins through indexes may not be optimal when many rows are returned.
• Views are used efficiently. Look at the SELECT list to see whether access to the view is
necessary.
• There are any unintentional Cartesian products (even with small tables).
• Each table is being accessed efficiently. Consider the predicates in the SQL statement and the number of rows in the table. Look for suspicious activity, such as a full table
scan on tables with large number of rows, which have predicates in the WHERE clause.
Also, a full table scan might be more efficient on a small table, or to leverage a better
join method (for example, hash join) for the number of rows returned.
If any of these conditions are not optimal, consider restructuring the SQL statement or the
indexes available on the tables. There are many ways to retrieve execution plans inside the database. The most well known
ones are listed in the slide:
• The EXPLAIN PLAN command enables you to view the execution plan that the
opt
SQL tuning An execution plan is the output of the optimizer and is presented to the execution engine for
implementation. It instructs the execution engine about the operations that it must perform for
most efficiently retrieving the data required by a query.
The EXPLAIN PLAN statement gathers execution plans chosen by the Oracle optimizer for
the SELECT, UPDATE, INSERT, and DELETE statements. The steps of the execution plan are
not performed in the order in which they are numbered. There is a parent-child relationship
between steps. The row source tree is the core of the execution plan. It shows the following
information: • An ordering of the tables referenced by the statement
• An access method for each table mentioned in the statement
• A join method for tables affected by join operations in the statement
• Data operations, such as filter, sort, or aggregation
In addition to the row source tree (or data flow tree for parallel operations), the plan table
contains information about the following:
• Optimization, such as the cost and cardinality of each operation
• Partitioning, such as the set of accessed partitions
• Parallel execution, such as the distribution method of join inputs
The EXPLAIN PLAN results help you determine whether the optimizer selects a particular When you tune a SQL statement in an OLTP environment, the goal is to drive from the table
that has the most selective filter. This means that there are fewer rows passed to the next
step. If the next step is a join, this means fewer rows are joined. Check to see whether the
access paths are optimal. When you examine the optimizer execution plan, check to confirm
the following:
• The plan is such that the driving table has the best filter.
• The join order in each step means that the fewest number of rows are returned to the
next step (that is, the join order should reflect going to the best not-yet-used filters).
• The join method is appropriate for the number of rows being returned. For example,
nested loops joins through indexes may not be optimal when many rows are returned.
• Views are used efficiently. Look at the SELECT list to see whether access to the view is
necessary.
• There are any unintentional Cartesian products (even with small tables).
• Each table is being accessed efficiently. Consider the predicates in the SQL statement and the number of rows in the table. Look for suspicious activity, such as a full table
scan on tables with large number of rows, which have predicates in the WHERE clause.
Also, a full table scan might be more efficient on a small table, or to leverage a better
join method (for example, hash join) for the number of rows returned.
If any of these conditions are not optimal, consider restructuring the SQL statement or the
indexes available on the tables. There are many ways to retrieve execution plans inside the database. The most well known
ones are listed in the slide:
• The EXPLAIN PLAN command enables you to view the execution plan that the
opt
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
