Using the indexes in the Voyager Oracle database can speed up your queries considerably. This presentation suggests ways to find out what is indexed and to use the indexes.
The document discusses database normalization and the three forms of normalization. It provides examples of tables that violate each normal form and explains how to modify the tables to conform to the normal forms. The first normal form requires each field to contain a single value and related data to be stored in separate tables or fields. The second normal form eliminates redundant data by creating separate tables for values that apply to multiple records. The third normal form ensures non-key fields are fully dependent on the primary key and removes transitive dependencies.
This document discusses execution plans in Oracle databases. It begins by defining an execution plan as the detailed steps the optimizer uses to execute a SQL statement, expressed as database operators. It then covers how to generate plans using EXPLAIN PLAN or V$SQL_PLAN, what constitutes a good plan for the optimizer in terms of cost and performance, and key aspects of plans including cardinality, access paths, join types, and join order. Examples are provided to illustrate each concept.
How to validate an email address syntaxBenHamminton
This document provides information on email address formats and restrictions. It explains that an email address has two parts separated by an @ sign, with the local part allowing 64 characters including some special characters, and the domain part allowing 255 characters following hostname requirements. It also provides a sample regular expression that checks the format but has some exceptions for valid addresses it does not pass.
The document discusses subqueries in SQL. It defines a subquery as a SELECT statement embedded within another SELECT statement. Subqueries allow queries to be built from simpler statements by executing an inner query and using its results to inform the conditions of the outer query. The key aspects covered are: subqueries can be used in the WHERE, HAVING, FROM and other clauses; single-row subqueries use single-value operators while multiple-row subqueries use operators like ANY and ALL; and subqueries execute before the outer query to provide their results.
MySQL optimization involves understanding the entire system to be optimized. The query optimizer attempts to determine the most efficient way to execute a query by considering possible query plans. Key aspects of optimization include data types and schema design, indexing, and query optimization. Smaller data types, simpler schemas, and indexes on commonly used columns can improve performance.
The document discusses database normalization. It defines normalization as reducing relations with anomalies to produce smaller, well-structured relations based on normal forms and functional dependencies. The document outlines the different normal forms including 1NF, 2NF, 3NF, BCNF, and discusses functional dependencies, anomalies, and the purpose of normalization to reduce redundancy and simplify data management. Examples are provided to illustrate concepts like update anomalies, insertion anomalies, and normal forms.
A subquery, or nested query, allows a query to be run within another SQL statement. Subqueries return intermediate results that can be used by the outer query. They are useful when the exact value needed is unknown but can be determined by a separate SELECT statement. Subqueries can be used with SELECT, INSERT, UPDATE, and DELETE statements. Correlated subqueries depend on the outer query, while non-correlated subqueries operate independently.
The document contains guidelines for testing various form objects and fields commonly found in applications. It includes test cases to validate text boxes, numeric fields, toolbars, list boxes, radio buttons, text areas, checkboxes, date fields, localization, passwords, emails, parameters, reports, multi-user access, login functionality, calculations and more. The guidelines are intended to ensure proper handling of data entry, formatting and validation for different objects, fields and scenarios.
The Microsoft Excel MATCH function searches for a value in an array and returns the relative position of that item.
The MATCH function is a built-in function in Excel that is categorized as a Lookup/Reference Function. It can be used as a worksheet function (WS) in Excel. As a worksheet function, the MATCH function can be entered as part of a formula in a cell of a worksheet.
In this part of the course, meta languages for describing grammars are introduced. Bottom-up and top-down parsers are derivation steps are described. Finally, ambiguous grammars are defined.
Nested subqueries contain a subquery within another subquery to retrieve data from a table. Correlated subqueries reference columns from the outer query so the inner query is executed once for each row returned by the outer query. This document provides examples of using nested and correlated subqueries to find the third highest salary or details of employees with specific highest salaries from an employee table.
The document describes a phone address booklet program with the following functions:
1) It allows the user to add new contacts by entering the contact's first name, last name, and telephone number into text fields.
2) It allows the user to search for existing contacts by entering their first name, last name, or telephone number.
3) It displays search results with the option to edit or cancel and returns the user to the main menu.
4) It handles errors if required fields are left empty and returns the user to the main menu.
This document discusses complex formulas in Excel. It defines complex formulas as those involving more than one mathematical operation. It reviews the order of operations as parentheses, exponents, multiplication/division (left to right), and addition/subtraction (left to right). Examples show how to write complex formulas using multiple operations and absolute vs. relative cell references. The document provides a challenge to create complex formulas using various operations and an absolute reference in a spreadsheet.
The document discusses various relational algebra operations. It describes select, project, join, union, intersection, difference, cartesian product and aggregate operations. It explains how these operations work and provides examples of relational algebra expressions to retrieve data from relations using these operations. These core relational algebra operations form the basis for implementing queries in relational database management systems.
The document provides instructions for creating tables in Microsoft Access 2010 using Table Design view. It describes how to define fields, data types, primary keys and field properties before entering data. Additional methods like creating a lookup field using the Lookup Wizard and freezing columns for data entry are also outlined. The summary provides the essential steps to set up a table structure and enter data.
Presentation given to the Voyager Users Group Meeting (VUGM) in 2001, later known as EndUser. This presentation is one of the most requested ones from any Voyager conference. It tells step by step how to use the "BLOB functions" that are part of the reports.mdb database for use with the Voyager Integrated Library System.
This document provides instructions for creating various types of queries in Microsoft Access 2010, including select, update, parameter, and totals queries. It explains how to add and sort fields, apply filters and criteria, perform calculations, and summarize data. Key steps covered include adding tables to a query, specifying criteria ranges, creating calculated and concatenated fields, running action queries, and using parameters and joins. The practice assignment demonstrates how to build queries that pull donor data, calculate contribution totals and averages, and allow filtering by date range.
A database is a collection of related tables or information that can be organized in many ways.
It is like keeping a set of file folder or record that contains any number of separate categories or entries which are called fields.
There are terminologies that are important to know when working with databases. They relate to the way data is organized in a table.
Microsoft Access is a database management program that allows users to create and manage databases. It allows users to create tables to store data in fields and records, and relate tables together. Users can also create forms and reports to display and organize the data. Advanced users can write code and applications to further manipulate the data. Access is used for personal, small business, departmental, and corporate applications to store and reference information.
This document provides an overview of Microsoft Access and databases, including opening and creating an Access database, creating tables and defining relationships between tables, and creating forms and reports to display and output data. It discusses the differences between file processing and database management systems (DBMS), and key characteristics of DBMS like self-describing metadata, program-data independence, and multiple user access.
This document provides an overview of Microsoft Access and database concepts. It includes sections on getting started with Access, navigating the environment, database terms like tables, queries, forms and reports, and how to create and manage a database including adding tables, fields, records, relationships and running queries. The document aims to introduce users to key Access features and the basics of setting up and working with an Access database.
Microsoft Access is a powerful program for creating and managing databases. It allows users to construct tables with fields and records to organize data. Users can view and manipulate this data in a table or in alternate views like forms and reports. Forms present data from tables in a graphical interface, allowing users to easily add, update and delete records. Reports take the data and present it in a printed format designed by the user. Overall, Microsoft Access provides tools for structuring, editing, and presenting database information in an intuitive interface.
This document provides an overview of creating and working with tables in Microsoft Access. It describes how to design a table by adding fields and setting their properties. Key points covered include data types, primary keys, field properties like format, validation and required fields. Navigation and editing features for working with table data are also summarized.
This document provides a summary of MySQL indexes and how to use the EXPLAIN statement to analyze query performance. It defines what indexes are, the different types of indexes like B-tree, hash, and full-text indexes. It also explains concepts like compound indexes, covering indexes, and partial indexes. The document demonstrates how to use the EXPLAIN statement to view and understand a query execution plan, including analyzing the possible and actual indexes used, join types, number of rows examined, and index usage. It provides examples of interpreting EXPLAIN output and analyzing performance bottlenecks.
The Microsoft Excel MATCH function searches for a value in an array and returns the relative position of that item.
The MATCH function is a built-in function in Excel that is categorized as a Lookup/Reference Function. It can be used as a worksheet function (WS) in Excel. As a worksheet function, the MATCH function can be entered as part of a formula in a cell of a worksheet.
In this part of the course, meta languages for describing grammars are introduced. Bottom-up and top-down parsers are derivation steps are described. Finally, ambiguous grammars are defined.
Nested subqueries contain a subquery within another subquery to retrieve data from a table. Correlated subqueries reference columns from the outer query so the inner query is executed once for each row returned by the outer query. This document provides examples of using nested and correlated subqueries to find the third highest salary or details of employees with specific highest salaries from an employee table.
The document describes a phone address booklet program with the following functions:
1) It allows the user to add new contacts by entering the contact's first name, last name, and telephone number into text fields.
2) It allows the user to search for existing contacts by entering their first name, last name, or telephone number.
3) It displays search results with the option to edit or cancel and returns the user to the main menu.
4) It handles errors if required fields are left empty and returns the user to the main menu.
This document discusses complex formulas in Excel. It defines complex formulas as those involving more than one mathematical operation. It reviews the order of operations as parentheses, exponents, multiplication/division (left to right), and addition/subtraction (left to right). Examples show how to write complex formulas using multiple operations and absolute vs. relative cell references. The document provides a challenge to create complex formulas using various operations and an absolute reference in a spreadsheet.
The document discusses various relational algebra operations. It describes select, project, join, union, intersection, difference, cartesian product and aggregate operations. It explains how these operations work and provides examples of relational algebra expressions to retrieve data from relations using these operations. These core relational algebra operations form the basis for implementing queries in relational database management systems.
The document provides instructions for creating tables in Microsoft Access 2010 using Table Design view. It describes how to define fields, data types, primary keys and field properties before entering data. Additional methods like creating a lookup field using the Lookup Wizard and freezing columns for data entry are also outlined. The summary provides the essential steps to set up a table structure and enter data.
Presentation given to the Voyager Users Group Meeting (VUGM) in 2001, later known as EndUser. This presentation is one of the most requested ones from any Voyager conference. It tells step by step how to use the "BLOB functions" that are part of the reports.mdb database for use with the Voyager Integrated Library System.
This document provides instructions for creating various types of queries in Microsoft Access 2010, including select, update, parameter, and totals queries. It explains how to add and sort fields, apply filters and criteria, perform calculations, and summarize data. Key steps covered include adding tables to a query, specifying criteria ranges, creating calculated and concatenated fields, running action queries, and using parameters and joins. The practice assignment demonstrates how to build queries that pull donor data, calculate contribution totals and averages, and allow filtering by date range.
A database is a collection of related tables or information that can be organized in many ways.
It is like keeping a set of file folder or record that contains any number of separate categories or entries which are called fields.
There are terminologies that are important to know when working with databases. They relate to the way data is organized in a table.
Microsoft Access is a database management program that allows users to create and manage databases. It allows users to create tables to store data in fields and records, and relate tables together. Users can also create forms and reports to display and organize the data. Advanced users can write code and applications to further manipulate the data. Access is used for personal, small business, departmental, and corporate applications to store and reference information.
This document provides an overview of Microsoft Access and databases, including opening and creating an Access database, creating tables and defining relationships between tables, and creating forms and reports to display and output data. It discusses the differences between file processing and database management systems (DBMS), and key characteristics of DBMS like self-describing metadata, program-data independence, and multiple user access.
This document provides an overview of Microsoft Access and database concepts. It includes sections on getting started with Access, navigating the environment, database terms like tables, queries, forms and reports, and how to create and manage a database including adding tables, fields, records, relationships and running queries. The document aims to introduce users to key Access features and the basics of setting up and working with an Access database.
Microsoft Access is a powerful program for creating and managing databases. It allows users to construct tables with fields and records to organize data. Users can view and manipulate this data in a table or in alternate views like forms and reports. Forms present data from tables in a graphical interface, allowing users to easily add, update and delete records. Reports take the data and present it in a printed format designed by the user. Overall, Microsoft Access provides tools for structuring, editing, and presenting database information in an intuitive interface.
This document provides an overview of creating and working with tables in Microsoft Access. It describes how to design a table by adding fields and setting their properties. Key points covered include data types, primary keys, field properties like format, validation and required fields. Navigation and editing features for working with table data are also summarized.
This document provides a summary of MySQL indexes and how to use the EXPLAIN statement to analyze query performance. It defines what indexes are, the different types of indexes like B-tree, hash, and full-text indexes. It also explains concepts like compound indexes, covering indexes, and partial indexes. The document demonstrates how to use the EXPLAIN statement to view and understand a query execution plan, including analyzing the possible and actual indexes used, join types, number of rows examined, and index usage. It provides examples of interpreting EXPLAIN output and analyzing performance bottlenecks.
This document discusses various strategies for optimizing MySQL queries and indexes, including:
- Using the slow query log and EXPLAIN statement to analyze slow queries.
- Avoiding correlated subqueries and issues in older MySQL versions.
- Choosing indexes based on selectivity and covering common queries.
- Identifying and addressing full table scans and duplicate indexes.
- Understanding the different join types and selecting optimal indexes.
This document explores the many indexing options available in Postgres, including traditional b-tree indexing, expression indexes, partial indexes, bitmap indexes, and non-b-tree index types like GIN, GIST, and SP-GIST. It discusses how to determine which index types support which data types and provides examples of how different index types are structured.
About Flexible Indexing
Postgres’ rich variety of data structures and data-type specific indexes can be confusing for newer and experienced Postgres users alike who may be unsure when and how to use them. For example, gin indexing specializes in the rapid lookup of keys with many duplicates — an area where traditional btree indexes perform poorly. This is particularly useful for json and full text searching. GiST allows for efficient indexing of two-dimensional values and range types.
To listen to the recorded presentation with Bruce Momjian, visit Enterprisedb.com > Resources > Webcasts > Ondemand Webcasts.
For product information and subscriptions, please email [email protected].
Ground Breakers Romania: Explain the explain_planMaria Colgan
This session was delivered as part of the EMEA Ground Breakers tour in Romania, Oct. 2019. The execution plan for a SQL statement can often seem complicated and hard to understand. Determining if the execution plan you are looking at is the best plan you could get or attempting to improve a poorly performing execution plan can be a daunting task even for the most experienced DBA or developer. This session examines the different aspects of an execution plan, from selectivity to parallel execution and explains what information you should be gleaming from the plan and how it affects the execution. It offers insight into what caused the Optimizer to make the decision it did as well as a set of corrective measures that can be used to improve each aspect of the plan.
Paper presented by Arno Brinkman https://www.abvisie.nl/firebird/
Global topics
- Introduction
- At which point does the optimizer his work
- Optimizer steps
- Index
Building Learning to Rank (LTR) search reranking models using Large Language ...Sujit Pal
Search engineers have many tools to address relevance. Older tools are typically unsupervised (statistical, rule based) and require large investments in manual tuning effort. Newer ones involve training or fine-tuning machine learning models and vector search, which require large investments in labeling documents with their relevance to queries.
Learning to Rank (LTR) models are in the latter category. However, their popularity has traditionally been limited to domains where user data can be harnessed to generate labels that are cheap and plentiful, such as e-commerce sites. In domains where this is not true, labeling often involves human experts, and results in labels that are neither cheap nor plentiful. This effectively becomes a roadblock to adoption of LTR models in these domains, in spite of their effectiveness in general.
Generative Large Language Models (LLMs) with parameters in the 70B+ range have been found to perform well at tasks that require mimicking human preferences. Labeling query-document pairs with relevance judgements for training LTR models is one such task. Using LLMs for this task opens up the possibility of obtaining a potentially unlimited number of query judgment labels, and makes LTR models a viable approach to improving the site’s search relevancy.
In this presentation, we describe work that was done to train and evaluate four LTR based re-rankers against lexical, vector, and heuristic search baselines. The models were a mix of pointwise, pairwise and listwise, and required different strategies to generate labels for them. All four models outperformed the lexical baseline, and one of the four models outperformed the vector search baseline as well. None of the models beat the heuristics baseline, although two came close – however, it is important to note that the heuristics were built up over months of trial and error and required familiarity of the search domain, whereas the LTR models were built in days and required much less familiarity.
This document provides an overview of search and indexing in Adobe Experience Manager using Apache Oak. It discusses Oak query implementation, cost calculation, and various index implementations like property, ordered, Lucene, Solr, and traversing indexes. It provides details on how indexes are defined, when to reindex, debugging cost calculation, and troubleshooting Solr. The primary messages are that search is significantly different between CRX2 and Oak, Oak provides more optimization opportunities but requires more configuration, and indexes need to be understood to optimize query performance.
Presented at 3|SHARE's EVOLVE'14 - The Adobe Experience Manager Community Summit on Wednesday November 19th, 2014 at the Hard Rock Hotel in San Diego, CA. evolve14.com
This document discusses how to query SAP tables from Java applications using the R3Table class. The R3Table class provides a wrapper for the RFC_READ_TABLE function module to make it easier to load table data into Java objects and models. The document describes using R3Table to get data from SAP tables, load it into list models, and display it in HTML controls. It also discusses enhancing security and functionality by creating a custom version of RFC_READ_TABLE called ZRFC_READ_TABLE.
This document provides an overview of performance tuning and indexing. It discusses indexing concepts like clustering factor and index data structures like B-trees. It also covers indexing strategies like reverse key indexes and the different types of histograms that can be created, including frequency, height-balanced, top frequency and hybrid histograms in 12c. The document concludes with discussing the basic statistics that are automatically collected on tables, columns and indexes to help with query optimization.
Indexes support efficient query execution in MongoDB. Without indexes, MongoDB would need to scan every document in a collection to find matching documents for a query. Indexes store a small portion of collection data in an easy-to-traverse form, ordered by the value of specific fields. To create an index, the db.collection.createIndex() method is used, specifying the field(s) to index. Indexes can be single-field, compound on multiple fields, or multikey for array values. The aggregation pipeline provides efficient data aggregation by transforming documents through a series of pipeline stages that can filter, sort, group, and perform calculations on document fields and arrays.
SH 2 - SES 3 - MongoDB Aggregation Framework.pptxMongoDB
The document provides an overview of MongoDB's aggregation framework. It explains that the aggregation framework allows users to process data from MongoDB collections and databases using aggregation pipeline stages similar to data aggregation operations in SQL like GROUP BY, JOIN, and filtering. The document then discusses several aggregation pipeline stages like $project, $lookup, $match, and $group. It also provides an example comparing an aggregation pipeline to a SQL query with GROUP BY and HAVING.
This document discusses various techniques for analyzing and tuning SQL queries to improve performance. It covers measurement methods like EXPLAIN and slow logs, database design optimizations like normalization and index usage, optimizing WHERE conditions to use indexes, choosing the best access methods, and join optimization techniques. Specific strategies mentioned include changing WHERE conditions to utilize indexes more efficiently, using STRAIGHT_JOIN to control join order, and optimizing queries that use filesort or joins vs subqueries.
Making materials findable at State Library Victoria, May 2015Alan Manifold
The document discusses issues around making materials from the State Library of Victoria findable online. It summarizes the different types of materials held in the collections, including books, maps, manuscripts, and digital resources. It also outlines the various sources of metadata that describe these materials, and the pathways that this metadata takes between different discovery platforms and databases. Key challenges mentioned include data synchronization between systems, complexity in handling different material formats, and addressing legacy descriptive practices and uncatalogued materials. The document seeks input on these findability challenges from the perspective of audiences and users.
A presentation on setting up Deep Links into the Primo discovery platform from an external website. Presented to the ANZREG Seminar, 6 February 2015, in Sydney, NSW.
Making Materials Findable at the State Library of VictoriaAlan Manifold
Discussion of some of the issues involved with the many data sources and repositories in use at the State Library of Victoria, how they interact and some of the solutions we have come up with to resolve them.
A collection of quotes and photographs related to the Baha'i electoral process. Suitable for use at Electoral Unit Conventions or other Baha'i election events
Photo retrospective of the life of Rosemary ManifoldAlan Manifold
This document contains information about two musical pieces performed by different groups. The first is Arcangelo Corelli's Christmas Concerto performed by the 1964 Epworth Forest Choir School Orchestra. The second is "Bali Ha'i" from the musical South Pacific, performed by Juanita Hall in the original Broadway production.
This keynote address to the Kentucky Voyager Users Group meeting in 2004 challenges librarians to think about how they do things in light of changed conditions in libraries and in the world. With new technologies both present and on the horizon, it is important that libraries make reviewing their workflows and policies part of their routine.
The PowerPoint slides make use of a variation of the Doobie Brothers' album cover for What Were Once Vices Are Now Habits.
Presented at the Endeavor Users Group (EndUser) meeting in 2004, this talk advocated for Endeavor to keep pushing their ENCompass product to the limits of functionality. Using for its starting point a tendency of some of the federated searching connectors to fail on a regular basis, it argues that it's important to keep having some level of failures, because otherwise it means the product only incorporates well-tested and proven technology, rather than exploring the edges of what is possible.
Queer Buy for the Straight Li-brary (Endeavor's ENCompass)Alan Manifold
This keynote address to the Great Lakes Users Group Meeting in 2003 discusses how ENCompass, a federated search product from Endeavor Information Systems, might have a hard time fitting into librarians' notions of how library systems ought to work.
This presentation was given by Alan Manifold at the 2014 ANZREG Seminar in Melbourne, VIC, held at the State Library of Victoria. It gives a quick overview of the creation by staff of the State Library of Victoria of a set of APIs (Application Programming Interface) to access directly the data within the DigiTool system. This allows users to create their own mashups and for the library to fashion a more flexible interface for using digital objects without being tied too closely to a DigiTool syntax and DigiTool viewers.
This presentation was given to the SCIS (Schools Catalogue Information Service) Asks forum in November 2013. It presents some thoughts about the way the use, and thus the structure and content of library metadata, have changed over the years. Some ideas about the future are explored. These topics are explored more fully in Alan's article in Connections (Issue 89 2014), "Libraries and metadata in a sea of information" (http://www2.curriculum.edu.au/scis/connections/issue_89_2014/articles/libraries_and_metadata_in_a_sea_of_information.html).
Using Access to Create Reports from Voyager (Microsoft Access with the Voyage...Alan Manifold
This document provides an overview of using Microsoft Access to create reports from Voyager library system data. It discusses database design principles, how to work with Access databases, look at tables, understand views versus tables, and create queries. Key points covered include linking tables in queries, using criteria such as patterns, dates, and expressions. Tips are provided for sorting results and prompting for criteria. Exercises are included to help users learn how to work with Access tables and create queries.
Reports and Forms: the finishing touches for Access Reporting (on the Voyager...Alan Manifold
This presentation was given at the Voyager Users Group Meeting (VUGM) in 2002. Many of the presentations at VUGM discussed various ways to create queries, but this one focused on Reports and Forms, which make the final product more professional and easier to use.
Buried Treasure: Finding Reporting Gold in the Voyager Tables (using Microsof...Alan Manifold
This presentation was given at the Voyager Users Group Meeting (VUGM) in 2002. It covers some exciting and interesting data fields and techniques for retrieving them out of the Voyager ILS's Oracle tables.
This presentation was given at the Getty Research Library and other locations through the years to sites that had already had Alan Manifold's Using Microsoft Access for Reporting from Voyager workshop. There is some overlap between the two, but this one goes into more depth on some special techniques that help create more complex reports and queries.
Access Reports for Tenderfeet (or is that tenderfoots?) Alan Manifold
This document provides an introduction to using Access reports with Voyager data. It discusses key concepts like queries, tables, fields, joins, criteria and grouping. It explains how to set up the database, link Voyager tables, run pre-existing queries and reports, and modify queries and reports. The goal is to help users who are new to Access ("Tenderfeet") get comfortable extracting and presenting data from Voyager through Access.
One More Thing: Tweaking and Embellishing Access QueriesAlan Manifold
This document provides tips for tweaking and maintaining Microsoft Access queries. It discusses analyzing queries to understand what they do, making queries flexible and easy to maintain, adding criteria and joins, handling date fields correctly, and using expressions. The key steps are to analyze the query, find needed tables and fields, make changes one at a time and test with each change, and address issues like criteria placement, operators, and how joins affect results.
Federated searching provides a more powerful search tool than Google in some ways, allowing users to search multiple databases simultaneously using a single query. However, federated searching systems currently face several problems, including a lack of standards, different data formats and protocols across databases, and difficulties with search definitions and connectors. Future challenges include improving authorization, response time, integration of new resources, and de-duplication of results. While federated searching works well in many libraries, more work is needed to match features like Google's ranking, relevancy, ease of use, and interface design to attract more patrons.
An Abecedary of Access Tips with the Voyager Integrated Library SystemAlan Manifold
This presentation pulls together a bunch of different tips that make using Access with Voyager more effective and efficient. It uses the alphabet to organize the tips.
Enhancing ICU Intelligence: How Our Functional Testing Enabled a Healthcare I...Impelsys Inc.
Impelsys provided a robust testing solution, leveraging a risk-based and requirement-mapped approach to validate ICU Connect and CritiXpert. A well-defined test suite was developed to assess data communication, clinical data collection, transformation, and visualization across integrated devices.
Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...Aqusag Technologies
In late April 2025, a significant portion of Europe, particularly Spain, Portugal, and parts of southern France, experienced widespread, rolling power outages that continue to affect millions of residents, businesses, and infrastructure systems.
Semantic Cultivators : The Critical Future Role to Enable AIartmondano
By 2026, AI agents will consume 10x more enterprise data than humans, but with none of the contextual understanding that prevents catastrophic misinterpretations.
TrsLabs - Fintech Product & Business ConsultingTrs Labs
Hybrid Growth Mandate Model with TrsLabs
Strategic Investments, Inorganic Growth, Business Model Pivoting are critical activities that business don't do/change everyday. In cases like this, it may benefit your business to choose a temporary external consultant.
An unbiased plan driven by clearcut deliverables, market dynamics and without the influence of your internal office equations empower business leaders to make right choices.
Getting things done within a budget within a timeframe is key to Growing Business - No matter whether you are a start-up or a big company
Talk to us & Unlock the competitive advantage
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxJustin Reock
Building 10x Organizations with Modern Productivity Metrics
10x developers may be a myth, but 10x organizations are very real, as proven by the influential study performed in the 1980s, ‘The Coding War Games.’
Right now, here in early 2025, we seem to be experiencing YAPP (Yet Another Productivity Philosophy), and that philosophy is converging on developer experience. It seems that with every new method we invent for the delivery of products, whether physical or virtual, we reinvent productivity philosophies to go alongside them.
But which of these approaches actually work? DORA? SPACE? DevEx? What should we invest in and create urgency behind today, so that we don’t find ourselves having the same discussion again in a decade?
The Evolution of Meme Coins A New Era for Digital Currency ppt.pdfAbi john
Analyze the growth of meme coins from mere online jokes to potential assets in the digital economy. Explore the community, culture, and utility as they elevate themselves to a new era in cryptocurrency.
Andrew Marnell: Transforming Business Strategy Through Data-Driven InsightsAndrew Marnell
With expertise in data architecture, performance tracking, and revenue forecasting, Andrew Marnell plays a vital role in aligning business strategies with data insights. Andrew Marnell’s ability to lead cross-functional teams ensures businesses achieve sustainable growth and operational excellence.
Spark is a powerhouse for large datasets, but when it comes to smaller data workloads, its overhead can sometimes slow things down. What if you could achieve high performance and efficiency without the need for Spark?
At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍
Explore delta-rs + CDC and how these open-source innovations power lightweight, high-performance data applications beyond Spark! 🚀
Technology Trends in 2025: AI and Big Data AnalyticsInData Labs
At InData Labs, we have been keeping an ear to the ground, looking out for AI-enabled digital transformation trends coming our way in 2025. Our report will provide a look into the technology landscape of the future, including:
-Artificial Intelligence Market Overview
-Strategies for AI Adoption in 2025
-Anticipated drivers of AI adoption and transformative technologies
-Benefits of AI and Big data for your business
-Tips on how to prepare your business for innovation
-AI and data privacy: Strategies for securing data privacy in AI models, etc.
Download your free copy nowand implement the key findings to improve your business.
HCL Nomad Web – Best Practices und Verwaltung von Multiuser-Umgebungenpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-nomad-web-best-practices-und-verwaltung-von-multiuser-umgebungen/
HCL Nomad Web wird als die nächste Generation des HCL Notes-Clients gefeiert und bietet zahlreiche Vorteile, wie die Beseitigung des Bedarfs an Paketierung, Verteilung und Installation. Nomad Web-Client-Updates werden “automatisch” im Hintergrund installiert, was den administrativen Aufwand im Vergleich zu traditionellen HCL Notes-Clients erheblich reduziert. Allerdings stellt die Fehlerbehebung in Nomad Web im Vergleich zum Notes-Client einzigartige Herausforderungen dar.
Begleiten Sie Christoph und Marc, während sie demonstrieren, wie der Fehlerbehebungsprozess in HCL Nomad Web vereinfacht werden kann, um eine reibungslose und effiziente Benutzererfahrung zu gewährleisten.
In diesem Webinar werden wir effektive Strategien zur Diagnose und Lösung häufiger Probleme in HCL Nomad Web untersuchen, einschließlich
- Zugriff auf die Konsole
- Auffinden und Interpretieren von Protokolldateien
- Zugriff auf den Datenordner im Cache des Browsers (unter Verwendung von OPFS)
- Verständnis der Unterschiede zwischen Einzel- und Mehrbenutzerszenarien
- Nutzung der Client Clocking-Funktion
Mobile App Development Company in Saudi ArabiaSteve Jonas
EmizenTech is a globally recognized software development company, proudly serving businesses since 2013. With over 11+ years of industry experience and a team of 200+ skilled professionals, we have successfully delivered 1200+ projects across various sectors. As a leading Mobile App Development Company In Saudi Arabia we offer end-to-end solutions for iOS, Android, and cross-platform applications. Our apps are known for their user-friendly interfaces, scalability, high performance, and strong security features. We tailor each mobile application to meet the unique needs of different industries, ensuring a seamless user experience. EmizenTech is committed to turning your vision into a powerful digital product that drives growth, innovation, and long-term success in the competitive mobile landscape of Saudi Arabia.
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025BookNet Canada
Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next.
Link to recording, transcript, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
Dev Dives: Automate and orchestrate your processes with UiPath MaestroUiPathCommunity
This session is designed to equip developers with the skills needed to build mission-critical, end-to-end processes that seamlessly orchestrate agents, people, and robots.
📕 Here's what you can expect:
- Modeling: Build end-to-end processes using BPMN.
- Implementing: Integrate agentic tasks, RPA, APIs, and advanced decisioning into processes.
- Operating: Control process instances with rewind, replay, pause, and stop functions.
- Monitoring: Use dashboards and embedded analytics for real-time insights into process instances.
This webinar is a must-attend for developers looking to enhance their agentic automation skills and orchestrate robust, mission-critical processes.
👨🏫 Speaker:
Andrei Vintila, Principal Product Manager @UiPath
This session streamed live on April 29, 2025, 16:00 CET.
Check out all our upcoming Dev Dives sessions at https://community.uipath.com/dev-dives-automation-developer-2025/.
Quantum Computing Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
Complete Guide to Advanced Logistics Management Software in Riyadh.pdfSoftware Company
Explore the benefits and features of advanced logistics management software for businesses in Riyadh. This guide delves into the latest technologies, from real-time tracking and route optimization to warehouse management and inventory control, helping businesses streamline their logistics operations and reduce costs. Learn how implementing the right software solution can enhance efficiency, improve customer satisfaction, and provide a competitive edge in the growing logistics sector of Riyadh.
Complete Guide to Advanced Logistics Management Software in Riyadh.pdfSoftware Company
Using Indexed field effectively in Access Queries with Voyager
1. USING
INDEXED FIELDS
EFFECTIVELY IN
ACCESS QUERIES
Alan Manifold
Systems Implementation Manager
Purdue University Libraries
European Endeavor Users Group 2005
Parallel sessions III
10 June 2005
12.00 - 12.45
2. Using Indexed Fields Effectively in Access Queries
PHONEBOOKS
Aaa—AlaA Ala—Bah A
A phonebook is an
index, but not the
efficient kind you would
want your computer to
use.
The basic lookup
process is a scan from
beginning to end,
looking at the index
tabs at the top of each
page.
3. Using Indexed Fields Effectively in Access Queries
AN IMPROVEMENT
ABCDEFGHIJKLM NOPQRSTUVWXYZ
ABCDEF GHIJKLM NOPQRS TUVWXYZ
ABC DEF GHI JKLM NOP QRS TUV WXYZ
JK LM
J K L M
N OP
WX YZ
W X Y Z
ABCDEFGHIJKLMNOPQRSTUVWXYZ
This index gets to any letter in a max of 6 tries
A BC D EF G HI
B C E F H I
Q RS T UV
O P R S U V
Bah-Bur Bur-Cam
4. Using Indexed Fields Effectively in Access Queries
EVEN BETTER
In a B-Tree, each node
can point to a number
of child nodes, so the
disk access is
minimized.
Nodes are (ideally)
sized to make
maximum use of
memory, which is
fast.
Cam-Cop Cop-Des
5. Using Indexed Fields Effectively in Access Queries
NOT OUR PROBLEM
We don’t have to worry
about how indexes
work, because Oracle
provides them for us.
We just want to make
sure we use them.
Des-Die Die-Ebs
6. Using Indexed Fields Effectively in Access Queries
WITHOUT INDEXES
The alternative to
using an index is a
“full table scan”. This
means looking at each
record in the database
in turn. Depending on
the size of the table,
this can be very slow.
SELECT BIB_ID
FROM BIB_TEXT
WHERE TITLE
LIKE "*turtles*";
“Let's see… Does record
1 have this? Nope. How
about record 2? Nope.
Record 3? Hmmm…”
Ebs-End End-Fax
7. Using Indexed Fields Effectively in Access Queries
IDENTIFYING INDEXES
Use the query on the
following slide in
SQL*Plus to identify
what indexes there are
in the Voyager
database.
Fax-Fly Fly-Gba
8. Using Indexed Fields Effectively in Access Queries
IDENTIFYING INDEXES
SELECT
USER_INDEXES.INDEX_NAME,
USER_INDEXES.TABLE_NAME,
COLUMN_POSITION,
SUBSTR(COLUMN_NAME,1,40) COL_NAME
FROM
USER_INDEXES,
USER_IND_COLUMNS
WHERE USER_INDEXES.INDEX_NAME =
USER_IND_COLUMNS.INDEX_NAME;
Gba-God God-Hel
9. Using Indexed Fields Effectively in Access Queries
OUR EXAMPLES
TABLE_NAME INDEX_NAME # COLUMN_NAME
ELINK_INDEX ELINK_INDEX_ELINK_ID 1 ELINK_ID
ELINK_INDEX ELINK_INDEX_LINK_NORMAL 1 LINK_TEXT_NORMAL
ELINK_INDEX ELINK_INDEX_RECID_RECTYPE_IDX 1 RECORD_ID
ELINK_INDEX ELINK_INDEX_RECID_RECTYPE_IDX 2 RECORD_TYPE
FINE_FEE FINE_FEE_IDX 1 FINE_FEE_ID
FINE_FEE FINE_FEE_IDX1 1 ITEM_ID
FINE_FEE FINE_FEE_IDX1 2 FINE_FEE_TYPE
FINE_FEE FINE_FEE_IDX1 3 PATRON_ID
FINE_FEE FINE_FEE_IDX1 4 FINE_FEE_BALANCE
FINE_FEE FINE_FEE_IDX1 5 FINE_FEE_ID
FINE_FEE FINE_FEE_PTN_IDX 1 PATRON_ID
FUND FUND_IDX 1 LEDGER_ID
FUND FUND_IDX 2 FUND_ID
FUND FUND_X_CODE 1 NORMAL_FUND_CODE
FUND FUND_X_NAME 1 NORMAL_FUND_NAME
FUND FUND_X_PARENT 1 LEDGER_ID
FUND FUND_X_PARENT 2 PARENT_FUND
We’ll use
these
records
to learn
how to
read the
results.
Hel-Hos Hos-Ico
10. Using Indexed Fields Effectively in Access Queries
THE SIMPLEST CASE
In this simplest case,
the index covers only
one field. The
COLUMN_POSITION
field is 1, and there is
no 2.
If you add criteria to
the ELINK_ID field or
link from this table to
another by it, the index
will be used and access
will be fast.
TABLE_NAME INDEX_NAME # COLUMN_NAME
ELINK_INDEX ELINK_INDEX_ELINK_ID 1 ELINK_ID
Ico-Inc Inc-Jar
11. Using Indexed Fields Effectively in Access Queries
A BIT HARDER
TABLE_NAME INDEX_NAME # COLUMN_NAME
FUND FUND_IDX 1 LEDGER_ID
FUND FUND_IDX 2 FUND_ID
In this case, you can
see there is a
COLUMN_POSITION
(#) field value of 2, as
well as one of 1.
If you search this field
by FUND_ID without
LEDGER_ID, it will do a
full table scan. You
must have the field
with position 1 before
it can use the index for
the field in position 2.
Jar-Jun Jun-Kaa
12. Using Indexed Fields Effectively in Access Queries
THESE CAN’t USE IT …
Because neither
of these two
queries refers to
the LEDGER_ID,
they can’t use
the FUND_IDX
index. They’ll
have to do a full
scan of the FUND
table.
Kaa-Kri Kri-Lap
13. Using Indexed Fields Effectively in Access Queries
… BUT THESE CAN
These two queries could
both use the FUND_IDX
index, as
they use
the
LEDGER_ID
as well as
the
FUND_ID
Lap-Lib Lib-Llo
14. Using Indexed Fields Effectively in Access Queries
A HARDER ONE YET
TABLE_NAME INDEX_NAME # COLUMN_NAME
FINE_FEE FINE_FEE_IDX1 1 ITEM_ID
FINE_FEE FINE_FEE_IDX1 2 FINE_FEE_TYPE
FINE_FEE FINE_FEE_IDX1 3 PATRON_ID
FINE_FEE FINE_FEE_IDX1 4 FINE_FEE_BALANCE
FINE_FEE FINE_FEE_IDX1 5 FINE_FEE_ID
To use this index, you
need an ITEM_ID, then
a FINE_FEE_TYPE, then
a PATRON_ID, then the
FINE_FEE_BALANCE,
then the FINE_FEE_ID.
You don’t have to use
all of these fields, but
you must use at least
the ones with smaller
numbers than the field
you’re interested in.
Llo-Max Max-Moo
15. Using Indexed Fields Effectively in Access Queries
USING THE INDEX
This query can use the
FINE_FEE_IDX1 index
since the first three
fields in the index
(ITEM_ID,
FINE_FEE_TYPE, and
PATRON_ID) are all
being used for links.
Moo-Nal Nal-Not
16. Using Indexed Fields Effectively in Access Queries
CRITERIA NOTES
Exact criteria such as:
“reference”
and
“ref main” Or “ref branch”
appear to work faster
than fuzzy criteria such
as:
Like “ref*”
Indexes are left-
anchored, so criteria
such as:
Like “*refe”
can’t use the indexes
Not-Oax Oax-Ole
17. Using Indexed Fields Effectively in Access Queries
THE FUDGE FACTOR
Where I have shown
examples of queries, I
have said the query
“could” or “can” use
the index. Whether it
does or not is up to
Oracle and the ODBC
drivers.
Ole-Opa Opa-Pac
18. Using Indexed Fields Effectively in Access Queries
EXAMPLE #1
Let’s look at
the indexes
related to this
query and see
how good it
is.
Pac-Pez Pez-Pur
19. Using Indexed Fields Effectively in Access Queries
THE LINKS WE USED
BIB_TEXT BIB_TEXT_BIB_ID_IDX 1 BIB_ID
LOCATION LOCATION_LOC_ID_IDX 1 LOCATION_ID
BIB_MFHD BIB_MFHD_BIBID_MFHDID_IDX 1 BIB_ID
BIB_MFHD BIB_MFHD_BIBID_MFHDID_IDX 2 MFHD_ID
BIB_MFHD BIB_MFHD_MFHDID_BIBID_IDX 1 MFHD_ID
BIB_MFHD BIB_MFHD_MFHDID_BIBID_IDX 2 BIB_ID
Most of the links we used were on indexed fields.
MFHD_MASTER MFHD_MASTER_MFHD_ID_IDX 1 MFHD_ID
MFHD_ITEM MFHD_ITEM_ITEM_IDX 1 ITEM_ID
MFHD_ITEM MFHD_ITEM_MFHD_IDX 1 MFHD_ID
ITEM ITEM_IDX 1 ITEM_ID
ITEM ITEM_TEMPLOC_PERMLOC_IDX 1 TEMP_LOCATION
ITEM ITEM_TEMPLOC_PERMLOC_IDX 2 PERM_LOCATION
Pur-Qua Qua-Quo
20. Using Indexed Fields Effectively in Access Queries
THE CRITERIA WE USED
MFHD_MASTER MFHD_MASTER_NORM_TYPE_LOC_IDX 1 NORMALIZED_CALL_NO
MFHD_MASTER MFHD_MASTER_NORM_TYPE_LOC_IDX 2 CALL_NO_TYPE
MFHD_MASTER MFHD_MASTER_NORM_TYPE_LOC_IDX 3 LOCATION_ID
MFHD_MASTER MFHD_MASTER_NORM_TYPE_LOC_IDX 4 SUPPRESS_IN_OPAC
MFHD_MASTER MFHD_MASTER_NORM_TYPE_LOC_IDX 5 MFHD_ID
MFHD_MASTER MFHD_MASTER_NORM_TYPE_LOC_IDX 6 DISPLAY_CALL_NO
MFHD_MASTER MFHD_MASTER_TYPE_IDX 1 CALL_NO_TYPE
MFHD_MASTER MFHD_MASTER_TYPE_IDX 2 MFHD_ID
CALL_NO_TYPE is indexed, but
NORMALIZED_CALL_NO would be a better choice
for criteria than DISPLAY_CALL_NO. In the
LOCATION table, only the LOCATION_ID is
indexed.
Quo-Ram Ram-Rev
21. Using Indexed Fields Effectively in Access Queries
DOES LOCATION MATTER?
Although neither the
LOCATION_CODE nor
the LOCATION_NAME
are indexed in
LOCATION, it makes
very little difference in
speed. The shorter
field (LOCATION_CODE)
is preferable for
criteria.
In a B-Tree structure,
the nodes are relatively
large, so they have lots
of records in them. For
a small table, a full-
table scan is equivalent
to an index lookup.
Rev-Rip Rip-Ror
23. Using Indexed Fields Effectively in Access Queries
ANALYSIS
All the links are on
indexed fields except to
PO_STATUS and
INVOICE_STATUS, which
are too small to count.
The criterion on the
INVOICE_STATUS_DESC
field from the
INVOICE_STATUS table
is okay, too.
San-Sol Sol-Sun
24. Using Indexed Fields Effectively in Access Queries
EXAMPLE #3
BIB_INDEX BIB_INDEX_BIB_ID_IDX 1 BIB_ID
BIB_INDEX BIB_INDEX_CODE_NORM_DISP_IDX 1 INDEX_CODE
BIB_INDEX BIB_INDEX_CODE_NORM_DISP_IDX 2 NORMAL_HEADING
BIB_INDEX BIB_INDEX_CODE_NORM_DISP_IDX 3 BIB_ID
BIB_INDEX BIB_INDEX_CODE_NORM_DISP_IDX 4 DISPLAY_HEADING
To use BIB_INDEX to get
all titles by an author
(100 or 700 fields),
specify the INDEX_CODE
field as well as the
NORMAL_HEADING field.
4 secs
>5 mins
0 secs
Sun-Tee Tee-Tod
25. Using Indexed Fields Effectively in Access Queries
EXAMPLE #4
There’s no help for this query. It needs to use
criteria that aren’t indexed anywhere. It will
take a long time to run no matter what.
Tod-Voy Voy-Wah
26. Using Indexed Fields Effectively in Access Queries
EXAMPLE #5
Not only does
this query
not use the
normalized
last name for
its criterion,
it also
requires the
user to enter
the names in
mixed case.
Wah-Who Who-You
27. Using Indexed Fields Effectively in Access Queries
SUMMARY
Using indexed fields in
queries is mostly a
matter of common
sense, and is not
difficult. But it can
make a major
difference in the speed
of queries.
You-Zen Zen-Zzy