XBRL dimensions allow tagging of multi-dimensional data in XBRL instance documents. Dimensions provide a way to add additional context to XBRL facts through members and domains. There are two types of dimensions - explicit and typed. Explicit dimensions specifically name each member, while typed dimensions define a domain without explicitly naming members. Dimensions are tagged in XBRL contexts using xbrldi:explicitMember or xbrldi:typedMember elements and allow automated processing of multi-dimensional financial reports.
Understanding InfluxDB Basics: Tags, Fields and MeasurementsInfluxData
Is it a table? No, it is much more! Finally understand tags, fields and measurements.
In this session, you will learn how to answer your real-life questions with data stored in InfluxDB. You will see that InfluxDB is more than some tables; it is a window to the world of your data. In particular, the usage of tags, fields and measurements enhances the time series database and helps answer your questions in a convenient and fast way, if you know what to do. Discover tips and tricks to use while implementing InfluxDB.
All topics are addressed in the context of IoT monitoring, predictive maintenance and medical applications.
SQL to NoSQL Best Practices with Amazon DynamoDB - AWS July 2016 Webinar Se...Amazon Web Services
Applications have traditionally stored data in a relational database management system (RDBMS) and have used a Structured Query Language (SQL) to retrieve and update that data. The growth of “internet scale” apps, such as e-commerce, social media, mobile apps, and the rise of big data have increased data throughput demands beyond the range of traditional relational databases. Non-relational (NoSQL) databases enables your application to scale more cost effectively, even for extraordinarily high demand. Amazon DynamoDB is a fully managed NoSQL database service that lets you focus on your app so you don’t have to worry about hardware acquisition or database management and lets you scale down your costs for off-peak periods. In this webinar, we’ll describe common database tasks, then compare and contrast SQL with equivalent DynamoDB operations.
Learning Objectives:
• Why consider the switch from SQL to NoSQL?
• Benefits of Amazon’s NoSQL database service
• Common SQL database operations and their DynamoDB equivalents
Understanding InfluxDB Basics: Tags, Fields and MeasurementsInfluxData
Is it a table? No, it is much more! Finally understand tags, fields and measurements.
In this session, you will learn how to answer your real-life questions with data stored in InfluxDB. You will see that InfluxDB is more than some tables; it is a window to the world of your data. In particular, the usage of tags, fields and measurements enhances the time series database and helps answer your questions in a convenient and fast way, if you know what to do. Discover tips and tricks to use while implementing InfluxDB.
All topics are addressed in the context of IoT monitoring, predictive maintenance and medical applications.
SQL to NoSQL Best Practices with Amazon DynamoDB - AWS July 2016 Webinar Se...Amazon Web Services
Applications have traditionally stored data in a relational database management system (RDBMS) and have used a Structured Query Language (SQL) to retrieve and update that data. The growth of “internet scale” apps, such as e-commerce, social media, mobile apps, and the rise of big data have increased data throughput demands beyond the range of traditional relational databases. Non-relational (NoSQL) databases enables your application to scale more cost effectively, even for extraordinarily high demand. Amazon DynamoDB is a fully managed NoSQL database service that lets you focus on your app so you don’t have to worry about hardware acquisition or database management and lets you scale down your costs for off-peak periods. In this webinar, we’ll describe common database tasks, then compare and contrast SQL with equivalent DynamoDB operations.
Learning Objectives:
• Why consider the switch from SQL to NoSQL?
• Benefits of Amazon’s NoSQL database service
• Common SQL database operations and their DynamoDB equivalents
Rethinking State Management in Cloud-Native Streaming Systems With Yingjun Wu...HostedbyConfluent
Rethinking State Management in Cloud-Native Streaming Systems With Yingjun Wu | Current 2022
Stream processing is becoming increasingly essential for extracting business value from data in real-time. To achieve strict user-defined SLAs under constantly changing workloads, modern streaming systems have started taking advantage of the cloud for scalable and resilient resources. New demand opens new opportunities and challenges for state management, which is at the core of streaming systems. Existing approaches typically use embedded key-value storage so that each worker can access it locally to achieve high performance. However, it requires an external durable file system for checkpointing, is complicated and time-consuming to redistribute state during scaling and migration, and is prone to performance throttling. Therefore, we propose shared storage based on LSM-tree. State gets stored at cloud object storage and seamlessly makes itself durable, and the high bandwidth of cloud storage enables fast recovery. The location of a partition of the state decouples with compute nodes thus making scaling straightforward and more efficient. Compaction in this shared LSM-tree is now globally coordinated with opportunistic serverless boosting instead of relying on individual compute nodes. We design a streaming-aware compaction and caching strategy to achieve smoother and better end-to-end performance.
DynamoDB is a NoSQL database service built for fast, scalable, consistent performance. This presentation introduces DynamoDB and discusses how to get started, provision throughput, design for the DynamoDB data model, query and scan tables and scale reads and writes without downtime.
WildFly AppServer - State of the Union
as presented at SoftShake Geneva, Oct 2015
http://soft-shake.ch/2015/en/
Covering the whole WildFly v8/9/10 series and the key aspects of the base AS7 architecture.
Fan-in Flames: Scaling Kafka to Millions of Producers With Ryanne Dolan | Cur...HostedbyConfluent
Fan-in Flames: Scaling Kafka to Millions of Producers With Ryanne Dolan | Current 2022
At supermassive scale, a perennial problem with Kafka is ""high fan-in"" -- a large number of producers sending records to a small number of brokers. Even a relatively modest amount of data can overwhelm a broker when there are hundreds of thousands of concurrent producer requests.
This talk discusses a few real-world applications where high fan-in becomes a problem, and presents a few strategies for dealing with it. These include: fronting Kafka with an ingestion layer; separating brokers into read-only and write-only subsets; implementing specialized partitioning strategies; and scaling across clusters with ""smart clients"".
Selenide alternative in Python - Introducing Selene [SeleniumCamp 2016]Iakiv Kramarenko
Talk given on SeleniumCamp 2016 about:
- What features should a "general web test automation tool" have
- Why Selenide is the one Java
- And Selene is the other in Python
- And how to use the latter
InfluxDB + Kepware: Start Monitoring Industrial Data QuicklyInfluxData
Kepware (a PTC Technology) is the market leader in Industrial Connectivity platforms, and is helping companies bridge the gap between IT and OT. For the past 25 years, Kepware has developed industrial connectivity tools including over 150 industrial drivers, and supporting over 300 protocols – from traditional automation protocols such as OPC UA or OPC DA to more modern IIoT protocols like MQTT or REST. Together with InfluxDB, customers achieve quick time to value, gaining insights from their industrial time-stamped data. Developers are able to collect, process, store and analyze thousands of metrics faster! Join this webinar to learn multiple approaches to sending IIoT data to a time series database.
In this webinar, Kyle Carreau and Jay Clifford dive into:
How to use Telegraf to send OPC UA and MQTT metrics to InfluxDB
The new Kepware IoT Gateway Advanced Template features to send data directly to InfluxDB
Best practices for using InfluxDB + Kepware for industrial automation – stick around for a demo!
Slides from my talk at the Feb 2011 Seattle Tech Startups meeting. More info here (along with powerpoint slides): http://www.startupmonkeys.com/2011/02/scala-frugal-mechanic/
Have you decided on Amazon Redshift as your data warehouse but want to learn the latest tips and tricks to get started? Watch our webinar on Tuesday, August 29th to learn how to get started and how using Redshift can help you quickly and easily analyze your data to make business critical decisions.
A mashup is a Web page or application that uses and combines data, presentation or functionality from two or more sources to create new services. The term implies easy, fast integration, frequently using open API (Application Programming Interface) and data sources to produce enriched results that were not necessarily the original reason for producing the raw source data.
Rethinking State Management in Cloud-Native Streaming Systems With Yingjun Wu...HostedbyConfluent
Rethinking State Management in Cloud-Native Streaming Systems With Yingjun Wu | Current 2022
Stream processing is becoming increasingly essential for extracting business value from data in real-time. To achieve strict user-defined SLAs under constantly changing workloads, modern streaming systems have started taking advantage of the cloud for scalable and resilient resources. New demand opens new opportunities and challenges for state management, which is at the core of streaming systems. Existing approaches typically use embedded key-value storage so that each worker can access it locally to achieve high performance. However, it requires an external durable file system for checkpointing, is complicated and time-consuming to redistribute state during scaling and migration, and is prone to performance throttling. Therefore, we propose shared storage based on LSM-tree. State gets stored at cloud object storage and seamlessly makes itself durable, and the high bandwidth of cloud storage enables fast recovery. The location of a partition of the state decouples with compute nodes thus making scaling straightforward and more efficient. Compaction in this shared LSM-tree is now globally coordinated with opportunistic serverless boosting instead of relying on individual compute nodes. We design a streaming-aware compaction and caching strategy to achieve smoother and better end-to-end performance.
DynamoDB is a NoSQL database service built for fast, scalable, consistent performance. This presentation introduces DynamoDB and discusses how to get started, provision throughput, design for the DynamoDB data model, query and scan tables and scale reads and writes without downtime.
WildFly AppServer - State of the Union
as presented at SoftShake Geneva, Oct 2015
http://soft-shake.ch/2015/en/
Covering the whole WildFly v8/9/10 series and the key aspects of the base AS7 architecture.
Fan-in Flames: Scaling Kafka to Millions of Producers With Ryanne Dolan | Cur...HostedbyConfluent
Fan-in Flames: Scaling Kafka to Millions of Producers With Ryanne Dolan | Current 2022
At supermassive scale, a perennial problem with Kafka is ""high fan-in"" -- a large number of producers sending records to a small number of brokers. Even a relatively modest amount of data can overwhelm a broker when there are hundreds of thousands of concurrent producer requests.
This talk discusses a few real-world applications where high fan-in becomes a problem, and presents a few strategies for dealing with it. These include: fronting Kafka with an ingestion layer; separating brokers into read-only and write-only subsets; implementing specialized partitioning strategies; and scaling across clusters with ""smart clients"".
Selenide alternative in Python - Introducing Selene [SeleniumCamp 2016]Iakiv Kramarenko
Talk given on SeleniumCamp 2016 about:
- What features should a "general web test automation tool" have
- Why Selenide is the one Java
- And Selene is the other in Python
- And how to use the latter
InfluxDB + Kepware: Start Monitoring Industrial Data QuicklyInfluxData
Kepware (a PTC Technology) is the market leader in Industrial Connectivity platforms, and is helping companies bridge the gap between IT and OT. For the past 25 years, Kepware has developed industrial connectivity tools including over 150 industrial drivers, and supporting over 300 protocols – from traditional automation protocols such as OPC UA or OPC DA to more modern IIoT protocols like MQTT or REST. Together with InfluxDB, customers achieve quick time to value, gaining insights from their industrial time-stamped data. Developers are able to collect, process, store and analyze thousands of metrics faster! Join this webinar to learn multiple approaches to sending IIoT data to a time series database.
In this webinar, Kyle Carreau and Jay Clifford dive into:
How to use Telegraf to send OPC UA and MQTT metrics to InfluxDB
The new Kepware IoT Gateway Advanced Template features to send data directly to InfluxDB
Best practices for using InfluxDB + Kepware for industrial automation – stick around for a demo!
Slides from my talk at the Feb 2011 Seattle Tech Startups meeting. More info here (along with powerpoint slides): http://www.startupmonkeys.com/2011/02/scala-frugal-mechanic/
Have you decided on Amazon Redshift as your data warehouse but want to learn the latest tips and tricks to get started? Watch our webinar on Tuesday, August 29th to learn how to get started and how using Redshift can help you quickly and easily analyze your data to make business critical decisions.
A mashup is a Web page or application that uses and combines data, presentation or functionality from two or more sources to create new services. The term implies easy, fast integration, frequently using open API (Application Programming Interface) and data sources to produce enriched results that were not necessarily the original reason for producing the raw source data.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2. 2
Session Overview
• What is an XBRL
Dimension?
• Tagging an XBRL
Dimension in an
Instance Document
Presented By: CA. Nirmal Ghorawat
3. 3
What is an XBRL Dimension?
Simply Stated – “Dimension” is a Technical Term in
XBRL used to Tag The Tables Typically found in
Financial Reports.
However, XBRL Dimension are more versatile – in as
much – they can be used to represent multi-
dimensional Data (2D{Table}, 3D, etc) in XBRL.
Presented By: CA. Nirmal Ghorawat
4. 4
What is an XBRL Dimension?
Ability to add multiple / additional identifiers to a piece
of data / information
Use of Dimension in non-forms-based Reporting
SIMPLIFY Tagging and reduces no. of elements in
Taxonomy
• Capability added to XBRL by XBRL Dimension 1.0
(18 September, 2006) (Technical Specification)
Presented By: CA. Nirmal Ghorawat
5. 5
The XBRL Dimension 1.0 Specification
Modular eXtension to the XBRL Specification (XBRL
2.1)
Enables Developers to add additional semantic
meaning to XBRL Taxonomies representing multi-
dimensional reports programmatically
Presented By: CA. Nirmal Ghorawat
6. 6
A Normal Table Featured in Financial Reports
Presented By: CA. Nirmal Ghorawat
7. 7
Terms Used in Dimensions
Dimension is a manner in which data might be presented
(=Axis) or say a categorization of facts.
eg. Sales can be presented by way Region or
Products.
Domain The range of valid values for a Dimension is
called its Domain.
eg. Products or Region are both Domain.
is a concept that is part of a domain. For
Domain example, “Food and Beverages” is a Domain
Member Member in the “Product” Domain.
Presented By: CA. Nirmal Ghorawat
8. 8
Terms Used in Dimensions
Hypercube A hypercube is a possible dimensional
(= Table) representation.
eg., Products and Regions form 2
Hypercubes.
Primary Item is a simple concept, or item, defined in an
XBRL schema that is part of a substitution
(=Line Items) group, and can therefore be represented in a
multi-dimensional manner.
eg. Sales
Presented By: CA. Nirmal Ghorawat
9. 9
Terms Used in XBRL Dimensions
Presented By: CA. Nirmal Ghorawat
10. 10
Why XBRL Dimensions?
Enables computer applications to consume and
process dynamic associations of Data
Automated processing of Multi-Dimensional Data for a
more complete analysis
Enables Developers to add additional semantic
meaning to XBRL Taxonomies representing multi-
dimensional reports programmatically
Presented By: CA. Nirmal Ghorawat
11. 11
Tuples v/s Dimensions
Tuples and Dimensions are both technical features in
XBRL Taxonomies which allow “Data” that is often
reported in the form of Tables in Financial / Business
Reports to be handled efficiently in XBRL.
Presented By: CA. Nirmal Ghorawat
12. 12
Tuples v/s Dimensions
Tuples Dimensions
Only 2-Dimensional. Not Versatile. Can be used for
possible to represent multi- representing multi-
dimensional data using dimensional data [2D/ 3D/
Tuples (n)D]
Not as functional as Dimensions are more
Dimensions Versatile and cover all
functional aspects of Tuples
and more
Inhibit Extensibility Exhibit Extensibility
Presented By: CA. Nirmal Ghorawat
13. 13
Tuples v/s Dimensions
Tuples Dimensions
Higher No. of Elements in Lower No. of Elements in
the Taxonomy (roughly: the Taxonomy (roughly:
Domain Members X Line Domain Members + Line
Items) Items)
No effect on Contexts. Higher No. of Contexts in
Instance Documents.
Presented By: CA. Nirmal Ghorawat
14. 14
TAGGING AN XBRL
DIMENSION IN AN INSTANCE
DOCUMENT
Presented By: CA. Nirmal Ghorawat
15. 15
xbrldi :: NameSpace
The instance document must contain reference
for the “xbrldi” namespaces
xbrldi http://xbrl.org/2006/xbrldi
Presented By: CA. Nirmal Ghorawat
16. 16
Explicit v/s Typed Dimension
Explicit Typed
You know exactly what are You don’t know the values
the Dimension Members but you know enough to
(i.e., the Domain explicitly define the members (i.e.,
names its members) not possible to explicitly
name its members)
There is a FINITE and There may be an INFINITE
MANAGEABLE number of (UNMANAGEABLE)
members number of (possible)
members.
Presented By: CA. Nirmal Ghorawat