IBI Open Visualizations provides access from any visualization or other tool that uses ODBC or JDBC to virtually any data source.
This is an update of a presentation from IBI (Information Builders) Virtual Summit (Users Group Meeting) in June 2020
The document introduces the Semantic Web (Web 3.0) as an evolution from the current Web 2.0. It discusses the limitations of Web 2.0 in that web pages are designed for humans rather than machines. The Semantic Web aims to add meaning to provide machines with understanding of web content so they can work with data more like humans do. Everything on the Semantic Web will be assigned a URI and represented as relationships between subjects, predicates and objects. This will allow machines to more effectively search, find and share information across the internet.
This document provides an introduction to knowledge graphs. It discusses:
- The foundation and origins of knowledge graphs in semantic networks from the 1950s-60s.
- Key applications of knowledge graphs at companies like Google, Amazon, Alibaba, and Microsoft.
- Standards for knowledge graphs including RDF, OWL, and SPARQL.
- Research topics related to knowledge graph construction, reasoning, and querying.
- Approaches to constructing knowledge graphs including mapping data from Wikipedia and using machine learning techniques.
- Reasoning with knowledge graphs using description logics, and approximate reasoning techniques.
- Knowledge graph embeddings for tasks like link prediction.
Introduction to Ontology Concepts and TerminologySteven Miller
The document introduces an ontology tutorial that will cover basic concepts of the Semantic Web, Linked Data, and the Resource Description Framework data model as well as the ontology languages RDFS and OWL. The tutorial is intended for information professionals who want to gain an introductory understanding of ontologies, ontology concepts, and terminology. The tutorial will explain how to model and structure data as RDF triples and create basic RDFS ontologies.
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
- SPARQL is a query language for retrieving and manipulating data stored in RDF format. It is similar to SQL but for RDF data.
- SPARQL queries contain prefix declarations, specify a dataset using FROM, and include a graph pattern in the WHERE clause to match triples.
- The main types of SPARQL queries are SELECT, ASK, DESCRIBE, and CONSTRUCT. SELECT returns variable bindings, ASK returns a boolean, DESCRIBE returns a description of a resource, and CONSTRUCT generates an RDF graph.
Understanding generative AI models A comprehensive overview.pdfStephenAmell4
Generative AI refers to a branch of artificial intelligence that focuses on enabling machines to generate new and original content. Unlike traditional AI systems that follow predefined rules and patterns, generative AI leverages advanced algorithms and neural networks to autonomously produce outputs that mimic human creativity and decision-making.
Basics of Generative AI: Models, Tokenization, Embeddings, Text Similarity, V...Robert McDermott
This document provides an overview of natural language processing techniques like language modeling, tokenization, embeddings, and semantic similarity. It discusses the basics of these concepts and how they relate to each other, such as how tokenization is used as a preprocessing step and embeddings are used to capture semantic meaning and relationships that allow measuring text similarity. It also presents examples to illustrate these techniques in action.
Power BI Summit 2023 - Embedding PowerBI reports in .NET MAUI mobile apps.pptxLuis775803
The document discusses embedding Power BI reports in .NET MAUI mobile apps. It begins with an introduction of .NET MAUI, a framework for building native cross-platform apps with shared code and resources. It then explains Power BI embedding, which allows integrating Power BI reports and analytics within apps. The remainder of the document outlines the steps needed to embed Power BI reports in a .NET MAUI mobile app, including fulfilling requirements, selecting an authentication method, registering an Azure AD application, creating a Power BI workspace, and embedding the report. It concludes with a demonstration of an example app.
Building NLP applications with TransformersJulien SIMON
The document discusses how transformer models and transfer learning (Deep Learning 2.0) have improved natural language processing by allowing researchers to easily apply pre-trained models to new tasks with limited data. It presents examples of how HuggingFace has used transformer models for tasks like translation and part-of-speech tagging. The document also discusses tools from HuggingFace that make it easier to train models on hardware accelerators and deploy them to production.
Artificial Intelligence in Life Sciences and Agriculture.Yannick Djoumbou
Artificial intelligence is increasingly being used in life sciences and agriculture to help address challenges in drug and pesticide development. Key applications of AI include computer-aided molecular design, synthesis planning, metabolism prediction, and quantitative structure-activity relationship modeling. These applications utilize machine learning algorithms to parse large amounts of data and gain insights that help streamline the drug and pesticide development process. However, challenges remain such as a lack of sufficiently large and diverse datasets as well as a shortage of AI expertise. Overall, AI is transforming the design-make-test-analyze cycle in molecular discovery and there is significant potential for continued innovation in this area.
The document discusses the importance of understanding sentence structure and the functions of words in context. It explains that there are four main functions - nominal, adjectival, adverbial, and verbal. Nominals include nouns, pronouns, and noun phrases. Adjectivals include adjectives and phrases that modify nouns. Adverbials modify verbs and adjectives and include adverbs and prepositional phrases. Verbals include main verbs and verbs combined with auxiliaries. Understanding functions provides insight into how words combine to form meaningful sentences.
8 Steps to Build a LangChain RAG Chatbot.Ritesh Kanjee
Retrieval Augmented Generation (RAG) is a methodology that enhances large language models (LLMs) by integrating external knowledge sources into their processes. Langchain implements RAG by adding a retrieval step to the prompt and LLM, forming a "retrieval-augmented generation" chain. This augmentation improves the accuracy of generated output by providing relevant context from external sources. RAG facilitates more informed and contextually accurate responses from LLMs, contributing to better performance in various tasks such as question answering and content generation.
Source - @akshay_pachaar
Am 18.10.2023 findet unser nächster Online Marketing Stammtisch statt.
An diesem Abend haben wir Knut Linke als Speaker eingeladen. Hauptsächlich werden wir uns mit den Eigenarten von LLMs, Chat GPT & KI beschäftigen.
"Freut euch auf einen spannenden Vortrag von KNUT LINKE mit den Themen: ChatGPT, KI, LLM Konzept & vielen mehr..."
Auf Du mit ChatGPT & Co. – Wie funktioniert generative KI?
In diesem Vortrag von Knut Linke werden wir uns mit den Themen künstliche Intelligenz und generative KI beschäftigen.
Thematisiert werden Grundlagen zur Funktionsweise aktueller KI-Lösungen sowie Vorteile und Risiken für bestimmte Berufe und unsere Arbeitswelt.
Den Tag lassen wir mit einem entspannten Diskurs im Monopol ausklingen.
Gerne sind wir aber auch für sonstige Fragen jederzeit für euch da.
Wir freuen uns auf neue Gesichter einen gelungenen Abend mit spannenden Gesprächen, Austausch und leckerer Versorgung von dem Monopol in Hameln.
AUGMENTING CREATIVITY USING GEN AI FOR DESIGN & INNOVATION | TOJIN T. EAPENTojin Eapen, PhD
Presentation slides from my September 2023 guest lecture on Generative AI and its impact on creativity. The lecture also highlights the key themes of my recent July/August 2023 Harvard Business Review (HBR) cover article, exploring the potential of Generative AI to enhance human creativity. Additionally, the presentation engages in a discussion regarding the emerging opportunities and challenges within this domain.
Generative AI (GAI) refers to a type of artificial intelligence that is able to generate new data or content, such as text, images, or music. This is typically done by training a model on a large dataset of existing data, and then using the model to generate new, similar data.
-Promote Divergent Thinking
-Challenge Expertise Bias
-Assist in Idea Evaluation
-Support Idea Refinement
-Facilitate Collaboration
https://hbr.org/2023/07/how-generative-ai-can-augment-human-creativity
One of the biggest opportunities generative AI offers to businesses and governments is to augment human creativity and overcome the challenges of democratizing innovation.
Prompt engineering is the practice of designing and refining specific text prompts to guide transformer-based language models, such as Large Language Models (LLMs), in generating desired outputs. It involves crafting clear and specific instructions and allowing the model sufficient time to process information. By carefully engineering prompts, practitioners can harness the capabilities of LLMs to achieve different goals.
1. AI can be used in software testing to generate test cases, execute tests, and analyze results in a way that mimics human testing activities.
2. Different types of AI like machine learning, natural language processing, and computer vision can be applied to testing scenarios.
3. The benefits of AI in testing include improved coverage, faster execution, early defect detection, and reduced costs. However, challenges include lack of domain knowledge, data quality issues, and initial setup efforts.
The document discusses explainability and bias in machine learning/AI models. It covers several topics:
1. Why explainability of models is important, including for laypeople using models and potential legal needs for explanations of decisions.
2. Methods for explainability including using interpretable models directly and post-hoc explainability methods like LIME and SHAP which provide feature attributions.
3. Issues with bias in machine learning models and different definitions of fairness. It also discusses techniques for measuring and mitigating bias, such as reweighting data or using adversarial learning.
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
Prompt Engineering - an Art, a Science, or your next Job Title?Maxim Salnikov
It's quite ironic that to interact with the most advanced AI in our history - Large Language Models: ChatGPT, etc. - we must use human language, not programming one. But how to get the most out of this dialogue i.e. how to create robust and efficient prompts so AI returns exactly what's needed for your solution on the first try? After my session, you can add the Junior (at least) Prompt Engineer skill to your CV: I will introduce Prompt Engineering as an emerging discipline with its own methodologies, tools, and best practices. Expect lots of examples that will help you to write ideal prompts for all occasions.
The document defines a UFO as an Unidentified Flying Object, a term created by the USAF in 1953. While some scientists believe that intelligent alien life may exist elsewhere in the galaxy, most say there is not enough evidence to confirm their existence or that aliens have visited Earth. Common explanations for UFO sightings include astronomical objects, aircraft, and balloons like weather balloons and research balloons. The document also notes that if an alien 65 million light years from Earth observed our planet with a powerful telescope, they would see dinosaurs, seeing Earth's past due to the long travel time of light.
Explainable AI (XAI) is becoming Must-Have NFR for most AI enabled product or solution deployments. Keen to know viewpoints and collaboration opportunities.
This document discusses Java Database Connectivity (JDBC) which provides Java applications with an API for accessing databases. It describes the four types of JDBC drivers: Type 1 uses JDBC-ODBC bridge, Type 2 uses native database APIs, Type 3 communicates through a middle-tier server, and Type 4 communicates directly via sockets. The document also outlines the basic steps to use JDBC for database connectivity including loading a driver, establishing a connection, creating statements, executing SQL, and processing result sets.
Access Data from XPages with the Relational ControlsTeamstudio
Did you know that Domino and XPages allows for the easy access of relational data? These exciting capabilities in the Extension Library can greatly enhance the capability of your applications and allow access to information beyond Domino. Howard and Paul will discuss what you need to get started, what controls allow access to relational data, and the new @Functions available to incorporate relational data in your Server Side JavaScript programming.
Detailed Installation Guide for using the Virtuoso ODBC Driver to connect Mac OS X Applications to the Linked (Open) Data Cloud and other Big Data sources.
Session presented at Oracle Developer Live - MySQL, 2020. Recording available at https://developer.oracle.com/developer-live/mysql/
Abstract:
MySQL Shell is the new, advanced command-line client and editor for MySQL. It sends SQL statements to MySQL server, supports both the classic MySQL protocol and the newer X protocol, and provides scripting capabilities for JavaScript and Python. But there's more to MySQL Shell than meets the eye. It delivers a natural and powerful interface for all DevOps tasks related to MySQL by providing APIs for development and administration. This session covers MySQL Shell's core features, along with demonstrations of how to use the various APIs and how to extend MySQL Shell. We’ll address the regular interaction with databases, the built-in tools that make DBAs and developers’ lives easier, the easy and flawless set up of HA architectures, and the plugins and extensions framework.
The document summarizes features of SQL Server 2005 Mobile Edition and the .NET Compact Framework v2.0 for accessing data. It discusses the architecture, integration with SQL Server 2005 and Visual Studio 2005, and synchronization options including remote data access and merge replication. Key points covered include improved performance, query optimization, updated cursors, and ease of development in Visual Studio 2005.
BI, Integration, and Apps on Couchbase using Simba ODBC and JDBCSimba Technologies
This document provides an overview of Simba Technologies' ODBC and JDBC drivers for Couchbase. It introduces Kyle from Simba and describes the drivers' features like allowing SQL access to Couchbase data and mapping Couchbase documents to a relational schema. The document also includes demos of driver functionality and best practices for using the ODBC and JDBC APIs.
Power BI Summit 2023 - Embedding PowerBI reports in .NET MAUI mobile apps.pptxLuis775803
The document discusses embedding Power BI reports in .NET MAUI mobile apps. It begins with an introduction of .NET MAUI, a framework for building native cross-platform apps with shared code and resources. It then explains Power BI embedding, which allows integrating Power BI reports and analytics within apps. The remainder of the document outlines the steps needed to embed Power BI reports in a .NET MAUI mobile app, including fulfilling requirements, selecting an authentication method, registering an Azure AD application, creating a Power BI workspace, and embedding the report. It concludes with a demonstration of an example app.
Building NLP applications with TransformersJulien SIMON
The document discusses how transformer models and transfer learning (Deep Learning 2.0) have improved natural language processing by allowing researchers to easily apply pre-trained models to new tasks with limited data. It presents examples of how HuggingFace has used transformer models for tasks like translation and part-of-speech tagging. The document also discusses tools from HuggingFace that make it easier to train models on hardware accelerators and deploy them to production.
Artificial Intelligence in Life Sciences and Agriculture.Yannick Djoumbou
Artificial intelligence is increasingly being used in life sciences and agriculture to help address challenges in drug and pesticide development. Key applications of AI include computer-aided molecular design, synthesis planning, metabolism prediction, and quantitative structure-activity relationship modeling. These applications utilize machine learning algorithms to parse large amounts of data and gain insights that help streamline the drug and pesticide development process. However, challenges remain such as a lack of sufficiently large and diverse datasets as well as a shortage of AI expertise. Overall, AI is transforming the design-make-test-analyze cycle in molecular discovery and there is significant potential for continued innovation in this area.
The document discusses the importance of understanding sentence structure and the functions of words in context. It explains that there are four main functions - nominal, adjectival, adverbial, and verbal. Nominals include nouns, pronouns, and noun phrases. Adjectivals include adjectives and phrases that modify nouns. Adverbials modify verbs and adjectives and include adverbs and prepositional phrases. Verbals include main verbs and verbs combined with auxiliaries. Understanding functions provides insight into how words combine to form meaningful sentences.
8 Steps to Build a LangChain RAG Chatbot.Ritesh Kanjee
Retrieval Augmented Generation (RAG) is a methodology that enhances large language models (LLMs) by integrating external knowledge sources into their processes. Langchain implements RAG by adding a retrieval step to the prompt and LLM, forming a "retrieval-augmented generation" chain. This augmentation improves the accuracy of generated output by providing relevant context from external sources. RAG facilitates more informed and contextually accurate responses from LLMs, contributing to better performance in various tasks such as question answering and content generation.
Source - @akshay_pachaar
Am 18.10.2023 findet unser nächster Online Marketing Stammtisch statt.
An diesem Abend haben wir Knut Linke als Speaker eingeladen. Hauptsächlich werden wir uns mit den Eigenarten von LLMs, Chat GPT & KI beschäftigen.
"Freut euch auf einen spannenden Vortrag von KNUT LINKE mit den Themen: ChatGPT, KI, LLM Konzept & vielen mehr..."
Auf Du mit ChatGPT & Co. – Wie funktioniert generative KI?
In diesem Vortrag von Knut Linke werden wir uns mit den Themen künstliche Intelligenz und generative KI beschäftigen.
Thematisiert werden Grundlagen zur Funktionsweise aktueller KI-Lösungen sowie Vorteile und Risiken für bestimmte Berufe und unsere Arbeitswelt.
Den Tag lassen wir mit einem entspannten Diskurs im Monopol ausklingen.
Gerne sind wir aber auch für sonstige Fragen jederzeit für euch da.
Wir freuen uns auf neue Gesichter einen gelungenen Abend mit spannenden Gesprächen, Austausch und leckerer Versorgung von dem Monopol in Hameln.
AUGMENTING CREATIVITY USING GEN AI FOR DESIGN & INNOVATION | TOJIN T. EAPENTojin Eapen, PhD
Presentation slides from my September 2023 guest lecture on Generative AI and its impact on creativity. The lecture also highlights the key themes of my recent July/August 2023 Harvard Business Review (HBR) cover article, exploring the potential of Generative AI to enhance human creativity. Additionally, the presentation engages in a discussion regarding the emerging opportunities and challenges within this domain.
Generative AI (GAI) refers to a type of artificial intelligence that is able to generate new data or content, such as text, images, or music. This is typically done by training a model on a large dataset of existing data, and then using the model to generate new, similar data.
-Promote Divergent Thinking
-Challenge Expertise Bias
-Assist in Idea Evaluation
-Support Idea Refinement
-Facilitate Collaboration
https://hbr.org/2023/07/how-generative-ai-can-augment-human-creativity
One of the biggest opportunities generative AI offers to businesses and governments is to augment human creativity and overcome the challenges of democratizing innovation.
Prompt engineering is the practice of designing and refining specific text prompts to guide transformer-based language models, such as Large Language Models (LLMs), in generating desired outputs. It involves crafting clear and specific instructions and allowing the model sufficient time to process information. By carefully engineering prompts, practitioners can harness the capabilities of LLMs to achieve different goals.
1. AI can be used in software testing to generate test cases, execute tests, and analyze results in a way that mimics human testing activities.
2. Different types of AI like machine learning, natural language processing, and computer vision can be applied to testing scenarios.
3. The benefits of AI in testing include improved coverage, faster execution, early defect detection, and reduced costs. However, challenges include lack of domain knowledge, data quality issues, and initial setup efforts.
The document discusses explainability and bias in machine learning/AI models. It covers several topics:
1. Why explainability of models is important, including for laypeople using models and potential legal needs for explanations of decisions.
2. Methods for explainability including using interpretable models directly and post-hoc explainability methods like LIME and SHAP which provide feature attributions.
3. Issues with bias in machine learning models and different definitions of fairness. It also discusses techniques for measuring and mitigating bias, such as reweighting data or using adversarial learning.
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
Prompt Engineering - an Art, a Science, or your next Job Title?Maxim Salnikov
It's quite ironic that to interact with the most advanced AI in our history - Large Language Models: ChatGPT, etc. - we must use human language, not programming one. But how to get the most out of this dialogue i.e. how to create robust and efficient prompts so AI returns exactly what's needed for your solution on the first try? After my session, you can add the Junior (at least) Prompt Engineer skill to your CV: I will introduce Prompt Engineering as an emerging discipline with its own methodologies, tools, and best practices. Expect lots of examples that will help you to write ideal prompts for all occasions.
The document defines a UFO as an Unidentified Flying Object, a term created by the USAF in 1953. While some scientists believe that intelligent alien life may exist elsewhere in the galaxy, most say there is not enough evidence to confirm their existence or that aliens have visited Earth. Common explanations for UFO sightings include astronomical objects, aircraft, and balloons like weather balloons and research balloons. The document also notes that if an alien 65 million light years from Earth observed our planet with a powerful telescope, they would see dinosaurs, seeing Earth's past due to the long travel time of light.
Explainable AI (XAI) is becoming Must-Have NFR for most AI enabled product or solution deployments. Keen to know viewpoints and collaboration opportunities.
This document discusses Java Database Connectivity (JDBC) which provides Java applications with an API for accessing databases. It describes the four types of JDBC drivers: Type 1 uses JDBC-ODBC bridge, Type 2 uses native database APIs, Type 3 communicates through a middle-tier server, and Type 4 communicates directly via sockets. The document also outlines the basic steps to use JDBC for database connectivity including loading a driver, establishing a connection, creating statements, executing SQL, and processing result sets.
Access Data from XPages with the Relational ControlsTeamstudio
Did you know that Domino and XPages allows for the easy access of relational data? These exciting capabilities in the Extension Library can greatly enhance the capability of your applications and allow access to information beyond Domino. Howard and Paul will discuss what you need to get started, what controls allow access to relational data, and the new @Functions available to incorporate relational data in your Server Side JavaScript programming.
Detailed Installation Guide for using the Virtuoso ODBC Driver to connect Mac OS X Applications to the Linked (Open) Data Cloud and other Big Data sources.
Session presented at Oracle Developer Live - MySQL, 2020. Recording available at https://developer.oracle.com/developer-live/mysql/
Abstract:
MySQL Shell is the new, advanced command-line client and editor for MySQL. It sends SQL statements to MySQL server, supports both the classic MySQL protocol and the newer X protocol, and provides scripting capabilities for JavaScript and Python. But there's more to MySQL Shell than meets the eye. It delivers a natural and powerful interface for all DevOps tasks related to MySQL by providing APIs for development and administration. This session covers MySQL Shell's core features, along with demonstrations of how to use the various APIs and how to extend MySQL Shell. We’ll address the regular interaction with databases, the built-in tools that make DBAs and developers’ lives easier, the easy and flawless set up of HA architectures, and the plugins and extensions framework.
The document summarizes features of SQL Server 2005 Mobile Edition and the .NET Compact Framework v2.0 for accessing data. It discusses the architecture, integration with SQL Server 2005 and Visual Studio 2005, and synchronization options including remote data access and merge replication. Key points covered include improved performance, query optimization, updated cursors, and ease of development in Visual Studio 2005.
BI, Integration, and Apps on Couchbase using Simba ODBC and JDBCSimba Technologies
This document provides an overview of Simba Technologies' ODBC and JDBC drivers for Couchbase. It introduces Kyle from Simba and describes the drivers' features like allowing SQL access to Couchbase data and mapping Couchbase documents to a relational schema. The document also includes demos of driver functionality and best practices for using the ODBC and JDBC APIs.
The document provides instructions on installing and configuring Provisioning Services. It discusses determining installation options, key components, integrating with Active Directory, preparing target devices, and supported operating systems, hypervisors, and databases. The summary includes an overview of Provisioning Services technology and components, farm and site design considerations, and the configuration steps to complete a Provisioning Services installation.
This document provides an overview of JDBC (Java Database Connectivity) including ODBC, the JDBC API, JDBC architecture and drivers, and the basic steps for using JDBC to connect to and query a database.
The GoodData platform utilizes a virtualized OpenStack environment and high performance redundant hardware infrastructure. It features services for data integration, analytics, visualization, automation, and security across multiple clusters managed through a cloud control center. The platform is designed for scalability, flexibility, and redundancy.
java database connectivity for java programmingrinky1234
- JDBC (Java Database Connectivity) is a Java API that allows Java programs to connect and execute queries with various databases. It uses JDBC drivers to connect to different database types.
- There are four main types of JDBC drivers: JDBC-ODBC bridge driver, native-API driver, network protocol driver, and thin driver. The thin driver provides the best performance as no additional software is required on the client or server side.
- To connect to a database using JDBC, a program loads the appropriate driver, establishes a connection, creates statements to execute queries, processes result sets, and closes the connection. The example shows how to connect to an Oracle database using JDB
Deep Dive - Usage of on premises data gateway for hybrid integration scenariosSajith C P Nair
Presentation delivered by Sajith C P, Integration Architect at the 2017 Global Integration Bootcamp, Bangalore.
https://www.biztalk360.com/gib2017-india/#speakers[inline]/7/
In this session the speaker talked about ‘on-premises data gateway’ as a secure centralized gateway that can be used for accessing on premise data from various Azure Services. He took a deep dive on how it works, how to install and various methods to troubleshoot connectivity. He concluded the session with few demos of its use in Azure Logic App, Microsoft Flow, Power Apps and Power BI.
This document discusses options for connecting OSIsoft PI data to relational database management systems (RDBMS). It outlines presentations on PI-ODBC, a new version with support for additional data types; PI-OLEDB, a prototype connector; and the PI RDBMS Interface for reading, writing, and modifying PI tag data in any RDBMS using ODBC. The interface allows querying PI data, modifying tags via PI-OLEDB, and automatically historizing changes in RDBMS tables. Future developments include improving configuration, supporting additional pointclass attributes, and adding scan-based output.
Dell PowerEdge zero touch provisioning with Auto Config speeds and simplifies server deployment. Using Server Configuration Profiles and your existing data center infrastructure, deploy one or thousands of PowerEdge servers reliably and repeatably. Learn more: http://www.dell.techcenter.com/LC
Discovery Day 2019 Sofia - Big data clustersIvan Donev
This document provides an overview of the architecture and components of SQL Server 2019 Big Data Clusters. It describes the key Kubernetes concepts used in Big Data Clusters like pods, services, and nodes. It then explains the different planes (control, compute, data) and nodes that make up a Big Data Cluster and their roles. Components in each plane like the SQL master instance, compute pools, storage pools, and data pools are also outlined.
This document provides an overview of database connectivity in Java. It discusses how Java applications can connect to databases using the JDBC API. It describes the different types of JDBC drivers (types I-IV) and how they work. The key steps in a JDBC program are outlined, including loading a driver, establishing a connection, creating statements, executing queries, and processing result sets. Common SQL statements like SELECT, INSERT, UPDATE and DELETE are explained. Overall, the document serves as an introduction to using the JDBC API to connect Java applications to various database management systems.
JDBC provides a standard Java API for accessing databases. It allows Java programs to connect to databases, send SQL statements, and process the results. The key steps in using JDBC include: 1) loading the appropriate JDBC driver, 2) establishing a connection, 3) creating statements to query the database, 4) processing result sets, and 5) closing connections. JDBC supports both two-tier and three-tier architectures and allows transactions to group SQL statements.
Mieke Jans is a Manager at Deloitte Analytics Belgium. She learned about process mining from her PhD supervisor while she was collaborating with a large SAP-using company for her dissertation.
Mieke extended her research topic to investigate the data availability of process mining data in SAP and the new analysis possibilities that emerge from it. It took her 8-9 months to find the right data and prepare it for her process mining analysis. She needed insights from both process owners and IT experts. For example, one person knew exactly how the procurement process took place at the front end of SAP, and another person helped her with the structure of the SAP-tables. She then combined the knowledge of these different persons.
AI Competitor Analysis: How to Monitor and Outperform Your CompetitorsContify
AI competitor analysis helps businesses watch and understand what their competitors are doing. Using smart competitor intelligence tools, you can track their moves, learn from their strategies, and find ways to do better. Stay smart, act fast, and grow your business with the power of AI insights.
For more information please visit here https://www.contify.com/
computer organization and assembly language : its about types of programming language along with variable and array description..https://www.nfciet.edu.pk/
2. Agenda
2
ODBC Connector
Connect from Microsoft Excel, Power BI,
Tableau and other tools
Visualization Tools
Access to data from Windows or Linux
Administer the server, Prepare data,
Manage resources
IBI Server
1
2
3
3. “Access from any analytics or
visualization tool to any data
source.”
3
—
5. 5
Connect any BI platform to any data source
Secure & Curated. Real-time & Trusted.
Your BI platform
• One connection to all data
• Real-time access
• Trusted data for dashboards
IBI Open Data Hub
• One central repository
• Data security
• Single metadata layer
• User governance
All enterprise data sources
• Databases
• Mainframes
• Files
• APIs
• Cloud infrastructure
• Applications
6. Components
6
• IBI Server (WebFOCUS Reporting Server)
Provides data virtualization
Provides configuration from a web browser
Provides data preparation, scheduling, usage reporting and security
• Adapters to data sources
Provides access to almost any database, column store, file, web service
• Updated Connectors
Provides access from any visualization tool
7. Two Personae
Business Analyst
● Uses a visualization tool
(MS Excel, Power BI, Tableau, etc.)
● Needs access to data accessible through the IBI Server
● Uses the ODBC (or JDBC) Connector for access to
curated or additional data sources
7
Administrator
● Uses the IBI Web Server’s console
● Configures Adapters and Connectors
● Uploads files to the server
● Creates metadata for tables, files and other data
sources
● Classifies data objects into folders
● Prepares data for analysis
● Runs resources usage reports
9. IBI Server
● Access data where it resides - Cross database and cross platform joins –
query multiple data sources in a single query
● Curation and control over what data sources are available
● Scalable server technology –
install on any platform: Windows, Linux UNIX, IBM i or Z
● Optimized SQL translation for all major relational databases
● Reads from files, legacy datasets, web services
● Data Flows to prepare and stage data for analysis – integrated scheduler
● Resource Analyzer reports show who used what tables and when
9
10. IBI ODBC Connector
● ODBC “Open Database Connectivity” is a database API specification
● The IBI ODBC Connector lets applications use the IBI Server as a database
● Install one ODBC Driver on each user’s PC
● Connect to any data source(S) your IBI Server(s) can access
10
13. 13
ODBC Data Sources (64 bit)
Driver Tab - shows versionSystem DSN Tab - shows IBI driver
Add and Configure
14. 14
Add or Configure
• Data Source Name – Letters, numbers, underscores
• Description – Optional
• TCP/IP Server – server name or IP Address
• Port – TCP Port for your server
(one less than HTTP Port)
Common values are 8100, 8116, 8120
• User/Password – Optional
• Test – connection to server
17. 17
Test ODBC with Rdaapp
RDAAPP: invoked from shell
Allocating environment handle...
List of available servers:
1 - LOOPBACK
2 - LNXX64R7
Enter corresponding server entry number or name (default=1): 1
Enter User Name: srvadmin
Enter Password: ********
Allocating connection handle...
Attempting connect to the datasource: LOOPBACK ...
Connect status = 0
New ODBC Extender Test.
RDAAPP Command Options:
S <SELECT SQL Statement to Execute> ;
Q Quit/Disconnect
? Help
Enter Command:
s select country from car ;
Alloc stmt ...
Return code from alloc stmt is 0
Issuing SQLPrepare call for select country from car ;
Return code from SQLPrepare call is 0
Executing select country from car ; stmt...
Issuing SQLNumResultCols call for select country from car ;
Number of resultset columns is 1
Printing select item descriptions:
Issuing SQLDescribeCol call for colNum=1
item #1
colname = COUNTRY
coltype = 1
precision = 10
scale = 0
nullable = 0
Binding columns...
Fetching report data...
ENGLAND
FRANCE
ITALY
JAPAN
W GERMANY
<<< 5 record(s) processed. >>>
18. 18
Test ODBC with Excel 2013, 2016 or 2019 installed
Create a text file with extension “dqy” with your server, user and password
Double click to run
19. Advanced
Data Source
• Set non-default server service
• Set schema name as folder name
or EDADBA (for backward compatibility
Global
• Enable tracing
23. 23
Install IBI Client
Expand the archive
$ tar –xvf i8207*
Optionally create directory for installation
$ sudo mkdir /ibi
$ sudo chown ibi:ibi user
Run the install program
$ ./isetup
(Default=localhost) : ibiserve
Enter outbound HTTP Listener Port.
The port is used by Web Console and DMC.
(Default=8121) :
Enter outbound TCP Listener Port.
The port is used by EDAAPI, JDBC, ODBC and WebFOCUS.
(Default=8120) :
-----------------------------------------------------------------------
The following selections have been made for ...
Install Options ...
media release name = R728207D
media gen number = 920 08/07/2020
INSTALLATION_DEVICE = /home/ibi/iserver.tar
PRODUCT = client
EDAHOME = /ibi/client82/home
LICENSE = XXX-XXX-XXXX
Configure Options ...
EDACONF = /ibi/client82/cln
EDAHOME = /ibi/client82/home
LICENSE = XXX-XXX-XXXX
PRODUCT = client
HTTP_OUTBOUND_SERVICE_NAME = 8121
OUTBOUND_SERVICE_NAME = 8129
OUTBOUND_HOST_NAME = edaserve
24. 24
Configure Client
For current user
$ nano .odbc.ini
For all users
$ sudo nano /etc/odbc.ini
Set variable and add to profile (.bash_profile or .profile)
$ export LD_LIBRARY_PATH=/ibi/client82/home/bin:$LD_LIBRARY_PATH
[ODBC Data Sources]
edaserve=IBI 82 Client ODBC Driver
[edaserve]
Driver=/ibi/client82/home/bin/libedaod3x.so
Description=IBI Server
Server=hostname
Port=8120
25. 25
ODBC Test Tool – isql
$ isql -v edaserve srvadmin srvadmin
+---------------------------------------+
| Connected! |
| |
| sql-statement |
| help [tablename] |
| quit |
| |
+---------------------------------------+
SQL> select country from car ;
+-----------+
| COUNTRY |
+-----------+
| ENGLAND |
| FRANCE |
| ITALY |
| JAPAN |
| W GERMANY |
+-----------+
5 rows fetched
SQL>
Start ODBC test tool (-v for verbose so you get
error messages)
$ isql -v server [userid [password]]
55. IBI JDBC Connector
● Installed with IBI Client – Windows and Linux
● Currently supports JDBC 2.0 – Upgrade development is ongoing
● Windows (default location) of “standalone” jar file
C:ibiclient82homeetcjavasrvrjlink_standalone.jar
● Driver Name
ibi.jlink.EdaDriver
● URL Format
jdbc:jlink://server:port
56. SQuirreL SQL
● Open Source JDBC Test Tool
● Runs on Windows and Linux
● Free download from
http://www.squirrelsql.org/#installation