NetVisn clearly identifies which objects will be impacted due to a database table/column changes and it does this across your entire Cognos environment. Impact can be viewed at a summary and detail level at the same time.
DataSplice provides software to optimize the palletization and disposition of removed assets using barcode scanning. The software allows assets to be scanned and automatically sorted onto pallets based on classification specifications, reducing multiple handling. Previously, assets would be manually sorted multiple times based on manufacturer, warranty status, and installation status. DataSplice streamlines the process by scanning assets once and using logic to determine the correct pallet based on the asset details.
This document summarizes different ways to organize data in Matlab, including cell arrays, function handles, and structures.
Cell arrays allow different data types to be stored in containers called cells. Function handles pass functions as inputs to other functions. Structures store selected data together in fields, where each field contains an array of a Matlab data type.
How to Use Basic Search in the SAO/NASA Astrophysics Data System Abstract Ser...Kayleigh Ayn Bohémier
The document introduces the SAO/NASA Astrophysics Data System Abstract Service, which provides a searchable database of astronomy and astrophysics literature dating back to 1975. It allows users to search over weekly updated abstracts and determine full-text article availability to support students and professionals. The system employs Boolean search operators to precisely locate relevant articles while excluding unwanted results.
Tool connectors in Warewolf are used to perform common tasks or data manipulation inside your service or microservice.
You do not need to go out of Warewolf and call a data connector to perform the task for you.
This document discusses key concepts related to data including:
1) Data sources can include databases, datasets, spreadsheets or hardcoded data.
2) A data lake stores all enterprise data in a single repository.
3) A data pipeline is a series of connected data processing elements used to move data between stores.
4) A data store connects to stored data whether in databases or files.
A student named Shashi Kumar Suman with roll number 16072 from section A gave a presentation on functional dependency in databases. The presentation covered the topic of functional dependency, which is a key concept in database design and normalization. It explained how functional dependencies constrain how values in one set of columns in a table determine values in another set of columns in that same table.
DataSplice provides software to optimize the palletization and disposition of removed assets using barcode scanning. The software allows assets to be scanned and automatically sorted onto pallets based on classification specifications, reducing multiple handling. Previously, assets would be manually sorted multiple times based on manufacturer, warranty status, and installation status. DataSplice streamlines the process by scanning assets once and using logic to determine the correct pallet based on the asset details.
This document summarizes different ways to organize data in Matlab, including cell arrays, function handles, and structures.
Cell arrays allow different data types to be stored in containers called cells. Function handles pass functions as inputs to other functions. Structures store selected data together in fields, where each field contains an array of a Matlab data type.
How to Use Basic Search in the SAO/NASA Astrophysics Data System Abstract Ser...Kayleigh Ayn Bohémier
The document introduces the SAO/NASA Astrophysics Data System Abstract Service, which provides a searchable database of astronomy and astrophysics literature dating back to 1975. It allows users to search over weekly updated abstracts and determine full-text article availability to support students and professionals. The system employs Boolean search operators to precisely locate relevant articles while excluding unwanted results.
Tool connectors in Warewolf are used to perform common tasks or data manipulation inside your service or microservice.
You do not need to go out of Warewolf and call a data connector to perform the task for you.
This document discusses key concepts related to data including:
1) Data sources can include databases, datasets, spreadsheets or hardcoded data.
2) A data lake stores all enterprise data in a single repository.
3) A data pipeline is a series of connected data processing elements used to move data between stores.
4) A data store connects to stored data whether in databases or files.
A student named Shashi Kumar Suman with roll number 16072 from section A gave a presentation on functional dependency in databases. The presentation covered the topic of functional dependency, which is a key concept in database design and normalization. It explained how functional dependencies constrain how values in one set of columns in a table determine values in another set of columns in that same table.
Functional dependencies play a key role in database design and normalization. A functional dependency (FD) is a constraint that one attribute determines another. FDs have various definitions but generally mean that given the value of one attribute (left side), the value of another attribute (right side) is determined. Armstrong's axioms are used to derive implied FDs from a set of FDs. The closure of an attribute set or set of FDs finds all attributes/FDs logically implied. Normalization aims to eliminate anomalies and is assessed using normal forms like 1NF, 2NF, 3NF, BCNF which impose additional constraints on table designs.
The document discusses different normal forms for organizing data in a database, including 1NF, 2NF, 3NF, and BCNF. 1NF requires attributes to be atomic and no repeating groups. 2NF removes partial dependencies by requiring non-prime attributes to depend on the whole primary key. 3NF removes non-key attributes that are not dependent on the primary key. BCNF is stronger than 3NF and requires all determinants to be candidate keys. Examples are provided to illustrate how relations can be decomposed to satisfy 3NF and BCNF.
Normalization is the process of organizing data in a database. This includes creating tables and establishing relationships between those tables according to rules designed both to protect the data and to make the database more flexible by eliminating redundancy and inconsistent dependency.
Functional dependencies and normalization for relational databasesJafar Nesargi
This document discusses guidelines for designing relational databases. It covers four informal measures of quality: semantics of attributes, reducing redundancy, reducing null values, and avoiding spurious tuples. The guidelines are: 1) design relations so their meaning is clear, 2) avoid anomalies like insertion, deletion and modification anomalies, 3) minimize null values in attributes, and 4) design relations to join without generating spurious tuples. The document uses examples to illustrate these concepts and their importance for database design.
The document defines functional dependencies and describes how they constrain relationships between attributes in a database relation. A functional dependency X → Y means the Y attribute is functionally determined by the X attribute(s). The closure of a set of functional dependencies includes all dependencies that can be logically derived. Normalization aims to eliminate anomalies by decomposing relations based on their functional dependencies until a desired normal form is reached.
B-Trees are tree data structures used to store data on disk storage. They allow for efficient retrieval of data compared to binary trees when using disk storage due to reduced height. B-Trees group data into nodes that can have multiple children, reducing the height needed compared to binary trees. Keys are inserted by adding to leaf nodes or splitting nodes and promoting middle keys. Deletion involves removing from leaf nodes, borrowing/promoting keys, or joining nodes.
NetVisn provides a one-stop view into the entire Cognos 10/8 security domain along with the ability to change security settings quickly and easily. Validating that your security is correctly applied across your entire environment is a simple task.
When NetVisn is installed, it automatically documents the entire Cognos Content Store, including all objects, properties, parameters, security settings, groups, roles, and accounts. The documentation mirrors Cognos Connection and provides hyperlinks to allow for fast drill-down and navigation of the fully documented Content Store.
This document discusses object dependency analysis performed on a Content Store. The analysis identifies all dependencies between objects, shows which objects will no longer function properly due to missing dependencies, and provides details on specific folders and objects with dependency issues. Administrators are advised to run this analysis daily to proactively manage objects with missing dependencies.
Model package dependency - reviewed 08-06-14Envisn
This document discusses dependencies for a package and model item. It lists the BI Reporting package as dependent on by other objects and identifies Quantity as a model item used by other listed objects.
This document discusses license management in 3 sections: the first shows assigned licenses by studio and permissions for each account; the second shows the same data but organized by account and group along with permissions; the third covers all Cognos capabilities and license categories and allows the user to select specific capabilities and an output format.
Documentation slides model - reviewed 08-06-14Envisn
The BI Reporting Model provides a summary of its content through a namespace matrix and allows for fast navigation between documented sections via hyperlinks. It also details the model's data security, object security, joins between tables, and database usage by table and column with references. Finally, it defines the data source details and attributes.
Presentation at IBM IOD 2011 by Larry Bob, BI Architect at The Boeing Company about what a skilled Framework Manager modeler can do in a large environment to advance both simplicity and excellence at the same time.
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
Generative Classifiers: Classifying with Bayesian decision theory, Bayes’ rule, Naïve Bayes classifier.
Discriminative Classifiers: Logistic Regression, Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Attribute selection measures- Gini impurity; Entropy, Regularization Hyperparameters, Regression Trees, Linear Support vector machines.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Functional dependencies play a key role in database design and normalization. A functional dependency (FD) is a constraint that one attribute determines another. FDs have various definitions but generally mean that given the value of one attribute (left side), the value of another attribute (right side) is determined. Armstrong's axioms are used to derive implied FDs from a set of FDs. The closure of an attribute set or set of FDs finds all attributes/FDs logically implied. Normalization aims to eliminate anomalies and is assessed using normal forms like 1NF, 2NF, 3NF, BCNF which impose additional constraints on table designs.
The document discusses different normal forms for organizing data in a database, including 1NF, 2NF, 3NF, and BCNF. 1NF requires attributes to be atomic and no repeating groups. 2NF removes partial dependencies by requiring non-prime attributes to depend on the whole primary key. 3NF removes non-key attributes that are not dependent on the primary key. BCNF is stronger than 3NF and requires all determinants to be candidate keys. Examples are provided to illustrate how relations can be decomposed to satisfy 3NF and BCNF.
Normalization is the process of organizing data in a database. This includes creating tables and establishing relationships between those tables according to rules designed both to protect the data and to make the database more flexible by eliminating redundancy and inconsistent dependency.
Functional dependencies and normalization for relational databasesJafar Nesargi
This document discusses guidelines for designing relational databases. It covers four informal measures of quality: semantics of attributes, reducing redundancy, reducing null values, and avoiding spurious tuples. The guidelines are: 1) design relations so their meaning is clear, 2) avoid anomalies like insertion, deletion and modification anomalies, 3) minimize null values in attributes, and 4) design relations to join without generating spurious tuples. The document uses examples to illustrate these concepts and their importance for database design.
The document defines functional dependencies and describes how they constrain relationships between attributes in a database relation. A functional dependency X → Y means the Y attribute is functionally determined by the X attribute(s). The closure of a set of functional dependencies includes all dependencies that can be logically derived. Normalization aims to eliminate anomalies by decomposing relations based on their functional dependencies until a desired normal form is reached.
B-Trees are tree data structures used to store data on disk storage. They allow for efficient retrieval of data compared to binary trees when using disk storage due to reduced height. B-Trees group data into nodes that can have multiple children, reducing the height needed compared to binary trees. Keys are inserted by adding to leaf nodes or splitting nodes and promoting middle keys. Deletion involves removing from leaf nodes, borrowing/promoting keys, or joining nodes.
NetVisn provides a one-stop view into the entire Cognos 10/8 security domain along with the ability to change security settings quickly and easily. Validating that your security is correctly applied across your entire environment is a simple task.
When NetVisn is installed, it automatically documents the entire Cognos Content Store, including all objects, properties, parameters, security settings, groups, roles, and accounts. The documentation mirrors Cognos Connection and provides hyperlinks to allow for fast drill-down and navigation of the fully documented Content Store.
This document discusses object dependency analysis performed on a Content Store. The analysis identifies all dependencies between objects, shows which objects will no longer function properly due to missing dependencies, and provides details on specific folders and objects with dependency issues. Administrators are advised to run this analysis daily to proactively manage objects with missing dependencies.
Model package dependency - reviewed 08-06-14Envisn
This document discusses dependencies for a package and model item. It lists the BI Reporting package as dependent on by other objects and identifies Quantity as a model item used by other listed objects.
This document discusses license management in 3 sections: the first shows assigned licenses by studio and permissions for each account; the second shows the same data but organized by account and group along with permissions; the third covers all Cognos capabilities and license categories and allows the user to select specific capabilities and an output format.
Documentation slides model - reviewed 08-06-14Envisn
The BI Reporting Model provides a summary of its content through a namespace matrix and allows for fast navigation between documented sections via hyperlinks. It also details the model's data security, object security, joins between tables, and database usage by table and column with references. Finally, it defines the data source details and attributes.
Presentation at IBM IOD 2011 by Larry Bob, BI Architect at The Boeing Company about what a skilled Framework Manager modeler can do in a large environment to advance both simplicity and excellence at the same time.
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
Generative Classifiers: Classifying with Bayesian decision theory, Bayes’ rule, Naïve Bayes classifier.
Discriminative Classifiers: Logistic Regression, Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Attribute selection measures- Gini impurity; Entropy, Regularization Hyperparameters, Regression Trees, Linear Support vector machines.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
06-20-2024-AI Camp Meetup-Unstructured Data and Vector DatabasesTimothy Spann
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of May 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
3. Database Dependency
Analysis can be refined to identify
table and column items and where
they are used.
This example shows those objects
Using Northwind table Order Details.
Database: Northwind
Column: Order Details