The document discusses temporal databases and time-varying data. It provides definitions and examples of temporal databases, time-varying data, and the two dimensions of time - valid and transaction time. It also summarizes different approaches to implementing temporal databases and reasons for their increasing relevance.
An overview of typical queries on a temporal database, e.g., temporal natural join, temporal coalescing, or temporal set operators. Examples are provided using valid-time, transaction-time and bitemporal databases.
Scott Bailey
Few things we model in our databases are as complicated as time. The major database vendors have struggled for years with implementing the base data types to represent time. And the capabilities and functionality vary wildly among databases. Fortunately PostgreSQL has one of the best implementations out there. We will look at PostgreSQL's core functionality, discuss temporal extensions, modeling temporal data, time travel and bitemporal data.
An overview of typical queries on a temporal database, e.g., temporal natural join, temporal coalescing, or temporal set operators. Examples are provided using valid-time, transaction-time and bitemporal databases.
Scott Bailey
Few things we model in our databases are as complicated as time. The major database vendors have struggled for years with implementing the base data types to represent time. And the capabilities and functionality vary wildly among databases. Fortunately PostgreSQL has one of the best implementations out there. We will look at PostgreSQL's core functionality, discuss temporal extensions, modeling temporal data, time travel and bitemporal data.
Presentation of the paper "Verification and Synthesis in Description Logic Based Dynamic Systems" at the 7th International Conference on Web Reasoning and Rule Systems (RR-2013). The paper received the best paper award.
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. In in this lecture we overview the mining of data streams
Temporálne databázy umožňujú zachytávať históriu biznisových a systémových zmien dát a poskytujú prostriedky na pohodlnú prácu s historickými dátami. Majú široké uplatnenie v rôznych sektoroch ako napríklad poisťovníctvo, bankovníctvo či rezervačné systémy. Umožňujú jednoduché kontrolovanie vykonaných zmien, návrat k predchádzajúcim stavom dát a rôzne analytické dotazy nad históriou. V rámci prezentácie sa budem venovať všeobecnému prehľadu oblasti, existujúcim štandardom (napr. SQL:2011) a riešeniam a na jednoduchých príkladoch predvediem hlavnú funkcionalitu temporálnych databáz. Nakoniec ešte načrtnem možnosti pridania temporálnej podpory do Postgresql a porozprávam aj o tom, ako sa snažím temporálnu podporu dostať do oficiálneho release a čo všetko také niečo obnáša.
This is an assignment produced by a student at the University of East Anglia which looks at how the retailer, WM Morrison's Supermarkets Plc, interacts as a system with its external environment in terms of the macro-environmental and economical factors associated with the UK retailer industry of 2009. Includes PESTLE Analysis and full reference/further reading lists.
In today's post I share my Top 10 Best Mobile. Presentations. Mobile Database Spatial databases provide structures for storage and analysis of spatial data; Spatial ... quickest route to victim
Presentation of the paper "Verification and Synthesis in Description Logic Based Dynamic Systems" at the 7th International Conference on Web Reasoning and Rule Systems (RR-2013). The paper received the best paper award.
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. In in this lecture we overview the mining of data streams
Temporálne databázy umožňujú zachytávať históriu biznisových a systémových zmien dát a poskytujú prostriedky na pohodlnú prácu s historickými dátami. Majú široké uplatnenie v rôznych sektoroch ako napríklad poisťovníctvo, bankovníctvo či rezervačné systémy. Umožňujú jednoduché kontrolovanie vykonaných zmien, návrat k predchádzajúcim stavom dát a rôzne analytické dotazy nad históriou. V rámci prezentácie sa budem venovať všeobecnému prehľadu oblasti, existujúcim štandardom (napr. SQL:2011) a riešeniam a na jednoduchých príkladoch predvediem hlavnú funkcionalitu temporálnych databáz. Nakoniec ešte načrtnem možnosti pridania temporálnej podpory do Postgresql a porozprávam aj o tom, ako sa snažím temporálnu podporu dostať do oficiálneho release a čo všetko také niečo obnáša.
This is an assignment produced by a student at the University of East Anglia which looks at how the retailer, WM Morrison's Supermarkets Plc, interacts as a system with its external environment in terms of the macro-environmental and economical factors associated with the UK retailer industry of 2009. Includes PESTLE Analysis and full reference/further reading lists.
In today's post I share my Top 10 Best Mobile. Presentations. Mobile Database Spatial databases provide structures for storage and analysis of spatial data; Spatial ... quickest route to victim
Save the Date for Quality Data: Making Use of DateTimeSafe Software
Date, time, and time zone fields can all hold as much value as the data itself. This information can be of further value when you extract and transform it to meet data requirements for your BI or CRM tools.
The problem is, “datetimes” are more complicated than you might think. Different formats need different structures, plus there are time zones to take into account, leap years, daylight savings time, and the dreaded international dateline! All of this can lead to a lot of manual effort (like python coding) for a seemingly simple task.
With FME, you can gain a complete understanding and have control over the datetimes in your data. Our datetime “Transformers” eliminate the need for coding and put you in control of data transformation and automation, all with the click of a button.
Join us to learn how you can use FME’s comprehensive datetime library, including:
- Managing date and time attributes (aka. DateTimes) using FME transformers and built-in functions.
- Calculating differences and testing datetime fields
- Parsing and formatting
- Dealing with different time zones
- How to manipulate date, time, and datetime values
By understanding how to apply the power of FME Engine to datetimes you can improve data quality and ultimately, clock out quicker!
Save the Date for Quality Data: Making Use of DateTimeSafe Software
Date, time, and time zone fields can all hold as much value as the data itself. This information can be of further value when you extract and transform it to meet data requirements for your BI or CRM tools.
The problem is, “datetimes” are more complicated than you might think. Different formats need different structures, plus there are time zones to take into account, leap years, daylight savings time, and the dreaded international dateline! All of this can lead to a lot of manual effort (like python coding) for a seemingly simple task.
With FME, you can gain a complete understanding and have control over the datetimes in your data. Our datetime “Transformers” eliminate the need for coding and put you in control of data transformation and automation, all with the click of a button.
Join us to learn how you can use FME’s comprehensive datetime library, including:
- Managing date and time attributes (aka. DateTimes) using FME transformers and built-in functions.
- Calculating differences and testing datetime fields
- Parsing and formatting
- Dealing with different time zones
- How to manipulate date, time, and datetime values
By understanding how to apply the power of FME Engine to datetimes you can improve data quality and ultimately, clock out quicker!
Project report on the design and build of a data warehouse from unstructured and structured data sources (Quandl, yelp and UK Office for National Statistics) using SQL Server 2016, MongoDB and IBM Watson. Design and implementation of business intelligence visualisations using Tableau to answer cross domain business questions
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
8. What are temporal databases? Valid (stated) Time Transaction (logged) Time The 2 dimensions of time
9. What are temporal databases? Valid (stated) Time Transaction (logged) Time Granularity of the time axis Chronons can be days, Seconds, milliseconds depending on the application domain
10. What are temporal databases? The moving point ‘now’ Valid (stated) Time Transaction (logged) Time
35. Demonstration SUPPLIER SUPPLIER PART Non Temporal Schema (SP) TNF Temporal Schema (TSP) Example schema taken from Temporal Data and the Relational Model by CJ Date, H Darwin, NA Lorentzos (2003)
97. DEMO 4 S1 Transaction time = now (showing one S relvar) t26(now-5days) ORA-20001: :Integrity Constraint violated – parent key not found S1,P1
98. DEMO 4 S1 Transaction time = now (showing one S relvar) t26(now-5days) Delete rule on foreign key constraint SP_S_FK is RESTRICT S1,P1 delete restrict
99. DEMO 4 S1 Transaction time = now (showing one S relvar) t26(now-5days) Delete rule on foreign key constraint SP_S_FK is CASCADE S1,P1 delete cascade
105. DEMO 5 S1,P2 Transaction time = now (showing all SP relvars) S1,P3 S2,P4 S2,P5 S2,P6 S3,P1 S3,P3 S3,P6 S1,P4 S1,P5 S1,P1
106. DEMO 5 S1,P2 Transaction time = now (showing all SP relvars) S1,P3 S2,P4 S2,P5 S2,P6 S3,P1 S3,P3 S3,P6 S1,P4 S1,P5 S1,P1 QUERY A – Page 74 List of dates each supplier was able to supply at least one part S1 S1 S3 S2
107. DEMO 5 Transaction time = now (showing all SP relvars) S2,P4 S2,P5 S2,P6 S3,P1 S3,P3 S3,P6 S1,P4 S1,P5 QUERY B – Page 75 List of dates each supplier was unable to supply at least one part S1,P2 S1,P3 S1,P1 S1 S1 S1 S2 S2 S3 S3
111. DEMO 6 Dept 10, Sales, New York Transaction time = now (showing Dept relvar) t28(now)
112. DEMO 6 Dept 10, Sales, New York Transaction time = now (showing Dept relvars) Dept 20, Finance, New York t29(now)
113. DEMO 6 Dept 10, Sales, New York Transaction time = now (showing Dept/Emp relvars) Dept 20, Finance, New York t30(now) Emp 1, John, Clerk,…,Dept 10
115. DEMO 6 Dept 10, Sales, New York Transaction time = now Dept 20, Finance, New York t31(now+20) Emp 1, John, Clerk,…,Dept 10 (showing Dept/Emp relvars)
117. DEMO 6 Dept 10, Sales, New York Transaction time = now Dept 20, Finance, New York t32(now) Emp 1, John, Clerk,…,Dept 10 ORA-20001: :Integrity Constraint violated – parent key not found delete restrict (showing Dept/Emp relvars)
118. DEMO 6 Dept 10, Sales, New York Transaction time = now Dept 20, Finance, New York t33(now) Emp 1, John, Clerk,…,Dept 20 delete cascade (showing Dept/Emp relvars)
120. DEMO 6 Dept 10, Sales, New York Transaction time = now Dept 20, Finance, New York t33(now) Emp 1, John, Clerk,…,Dept 20 transferable (showing Dept/Emp relvars)
121. DEMO 6 Dept 10, Sales, New York Transaction time = now Dept 20, Finance, New York t34(now) Emp 1, John, Clerk,…,Dept 20 Non transferable ORA-20001: :Illegal attempt to modify non-transferable foreign key. (showing Dept/Emp relvars)
122. DEMO 6 Dept 10, Sales, New York Transaction time = now Dept 20, Finance, New York t34(now) Emp 1, John, Clerk,…,Dept 20 Non transferable (showing Dept/Emp relvars)
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125. A Q & Q U E S T I O N S A N S W E R S Rob Squire UK Consulting [email_address]