Session about types of analytics. Descriptive, diagnostic, predictive and prescriptive analytics.
Conference DATA ANALYSIS DEVELOPMENT 2016 by RZECZPOSPOLITA.
What Is Prescriptive Analytics? Your 5-Minute OverviewShannon Kearns
This slide deck walks you through the basis of understanding prescriptive analytics. Understand the different kinds of prescriptive analytics, how it works, its value, where to find use cases and more!
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
Analytics with Descriptive, Predictive and Prescriptive Techniquesleadershipsoil
How the analytics industry has been affected by descriptive, predictive and prescriptive techniques and how these traditional analytical techniques are going to transform the industry in future
Discussed what is Prescriptive Analytics, comparison between Descriptive and Prescriptive Analytics, process, methods and tools. A report presentation conducted at University of East - Manila, Philippines dated July 6, 2017.
What Is Prescriptive Analytics? Your 5-Minute OverviewShannon Kearns
This slide deck walks you through the basis of understanding prescriptive analytics. Understand the different kinds of prescriptive analytics, how it works, its value, where to find use cases and more!
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
Analytics with Descriptive, Predictive and Prescriptive Techniquesleadershipsoil
How the analytics industry has been affected by descriptive, predictive and prescriptive techniques and how these traditional analytical techniques are going to transform the industry in future
Discussed what is Prescriptive Analytics, comparison between Descriptive and Prescriptive Analytics, process, methods and tools. A report presentation conducted at University of East - Manila, Philippines dated July 6, 2017.
Highlights of the Business Analytics seminar by Gary Cokins from October 21, 2014 presentation with Illinois CPA Society.
Gary Cokins is an internationally recognized expert, speaker, and author in performance improvement systems and cost management.
http://www.GaryCokins.com
DI&A Slides: Descriptive, Prescriptive, and Predictive AnalyticsDATAVERSITY
Data analysis can be divided into descriptive, prescriptive and predictive analytics. Descriptive analytics aims to help uncover valuable insight from the data being analyzed. Prescriptive analytics suggests conclusions or actions that may be taken based on the analysis. Predictive analytics focuses on the application of statistical models to help forecast the behavior of people and markets.
This webinar will compare and contrast these different data analysis activities and cover:
- Statistical Analysis – forming a hypothesis, identifying appropriate sources and proving / disproving the hypothesis
- Descriptive Data Analytics – finding patterns
- Predictive Analytics – creating models of behavior
- Prescriptive Analytics – acting on insight
- How the analytic environment differs for each
It has been said that Mobiles +Cloud + Social + Big Data = Better Run The World. IBM has invested over $20 billion since 2005 to grow its analytics business, many companies will invest more than $120 billion by 2015 on analytics, hardware, software and services critical in almost every industry like ; Healthcare, media, sports, finance, government, etc.
It has been estimated that there is a shortage of 140,000 – 190,000 people with deep analytical skills to fill the demand of jobs in the U.S. by 2018.
Decoding the human genome originally took 10 years to process; now it can be achieved in one week with the power of Analytic and BI (Business Intelligence). This lecture’s Key Messages is that Analytics provide a competitive edge to individuals , companies and institutions and that Analytics and BI are often critical to the success of any organization.
Methodology used is to teach analytic techniques through real world examples and real data with this goal to convince audience of the Analytics Edge and power of BI, and inspire them to use analytics and BI in their career and their life.
Data Analytics with R, Contents and Course materials, PPT contents. Developed by K K Singh, RGUKT Nuzvid.
Contents:
Introduction to Data, Information and Data Analytics,
Types of Variables,
Types of Analytics
Life cycle of data analytics.
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
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Business Analytics to solve your Business ProblemsVishal Pawar
Business Analytics Solution in 12 Steps
What is Business Analytics ?
Why we need it ?
Identify your Focus Area and Target Applications
Importance of Business Analytics with different Roles
Confirming your Business Goal with value of Business Analytics
Differentiating Business Analysis , Analyst & Intelligence
What world is doing for Business Analytics Problems
Segregate solution with Data Discovery , Analytics & Science
Gathering information with All Available Data Source
Developing Business Analytics Framework & Components
Developing Visualization with Best User Experience
Improvising Maturity Level of Business Analytics
Get Connected with Expert Team , Who know technology !
Beyond analytics: Prescriptive analytics for the future of your business by Á...Big Data Spain
Analytics has undoubtedly changed the way businesses are operated. It has made clearer than ever that what cannot be measured cannot be managed. However, about 80% of Big Data projects still merely rely on descriptive analytics. While clever visualizations of the business data can be of great aid in the decision making process, there is much more value to be explored through deeper analytical processes. Whenever information about business rules and costs is available, prescriptive analytics can recommend efficient courses of action to optimize costs or revenues.
Session presented at Big Data Spain 2015 Conference
16th Oct 2015
Kinépolis Madrid
http://www.bigdataspain.org
Event promoted by: http://www.paradigmatecnologico.com
Abstract: http://www.bigdataspain.org/program/fri/slot-29.html#spch29.2
Predictive Analytics: Context and Use Cases
Historical context for successful implementation of predictive analytic techniques and examples of implementation of successful use cases.
Highlights of the Business Analytics seminar by Gary Cokins from October 21, 2014 presentation with Illinois CPA Society.
Gary Cokins is an internationally recognized expert, speaker, and author in performance improvement systems and cost management.
http://www.GaryCokins.com
DI&A Slides: Descriptive, Prescriptive, and Predictive AnalyticsDATAVERSITY
Data analysis can be divided into descriptive, prescriptive and predictive analytics. Descriptive analytics aims to help uncover valuable insight from the data being analyzed. Prescriptive analytics suggests conclusions or actions that may be taken based on the analysis. Predictive analytics focuses on the application of statistical models to help forecast the behavior of people and markets.
This webinar will compare and contrast these different data analysis activities and cover:
- Statistical Analysis – forming a hypothesis, identifying appropriate sources and proving / disproving the hypothesis
- Descriptive Data Analytics – finding patterns
- Predictive Analytics – creating models of behavior
- Prescriptive Analytics – acting on insight
- How the analytic environment differs for each
It has been said that Mobiles +Cloud + Social + Big Data = Better Run The World. IBM has invested over $20 billion since 2005 to grow its analytics business, many companies will invest more than $120 billion by 2015 on analytics, hardware, software and services critical in almost every industry like ; Healthcare, media, sports, finance, government, etc.
It has been estimated that there is a shortage of 140,000 – 190,000 people with deep analytical skills to fill the demand of jobs in the U.S. by 2018.
Decoding the human genome originally took 10 years to process; now it can be achieved in one week with the power of Analytic and BI (Business Intelligence). This lecture’s Key Messages is that Analytics provide a competitive edge to individuals , companies and institutions and that Analytics and BI are often critical to the success of any organization.
Methodology used is to teach analytic techniques through real world examples and real data with this goal to convince audience of the Analytics Edge and power of BI, and inspire them to use analytics and BI in their career and their life.
Data Analytics with R, Contents and Course materials, PPT contents. Developed by K K Singh, RGUKT Nuzvid.
Contents:
Introduction to Data, Information and Data Analytics,
Types of Variables,
Types of Analytics
Life cycle of data analytics.
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Business Analytics to solve your Business ProblemsVishal Pawar
Business Analytics Solution in 12 Steps
What is Business Analytics ?
Why we need it ?
Identify your Focus Area and Target Applications
Importance of Business Analytics with different Roles
Confirming your Business Goal with value of Business Analytics
Differentiating Business Analysis , Analyst & Intelligence
What world is doing for Business Analytics Problems
Segregate solution with Data Discovery , Analytics & Science
Gathering information with All Available Data Source
Developing Business Analytics Framework & Components
Developing Visualization with Best User Experience
Improvising Maturity Level of Business Analytics
Get Connected with Expert Team , Who know technology !
Beyond analytics: Prescriptive analytics for the future of your business by Á...Big Data Spain
Analytics has undoubtedly changed the way businesses are operated. It has made clearer than ever that what cannot be measured cannot be managed. However, about 80% of Big Data projects still merely rely on descriptive analytics. While clever visualizations of the business data can be of great aid in the decision making process, there is much more value to be explored through deeper analytical processes. Whenever information about business rules and costs is available, prescriptive analytics can recommend efficient courses of action to optimize costs or revenues.
Session presented at Big Data Spain 2015 Conference
16th Oct 2015
Kinépolis Madrid
http://www.bigdataspain.org
Event promoted by: http://www.paradigmatecnologico.com
Abstract: http://www.bigdataspain.org/program/fri/slot-29.html#spch29.2
Predictive Analytics: Context and Use Cases
Historical context for successful implementation of predictive analytic techniques and examples of implementation of successful use cases.
The art technique of data visualizationUday Kothari
Decision making based on information has been the single most important objective of a data warehousing or big data pursuit. No matter how big, fast and varied data are generated and processed; decision makers are only concerned with the consumption of its end result – data visualization.
Data visualization simply means representing data in a visually appealing manner to enable understanding of the context in which we operate. Data visualization is a “moment of truth” that stems from a data management initiative. It is a very linear process of decision making; and hence, critical to its success. However, data visualizations also possess the potential to put an end to such initiatives; especially, when they are either heavily biased on just the design or contain information overload.
This webinar on the art and technique of data visualization focuses sharply on the one thing that matters most to qualify for effective data visualization: the truth that comes out from data. We have facilitated the discussion with the help of our 3D framework: Design, Discovery & Data.
After registering, you will receive a confirmation email containing information about joining the webinar.
Data visualization is a complex set of processes which is like an umbrella that covers both information and scientific visualization simultaneously. We can’t ignore the benefits of data visualization for its accurate quantities, as it is easily comparable. It also lends valuable suggestion pertaining to the usage of its technique and tools. Scientifically its effectiveness lies in our brain's ability to maintain a proper balance between perception and cognition through visualization.
Data visualizations make huge amounts of data more accessible and understandable. Data visualization, or "data viz," is becoming largely important as the amount of data generated is increasing and big data tools are helping to create meaning behind all of that data.
This SlideShare presentation takes you through more details around data visualization and includes examples of some great data visualization pieces.
Very basic Introduction to Big Data. Touches on what it is, characteristics, some examples of Big Data frameworks. Hadoop 2.0 example - Yarn, HDFS and Map-Reduce with Zookeeper.
The objective of this course is to introduce the principles and techniques of data visualization. Students will learn the basic concepts of communicating information through graphics and apply these concepts in building a visualization of their own.
Lesson 0: What is Data Visualization?
The future needs addressing now. Technology is driving change at dog year rates (x7) see the future now and build competitive advantage, navigate the unknown with cybertreking
This is the presentation I gave to the HIMSS Management Engineering and Process Improvement (ME-PI) Community on predictive analytics healthcare usage.
Product managers personality and career pathTatuLund
In this document I am researching product managers persona from organizational , personality and career path perspectives. I think these viewpoints are essentials to understand in order to recruit new product managers effectively. I was chairing a session on this topic in ProductCamp Helsinki event 18.4.2015.
Building a Real-Time IoT monitoring application with AzureDavide Mauri
Being able to analyze data in real-time is a very hot topic already and it will be more and more in. From product recommendations to fraud detection alarms a lot of stuff would be perfect if it could happen in real time. In this session a sample solution using the serverless capabilities of Azure will be developed, right from the ingestion of sensor data to their analysis and recommendation using AI in real time. Come to see how you could do the same in your environment, moving your application capabilities to the next level.
Power BI for Big Data and the New Look of Big Data SolutionsJames Serra
New features in Power BI give it enterprise tools, but that does not mean it automatically creates an enterprise solution. In this talk we will cover these new features (composite models, aggregations tables, dataflow) as well as Azure Data Lake Store Gen2, and describe the use cases and products of an individual, departmental, and enterprise big data solution. We will also talk about why a data warehouse and cubes still should be part of an enterprise solution, and how a data lake should be organized.
Agile Testing Days 2017 Introducing AgileBI SustainablyRaphael Branger
"We now do Agile BI too” is often heard in todays BI community. But can you really "create" agile in Business Intelligence projects? This presentation shows that Agile BI doesn't necessarily start with the introduction of an iterative project approach. An organisation is well advised to establish first the necessary foundations in regards to organisation, business and technology in order to become capable of an iterative, incremental project approach in the BI domain. In this session you learn which building blocks you need to consider. In addition you will see what a meaningful sequence to these building blocks is. Selected aspects like test automation, BI specific design patterns as well as the Disciplined Agile Framework will be explained in more and practical details.
So you got a handle on what Big Data is and how you can use it to find business value in your data. Now you need an understanding of the Microsoft products that can be used to create a Big Data solution. Microsoft has many pieces of the puzzle and in this presentation I will show how they fit together. How does Microsoft enhance and add value to Big Data? From collecting data, transforming it, storing it, to visualizing it, I will show you Microsoft’s solutions for every step of the way
Secrets of Enterprise Data Mining: SQL Saturday Oregon 201411Mark Tabladillo
If you have a SQL Server license (Standard or higher) then you already have the ability to start data mining. In this new presentation, you will see how to scale up data mining from the free Excel 2013 add-in to production use. Aimed at beginning to intermediate data miners, this presentation will show how mining models move from development to production. We will use SQL Server 2014 tools including SSMS, SSIS, and SSDT.
A competent professional offering over 7+ years of experience in Business Intelligence & Data Warehousing working in roles such as Business Analyst, Data Analyst & Reporting Developer, ETL Architect, Data Architect. Extensively worked on a wide range of tools such as Tableau, Spotfire & SAP Business Objects. Skilled in Teradata database, Oracle Database, MS SQL Server, MS Access, PostgreSQL, Requirements Analysis, reporting, Extract, Transform, Load (ETL), Data Warehousing, End-to-end execution of BI Projects, System Integration, Value Delivery, Team Leadership, Onshore-Offshore working model.
Business Expertise: Power & Engineering(GE POWER), Banking and Financial Services(GE CAPITAL), Leasing domain .
Interests: Data Science.
Meetup Toulouse Microsoft Azure : Bâtir une solution IoTAlex Danvy
Un tour d'horizon des solutions disponibles chez Microsoft pour bâtir une solution IoT. Il est question de Microsoft Azure bien-sûr, mais pas seulement. Windows, Machine Learning, Bots, OCF/AllJoyn, Hololens
Office 365 provides the core creative tools for over 120 million monthly active users -- and while there are many amazing capabilities within it, you can dramatically increase the effectiveness and productivity of users by tailoring the apps to roles or tasks. Come see all the ways you can customize and build next-level tools for users, along with what's new, with Office 365. From new Graph APIs, to deep web extensions across Office products, to new ways to extend conversations, make your users more productive and effective by integrating with Office 365.
Office 365 provides the core creative tools for over 120 million monthly active users -- and while there are many amazing capabilities within it, you can dramatically increase the effectiveness and productivity of users by tailoring the apps to roles or tasks. Come see all the ways you can customize and build next-level tools for users, along with what's new, with Office 365. From new Graph APIs, to deep web extensions across Office products, to new ways to extend conversations, make your users more productive and effective by integrating with Office 365.
Microsoft Fabric is the next version of Azure Data Factory, Azure Data Explorer, Azure Synapse Analytics, and Power BI. It brings all of these capabilities together into a single unified analytics platform that goes from the data lake to the business user in a SaaS-like environment. Therefore, the vision of Fabric is to be a one-stop shop for all the analytical needs for every enterprise and one platform for everyone from a citizen developer to a data engineer. Fabric will cover the complete spectrum of services including data movement, data lake, data engineering, data integration and data science, observational analytics, and business intelligence. With Fabric, there is no need to stitch together different services from multiple vendors. Instead, the customer enjoys end-to-end, highly integrated, single offering that is easy to understand, onboard, create and operate.
This is a hugely important new product from Microsoft and I will simplify your understanding of it via a presentation and demo.
Agenda:
What is Microsoft Fabric?
Workspaces and capacities
OneLake
Lakehouse
Data Warehouse
ADF
Power BI / DirectLake
Resources
DataMass Summit - Machine Learning for Big Data in SQL ServerŁukasz Grala
Sesja pokazująca zarówno Machine Learning Server (czyli algorytmy uczenia maszynowego w językach R i Python), ale także możliwość korzystania z danych JSON w SQL Server, czy też łączenia się do danych znajdujących się na HDFS, HADOOP, czy Spark poprzez Polybase w SQL Server, by te dane wykorzystywać do analizy, predykcji poprzez modele w językach R lub Python.
Sesja na temat analizy sentymentu, ale także i algorytmów uczenia maszynowego w bibliotekach do języka R Microsoft. Sesja była prezentowana na konferencji WhyR? w Warszawie
AnalyticsConf2016 - Innowacyjność poprzez inteligentną analizę informacji - C...Łukasz Grala
Sesja o wprowadzeniu do sztucznej inteligencji, cognitive services i uczeniu maszynowym. Na wyciągnięcie ręki serwisy, które dają możliwość analizy obrazu, dźwięku, tekstów. Analiza obrazu w czasie rzeczywitym, rozpoznawanie twarzy i ludzi, ruchu, emocji. Odczytywanie tekstów z video, oraz boty.
AnalyticsConf2016 - Zaawansowana analityka na platformie Azure HDInsightŁukasz Grala
Sesja or ozwiązaniu Big Data Analytics Microsoft. Jest to Hortonowrks (HADOOP, HBase, Storm, Spark), wraz z wydajnym R Server. Zaawansowana analityka przy użyciui RevoScaleR
eRum2016 -RevoScaleR - Performance and Scalability RŁukasz Grala
Conference eRum2016.
European R users meeting (eRum) is an international conference that aims at integrating users of the R language. eRum 2016 will be a good chance to exchange experiences, broaden knowledge on R and collaborate. One can participate in eRum 2016: (1) with a regular oral presentation, (2) with a lightning talk, (3) with a poster presentation, (4) or attending without presentation or poster. Due to space available at the conference venue, organizers set limit of participants at 250.
Session about RevoScale R.
AzureDay North 2016. Conference about cloud solutions.
What is Machine Learning? Why we need Machine Learning? Where and When we use this? What is Azure Machine Learning and language R. Session introduce to paradigm machine learning, data mining, classes of problems and fundamentals of algorithms.
By Data Scientist as a Service.
AzureDay - Introduction Big Data Analytics.Łukasz Grala
AzureDay North 2016. Conference about cloud solutions.
What is Analytics? What is Big Data? Why Big Data we have in the cloud. What offer Microsoft for Big Data Analytics. How to start with Big Data Analytics or Advanced Analytics? Session introduce fundamentals for Big Data and Advanced Analytics.
By Data Scientist as a Service
WyspaIT 2016 - Azure Stream Analytics i Azure Machine Learning w analizie str...Łukasz Grala
Wzrost ilości danych w postaci strumieni danych spowodował potrzebę analizy danych w czasie rzecyzwistych będących strumieniami. W czasie sesji pokazano połączenie:
- event hub/Iot hub
- Azure Stream Analytics
- Azure Machine Learning
20160405 Cloud Community Poznań - Cloud Analytics on AzureŁukasz Grala
Cloud Analytics on Platform Azure. Overview about analytics. Talking about Azure Data Lake Storage & Analytics, Azure Stream Analytics, HDInsight, Hortonowrks, PowerBI...
Pierwsza edycja konferencji AzureDay Poland 2016. W ramach tej konferencji sesja o analizie danych strumieniowych przy użyciu Azure Stream Analytics, rozszerzone o możliwości algorytmów uczenia maszynowego przetwarzane w Azure Machine Learning
Wprowadzenie do składowania danych w chmurze. Od relacyjnych Azure SQL Database, Azure SQL Data Warehouse, NoSQL - Azure DocumentDB, HDInsight (Hadoop, Spark, Hbase), Azure Search i Azure Data Factory
Wprowadzenie do analizy danych w chmurze. Między innymi o Azure Stream Analytics, Azure Data Lake Analytics, Azure Machine Learning, ale też i o rozwiazaniach OpenSource (Spark, Yupiter, Storm, Zepelin)
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
2. Łukasz Grala
Architect Data
Platform
& BI Solutions |
Microsoft MVP Data
Platform
Architekt rozwiązań platformy danych i BI
Projektant, trener i konsultant
Wykładowca na wyższych uczelniach
Autor artykułów, publikacji i webcastów
Związany naukowo z Wydziałem Informatyki Politechniki
Poznańskiej (architektura baz i hurtowni danych, uczenie
maszynowe, eksploracja danych)
Zawodowo architekt freelancer w TIDK, trener i ekspert w
SQLExpert, współpracownik technologiczny Microsoft
Członek PLSSUG, PTI
Prelegent na licznych konferencjach
lukasz@tidk.pl
3. Agenda
Types of Analytics and Business
Intelligence
Prescriptive analytics
Summary
lukasz@tidk.pl
6. Business
Intelligence
lukasz@tidk.pl
ETL
BI
Applications
Enterprise Data
Warehouse (EDW)
Normalized tables
(3NF)
Atomic data
User queryable
ETL
Presentation Area:
Dimensional (star
schema or OLAP
cube)
Atomic and summary
data
Organized by
business process
Uses conformed
dimensions
Enterprise DW Bus
Architecture
Source
Transactions
Back Room Front Room
Source: Hybrid Hub-and-Spoke and Kimball Architecture (R.Kimball, M.Ross „The DataWarehouse Toolkit” 3ed - 2013
12. BI Solutions
ETL Tool
(SSIS, etc) EDW
(SQL Server, Teradata, etc)
Extract
Original Data
Load
Transformed
Data
Transform
BI Tools
Ingest (EL)
Original Data
Scale-out
Storage &
Compute
(HDFS, Blob Storage,
etc)
Transform & Load
Data Marts
Data Lake(s)
Dashboards
Apps
Streaming data
lukasz@tidk.pl
18. Classes
Learning
Problems
lukasz@tidk.pl
Classification: Assign a category to each item (Chinese | French
| Indian | Italian | Japanese restaurant).
Regression: Predict a real value for each item
(stock/currency value, temperature).
Ranking: Order items according to some criterion
(web search results relevant to a user query).
Clustering: Partition items into homogeneous groups
(clustering twitter posts by topic).
Dimensionality reduction: Transform an initial representation of items
into a lower-dimensional representation while preserving some
properties (preprocessing of digital images).
23. Prescriptive
Analytics
lukasz@tidk.pl
e.g. business rules, regulatory requirments,
technolgoy requirments, HR policies
e.g. profit per item, per unit,
throughput per hour
Find best solutions (variable values) to meet objectives
e.g. maximise profit, minimise cost, minimise downtime