Applying Data Science to Move Beyond Keywords for Social Analysis
•Download as PPTX, PDF•
2 likes•536 views
At DataSift we're constantly looking for ways to help customers build better insights from data. We're increasingly looking to data science techniques to achieve this. One area we're currently working on is moving beyond keywords when building filters and classifiers. If you're a member of DataSift FORUM you'll be aware that we recently released our Keyword Relationship Explorer tool. This tool was a first step towards our goal. Join us for our first in a series of webinars for the DataSift FORUM where you will: Understand how we built the model that sits behind the tool Learn how you can build your own model from your own data See how we're looking to build the concept of similarity into our platform and how it will transform the way customers will filter and classify data
Report
Share
Report
Share
Recommended
Facebook Topic Data in 2016: The Update
With 2016 just round the corner, we’re kicking off our New Year efforts to keep you up to date on all things Facebook topic data. We’ll begin with an update session on Wednesday, December 16 which will cover:
What you can expect from DataSift’s marketing team in terms of support
What’s new in the Facebook topic data guidelines for partners
Facebook topic data use cases: what type of insight use cases get most traction
Facebook topic data collateral at your disposal
We plan to hold these session every other month. Please register today for the webinar on Wednesday December 16 and, if you can’t attend, we’ll send you a recorded webcast.
DataSift VEDO FOCUS introduction
VEDO FOCUS is an AI tool that analyzes over 1 billion keywords to categorize data into 450,000 categories applied at the firehose level. It allows users to filter collected data by category, analyze the output of categorized data, and combine categorized data with demographic and sentiment data to map brands, advocacy, features across audiences.
DataSift September '12 Release Overview
The document introduces new features and enhancements to DataSift's products in September 2012, including an improved query builder that allows for visually creating streams without coding; streamlined stream management with private streams and monitoring capabilities; and a new "Push" feature to deliver filtered data directly into users' platforms using connectors.
Turning Social Data Into Strategic Advantage: What Marketers Need to Know
1) The document discusses how social media data has become huge in volume, with over 1.8 billion people on social networks generating 400 million tweets per day and 3.2 billion "likes" per day.
2) It describes how social data has become more complex, with the average tweet now containing up to 150 fields of metadata. Early social media monitoring relied on manually eyeballing tweets but this does not scale effectively.
3) The document introduces DataSift as a social data platform that ingests and normalizes social data from over 50 sources in real time, analyzing and augmenting it with over 40 insights within half a second for enterprise use.
What's New in Facebook Topic Data
With Facebook’s 1.49 billion million active users and just having taken over from Google as the traffic source for news sites, can you afford not to pay attention?
Facebook topic data is now available and it can benefit your business in ways you haven’t considered before. It’s a rare opportunity to set yourself apart from your peers and demonstrate a real competitive differentiation.
We’ve just added many great features to PYLON for Facebook Topic Data: new countries, additional languages and many more. Join us for our next live webinar where you will learn:
About our recent updates to PYLON for Facebook Topic Data and how these new features will make your life easier when analyzing Facebook topic data
How you can create training sets for classifiers and machine learning algorithms
See how Facebook topic data works in action and what use cases it supports
Spaces are limited, sign up to save your seat today!
Get a Clearer Picture of Your Target Audience with Facebook Topic Data
Did you hear the one about a snack brand that made assumptions based on focus group data that turned out completely wrong?
According to Facebook topic data, we’re snacking with family not friends when watching big sports events. Who knew?!
With Facebook on its way to capture 65% of social network ad revenues in 2015, Facebook topic data is uniquely placed to deliver invaluable insights into the opinions of 1.49 billion of Facebook’s monthly users. Brands can now work with one of DataSift’s partners to gain access to this data and test their hypotheses.
Join us for our upcoming webinar where you will:
See real-life use case examples of how marketers and agencies are leveraging Facebook topic data
Learn about our recent updates to PYLON for Facebook topic data and how these new features will make your life easier when analyzing Facebook topic data
Have your questions about Facebook topic data answered
Improved Speed + Accuracy for Research with Datasift Demographics
Social media is a treasure trove of market research data that is largely untapped. These social interactions come with rich metadata that can not only tell you what people say, but who is saying it and from where.
In this webinar, we'll be discussing what data is publicly available, the research that has already been done using both historical and real-time, and how you can get started with social data for your own market research.
To discuss your specific use cases for demographic social data, contact us today:
http://datasift.com/contact-us/
SEO on a Budget - Search London - July 30 2014
Jo Turnbull demonstrates how to implement SEO on a budget using three examples. Find out how you can achieve results with SEO, even on a small budget.
Recommended
Facebook Topic Data in 2016: The Update
With 2016 just round the corner, we’re kicking off our New Year efforts to keep you up to date on all things Facebook topic data. We’ll begin with an update session on Wednesday, December 16 which will cover:
What you can expect from DataSift’s marketing team in terms of support
What’s new in the Facebook topic data guidelines for partners
Facebook topic data use cases: what type of insight use cases get most traction
Facebook topic data collateral at your disposal
We plan to hold these session every other month. Please register today for the webinar on Wednesday December 16 and, if you can’t attend, we’ll send you a recorded webcast.
DataSift VEDO FOCUS introduction
VEDO FOCUS is an AI tool that analyzes over 1 billion keywords to categorize data into 450,000 categories applied at the firehose level. It allows users to filter collected data by category, analyze the output of categorized data, and combine categorized data with demographic and sentiment data to map brands, advocacy, features across audiences.
DataSift September '12 Release Overview
The document introduces new features and enhancements to DataSift's products in September 2012, including an improved query builder that allows for visually creating streams without coding; streamlined stream management with private streams and monitoring capabilities; and a new "Push" feature to deliver filtered data directly into users' platforms using connectors.
Turning Social Data Into Strategic Advantage: What Marketers Need to Know
1) The document discusses how social media data has become huge in volume, with over 1.8 billion people on social networks generating 400 million tweets per day and 3.2 billion "likes" per day.
2) It describes how social data has become more complex, with the average tweet now containing up to 150 fields of metadata. Early social media monitoring relied on manually eyeballing tweets but this does not scale effectively.
3) The document introduces DataSift as a social data platform that ingests and normalizes social data from over 50 sources in real time, analyzing and augmenting it with over 40 insights within half a second for enterprise use.
What's New in Facebook Topic Data
With Facebook’s 1.49 billion million active users and just having taken over from Google as the traffic source for news sites, can you afford not to pay attention?
Facebook topic data is now available and it can benefit your business in ways you haven’t considered before. It’s a rare opportunity to set yourself apart from your peers and demonstrate a real competitive differentiation.
We’ve just added many great features to PYLON for Facebook Topic Data: new countries, additional languages and many more. Join us for our next live webinar where you will learn:
About our recent updates to PYLON for Facebook Topic Data and how these new features will make your life easier when analyzing Facebook topic data
How you can create training sets for classifiers and machine learning algorithms
See how Facebook topic data works in action and what use cases it supports
Spaces are limited, sign up to save your seat today!
Get a Clearer Picture of Your Target Audience with Facebook Topic Data
Did you hear the one about a snack brand that made assumptions based on focus group data that turned out completely wrong?
According to Facebook topic data, we’re snacking with family not friends when watching big sports events. Who knew?!
With Facebook on its way to capture 65% of social network ad revenues in 2015, Facebook topic data is uniquely placed to deliver invaluable insights into the opinions of 1.49 billion of Facebook’s monthly users. Brands can now work with one of DataSift’s partners to gain access to this data and test their hypotheses.
Join us for our upcoming webinar where you will:
See real-life use case examples of how marketers and agencies are leveraging Facebook topic data
Learn about our recent updates to PYLON for Facebook topic data and how these new features will make your life easier when analyzing Facebook topic data
Have your questions about Facebook topic data answered
Improved Speed + Accuracy for Research with Datasift Demographics
Social media is a treasure trove of market research data that is largely untapped. These social interactions come with rich metadata that can not only tell you what people say, but who is saying it and from where.
In this webinar, we'll be discussing what data is publicly available, the research that has already been done using both historical and real-time, and how you can get started with social data for your own market research.
To discuss your specific use cases for demographic social data, contact us today:
http://datasift.com/contact-us/
SEO on a Budget - Search London - July 30 2014
Jo Turnbull demonstrates how to implement SEO on a budget using three examples. Find out how you can achieve results with SEO, even on a small budget.
Marketers, Rev Your Engines: Facebook Topic Data is Available Now
Marketers who want their message to stand out from the noise today are seriously challenged. To create truly compelling content that resonates with the right audience, you need a serious edge. Marketers have had great insights into paid Facebook content and corporate-owned pages, but never what people share and engage with directly in their news feeds. How should marketers today curate effective content for their relevant audience?
With insights from Facebook topic data you can get the edge you need
Topic data is what people share and engage with directly in their Facebook news feeds, and for the first time it is now available through a number of DataSift certified social companies and agencies listed on our partner portal. This is the first time that you as a marketer or agency can define a topic (your brand, competitors, events, etc.) and understand the audience around that topic.
Jason Rose, DataSift's SVP Marketing, will host our next live webinar where brand marketers and agencies will learn:
How Facebook topic data enables you to understand more about your target audience
About new strategies to reach targeted demographics that we revealed for a major automotive launch using analyzing topic data
How you can be one of the first to begin leveraging topic data from Facebook’s 1.44 Billion monthly active users
Spaces are limited, so sign up to save your seat today!
How to Build Innovative Products with Facebook Topic Data
From open communication to closed interaction, the ecosystem of social data is constantly changing and evolving. As a social analytics provider, how do you adapt and capture the new norms of social insights?
Facebook topic data is born in the wake of shifting consumer behaviors and growing privacy concerns. With its privacy first model, the new type of aggregated and anonymized data coupled with its multi-dimensionality allow for virtually unlimited number of ways to surface audience insights from the largest source of public opinion.
The good news is that we already handled the heavy lifting in processing the billions of daily interactions on Facebook. The rest lies in how you can leverage PYLON and the tools we created for you to innovate and differentiate your product in the new paradigm of audience insights.
Join us for our upcoming webinar and learn:
About the difference between public and non-public data sources and the philosophy behind our PYLON design
Explore the tools and techniques we developed to help you innovate and differentiate your product
Have your questions about Facebook topic data answered
10 Reasons Facebook Topic Data Will Change Your World
Facebook represents by far the largest record of public opinion that has ever existed. With 1.55 billion monthly active users, that doesn’t come as a surprise.
Facebook topic data is the only way to understand the entire Facebook audience engaging around the brands, products, events and competitors that businesses care about. Facebook topic data represents the start of a new era of social data.
Join us for our webinar where you will learn:
How Facebook topic data differs from anything you have ever seen before and the new types of insights you can now gain
How you can build the most unique and innovative products and provide important insights to your customers
About our recent updates to PYLON for Facebook Topic Data including the availability of Super Public Text Samples
Spaces are limited, sign up to save your seat today!
Five Things You Didn't Know DataSift Can Do
There are billions of human interactions on social media every day. How do you tune in to extract value from disparate and unstructured pieces of social data?
DataSift was built to solve this very problem. The platform unifies all the types of social, news and blogs data in a single place. And our unique coding language helps you find meaning in the data faster.
Learn about:
-The basics of the DataSift Platform
-What types of data you can access and query on our platform (hint: there are LOTS!)
-How you can enrich social data with DataSift in terms of context, sentiment, demographics and links content
-Our magic ingredients - CSDL and VEDO that revolutionize social data analysis
DataSift's Rob Bailey at The Social Media Strategies Summit
Social monitoring has gone from a fancy nice-to-have, to an essential strategic tool for all organizations. DataSift provides insights into customers, the market and trending topics.
DataSift attended the Social Media Strategies Summit in San Francisco, where Rob Bailey, our CEO, discussed the seven measurable actions that further your company’s marketing and social media efforts. In addition to providing a framework for moving your company’s efforts from beginner to pro, Rob highlighted companies who are thinking strategically about social media, such as Taco Bell, Burberry and KFC.
Boosting Your Brand Marketing with Facebook Topic Data Insights
You have a new market entrant. You would like to know how it’s impacting your brand. So the first thing you do is to find out your share of voice in the market. What now?
Indeed, knowing your brand’s share of voice is important for monitoring your brand health, but wouldn’t it be even better if you know how it varies among different demographic groups, how audiences are engaging with your brand and what features or products are driving your top engagements?
We believe so. It is in the details that makes the difference and can truly impact your brand marketing. The share of voice use case is but one of the many Facebook topic data insights that can help you turn a good first start into a full fledged brand marketing strategy.
Join us for our upcoming webinar and learn:
About the different ways your brand can apply Facebook topic data to achieve the brand marketing results you’ve always wanted
Explore the brand health use case and three other examples of how Facebook topic data insights can help you discover the undiscoverable
Have your questions about Facebook topic data answered
Sign up to save your seat today!
Social Media Management in Crisis Communication
Presentation from David VIcent, Relational Marketeer about the main importance nowadays of social media in crisis Communication. UNWTO Themis Capacity programme, North Africa and Middle East Countries.
Staying on the Right Side of the Fence when Analyzing Human Data
This document summarizes a presentation on analyzing human data ethically. It discusses how data collection has become more intimate, consumers lack control over their data and distrust how it is used. Five principles of ethical data use are outlined: being beneficial, progressive, sustainable, respectful and fair. Case studies demonstrate how companies apply these principles in areas like continuous improvement, transparency and considering all stakeholders. The presentation argues for a privacy-first approach to data insights that provides aggregate, anonymized analysis to benefit both businesses and consumers in a way that builds trust.
Taming Social Data: How Social Data Framing liberates analysis and accelerate...
As potent as direct access to social data can be, the power multiplies many times over when social data is combined with your business data. When these data sets come together, insights pop out like fireworks. And the relevance of the social noise to your business becomes clear as day.
Transforming Customer Experience: From Moments to Journeys
Why do companies need to manage the entire customer experience? New analysis reveals that the entire customer journey - the series of interactions with a brand - is more important than any single touchpoint experience. Leading companies identify and effectively manage a few "key journeys." When companies perfect managing the entire customer journey, they reap significant benefits—including enhanced customer and employee satisfaction, reduced customer churn, increased revenue, lower costs, improved organizational collaboration, and competitive advantage. Presented at the Harvard Business Review webinar. For more on customer decision journeys: http://mckinseyonmarketingandsales.com/topics/customer-decision-journey
Customer Journey Analytics and Big Data
This document summarizes key insights from a McKinsey presentation on customer journey analytics and big data. It finds that companies are storing large amounts of data but few know how to extract value from it. Analyzing customer journeys rather than individual touchpoints provides more predictive insights into customer satisfaction and churn. Mapping important customer journeys in an industry reveals opportunities to improve the customer experience and reduce costs. The presentation provides an example of a retail bank that identified ways to decrease service costs and improve customer satisfaction by analyzing its customer journey data.
Back to the Future: The Reinvention of the Library Catalog, Yesterday, Today,...
American Library Association
Rare Book and Manuscript Section
Pre-Conference, Las Vegas, NV, June 26, 2014
Eric Miller, em@zepheira.com
Recurrent Neural Network : Multi-Class & Multi Label Text Classification
This is the content for the 4th International Conference on Data Management, Analytics and Innovation - ICDMAI 2020.
Here we talk & discuss LSTM and Text Classification for Multi-Class & Multi-Label problem statement.
Code : https://github.com/amitbcp/icdmai_2020
Introducción a NoSQL
No se pierda esta oportunidad de conocer las ventajas de NoSQL. Participe en nuestro seminario web y descubra:
Qué significa el término NoSQL
Qué diferencias hay entre los almacenes clave-valor, columna ancha, grafo y de documentos
Qué significa el término «multimodelo»
Tomáš Mikolov - Distributed Representations for NLP
The document discusses word embedding techniques, specifically Word2vec. It introduces the motivation for distributed word representations and describes the Skip-gram and CBOW architectures. Word2vec produces word vectors that encode linguistic regularities, with simple examples showing words with similar relationships have similar vector offsets. Evaluation shows Word2vec outperforms previous methods, and its word vectors are now widely used in NLP applications.
Learning Multilingual Semantic Parsers for Question Answering over Linked Dat...
The document summarizes a PhD dissertation defense talk on learning multilingual semantic parsers for question answering over linked data. It discusses comparing neural and probabilistic graphical model architectures for semantic parsing to map natural language to formal meaning representations. The talk outlines introducing dependency parse tree-based approaches, evaluating different model architectures, and addressing challenges in building multilingual question answering systems over structured knowledge bases.
Red Hat Summit Connect 2023 - Redis Enterprise, the engine of Generative AI
Redis è conosciuto come un database in tempo reale che può essere utilizzato come cache, per memorizzare sessioni utente o immagazzinare token d’autenticazione, documenti JSON, per gestire inventari in tempo reale, dati geografici, come feature store in scenari di machine learning, gestione di code, broker, stream e molto altro. Ma non tutti sanno che Redis può memorizzare e indicizzare vettori di embeddings, ovvero quelle strutture dati che sono alla base di applicativi come ChatGPT. In questo talk, esploreremo come utilizzare Redis come un database vettoriale per implementare casi d’uso moderni.
Vectors in Search - Towards More Semantic Matching
With the advent of deep learning and algorithms like word2vec and doc2vec, vectors-based representations are increasingly being used in search to represent anything from documents to images and products. However, search engines work with documents made of tokens, and not vectors, and are typically not designed for fast vector matching out of the box. In this talk, I will give an overview of how vectors can be derived from documents to produce a semantic representation of a document that can be used to implement semantic / conceptual search without hurting performance. I will then I will describe a few different techniques for efficiently searching vector-based representations in an inverted index, such as learning sparse representations of vectors, clustering, and learning binary vectors. Finally, I will discuss some of the pitfalls of vector-based search, and how to get the best of both worlds by combining vector-based scoring with traditional relevancy metrics such as BM25.
Vectors in Search – Towards More Semantic Matching - Simon Hughes, Dice.com
This document summarizes Simon Hughes' presentation on using vector representations for semantic matching in search. It discusses using word embeddings to learn vector representations of words that capture their semantic meaning based on context. Approaches for searching with word embeddings include expanding queries with related terms from the embedding model or clustering the embeddings and mapping queries to clusters. The document also covers techniques for indexing and searching vector representations in an inverted index, such as using locality-sensitive hashing or k-means trees to map vectors to discrete tokens that can be indexed.
Ssas dmx ile kurum içi verilerin i̇şlenmesi
This document discusses business intelligence and data mining. It begins by explaining the evolution of data usage from descriptive to prescriptive analytics. It then contrasts legacy vs modern business intelligence tools, listing Microsoft's SQL Server tools. Common data mining tools and languages are also outlined, including their advantages and uses. Finally, the document demonstrates data mining techniques like association and classification using a scenario-based approach.
NLP & DBpedia
This document describes an approach for bridging the gap between natural language queries and linked data concepts using BabelNet. The approach uses BabelNet for word sense disambiguation, named entity recognition and disambiguation. It parses queries, matches terms to ontology concepts and properties, generates candidate triples, and integrates the triples to produce SPARQL queries. The approach was evaluated on test data from QALD-2, achieving a promising 76% of questions answered correctly.
Semantics at Scale: A Distributional Approach
1) The document discusses using distributional semantics to build robust semantic models that can handle large amounts of data and enable semantic computing at scale.
2) It describes how distributional semantic models can be used to represent word meanings based on their linguistic contexts, allowing semantic knowledge bases to be automatically constructed from large text corpora.
3) The author proposes a schema-agnostic approach using distributional semantics to enable querying databases without prior knowledge of schemas, addressing problems of vocabulary and structural differences between queries and data.
More Related Content
Viewers also liked
Marketers, Rev Your Engines: Facebook Topic Data is Available Now
Marketers who want their message to stand out from the noise today are seriously challenged. To create truly compelling content that resonates with the right audience, you need a serious edge. Marketers have had great insights into paid Facebook content and corporate-owned pages, but never what people share and engage with directly in their news feeds. How should marketers today curate effective content for their relevant audience?
With insights from Facebook topic data you can get the edge you need
Topic data is what people share and engage with directly in their Facebook news feeds, and for the first time it is now available through a number of DataSift certified social companies and agencies listed on our partner portal. This is the first time that you as a marketer or agency can define a topic (your brand, competitors, events, etc.) and understand the audience around that topic.
Jason Rose, DataSift's SVP Marketing, will host our next live webinar where brand marketers and agencies will learn:
How Facebook topic data enables you to understand more about your target audience
About new strategies to reach targeted demographics that we revealed for a major automotive launch using analyzing topic data
How you can be one of the first to begin leveraging topic data from Facebook’s 1.44 Billion monthly active users
Spaces are limited, so sign up to save your seat today!
How to Build Innovative Products with Facebook Topic Data
From open communication to closed interaction, the ecosystem of social data is constantly changing and evolving. As a social analytics provider, how do you adapt and capture the new norms of social insights?
Facebook topic data is born in the wake of shifting consumer behaviors and growing privacy concerns. With its privacy first model, the new type of aggregated and anonymized data coupled with its multi-dimensionality allow for virtually unlimited number of ways to surface audience insights from the largest source of public opinion.
The good news is that we already handled the heavy lifting in processing the billions of daily interactions on Facebook. The rest lies in how you can leverage PYLON and the tools we created for you to innovate and differentiate your product in the new paradigm of audience insights.
Join us for our upcoming webinar and learn:
About the difference between public and non-public data sources and the philosophy behind our PYLON design
Explore the tools and techniques we developed to help you innovate and differentiate your product
Have your questions about Facebook topic data answered
10 Reasons Facebook Topic Data Will Change Your World
Facebook represents by far the largest record of public opinion that has ever existed. With 1.55 billion monthly active users, that doesn’t come as a surprise.
Facebook topic data is the only way to understand the entire Facebook audience engaging around the brands, products, events and competitors that businesses care about. Facebook topic data represents the start of a new era of social data.
Join us for our webinar where you will learn:
How Facebook topic data differs from anything you have ever seen before and the new types of insights you can now gain
How you can build the most unique and innovative products and provide important insights to your customers
About our recent updates to PYLON for Facebook Topic Data including the availability of Super Public Text Samples
Spaces are limited, sign up to save your seat today!
Five Things You Didn't Know DataSift Can Do
There are billions of human interactions on social media every day. How do you tune in to extract value from disparate and unstructured pieces of social data?
DataSift was built to solve this very problem. The platform unifies all the types of social, news and blogs data in a single place. And our unique coding language helps you find meaning in the data faster.
Learn about:
-The basics of the DataSift Platform
-What types of data you can access and query on our platform (hint: there are LOTS!)
-How you can enrich social data with DataSift in terms of context, sentiment, demographics and links content
-Our magic ingredients - CSDL and VEDO that revolutionize social data analysis
DataSift's Rob Bailey at The Social Media Strategies Summit
Social monitoring has gone from a fancy nice-to-have, to an essential strategic tool for all organizations. DataSift provides insights into customers, the market and trending topics.
DataSift attended the Social Media Strategies Summit in San Francisco, where Rob Bailey, our CEO, discussed the seven measurable actions that further your company’s marketing and social media efforts. In addition to providing a framework for moving your company’s efforts from beginner to pro, Rob highlighted companies who are thinking strategically about social media, such as Taco Bell, Burberry and KFC.
Boosting Your Brand Marketing with Facebook Topic Data Insights
You have a new market entrant. You would like to know how it’s impacting your brand. So the first thing you do is to find out your share of voice in the market. What now?
Indeed, knowing your brand’s share of voice is important for monitoring your brand health, but wouldn’t it be even better if you know how it varies among different demographic groups, how audiences are engaging with your brand and what features or products are driving your top engagements?
We believe so. It is in the details that makes the difference and can truly impact your brand marketing. The share of voice use case is but one of the many Facebook topic data insights that can help you turn a good first start into a full fledged brand marketing strategy.
Join us for our upcoming webinar and learn:
About the different ways your brand can apply Facebook topic data to achieve the brand marketing results you’ve always wanted
Explore the brand health use case and three other examples of how Facebook topic data insights can help you discover the undiscoverable
Have your questions about Facebook topic data answered
Sign up to save your seat today!
Social Media Management in Crisis Communication
Presentation from David VIcent, Relational Marketeer about the main importance nowadays of social media in crisis Communication. UNWTO Themis Capacity programme, North Africa and Middle East Countries.
Staying on the Right Side of the Fence when Analyzing Human Data
This document summarizes a presentation on analyzing human data ethically. It discusses how data collection has become more intimate, consumers lack control over their data and distrust how it is used. Five principles of ethical data use are outlined: being beneficial, progressive, sustainable, respectful and fair. Case studies demonstrate how companies apply these principles in areas like continuous improvement, transparency and considering all stakeholders. The presentation argues for a privacy-first approach to data insights that provides aggregate, anonymized analysis to benefit both businesses and consumers in a way that builds trust.
Taming Social Data: How Social Data Framing liberates analysis and accelerate...
As potent as direct access to social data can be, the power multiplies many times over when social data is combined with your business data. When these data sets come together, insights pop out like fireworks. And the relevance of the social noise to your business becomes clear as day.
Transforming Customer Experience: From Moments to Journeys
Why do companies need to manage the entire customer experience? New analysis reveals that the entire customer journey - the series of interactions with a brand - is more important than any single touchpoint experience. Leading companies identify and effectively manage a few "key journeys." When companies perfect managing the entire customer journey, they reap significant benefits—including enhanced customer and employee satisfaction, reduced customer churn, increased revenue, lower costs, improved organizational collaboration, and competitive advantage. Presented at the Harvard Business Review webinar. For more on customer decision journeys: http://mckinseyonmarketingandsales.com/topics/customer-decision-journey
Customer Journey Analytics and Big Data
This document summarizes key insights from a McKinsey presentation on customer journey analytics and big data. It finds that companies are storing large amounts of data but few know how to extract value from it. Analyzing customer journeys rather than individual touchpoints provides more predictive insights into customer satisfaction and churn. Mapping important customer journeys in an industry reveals opportunities to improve the customer experience and reduce costs. The presentation provides an example of a retail bank that identified ways to decrease service costs and improve customer satisfaction by analyzing its customer journey data.
Viewers also liked (11)
Marketers, Rev Your Engines: Facebook Topic Data is Available Now
Marketers, Rev Your Engines: Facebook Topic Data is Available Now
How to Build Innovative Products with Facebook Topic Data
How to Build Innovative Products with Facebook Topic Data
10 Reasons Facebook Topic Data Will Change Your World
10 Reasons Facebook Topic Data Will Change Your World
DataSift's Rob Bailey at The Social Media Strategies Summit
DataSift's Rob Bailey at The Social Media Strategies Summit
Boosting Your Brand Marketing with Facebook Topic Data Insights
Boosting Your Brand Marketing with Facebook Topic Data Insights
Staying on the Right Side of the Fence when Analyzing Human Data
Staying on the Right Side of the Fence when Analyzing Human Data
Taming Social Data: How Social Data Framing liberates analysis and accelerate...
Taming Social Data: How Social Data Framing liberates analysis and accelerate...
Transforming Customer Experience: From Moments to Journeys
Transforming Customer Experience: From Moments to Journeys
Similar to Applying Data Science to Move Beyond Keywords for Social Analysis
Back to the Future: The Reinvention of the Library Catalog, Yesterday, Today,...
American Library Association
Rare Book and Manuscript Section
Pre-Conference, Las Vegas, NV, June 26, 2014
Eric Miller, em@zepheira.com
Recurrent Neural Network : Multi-Class & Multi Label Text Classification
This is the content for the 4th International Conference on Data Management, Analytics and Innovation - ICDMAI 2020.
Here we talk & discuss LSTM and Text Classification for Multi-Class & Multi-Label problem statement.
Code : https://github.com/amitbcp/icdmai_2020
Introducción a NoSQL
No se pierda esta oportunidad de conocer las ventajas de NoSQL. Participe en nuestro seminario web y descubra:
Qué significa el término NoSQL
Qué diferencias hay entre los almacenes clave-valor, columna ancha, grafo y de documentos
Qué significa el término «multimodelo»
Tomáš Mikolov - Distributed Representations for NLP
The document discusses word embedding techniques, specifically Word2vec. It introduces the motivation for distributed word representations and describes the Skip-gram and CBOW architectures. Word2vec produces word vectors that encode linguistic regularities, with simple examples showing words with similar relationships have similar vector offsets. Evaluation shows Word2vec outperforms previous methods, and its word vectors are now widely used in NLP applications.
Learning Multilingual Semantic Parsers for Question Answering over Linked Dat...
The document summarizes a PhD dissertation defense talk on learning multilingual semantic parsers for question answering over linked data. It discusses comparing neural and probabilistic graphical model architectures for semantic parsing to map natural language to formal meaning representations. The talk outlines introducing dependency parse tree-based approaches, evaluating different model architectures, and addressing challenges in building multilingual question answering systems over structured knowledge bases.
Red Hat Summit Connect 2023 - Redis Enterprise, the engine of Generative AI
Redis è conosciuto come un database in tempo reale che può essere utilizzato come cache, per memorizzare sessioni utente o immagazzinare token d’autenticazione, documenti JSON, per gestire inventari in tempo reale, dati geografici, come feature store in scenari di machine learning, gestione di code, broker, stream e molto altro. Ma non tutti sanno che Redis può memorizzare e indicizzare vettori di embeddings, ovvero quelle strutture dati che sono alla base di applicativi come ChatGPT. In questo talk, esploreremo come utilizzare Redis come un database vettoriale per implementare casi d’uso moderni.
Vectors in Search - Towards More Semantic Matching
With the advent of deep learning and algorithms like word2vec and doc2vec, vectors-based representations are increasingly being used in search to represent anything from documents to images and products. However, search engines work with documents made of tokens, and not vectors, and are typically not designed for fast vector matching out of the box. In this talk, I will give an overview of how vectors can be derived from documents to produce a semantic representation of a document that can be used to implement semantic / conceptual search without hurting performance. I will then I will describe a few different techniques for efficiently searching vector-based representations in an inverted index, such as learning sparse representations of vectors, clustering, and learning binary vectors. Finally, I will discuss some of the pitfalls of vector-based search, and how to get the best of both worlds by combining vector-based scoring with traditional relevancy metrics such as BM25.
Vectors in Search – Towards More Semantic Matching - Simon Hughes, Dice.com
This document summarizes Simon Hughes' presentation on using vector representations for semantic matching in search. It discusses using word embeddings to learn vector representations of words that capture their semantic meaning based on context. Approaches for searching with word embeddings include expanding queries with related terms from the embedding model or clustering the embeddings and mapping queries to clusters. The document also covers techniques for indexing and searching vector representations in an inverted index, such as using locality-sensitive hashing or k-means trees to map vectors to discrete tokens that can be indexed.
Ssas dmx ile kurum içi verilerin i̇şlenmesi
This document discusses business intelligence and data mining. It begins by explaining the evolution of data usage from descriptive to prescriptive analytics. It then contrasts legacy vs modern business intelligence tools, listing Microsoft's SQL Server tools. Common data mining tools and languages are also outlined, including their advantages and uses. Finally, the document demonstrates data mining techniques like association and classification using a scenario-based approach.
NLP & DBpedia
This document describes an approach for bridging the gap between natural language queries and linked data concepts using BabelNet. The approach uses BabelNet for word sense disambiguation, named entity recognition and disambiguation. It parses queries, matches terms to ontology concepts and properties, generates candidate triples, and integrates the triples to produce SPARQL queries. The approach was evaluated on test data from QALD-2, achieving a promising 76% of questions answered correctly.
Semantics at Scale: A Distributional Approach
1) The document discusses using distributional semantics to build robust semantic models that can handle large amounts of data and enable semantic computing at scale.
2) It describes how distributional semantic models can be used to represent word meanings based on their linguistic contexts, allowing semantic knowledge bases to be automatically constructed from large text corpora.
3) The author proposes a schema-agnostic approach using distributional semantics to enable querying databases without prior knowledge of schemas, addressing problems of vocabulary and structural differences between queries and data.
SOLID Principles and Design Patterns
The document discusses software design principles and patterns, including the SOLID principles, design patterns like factory method and abstract factory, code smells like duplicated code and feature envy, and refactoring techniques to address smells like extracting classes and collapsing hierarchies. It provides examples of applying principles and patterns to real code and suggests design is key to creating high-quality, maintainable software.
Deep Learning for Chatbot (3/4)
This document summarizes the key topics covered in Day 3 of a DL Chatbot seminar, including Seq2Seq models with attention mechanisms, advanced Seq2Seq architectures, and advanced attention mechanisms. The topics covered RNN encoder-decoder models, attention scoring methods, hierarchical models, personalized embeddings, copying mechanisms, bidirectional attention, self-attention models like Transformer, and various Seq2Seq implementations in PyTorch. Example papers and concepts are referenced throughout relating to sequence generation, machine translation, image captioning, and question answering.
The web of interlinked data and knowledge stripped
Linked Data approaches can help solve enterprise information integration (EII) challenges by complementing text on web pages with structured, linked open data from different sources. This allows for intelligently combining, integrating, and joining structured information across heterogeneous systems. A distributed, iterative, bottom-up integration approach using Linked Data may help solve the EII problem in large companies by taking a pay-as-you-go approach.
Bhavesh ro r
The document summarizes a workshop on Drupal and Ruby on Rails that was held on September 10-11, 2011 at DDIT, Nadiad. It provides overviews of Ruby and Ruby on Rails, their uses, strengths, directory structures, MVC architecture, and supporting technologies like HTML5, CoffeeScript, Sass/SCSS, and CSS3. It also lists some major companies that use Ruby on Rails for their web applications and products.
Haystack 2019 - Search with Vectors - Simon Hughes
With the advent of deep learning and algorithms like word2vec and doc2vec, vectors-based representations are increasingly being used in search to represent anything from documents to images and products. However, search engines work with documents made of tokens, and not vectors, and are typically not designed for fast vector matching out of the box. In this talk, I will give an overview of how vectors can be derived from documents to produce a semantic representation of a document that can be used to implement semantic / conceptual search without hurting performance. I will then describe a few different techniques for efficiently searching vector-based representations in an inverted index, including LSH, vector quantization and k-means tree, and compare their performance in terms of speed and relevancy. Finally, I will describe how each technique can be implemented efficiently in a lucene-based search engine such as Solr or Elastic Search.
Searching with vectors
Talk by Simon Hughes at the Haystack search conference in 2019. Discussing how to implement semantic search within an inverted index using vectors.
LLMs in Production: Tooling, Process, and Team Structure
Join Dr. Greg Loughnane and Chris Alexiuk in this exciting webinar to learn all about the tooling, processes, and team structure you need to build and operate performant, reliable, and scalable production-grade LLM applications!
A Panorama of Natural Language Processing
Presented by Ted Xiao at RobotXSpace on 4/18/2017. This workshop covers the fundamentals of Natural Language Processing, crucial NLP approaches, and an overview of NLP in industry.
The Semantic Knowledge Graph
Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016)
Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.
Similar to Applying Data Science to Move Beyond Keywords for Social Analysis (20)
Back to the Future: The Reinvention of the Library Catalog, Yesterday, Today,...
Back to the Future: The Reinvention of the Library Catalog, Yesterday, Today,...
Recurrent Neural Network : Multi-Class & Multi Label Text Classification
Recurrent Neural Network : Multi-Class & Multi Label Text Classification
Tomáš Mikolov - Distributed Representations for NLP
Tomáš Mikolov - Distributed Representations for NLP
Learning Multilingual Semantic Parsers for Question Answering over Linked Dat...
Learning Multilingual Semantic Parsers for Question Answering over Linked Dat...
Red Hat Summit Connect 2023 - Redis Enterprise, the engine of Generative AI
Red Hat Summit Connect 2023 - Redis Enterprise, the engine of Generative AI
Vectors in Search - Towards More Semantic Matching
Vectors in Search - Towards More Semantic Matching
Vectors in Search – Towards More Semantic Matching - Simon Hughes, Dice.com
Vectors in Search – Towards More Semantic Matching - Simon Hughes, Dice.com
The web of interlinked data and knowledge stripped
The web of interlinked data and knowledge stripped
Haystack 2019 - Search with Vectors - Simon Hughes
Haystack 2019 - Search with Vectors - Simon Hughes
LLMs in Production: Tooling, Process, and Team Structure
LLMs in Production: Tooling, Process, and Team Structure
Recently uploaded
Astute Business Solutions | Oracle Cloud Partner |
Your goto partner for Oracle Cloud, PeopleSoft, E-Business Suite, and Ellucian Banner. We are a firm specialized in managed services and consulting.
JavaLand 2024: Application Development Green Masterplan
My presentation slides I used at JavaLand 2024
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Introduction of Cybersecurity with OSS at Code Europe 2024
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
"Choosing proper type of scaling", Olena Syrota
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Apps Break Data
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.Mutation Testing for Task-Oriented Chatbots
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
Essentials of Automations: Exploring Attributes & Automation Parameters
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...Pitangent Analytics & Technology Solutions Pvt. Ltd
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Leveraging the Graph for Clinical Trials and Standards
Katja Glaß
OpenStudyBuilder Community Manager - Katja Glaß Consulting
Marius Conjeaud
Principal Consultant - Neo4j
Recently uploaded (20)
Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
Essentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation Parameters
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Dandelion Hashtable: beyond billion requests per second on a commodity server
Dandelion Hashtable: beyond billion requests per second on a commodity server
Leveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and Standards
Applying Data Science to Move Beyond Keywords for Social Analysis
- 1. Applying Data Science to Move Beyond Keywords for Social Analysis
- 2. Richard Caudle Director Developer Relations Claudio Weeraratne Director Product Management
- 6. AMBIGUITY OF NATURAL LANGUAGE
- 8. MOVING BEYOND KEYWORDS ba nk similarity x wit h- dr aw similarity y AND interaction.content any "rbs,lloyds,hsbc,barclays" AND interaction.content any "withdraw,close,cashpoint,atm"
- 12. OUR APPROACH • Produce a vector space where words are grouped by their context • Context of a word is given by surrounding words • Perform unsupervised machine learning to learn topics • word2vec is a well known implementation • gensim is a Python library that simplifies word2vec usage • Resulting model is queryable for similarity (of word vectors) • Language-agnostic solution
- 13. LEARNING SIMILARITY Learn to predict a word from surrounding words "I'm heading to #rbs to close my account" rbs account close rbs account close hsbc barclays withdraw balance cash money (1000's posts) CONCEPT 'BANK' NEURALNETWORK
- 15. DEMO
- 16. IMPROVED FILTERING & CLASSIFICATION interaction.content similar "bank,hsbc:0.7" AND interaction.content similar "withdraw:0.8" interaction.content any "rbs,lloyds,hsbc,barclays" AND interaction.content any "withdraw,close,cashpoint,atm" CONCISE INTUITIVE MAINTAINABLE UP-TO-DATE HIGHER COVERAGE ACCURACY
- 17. IMPROVING OUR PLATFORM • Further validation of approach • Operationalization of model production • Creation new models for different audiences • Automated updating of models • Implementation of 'similarity' in CSDL
- 18. Q&A
- 20. THANK YOU