The document discusses a master's thesis that examines using Qtags to aggregate and represent collective opinions from very large online conversations. Qtags are a proposed system for tagging items that uses plus and minus signs to indicate positive and negative ratings. The thesis has two parts: 1) introducing Qtags and experiments showing they increase shared tags over conventional tagging, and 2) applying Qtags to aggregate public opinions from comments and improve debate.
FACILITATING VIDEO SOCIAL MEDIA SEARCH USING SOCIAL-DRIVEN TAGS COMPUTINGcsandit
Online video search or stream live on social media has become tremendous widespread and
speedy increased continuously in recent years. Most of the videos shared on social media are
aimed at the more number of views from audiences. What and how many videos the users
shared all around the world have created a great amount and varied videos and the other data
into Internet cloud’s database and even can be viewed as a kind of big data of digital contents.
This research is to present how to implement a social-driven tags computing (SDT) which can
be used to facilitate online video search on social media platforms
Search logs from user interactions with image archives can be analyzed and utilized in three ways:
1. To understand user search behavior and how professional users search differently than average users.
2. As training data to automatically annotate images with concepts using similar queries and clicked images, though reliability varies by concept.
3. As additional positive training samples to improve automated image classification systems, especially when combined with manually annotated samples.
This document discusses several research problems related to analyzing social communication in online media:
1. The first problem examines why people repeatedly view the same YouTube video and proposes that it is the interesting conversations around the video, not just the video itself. An approach is presented to model conversational themes and interestingness over time.
2. The second problem introduces a framework for predicting "social synchrony" in social media by learning user actions and evolving models of the social network over time.
3. The third problem examines how to characterize communication at both the individual and group levels in order to extract and analyze prototypical social groups within a topic in the blogosphere.
Over the past year Community question answering (cQA) services have Achieved popularity. It allow
members to post and answer questions as enables general users to seek information from comprehensive set of
well-answered questions. But Still, existing cQA forums usually only provide textual answers, for many questions
which are not informative enough. In this paper, we propose a schema that is able to enrich textual answers in
CQA with Appropriate media data. For multimedia search, and multimedia data selection and presentation our
scheme consists of three components: answer medium selection, question base classification ,query generation,
MM data selection and presentation. This method automatically decides which type of media information should
be added for textual answer. It then automatically gathers data from the web. Our approach can enable a novel
multimedia question answering (MMQA) approach by processing large set of QA pairs and adding them to the
pool. users can find multimedia answers by matching their questions with those in a pool. Our approach is based
on community-contributed textual answers and thus it is able to deal with more complex questions.
Using Social Media to Compete with Big BusinessJulie Gomoll
Small businesses have advantages over larger competitors in authenticity, flexibility, and expertise. Using social media, small businesses can gain wider reach and more exposure at a lower cost than traditional advertising. To be successful, a social media strategy should be integrated across all business areas and tools like blogs, LinkedIn, Twitter and email. Social media also requires interacting authentically with customers by showing personality, admitting mistakes, and offering as well as asking for help.
FACILITATING VIDEO SOCIAL MEDIA SEARCH USING SOCIAL-DRIVEN TAGS COMPUTINGcsandit
Online video search or stream live on social media has become tremendous widespread and
speedy increased continuously in recent years. Most of the videos shared on social media are
aimed at the more number of views from audiences. What and how many videos the users
shared all around the world have created a great amount and varied videos and the other data
into Internet cloud’s database and even can be viewed as a kind of big data of digital contents.
This research is to present how to implement a social-driven tags computing (SDT) which can
be used to facilitate online video search on social media platforms
Search logs from user interactions with image archives can be analyzed and utilized in three ways:
1. To understand user search behavior and how professional users search differently than average users.
2. As training data to automatically annotate images with concepts using similar queries and clicked images, though reliability varies by concept.
3. As additional positive training samples to improve automated image classification systems, especially when combined with manually annotated samples.
This document discusses several research problems related to analyzing social communication in online media:
1. The first problem examines why people repeatedly view the same YouTube video and proposes that it is the interesting conversations around the video, not just the video itself. An approach is presented to model conversational themes and interestingness over time.
2. The second problem introduces a framework for predicting "social synchrony" in social media by learning user actions and evolving models of the social network over time.
3. The third problem examines how to characterize communication at both the individual and group levels in order to extract and analyze prototypical social groups within a topic in the blogosphere.
Over the past year Community question answering (cQA) services have Achieved popularity. It allow
members to post and answer questions as enables general users to seek information from comprehensive set of
well-answered questions. But Still, existing cQA forums usually only provide textual answers, for many questions
which are not informative enough. In this paper, we propose a schema that is able to enrich textual answers in
CQA with Appropriate media data. For multimedia search, and multimedia data selection and presentation our
scheme consists of three components: answer medium selection, question base classification ,query generation,
MM data selection and presentation. This method automatically decides which type of media information should
be added for textual answer. It then automatically gathers data from the web. Our approach can enable a novel
multimedia question answering (MMQA) approach by processing large set of QA pairs and adding them to the
pool. users can find multimedia answers by matching their questions with those in a pool. Our approach is based
on community-contributed textual answers and thus it is able to deal with more complex questions.
Using Social Media to Compete with Big BusinessJulie Gomoll
Small businesses have advantages over larger competitors in authenticity, flexibility, and expertise. Using social media, small businesses can gain wider reach and more exposure at a lower cost than traditional advertising. To be successful, a social media strategy should be integrated across all business areas and tools like blogs, LinkedIn, Twitter and email. Social media also requires interacting authentically with customers by showing personality, admitting mistakes, and offering as well as asking for help.
This document discusses facilitating the emergence of an online community of practice around user engagement in education technology. It introduces concepts like asset-based community development and appreciative inquiry to guide the process. Participants share experiences from successful past projects and discuss what made them feel successful. The goal is to support the formation of an effective, sustainable community through open discussion and by applying principles of appreciative inquiry and user-centered design.
Design of Automated Sentiment or Opinion Discovery System to Enhance Its Perf...idescitation
In today’s social networking era, if one has to make
decision about any product, service or individual performance,
the availability of various comments, suggestions, ratings,
and feedbacks are abundant. The required decision support
data can be collected through different sources of Medias like
newspapers, blogs, and discussion forums and from internet
too. So surely, it leads to the selection of best product, service
or individual if it is analyzed efficiently. In leading and
competitive world, this is huge and practical need of industries,
organizations to empower their qualities. In the recent years,
the significant study is done in the field of sentiment analysis.
However, the earlier work focused the implementation and
evaluation of individual sub technique of sentiment analysis.
Though these implementations produces significant results
of sentiment or opinion analysis, the trust of decision makers
is still in dangling to accept the results of such analysis. In
this paper, initially, we have been described the brief review
about the sentiment or opinion analysis system. Then the
details are provided about the design and about how to build
an automated opinion discovery system to enhance
performance of sentiment or opinion analysis based on feature
extraction sentiment analysis sub technique, natural language
processing and data mining techniques in an integrated way
The document presents a proposed approach for sentiment analysis on big social data using Spark. It discusses collecting opinions from social media to analyze large events by tracking public behavior in real-time. The proposed system provides a adaptable sentiment analysis approach using Spark that analyzes social media posts and classifies them by subject in real-time. It also discusses using sentiment data from social media to inform decisions.
An Unsupervised Approach For Reputation GenerationKayla Jones
This document describes a proposed unsupervised approach for generating reputation values based on opinion clustering and semantic analysis. The approach involves collecting opinion data, preprocessing it, applying latent semantic analysis and k-means clustering to group opinions into clusters, and then aggregating statistics from the clusters to generate a single reputation value for an entity. The approach is evaluated on a dataset of movie reviews from IMDB by comparing the generated reputation values to IMDB's weighted average user ratings. The results show the approach can accurately generate reputation values when choosing an optimal number of clusters.
Profile Analysis of Users in Data Analytics DomainDrjabez
Data Analytics and Data Science is in the fast forward
mode recently. We see a lot of companies hiring people for data
analysis and data science, especially in India. Also, many
recruiting firms use stackoverflow to fish their potential
candidates. The industry has also started to recruit people based
on the shapes of expertise. Expertise of a personal is
metaphorically outlined by shapes of letters like I, T, M and
hyphen betting on her experiencein a section (depth) and
therefore the variety of areas of interest (width).This proposal
builds upon the work of mining shapes of user expertise in a
typical online social Question and Answer (Q&A) community
where expert users often answer questions posed by other
users.We have dealt with the temporal analysis of the expertise
among the Q&A community users in terms how the user/ expert
have evolved over time.
Keywords— Shapes of expertise, Graph communities, Expertise
evolution, Q&A community
Enabling reuse of arguments and opinions in open collaboration systems PhD vi...jodischneider
This document summarizes a PhD thesis on enabling the reuse of arguments and opinions in open collaboration systems. It discusses three research questions: 1) opportunities and requirements for argumentation support, 2) common arguments used in these systems, and 3) structuring arguments to support reuse. The methodology involved analyzing discussions from Wikipedia and open collaboration projects using argumentation theories like Walton's schemes and factors analysis. The goal is to develop semantic structures and visualizations to help people understand diverse opinions and make collaborative decisions. A prototype system tested with users found structuring discussions by key factors helped people evaluate arguments more effectively.
The document proposes an Incremental Short Text Summarization (IncreSTS) algorithm to generate summaries of comment streams on social media in real-time. IncreSTS models the problem as incremental clustering and can update clustering results with new comments. It identifies top-k clusters of comments expressing different opinions. For each cluster, key terms are extracted to create a visual summary cloud allowing users to easily understand the main points without reading all comments. The algorithm is efficient, scalable, and can handle outliers to meet the real-time needs of social media comment stream summarization.
This document discusses folksonomies, which are classification systems created by users tagging online items. It notes that while tags may be imprecise, ambiguous, or personalized, evidence shows tagging vocabularies converge over time. The author studies tag distributions and finds single-use tags do not dominate. The document considers how to foster better tagging through educating users and improving systems, but recognizes that limiting tags risks losing valuable metadata and the diversity inherent in folksonomies. It concludes the best approach is remaining open-minded and retaining as much user-generated metadata as possible.
Self-modeling and self-reflection of E-learning communitiesZina Petrushyna
1. The document discusses self-modeling and self-reflection of e-learning communities.
2. It proposes that communities of learners can self-reflect on their goals, properties, and changes over time through techniques like pattern sharing and modeling the community using competencies and social network analysis.
3. Future work discussed includes researching the relationship between self-modeling and self-reflection phases, assessing learner competencies and patterns in goal models of communities, and discovering patterns over time in real e-learning communities.
1. The document discusses how communities shape knowledge structures through media and discusses case studies of disturbances in digital social networks.
2. It presents models for analyzing the evolution of scientific communities and factors that contribute to community success.
3. Methods are proposed for expert finding in communities by analyzing storytelling and recommending experts based on their values and knowledge in the community.
Enhancing social tagging with a knowledge organization systemMichael Day
Presentation slides associated with the paper "Enhancing Social Tagging with a Knowledge Organization System" written by Koraljka Golub, Jim Moon, Douglas Tudhope and Marianne Lykke Nielsen, accepted for the IFLA Satellite Meeting, Emerging Trends in Technology: Libraries Between Web 2.0, Semantic Web and Search, Florence, 19-20 August 2009. Much of the content of the slides is taken from previous presentations given by Koraljka Golub of UKOLN and Brian Matthews of STFC
This document analyzes aspects of broad folksonomies based on a sample of over 800,000 bookmarks from Delicious. It finds that:
1) The frequency-rank distribution of co-occurring tags follows a power law for around 80% of tags analyzed.
2) When analyzing resource-based and user-based tagging characteristics, only around 18-13% of tags respectively followed a power law distribution, indicating folksonomies have a more complex underlying structure.
3) Tags provide added value for retrieval over title alone, though precision and recall were mostly below 0.5, showing tags are not redundant but current usage may not optimize retrieval performance.
Getting Started with User Research - Stir Trek 2011Carol Smith
Presented at Stir Trek: Thor Edition, in Columbus, Ohio on May 6, 2011.
Once you know who uses your product, all sorts of new questions start to emerge. How are they using the product? Why are they using it? What else might they want? In this session you will learn about three quick and easy methods to understand the users desires, needs and abilities. The basics of observations, interviews and card sorting will be covered. You will also learn ways to effectively share and communicate what you learn with your team.
This dissertation analyzes social media data and outlines approaches for understanding online communication and collaboration. It presents algorithms for detecting communities using structural and semantic properties. It analyzes blog subscription patterns and the microblogging phenomenon. Systems are developed for opinion retrieval from blogs and identifying influential users. The growth of social media and tagging behavior are also studied through analysis of tags and social graphs.
"Engaging Students in Distance Learning." Presentation given at 7th Drexel e-Learning Conference, March 26th, 2009, by Dr Jim Waters & Dr Susan Gasson.
Recommendation System Using Social Networking ijcseit
With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
Users are Losers! They’ll Like Whatever we Make! and Other Fallacies.Carol Smith
Presented at CodeMash 2013.
If this sounds familiar it is time to make big changes or look for a new job. Failing your users will only end badly. In this session we look at the assumptions that are all-too-often made about users, usability and the User Experience (UX). In response to each of these misguided statements Carol will provide a quick method you can conduct with little or no resources to debunk these myths.
How are project-specific forums utilized? A study of participation, content, ...Yusuf Sulistyo Nugroho
This presentation slide describes the detailed findings of the analyses on how project-specific forums in the Eclipse ecosystem are utilized in terms of participation, content, and sentiment. This is presented in the Journal-First Track of The ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 2022.
Related video: https://youtu.be/CuG00prD_jo
- The document discusses models of collective intelligence and challenges in designing systems to support collective intelligence when dealing with complex problems.
- It describes how argument mapping tools can help address issues like lack of insight into logical structures, poor idea evaluation, and shallow contributions that hamper online debates.
- A case study discussed how an argument mapping tool called LiteMap was used to collaboratively map discussions on an online platform about sustainable living. Mappers found the process challenging, especially for ill-defined topics.
Lee has over 10 years of experience designing and developing websites and web services. Some of his most notable projects include designing the user interface for Naver Office, Samsung's Power Infolink TV widgets, and introducing the novel Qtag qualitative tagging system to allow more effective sharing of descriptive tags. He has strong skills in establishing strategies, specifying requirements, designing interfaces, and managing projects from start to release.
This document discusses facilitating the emergence of an online community of practice around user engagement in education technology. It introduces concepts like asset-based community development and appreciative inquiry to guide the process. Participants share experiences from successful past projects and discuss what made them feel successful. The goal is to support the formation of an effective, sustainable community through open discussion and by applying principles of appreciative inquiry and user-centered design.
Design of Automated Sentiment or Opinion Discovery System to Enhance Its Perf...idescitation
In today’s social networking era, if one has to make
decision about any product, service or individual performance,
the availability of various comments, suggestions, ratings,
and feedbacks are abundant. The required decision support
data can be collected through different sources of Medias like
newspapers, blogs, and discussion forums and from internet
too. So surely, it leads to the selection of best product, service
or individual if it is analyzed efficiently. In leading and
competitive world, this is huge and practical need of industries,
organizations to empower their qualities. In the recent years,
the significant study is done in the field of sentiment analysis.
However, the earlier work focused the implementation and
evaluation of individual sub technique of sentiment analysis.
Though these implementations produces significant results
of sentiment or opinion analysis, the trust of decision makers
is still in dangling to accept the results of such analysis. In
this paper, initially, we have been described the brief review
about the sentiment or opinion analysis system. Then the
details are provided about the design and about how to build
an automated opinion discovery system to enhance
performance of sentiment or opinion analysis based on feature
extraction sentiment analysis sub technique, natural language
processing and data mining techniques in an integrated way
The document presents a proposed approach for sentiment analysis on big social data using Spark. It discusses collecting opinions from social media to analyze large events by tracking public behavior in real-time. The proposed system provides a adaptable sentiment analysis approach using Spark that analyzes social media posts and classifies them by subject in real-time. It also discusses using sentiment data from social media to inform decisions.
An Unsupervised Approach For Reputation GenerationKayla Jones
This document describes a proposed unsupervised approach for generating reputation values based on opinion clustering and semantic analysis. The approach involves collecting opinion data, preprocessing it, applying latent semantic analysis and k-means clustering to group opinions into clusters, and then aggregating statistics from the clusters to generate a single reputation value for an entity. The approach is evaluated on a dataset of movie reviews from IMDB by comparing the generated reputation values to IMDB's weighted average user ratings. The results show the approach can accurately generate reputation values when choosing an optimal number of clusters.
Profile Analysis of Users in Data Analytics DomainDrjabez
Data Analytics and Data Science is in the fast forward
mode recently. We see a lot of companies hiring people for data
analysis and data science, especially in India. Also, many
recruiting firms use stackoverflow to fish their potential
candidates. The industry has also started to recruit people based
on the shapes of expertise. Expertise of a personal is
metaphorically outlined by shapes of letters like I, T, M and
hyphen betting on her experiencein a section (depth) and
therefore the variety of areas of interest (width).This proposal
builds upon the work of mining shapes of user expertise in a
typical online social Question and Answer (Q&A) community
where expert users often answer questions posed by other
users.We have dealt with the temporal analysis of the expertise
among the Q&A community users in terms how the user/ expert
have evolved over time.
Keywords— Shapes of expertise, Graph communities, Expertise
evolution, Q&A community
Enabling reuse of arguments and opinions in open collaboration systems PhD vi...jodischneider
This document summarizes a PhD thesis on enabling the reuse of arguments and opinions in open collaboration systems. It discusses three research questions: 1) opportunities and requirements for argumentation support, 2) common arguments used in these systems, and 3) structuring arguments to support reuse. The methodology involved analyzing discussions from Wikipedia and open collaboration projects using argumentation theories like Walton's schemes and factors analysis. The goal is to develop semantic structures and visualizations to help people understand diverse opinions and make collaborative decisions. A prototype system tested with users found structuring discussions by key factors helped people evaluate arguments more effectively.
The document proposes an Incremental Short Text Summarization (IncreSTS) algorithm to generate summaries of comment streams on social media in real-time. IncreSTS models the problem as incremental clustering and can update clustering results with new comments. It identifies top-k clusters of comments expressing different opinions. For each cluster, key terms are extracted to create a visual summary cloud allowing users to easily understand the main points without reading all comments. The algorithm is efficient, scalable, and can handle outliers to meet the real-time needs of social media comment stream summarization.
This document discusses folksonomies, which are classification systems created by users tagging online items. It notes that while tags may be imprecise, ambiguous, or personalized, evidence shows tagging vocabularies converge over time. The author studies tag distributions and finds single-use tags do not dominate. The document considers how to foster better tagging through educating users and improving systems, but recognizes that limiting tags risks losing valuable metadata and the diversity inherent in folksonomies. It concludes the best approach is remaining open-minded and retaining as much user-generated metadata as possible.
Self-modeling and self-reflection of E-learning communitiesZina Petrushyna
1. The document discusses self-modeling and self-reflection of e-learning communities.
2. It proposes that communities of learners can self-reflect on their goals, properties, and changes over time through techniques like pattern sharing and modeling the community using competencies and social network analysis.
3. Future work discussed includes researching the relationship between self-modeling and self-reflection phases, assessing learner competencies and patterns in goal models of communities, and discovering patterns over time in real e-learning communities.
1. The document discusses how communities shape knowledge structures through media and discusses case studies of disturbances in digital social networks.
2. It presents models for analyzing the evolution of scientific communities and factors that contribute to community success.
3. Methods are proposed for expert finding in communities by analyzing storytelling and recommending experts based on their values and knowledge in the community.
Enhancing social tagging with a knowledge organization systemMichael Day
Presentation slides associated with the paper "Enhancing Social Tagging with a Knowledge Organization System" written by Koraljka Golub, Jim Moon, Douglas Tudhope and Marianne Lykke Nielsen, accepted for the IFLA Satellite Meeting, Emerging Trends in Technology: Libraries Between Web 2.0, Semantic Web and Search, Florence, 19-20 August 2009. Much of the content of the slides is taken from previous presentations given by Koraljka Golub of UKOLN and Brian Matthews of STFC
This document analyzes aspects of broad folksonomies based on a sample of over 800,000 bookmarks from Delicious. It finds that:
1) The frequency-rank distribution of co-occurring tags follows a power law for around 80% of tags analyzed.
2) When analyzing resource-based and user-based tagging characteristics, only around 18-13% of tags respectively followed a power law distribution, indicating folksonomies have a more complex underlying structure.
3) Tags provide added value for retrieval over title alone, though precision and recall were mostly below 0.5, showing tags are not redundant but current usage may not optimize retrieval performance.
Getting Started with User Research - Stir Trek 2011Carol Smith
Presented at Stir Trek: Thor Edition, in Columbus, Ohio on May 6, 2011.
Once you know who uses your product, all sorts of new questions start to emerge. How are they using the product? Why are they using it? What else might they want? In this session you will learn about three quick and easy methods to understand the users desires, needs and abilities. The basics of observations, interviews and card sorting will be covered. You will also learn ways to effectively share and communicate what you learn with your team.
This dissertation analyzes social media data and outlines approaches for understanding online communication and collaboration. It presents algorithms for detecting communities using structural and semantic properties. It analyzes blog subscription patterns and the microblogging phenomenon. Systems are developed for opinion retrieval from blogs and identifying influential users. The growth of social media and tagging behavior are also studied through analysis of tags and social graphs.
"Engaging Students in Distance Learning." Presentation given at 7th Drexel e-Learning Conference, March 26th, 2009, by Dr Jim Waters & Dr Susan Gasson.
Recommendation System Using Social Networking ijcseit
With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
Users are Losers! They’ll Like Whatever we Make! and Other Fallacies.Carol Smith
Presented at CodeMash 2013.
If this sounds familiar it is time to make big changes or look for a new job. Failing your users will only end badly. In this session we look at the assumptions that are all-too-often made about users, usability and the User Experience (UX). In response to each of these misguided statements Carol will provide a quick method you can conduct with little or no resources to debunk these myths.
How are project-specific forums utilized? A study of participation, content, ...Yusuf Sulistyo Nugroho
This presentation slide describes the detailed findings of the analyses on how project-specific forums in the Eclipse ecosystem are utilized in terms of participation, content, and sentiment. This is presented in the Journal-First Track of The ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 2022.
Related video: https://youtu.be/CuG00prD_jo
- The document discusses models of collective intelligence and challenges in designing systems to support collective intelligence when dealing with complex problems.
- It describes how argument mapping tools can help address issues like lack of insight into logical structures, poor idea evaluation, and shallow contributions that hamper online debates.
- A case study discussed how an argument mapping tool called LiteMap was used to collaboratively map discussions on an online platform about sustainable living. Mappers found the process challenging, especially for ill-defined topics.
Similar to Lee Sung Eob Mastersthesisproposal03 (20)
Lee has over 10 years of experience designing and developing websites and web services. Some of his most notable projects include designing the user interface for Naver Office, Samsung's Power Infolink TV widgets, and introducing the novel Qtag qualitative tagging system to allow more effective sharing of descriptive tags. He has strong skills in establishing strategies, specifying requirements, designing interfaces, and managing projects from start to release.
This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
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 .
"NATO Hackathon Winner: AI-Powered Drug Search", Taras KlobaFwdays
This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application.
In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy.
We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
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).
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
High performance Serverless Java on AWS- GoTo Amsterdam 2024Vadym Kazulkin
Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
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Lee Sung Eob Mastersthesisproposal03
1. Expanded Reproduction of Socially Shared Opinions via Qtag A presentation about a Master Thesis KAIST Graduate School of Culture Technology Affiliation Lee, Sung Eob Written & Presented by Han, ‘Steve’ SangKi Advisor Professor
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6. From Previous Studies Preliminaries People tag to rate & express their opinions However, this kind of tags hardly shared among users Because, users tend to use diversified expressions
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9. We propose Qtag Objective Qtag expands user experience of collaborative tagging Q stands for Qualitative, Qtag is made to… 1. To Rate and to express Opinions 2. To produce more sharable tags 3. To provide fast & intuitive interpretation
10. Simple Idea of Qtag Proposed Scheme Qtag is simple augmentation of Plus(+) and Minus(-) signs Positive Tags Positive Rating & Opinions Negative Tags Negative Rating & Opinions Neutral Tags Conventional Tags Style+ Sound+ KFed+ Talent- Life- music- Pop Music KFed Britney Simple
11. How Qtag Makes Tags more Sharable Proposed Scheme Qtag will reduce diversification of expression via formulated expressions (Qtag) Music-
12. Qtag Experiments Experiments In order to evaluate Qtag, we built a conceptual model 1042 distinct tags were tagged 4083 times by 126 participants A series of questionnaire is also conducted in order to elaborate Adoption and Satisfaction of Qtag In order to aggregate Qtags, four digital product reviews were chosen from ZDNet Korea (http://www.zdnet.co.kr/) and four articles concerning celebrities published on Joins (http://www.joins.com/), one of the major internet newspapers in Korea. 1 st Experiment Each participant is asked to tag conventionally & Qtag 2 articles & 2 reviews 2 nd Experiment Participants answered a series of questionnaires The same tagging interface is applied as del.icio.us ( Free form / Bag-model / *Blind Tagging )
13. Qtag Cloud (1) Experiments Total Tag Count(709) / Positive Tag Count(344) / Negative Tag Count(210) Total Tag Count Rating: +189 After conducting experiments, we visualized Qtag clouds A review article about Sanyo Xacti
14. Qtag Cloud (2) Experiments Total Tag Count(414) / Positive Tag Count(35) / Negative Tag Count(176) Total Tag Count Rating: -141 A celebrity article about Britney Spears After conducting experiments, we visualized Qtag clouds
15. Tagging vs. Qtagging (1) 1 1 2 Results Changes in quantity and quality of tags Shared tags increased, It elaborates that Qtag filters meta-noise Total Distinct Tags Total Shared 400 128 1841 Conventional Tagging 642 219 2242 Qtagging Increased By Tagging Frequency 60.0% 71.1% 21.8% Entropy of tag data increased However, shared tags increased 1 2
16. Tagging vs. Qtagging (3) Results 1 2 The difference of probabilities between QT & CT is calculated Calculating Normal Distribution for ‘Conventional Tagging’ Calculation for normal distribution Calculating Normal Distribution for ‘Qtagging’ Calculation for normal distribution Qtagging has absolutely higher 'Shared Tags' 2 1 Qtag improves tag sharing
17. Implementation & Contribution Conclusion Qtag expands user experience of collaborative tagging 2. Qtag encourages users to tag more sharable tags in case of rating & self-expression 3. Participants generally accepted & Qtag system 1. Proposed a new formulated means of tagging for rating and self-expression
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19. Collective Intelligence Preliminaries Nobody knows everything, but everybody knows something Collaborative Tagging & Qtagging is also tools for harnessing Collective Intelligence or Wisdom of crowds Collective Intelligence is a form of intelligence that emerges from the collaboration and competition of many individuals. Appears in a wide variety of forms of consensus decision making in Bacteria, Animals, Human and Computers
20. New Domain of Qtag Preliminaries Qtag can be a tool of extracting public opinions from comments We define this data aggregating as “Collective Opinion” Qtag will expend the domain of collective intelligence There must be dominant opinions about this article If public opinions could be revealed, more productive debate via VLSC would be possible However, readers put their eyes on comment lists about 3 to 15 seconds Current interface makes hard to extract dominant opinions among VLSC
21. Collective Opinion vs. Intelligence Preliminaries Collective Opinion is subsidiary to Collective Intelligence According to degree of interactivity, Collective Opinion is the link between Collective Intelligence & Wisdom of Crowds Collective Intelligence Collective Opinion Wisdom of Crowds Abstraction & Enhancement Abstraction & Grouping Social Proofing Problem Solving High degree of Interactivity Diversified degree of interactivity Minimum degree of Interactivity Independency Among Participants Similarity Data size matters / Shares similar process (Data Aggregation & Mining) Knowledge Production (Wikipedia) Aggregation of public opinions (Comments) Problem Solving (Recommendation) Applications
22. What is Collective Opinion Preliminaries Collective Opinion Collective Intelligence Collective opinion is mining public opinions from a very large scale conversation (Comments) Qtag will harness Collective Opinion & encourage debates among a Very Large Scale Conversation (VLSC) Problem Solving / Data Producing Oriented Omni-directional Approach Data Aggregation Oriented Multi-directional Approach
23. Sharing Opinions via Qtag Objective Qtag expands user experience of collaborative tagging Qtag is applied in order to enhance VLSC environment 1. To provide breadcrumbs to access old comments 2. To aggregate & represent public opinions 3. To provide guides for writing new comments
24. User Scenario Proposed Scheme Massing process for dominant public opinions may be similar to following example Qtags JYP Wondergirls- 가창력 - Qtags 박진영 Wondergirls+ 완소희 + Comments 난 상관없다 . 원더걸스 너무 좋아 . 특히 완소희… Comments JYP 의 원더걸스 노래 너무 못한다 . Comments 원더걸스가 뭐가 좋다고…짜증해서 태깅한다 Qtags JYP Wondergirls- Tell Me- Qtag may encourage debates & represent dominant public opinions for a very large scale conversation
25. 2 nd Set of Qtag Experiments Experiments This set of experiments will evaluate Qtag for accumulating & representing Collective Opinion In order to aggregate Qtags, A number of internet articles which can trigger debates will be picked from renowned sources Experiments is openly deployed to attract wide range of internet users 1 st Experiment Qtag will be collected and a set of questionnaire will be conducted 2 nd Experiment Participants will answer a series of questionnaires about usability Tagging interface will be different from the first set of experiments ( Free form / Bag-model / *Open Tagging / synonym) 1 st Experiments conducted for one week from 25 Nov to 2 Dec 12 popular contents are posted, 401 participants participated 1531 comments & 5014 times tagged
26. Implementation & Contribution Conclusion VLSC environment can be improved via Qtag 2. Public opinions among VLSC can be aggregated & represented by Qtag 3. We speculate that comment writing may assisted by Qtag 1. Access to old comments can be improved via Qtag (breadcrumbs)
29. An Extreme Case Tags for ratings and opinion-expression Is a common phenomenon
30. Tag Sharing Status We analyzed tag data from mar.gar.in. To elaborate whether tags for rating can be shared or not Introducing mar.gar.in, Korean replica of del.icio.us 3000 Registered Users 73,001 bookmarks 24,000 distinct tags
31. Tag Data Analysis Our tag classification is based on Golder & Huberman’s Sen et al. customized Golder & Huberman’s tag classifications for their own use. So did we Functions Tag Perform for bookmarks Classified by Golder and Huberman Types of Tags optimized for social shopping and networking Identifying What (or Who) it is about Identifying What It is Identifying Who Owns It Refining Categories Identifying Qualities or Characteristics Self Reference Task Organizing Identifying Tags Descriptive Tags Personal Tags
32. Sharable tag distribution We analyzed tag data which is shared by two or more users to elaborate whether tags for ratings and expressing opinion can be shared or not We assigned two coders, and they distributed tags manually and reached consensus for tags failed to yield agreement
33. Answer for 1 st Research Question RQ1. Are tags for rating and expressing opinions shared easily? Tags for rating and expression are hardly shared 4.8% Of sharable tags are “Descriptive Tags”
34. Qtag Design Basically Qtag system is not much different from current tagging system Qtag is consisted of four following components Featured Item or People A homogeneous kinds of products or people which shares the same qualities Related Contents Reviews or Articles about featured items (Actual Tagging Source) Qtags KFed - Music- Talent- Life- Dance+ Pop Music Fashion+ Dance Music Positive / Negative / Neutral Tags Qtag Counts Demonstrate overall ratings and reputation Positive Tag Neutral Tag Negative Tag 10 5 20 -10 (35)
35. Domains of Qtag A homogeneous product line-up & people which & who shares the same qualities for comparison Qtag may perform well for Social Shopping & SNS A homogeneous product line-up shares the same specifications CCD+ Grip- Dslr Weight- Possible Qtags People who shares similar qualities Look+ personality- Students Possible Qtags
36. Qtag May Control Wild Expressions of Tags Proposed Scheme The causes of expression may be limited Music Privacy Appearance Dance General Reputation Qtag will provide a guideline of formulated expression via augmenting signs
37. Tagging vs. Qtagging (2) Results When there are 642 distinct tags When there are 400 distinct tags A Simple speculation about the probability of sharing tags Qtag System Conventional Tagging System When the number of distinct tag increases, the probability of sharing tags among users decreases. The same number of participants 126 Personnel Although there were more distinct tags, There were more shared tags among Qtag dataset This proves Qtag provides more chance of sharing tags
38. Research Questions Will Qtag work? We will answer following research questions RQ1. Are tags for rating and expressing opinions shared easily? RQ2. Are users able to apply the Qtag without difficulties? RQ3. Are users able to interpret valid information from Qtag? RQ4. Are there changes in quantity and quality of tags? We analyzed conventional tag data & conducted experiments with a Qtag Conceptual model
39. Answer for 2 nd Research Question RQ2. Are users able to apply the Qtag without difficulties? From both aspects of understanding the concept of Qtag & the actual tag count, participants applied Qtag without difficulties The answer based on Questionnaire After a text-based introduction of Qtag was provided, participants responded on a five-point scale as to whether the concept of Qtag was easy to understand. 4.15 stars The answer based on aggregated tag data Comparison of tag counts between conventional neutral tags and tags augmented with positive(+) or negative(-) signs Augmented Tag Count Neutral Tag Count
40. Answer for 3 rd Research Question RQ3. Are users able to interpret valid information from Qtag? Participants generally interpret valuable information from Qtag Clouds Participants responded on a five-point scale as to whether Qtag clouds convey valuable information or not 3.59 stars Participants requested for better visualization of Qtag clouds
41. Answer for 4 th Research Question 1 1 2 RQ4. Are there changes in quantity and quality of tags? Shared tags increased, It elaborates that Qtag filters meta-noise Total Distinct Tags Total Shared 400 128 1841 Conventional Tagging 642 219 2242 Qtagging Increased By Tagging Frequency 60.0% 71.1% 21.8% Entropy of tag data increased However, shared tags increased 1 2
42. Extreme Tags Tags for ratings and opinion-expression Is a common phenomenon We often tag to rate & express
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44. Massing Snow into Balls Preliminaries Proposed Scheme Qtag is the tool for making dominant public opinion standout Snow = A Very Large Scale Conversation snowballs = Dominant Public Opinions Dominant public opinions can be more noticeable via Qtag (Qtag: Tagging as Means of Massing Public Opinions)
45. Tagging vs. Qtagging (2) Results Let be a total set such that each element (tagging object) has at least tags on it. Then we have a nested property such that In our case, the 'Total distinct Tag' set is , and 'Shared Tag' set is . And from the nested property, . The number of total distinct tags : (tags) The number of participants : (person) The average number of each participants tagging frequency : (tags/person)
46. Tagging vs. Qtagging (2) Problem Definition Q: What is the probability of any random object is also a element in ? Solution We will think about the complimentary case. What is the probability of with ? Then only one person should pick selected object and others don't.
47. Extreme Case Checking If ,then , : it means that if gets larger, less chance of 'Shared' case. If , then , : it means that if gets smaller, high chance of 'Shared' Tagging vs. Qtagging (2) From the extreme cases, we could see that the derivation of probability is reasonable. And since is constant for each selection in , we can see that distribution of number of shared tagging follows 'Binomial Distribution' and for large , it can be approximated by 'Normal Distribution‘ .
48. Checking ‘Conventional Tagging’ 1 2 Participants tagged average 14.6 tags for ‘Conventional Tagging Model’ The unshared tag production probability reaches near 1.0. This result means that in the case of random tag selection, most tags should be shared. In the random tag selection case, the number of shared tags supposed to be 383.3. Variance is calculated to calculate normal distribution. 3 4 1 2 3 4 Tagging vs. Qtagging (2) Then
49. Checking ‘Qtagging’ Then 1 2 3 4 Participants tagged average 17.6 tags for ‘Conventional Tagging Model’ The unshared tag production probability reaches near 1.0. This result means that in the case of random tag selection, 90% of tags should be shared. In the random tag selection case, the number of shared tags supposed to be 577.8. Variance is calculated to calculate normal distribution. 1 2 3 4 Tagging vs. Qtagging (2)
50. Calculating Normal Distribution for ‘Conventional Tagging’ Calculating Normal Distribution for ‘Qtagging’ 1 2 Tagging vs. Qtagging (2) Calculation for normal distribution Calculation for normal distribution Qtagging has absolutely higher 'Shared Tags' 2 1
Editor's Notes
Expanded Reproduction of Socially Shared Opinions via Qtag