The availability of social tags has greatly enhanced access to information.
Tag clouds have emerged as a new “social” way to find
and visualize information, providing both one-click access to information
and a snapshot of the “aboutness” of a tagged collection.
A range of research projects explored and compared different tag
artifacts for information access ranging from regular tag clouds to
tag hierarchies. At the same time, there is a lack of user studies that
compare the effectiveness of different types of tag-based browsing
interfaces from the users point of view. This paper contributes to
the research on tag-based information access by presenting a controlled
user study that compared three types of tag-based interfaces
on two recognized types of search tasks – lookup and exploratory
search. Our results demonstrate that tag-based browsing interfaces
significantly outperform traditional search interfaces in both performance
and user satisfaction. At the same time, the differences
between the two types of tag-based browsing interfaces explored in
our study are not as clear.
Recommending Items in Social Tagging Systems Using Tag and Time InformationChristoph Trattner
In this work we present a novel item recommendation ap- proach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this can- didate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation com- ing from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function.
As the results of our extensive evaluation conducted on data- sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.
Je t’aime… moi non plus: reporting on the opportunities, expectations and cha...Christoph Trattner
This presentation will be a live exchange of ideas & arguments, between a representative of a start up working on agricultural information management and discovery, and a representative of academia that has recently completed his PhD and is now leading a young and promising research team.
The two presenters will focus on the case of a recommendation service that is going to be part of a web portal for organic agriculture researchers and educators (called Organic.Edunet), which will help users find relevant educational material and bibliography. They currently develop this as part of an EU-funded initiative but would both be interested to find a way to further sustain this work: the start up by including this to the bundle of services that it offers to the users of its information discovery packages, and the research team by attracting more funding to further explore recommendation technologies.
The start up representative will describe his evergoing, helpless and aimless efforts to include a research activity on recommender systems within the R&D strategy of the company, for the sakes of the good-old-PhD-times. And will explain why this failed.
The academia representative will describe the great things that his research can do to boost the performance of recommendation services in such portals. And why this does-not-work-yet-operationally because he cannot find real usage data that can prove his amazing algorithm outside what can be proven in offline lab experiments using datasets from other domains (like MovieLens and CiteULike).
Both will explain how they started working together in order to design, experimentally test, and deploy the Organic.Edunet recommendation service. And will describe their expectations from this academic-industry collaboration. Then, they will reflect on the challenges they see in such partnerships and how (if) they plan to overcome them.
Towards a Big Data Recommender Engine for Online and Offline MarketplacesChristoph Trattner
Recommender systems aim at helping users to find relevant information in an overloaded information space.
Although there are well known methods (Content-based, Collaborative Filtering, Matrix Factorization) and libraries to implement, evaluate and extend recommenders (Apache Mahout, Graphlab, MyMediaLite, among others), the deployment of a real-time recommender from scratch which considers a combination of algorithms and various data sources (e.g., social, transactional, and location) remains unsolved.
In this talk, we report on the challenges towards such a recommender systems in the context of online of offline marketplaces. In particular, we describe our solution in terms of the requirements, the data model and algorithms that allows modularity and extensibility, as well as the system architecture to facilitate the scaling of our approach to big data for online and offline marketplaces.
Recommending Items in Social Tagging Systems Using Tag and Time InformationChristoph Trattner
In this work we present a novel item recommendation ap- proach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this can- didate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation com- ing from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function.
As the results of our extensive evaluation conducted on data- sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.
Je t’aime… moi non plus: reporting on the opportunities, expectations and cha...Christoph Trattner
This presentation will be a live exchange of ideas & arguments, between a representative of a start up working on agricultural information management and discovery, and a representative of academia that has recently completed his PhD and is now leading a young and promising research team.
The two presenters will focus on the case of a recommendation service that is going to be part of a web portal for organic agriculture researchers and educators (called Organic.Edunet), which will help users find relevant educational material and bibliography. They currently develop this as part of an EU-funded initiative but would both be interested to find a way to further sustain this work: the start up by including this to the bundle of services that it offers to the users of its information discovery packages, and the research team by attracting more funding to further explore recommendation technologies.
The start up representative will describe his evergoing, helpless and aimless efforts to include a research activity on recommender systems within the R&D strategy of the company, for the sakes of the good-old-PhD-times. And will explain why this failed.
The academia representative will describe the great things that his research can do to boost the performance of recommendation services in such portals. And why this does-not-work-yet-operationally because he cannot find real usage data that can prove his amazing algorithm outside what can be proven in offline lab experiments using datasets from other domains (like MovieLens and CiteULike).
Both will explain how they started working together in order to design, experimentally test, and deploy the Organic.Edunet recommendation service. And will describe their expectations from this academic-industry collaboration. Then, they will reflect on the challenges they see in such partnerships and how (if) they plan to overcome them.
Towards a Big Data Recommender Engine for Online and Offline MarketplacesChristoph Trattner
Recommender systems aim at helping users to find relevant information in an overloaded information space.
Although there are well known methods (Content-based, Collaborative Filtering, Matrix Factorization) and libraries to implement, evaluate and extend recommenders (Apache Mahout, Graphlab, MyMediaLite, among others), the deployment of a real-time recommender from scratch which considers a combination of algorithms and various data sources (e.g., social, transactional, and location) remains unsolved.
In this talk, we report on the challenges towards such a recommender systems in the context of online of offline marketplaces. In particular, we describe our solution in terms of the requirements, the data model and algorithms that allows modularity and extensibility, as well as the system architecture to facilitate the scaling of our approach to big data for online and offline marketplaces.
From Search to Predictions in Tagged Information SpacesChristoph Trattner
Tagging gained tremendously in popularity over past few years. When looking into the literature of tagging we find a lot of work regarding people's tagging motivation, their behavior, models that describe the folksonomy generation process, emergent semantic structures, etc., but interestingly we find quite little research showing the value of tags for searching an overloaded information space. Furthermore, there is lot of literature on the tag or item prediction problem, but interestingly almost all of them lookat the issue from a data-driven perspective. To bridge this gap in the literature, we have conducted several in-depth studies in the past showing the value of tags for lookup and exploratory search. We looked at the problem from a network theoretic and interface perspective and we will show how useful tags are for searching. Furthermore, we reviewed literature on memory processes from cognitive science and have invented a number of novel recommender algorithms based on the ACT-R and MINERVA2 theory. We will show that these approaches can not only predict tags and items extremely well, but also reveal how these models can help in explaining the recommendation processes better than current approaches.
Studying Online Food Consumption and Production Patterns: Recent Trends and C...Christoph Trattner
Food is a fundamental concept in our daily lives and is one of the most important factors that shape how healthy we are or how good we feel. Although research on the users’ food preferences has been a well-established research area over the last decades, only very little research was devoted yet to understand, how the World Wide Web influences the way we consume or produce food offline.
In this talk, I will therefore highlight recent research in online food communities and interesting findings in terms of online food recipe consumption and production patterns. I will show, how these studies might be useful when drawing conclusions about health related issues, such as obesity or diabetes of a large population, and how these insights might be used to tune current recommender approaches in this domain.
Last but not least, I will discuss the limitations of these studies and will highlight the need for a joined European taskforce, that studies food, health related issues and recommender systems on a much larger and more useful way, as proposed by current research in this area.
Understanding the Impact of Weather for POI RecommendationsChristoph Trattner
POI recommender systems for location-based social network services, such as Foursquare or Yelp, have gained tremendous popularity in the past few years. Much work has been dedicated into improving recommendation services in such systems by integrating different features that are assumed to have an impact on people's preferences for POIs, such as time and geolocation. Yet, little attention has been paid to the impact of weather on the users' final decision to visit a recommended POI. In this paper we contribute to this area of research by presenting the first results of a study that aims to recommend POIs based on weather data. To this end, we extend the state-of-the-art Rank-GeoFM POI recommender algorithm with additional weather-related features, such as temperature, cloud cover, humidity and precipitation intensity. We show that using weather data not only significantly increases the recommendation accuracy in comparison to the original algorithm, but also outperforms its time-based variant. Furthermore, we present the magnitude of impact of each feature on the recommendation quality, showing the need to study the weather context in more detail in the light of POI recommendation systems.
Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PU...Denis Parra Santander
- First version was a guest lecture about Network Visualization in the class "Data Visualization" taught by Dr. Sharon Hsiao in the QMSS program at Columbia University http://www.columbia.edu/~ih2240/dataviz/index.htm
- This updated version was delivered in our class on SNA at PUC Chile in the MPGI master program.
Social Computing in the area of Big Data at the Know-Center Austria's leading...Christoph Trattner
Nowadays, social networks and media, such as Facebook, Twitter & Co, affect our communication and our exchange of knowledge more than ever. But which additional benefits can offer social media apart from easy interaction with friends and how can they be used to create additional value for companies and institutions? These are the questions that the area Social Computing at Know-Center addresses in detail.
In this talk we will give a brief overview of industry and non-industry related research projects which we have been involved in recently with my group, Social Computing at the Know-Center, in the context of Big Data and social media. In particular, the talk will highlight specific research project outcomes and work-in-progress that make use of social media data to help people to explore the vastly growing overloaded information space more efficiently.
Recommending Tags with a Model of Human CategorizationChristoph Trattner
Social tagging involves complex processes of human categorization that have been the topic of much research in the cognitive sciences. In this paper we present a recommender approach for social tags whose principles are derived from some of the more prominent and empirically well-founded models from this research tradition. The basic architecture is a simple three-layers connectionist model. The input layer encodes patterns of semantic features of a user-specific re- source, which are either latent topics elicited through Latent Dirichlet Allocation (LDA) or available external categories. The hidden layer categorizes the resource by matching the encoded pattern against already learned exemplar patterns. The latter are composed of unique feature patterns and associated tag distributions. Finally, the output layer samples tags from the associated tag distributions to verbalize the preceding categorization process. We have evaluated this approach on a real-world folksonomy gathered from Wikipedia bookmarks in Delicious. In the experiment our approach outperformed LDA, a well-established algorithm. We at- tribute this to the fact that our approach processes seman- tic information (either latent topics or external categories) across the three different layers, and this substantially enhances the recommendation performance. With this paper, we demonstrate that a theoretically guided design of algorithms not only holds potential for improving existing recommendation mechanisms, but it also allows us to derive more generalizable insights about how human information interaction on the Web is determined by both semantic and verbal processes.
apidays LIVE Australia 2021 - Tracing across your distributed process boundar...apidays
apidays LIVE Australia 2021 - Accelerating Digital
September 15 & 16, 2021
Tracing across your distributed process boundaries using OpenTelemetry
Dasith Wijes, Senior Consultant at Microsoft (Azure Cloud & AI Team)
Facilitating Data Curation: a Solution Developed in the Toxicology DomainChristophe Debruyne
Christophe Debruyne, Jonathan Riggio, Emma Gustafson, Declan O'Sullivan, Mathieu Vinken, Tamara Vanhaecke, Olga De Troyer.
Presented at the 2020 IEEE 14th International Conference on Semantic Computing, San Diego, California, 3-5 February 2020
Toxicology aims to understand the adverse effects of
chemical compounds or physical agents on living organisms. For
chemicals, much information regarding safety testing of cosmetic
ingredients is now scattered in a plethora of safety evaluation
reports. Toxicologists in our university intend to collect this
information into a single repository. Their current approach uses
spreadsheets, does not scale well, and makes data curation and
querying cumbersome. Semantic technologies (e.g., RDF, OWL,
and Linked Data principles) would be more appropriate for
this purpose. However, this technology is not very accessible to
toxicologists without extensive training. In this paper, we report
on a tool that supports subject matter experts in the construction
of an RDF–based knowledge base for the toxicology domain. The
tool is using the jigsaw metaphor for guiding the subject matter
experts. We demonstrate that the jigsaw metaphor is a viable
option for generating RDF. Future work includes investigating
appropriate methods and tools for knowledge evolution and data
analysis.
The Apache Solr Semantic Knowledge GraphTrey Grainger
What if instead of a query returning documents, you could alternatively return other keywords most related to the query: i.e. given a search for "data science", return me back results like "machine learning", "predictive modeling", "artificial neural networks", etc.? Solr’s Semantic Knowledge Graph does just that. It leverages the inverted index to automatically model the significance of relationships between every term in the inverted index (even across multiple fields) allowing real-time traversal and ranking of any relationship within your documents. Use cases for the Semantic Knowledge Graph include disambiguation of multiple meanings of terms (does "driver" mean truck driver, printer driver, a type of golf club, etc.), searching on vectors of related keywords to form a conceptual search (versus just a text match), powering recommendation algorithms, ranking lists of keywords based upon conceptual cohesion to reduce noise, summarizing documents by extracting their most significant terms, and numerous other applications involving anomaly detection, significance/relationship discovery, and semantic search. In this talk, we'll do a deep dive into the internals of how the Semantic Knowledge Graph works and will walk you through how to get up and running with an example dataset to explore the meaningful relationships hidden within your data.
Building a semantic search system - one that can correctly parse and interpret end-user intent and return the ideal results for users’ queries - is not an easy task. It requires semantically parsing the terms, phrases, and structure within queries, disambiguating polysemous terms, correcting misspellings, expanding to conceptually synonymous or related concepts, and rewriting queries in a way that maps the correct interpretation of each end user’s query into the ideal representation of features and weights that will return the best results for that user. Not only that, but the above must often be done within the confines of a very specific domain - ripe with its own jargon and linguistic and conceptual nuances.
This talk will walk through the anatomy of a semantic search system and how each of the pieces described above fit together to deliver a final solution. We'll leverage several recently-released capabilities in Apache Solr (the Semantic Knowledge Graph, Solr Text Tagger, Statistical Phrase Identifier) and Lucidworks Fusion (query log mining, misspelling job, word2vec job, query pipelines, relevancy experiment backtesting) to show you an end-to-end working Semantic Search system that can automatically learn the nuances of any domain and deliver a substantially more relevant search experience.
Matthias Vallentin - Towards Interactive Network Forensics and Incident Respo...boundary_slides
Incident response, post-facto forensics, and network troubleshooting rely on the ability to quickly extract relevant information. To this end, security analysts and network operators need a system that (i) allows for directly expressing a query using domain-specific constructs, (ii) that delivers the performance required for interactive analysis, and (iii) that is not affected by a continuously arriving stream of semi-structured data.
This talk covers the design and implementation plans of a distributed analytics platform that meets these requirements. Well-proven Google architectures like GFS, BigTable, Chubby, and Dremel heavily influenced the design of the system, which leverages bitmap indexes to meet the interactive query requirements. The goal is to develop a prototype ready for production usage in the next few months and obtain feedback from using it on various large-scale sites serving tens of thousands of machines.
DN18 | The Evolution and Future of Graph Technology: Intelligent Systems | Ax...Dataconomy Media
Abstract of the Prersentation:
The field of graph technology has developed rapidly in recent years and established itself as an independent technology sector that will probably even receive its own query language standard (GQL). As almost any business benefits from graph platforms it is no wonder that adoption is broad and fast. There must be good reasons for that. In his talk Axel will give an overview of the evolution of technology and products in the Graph Space from the early beginnings up to current developments in machine learning and artificial intelligence. He will also give some examples and explain why graph technology is so well suited for most use cases and to build intelligent systems.
About the Author:
Axel Morgner started Structr in 2010 to create the next-gen CMS. Previously, he worked for Oracle and founded an ECM company. Axel loves Open Source. As CEO, he’s responsible for the company behind Structr and the project itself, with focus on the front end.
From Search to Predictions in Tagged Information SpacesChristoph Trattner
Tagging gained tremendously in popularity over past few years. When looking into the literature of tagging we find a lot of work regarding people's tagging motivation, their behavior, models that describe the folksonomy generation process, emergent semantic structures, etc., but interestingly we find quite little research showing the value of tags for searching an overloaded information space. Furthermore, there is lot of literature on the tag or item prediction problem, but interestingly almost all of them lookat the issue from a data-driven perspective. To bridge this gap in the literature, we have conducted several in-depth studies in the past showing the value of tags for lookup and exploratory search. We looked at the problem from a network theoretic and interface perspective and we will show how useful tags are for searching. Furthermore, we reviewed literature on memory processes from cognitive science and have invented a number of novel recommender algorithms based on the ACT-R and MINERVA2 theory. We will show that these approaches can not only predict tags and items extremely well, but also reveal how these models can help in explaining the recommendation processes better than current approaches.
Studying Online Food Consumption and Production Patterns: Recent Trends and C...Christoph Trattner
Food is a fundamental concept in our daily lives and is one of the most important factors that shape how healthy we are or how good we feel. Although research on the users’ food preferences has been a well-established research area over the last decades, only very little research was devoted yet to understand, how the World Wide Web influences the way we consume or produce food offline.
In this talk, I will therefore highlight recent research in online food communities and interesting findings in terms of online food recipe consumption and production patterns. I will show, how these studies might be useful when drawing conclusions about health related issues, such as obesity or diabetes of a large population, and how these insights might be used to tune current recommender approaches in this domain.
Last but not least, I will discuss the limitations of these studies and will highlight the need for a joined European taskforce, that studies food, health related issues and recommender systems on a much larger and more useful way, as proposed by current research in this area.
Understanding the Impact of Weather for POI RecommendationsChristoph Trattner
POI recommender systems for location-based social network services, such as Foursquare or Yelp, have gained tremendous popularity in the past few years. Much work has been dedicated into improving recommendation services in such systems by integrating different features that are assumed to have an impact on people's preferences for POIs, such as time and geolocation. Yet, little attention has been paid to the impact of weather on the users' final decision to visit a recommended POI. In this paper we contribute to this area of research by presenting the first results of a study that aims to recommend POIs based on weather data. To this end, we extend the state-of-the-art Rank-GeoFM POI recommender algorithm with additional weather-related features, such as temperature, cloud cover, humidity and precipitation intensity. We show that using weather data not only significantly increases the recommendation accuracy in comparison to the original algorithm, but also outperforms its time-based variant. Furthermore, we present the magnitude of impact of each feature on the recommendation quality, showing the need to study the weather context in more detail in the light of POI recommendation systems.
Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PU...Denis Parra Santander
- First version was a guest lecture about Network Visualization in the class "Data Visualization" taught by Dr. Sharon Hsiao in the QMSS program at Columbia University http://www.columbia.edu/~ih2240/dataviz/index.htm
- This updated version was delivered in our class on SNA at PUC Chile in the MPGI master program.
Social Computing in the area of Big Data at the Know-Center Austria's leading...Christoph Trattner
Nowadays, social networks and media, such as Facebook, Twitter & Co, affect our communication and our exchange of knowledge more than ever. But which additional benefits can offer social media apart from easy interaction with friends and how can they be used to create additional value for companies and institutions? These are the questions that the area Social Computing at Know-Center addresses in detail.
In this talk we will give a brief overview of industry and non-industry related research projects which we have been involved in recently with my group, Social Computing at the Know-Center, in the context of Big Data and social media. In particular, the talk will highlight specific research project outcomes and work-in-progress that make use of social media data to help people to explore the vastly growing overloaded information space more efficiently.
Recommending Tags with a Model of Human CategorizationChristoph Trattner
Social tagging involves complex processes of human categorization that have been the topic of much research in the cognitive sciences. In this paper we present a recommender approach for social tags whose principles are derived from some of the more prominent and empirically well-founded models from this research tradition. The basic architecture is a simple three-layers connectionist model. The input layer encodes patterns of semantic features of a user-specific re- source, which are either latent topics elicited through Latent Dirichlet Allocation (LDA) or available external categories. The hidden layer categorizes the resource by matching the encoded pattern against already learned exemplar patterns. The latter are composed of unique feature patterns and associated tag distributions. Finally, the output layer samples tags from the associated tag distributions to verbalize the preceding categorization process. We have evaluated this approach on a real-world folksonomy gathered from Wikipedia bookmarks in Delicious. In the experiment our approach outperformed LDA, a well-established algorithm. We at- tribute this to the fact that our approach processes seman- tic information (either latent topics or external categories) across the three different layers, and this substantially enhances the recommendation performance. With this paper, we demonstrate that a theoretically guided design of algorithms not only holds potential for improving existing recommendation mechanisms, but it also allows us to derive more generalizable insights about how human information interaction on the Web is determined by both semantic and verbal processes.
apidays LIVE Australia 2021 - Tracing across your distributed process boundar...apidays
apidays LIVE Australia 2021 - Accelerating Digital
September 15 & 16, 2021
Tracing across your distributed process boundaries using OpenTelemetry
Dasith Wijes, Senior Consultant at Microsoft (Azure Cloud & AI Team)
Facilitating Data Curation: a Solution Developed in the Toxicology DomainChristophe Debruyne
Christophe Debruyne, Jonathan Riggio, Emma Gustafson, Declan O'Sullivan, Mathieu Vinken, Tamara Vanhaecke, Olga De Troyer.
Presented at the 2020 IEEE 14th International Conference on Semantic Computing, San Diego, California, 3-5 February 2020
Toxicology aims to understand the adverse effects of
chemical compounds or physical agents on living organisms. For
chemicals, much information regarding safety testing of cosmetic
ingredients is now scattered in a plethora of safety evaluation
reports. Toxicologists in our university intend to collect this
information into a single repository. Their current approach uses
spreadsheets, does not scale well, and makes data curation and
querying cumbersome. Semantic technologies (e.g., RDF, OWL,
and Linked Data principles) would be more appropriate for
this purpose. However, this technology is not very accessible to
toxicologists without extensive training. In this paper, we report
on a tool that supports subject matter experts in the construction
of an RDF–based knowledge base for the toxicology domain. The
tool is using the jigsaw metaphor for guiding the subject matter
experts. We demonstrate that the jigsaw metaphor is a viable
option for generating RDF. Future work includes investigating
appropriate methods and tools for knowledge evolution and data
analysis.
The Apache Solr Semantic Knowledge GraphTrey Grainger
What if instead of a query returning documents, you could alternatively return other keywords most related to the query: i.e. given a search for "data science", return me back results like "machine learning", "predictive modeling", "artificial neural networks", etc.? Solr’s Semantic Knowledge Graph does just that. It leverages the inverted index to automatically model the significance of relationships between every term in the inverted index (even across multiple fields) allowing real-time traversal and ranking of any relationship within your documents. Use cases for the Semantic Knowledge Graph include disambiguation of multiple meanings of terms (does "driver" mean truck driver, printer driver, a type of golf club, etc.), searching on vectors of related keywords to form a conceptual search (versus just a text match), powering recommendation algorithms, ranking lists of keywords based upon conceptual cohesion to reduce noise, summarizing documents by extracting their most significant terms, and numerous other applications involving anomaly detection, significance/relationship discovery, and semantic search. In this talk, we'll do a deep dive into the internals of how the Semantic Knowledge Graph works and will walk you through how to get up and running with an example dataset to explore the meaningful relationships hidden within your data.
Building a semantic search system - one that can correctly parse and interpret end-user intent and return the ideal results for users’ queries - is not an easy task. It requires semantically parsing the terms, phrases, and structure within queries, disambiguating polysemous terms, correcting misspellings, expanding to conceptually synonymous or related concepts, and rewriting queries in a way that maps the correct interpretation of each end user’s query into the ideal representation of features and weights that will return the best results for that user. Not only that, but the above must often be done within the confines of a very specific domain - ripe with its own jargon and linguistic and conceptual nuances.
This talk will walk through the anatomy of a semantic search system and how each of the pieces described above fit together to deliver a final solution. We'll leverage several recently-released capabilities in Apache Solr (the Semantic Knowledge Graph, Solr Text Tagger, Statistical Phrase Identifier) and Lucidworks Fusion (query log mining, misspelling job, word2vec job, query pipelines, relevancy experiment backtesting) to show you an end-to-end working Semantic Search system that can automatically learn the nuances of any domain and deliver a substantially more relevant search experience.
Matthias Vallentin - Towards Interactive Network Forensics and Incident Respo...boundary_slides
Incident response, post-facto forensics, and network troubleshooting rely on the ability to quickly extract relevant information. To this end, security analysts and network operators need a system that (i) allows for directly expressing a query using domain-specific constructs, (ii) that delivers the performance required for interactive analysis, and (iii) that is not affected by a continuously arriving stream of semi-structured data.
This talk covers the design and implementation plans of a distributed analytics platform that meets these requirements. Well-proven Google architectures like GFS, BigTable, Chubby, and Dremel heavily influenced the design of the system, which leverages bitmap indexes to meet the interactive query requirements. The goal is to develop a prototype ready for production usage in the next few months and obtain feedback from using it on various large-scale sites serving tens of thousands of machines.
DN18 | The Evolution and Future of Graph Technology: Intelligent Systems | Ax...Dataconomy Media
Abstract of the Prersentation:
The field of graph technology has developed rapidly in recent years and established itself as an independent technology sector that will probably even receive its own query language standard (GQL). As almost any business benefits from graph platforms it is no wonder that adoption is broad and fast. There must be good reasons for that. In his talk Axel will give an overview of the evolution of technology and products in the Graph Space from the early beginnings up to current developments in machine learning and artificial intelligence. He will also give some examples and explain why graph technology is so well suited for most use cases and to build intelligent systems.
About the Author:
Axel Morgner started Structr in 2010 to create the next-gen CMS. Previously, he worked for Oracle and founded an ECM company. Axel loves Open Source. As CEO, he’s responsible for the company behind Structr and the project itself, with focus on the front end.
An Ad-hoc Smart Gateway Platform for the Web of Things (IEEE iThings 2013 Bes...Darren Carlson
The Web of Things (WoT) aims to extend the Web into the physical world by promoting the adoption of Web protocols by situated services and smart objects (ambient artifacts). However, real-world ambient artifacts often adopt proprietary and/or non-Web protocols, making them invisible to Web search engines and inaccessible to conventional Web agents. Smart Gateways have been proposed as a way to “Web-enable” proprietary ambient artifacts through intermediary proxy nodes; however, the requisite infrastructure is difficult to deploy at Web scale. To address such challenges, we are developing Ambient Dynamix (Dynamix): a plug-and-play context framework for mobile devices, which enables Web agents to interoperate with non-Web ambient artifacts – directly from the browser. In this paper, we describe how Dynamix can be used to transform the user’s device into an ad-hoc Smart Gateway in-situ, enabling Web applications (in the device’s browser) to seamlessly interact with non-Web ambient artifacts in the physical environment. We describe an operational prototype implementation, which enables Web apps to discover and control nearby UPnP and AirPlay media devices uniformly. We also present a performance evaluation that indicates the prototype imposes low processing and memory overhead, and is suitable for deployment on many commodity mobile devices.
MuCon 2019: Exploring Your Microservices Architecture Through Network Science...OpenCredo
Your microservice system has been up and running for a while. You know you’ve diligently employed every ounce of your experience and knowledge over time to design a sensible application architecture, with hopefully sensible boundaries. But time is now throwing new questions your way: Are my boundaries still sensible?
Have any anti-patterns crept in, do I have the dreaded distributed monolith?
This talk explores how network science techniques can be applied to help gain insight into, and explore questions about your microservices architecture.
To architect or engineer? Lessons from DataPool on building RDM repositoriesjiscdatapool
There cannot be many mature products where development meetings have not been interrupted with a rueful declaration that to make further progress “you wouldn’t start from here”. This encapsulates one key difference between the architect and engineer, the latter prepared to work with the set of tools provided, the other preferring to start with a blank sheet of paper or an open space. In building research data repositories using two different softwares, Microsoft Sharepoint and EPrints, the DataPool Project is working somewhere between these extremes. Which approach will prove to be the more resilient for research data management (RDM)? In this talk we will look at the relevant factors.
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Evaluating Tag-Based Information Access in Image Collections
1. Graz University of Technology
Evaluating Tag-Based Information Access
in Image Collections
Christoph Trattner*,
Yiling Lin, Denis Parra, Zhen Yue, Peter Brusilovsky
*Graz University of Technology, Austria
University of Pittsburgh, USA
Christoph Trattner Hypertext 2012
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2. Graz University of Technology
Tagging Systems
“Tagging gained tremendously in popularity over the
past few years”
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6. Graz University of Technology
Problem Statement
Not surprisingly, there was a lot of research in the past
few years that for instance investigated the value of
tags for efficient search and information retrieval in
online information systems
Surprisingly, most of the studies only use information
retrieval or network-theoretic measures and ignore the
user side
To contribute to this field of research we conducted a
controlled user study
1 2 3
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7. Graz University of Technology
What will be presented?
Are tags useful to be used in search interfaces?
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8. Graz University of Technology
Dataset
~ 2,000 images
~ 4,200 tags
~ 16,000 tag assignments
Interesting Fact:
Tags were generated by
~100 users from Amazon
Mechanical Turk
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9. Graz University of Technology
Interfaces
1 Baseline
2 Tag Cloud Search Interface
Faceted Tag Cloud Search Interface
3
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10. Graz University of Technology
How were the interfaces evaluated?
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11. Graz University of Technology
Evaluation
Within-subject design, i.e. all of or subjects
evaluated all interfaces during the study.
Interfaces were counter balanced
Baseline Tag Cloud Faceted Tag Cloud
1 2 3
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12. Graz University of Technology
Evaluation
2 types of tasks:
Look-up search task
9 images with different difficulty level hard
200
Rank Pos
150 medium easy
Exploratory search task 100
50
3 tasks with different difficulty level 0
1
118
235
352
469
586
703
820
937
1054
1171
1288
1405
1522
1639
1756
1873
Sample Task:
“Find at least 8 different types of stores/shops in Pittsburgh! Each type of
store/shop should have at least two images from different locations, i.e.
in total you will have to find at least 16 images.”
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13. Graz University of Technology
Evaluation: Look-up Task
Look-up task
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14. Graz University of Technology
Evaluation: Exploratory Search Task
Exploratroy search
task
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15. Graz University of Technology
Evaluation
All in all, 24 subjects
Median age 31
19 reported to be familiar with tagging systems
All reported to be to use computers more than 5
hours a day
All of them reported to be familiar with search
engines
One session took 90 mins
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16. Graz University of Technology
What are the results?
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17. Graz University of Technology
Results: Performance (1/2)
Variables:
Question 1: What interface performs best? • Total Actions
• Search Time
1 2 3
Look-up: no sign. differences between interfaces
Exploratory:
Tag Cloud Interface out-performs baseline
Faceted Tag Cloud Interface almost as slow as baseline
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18. Graz University of Technology
Results: Performance (2/2)
Variables:
Question 2: What is the effect of familiarity and difficulty
• Total Actions
on the performance of the interfaces?
• Search Time
1 2 3
On medium difficultly level…
Tag Cloud Interface out-performs baseline interface in search time
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19. Graz University of Technology
Results: Usage (1/2)
Question 3: How are the interfaces used?
=> Log analysis
Results:
Search action and click image action used most often
Add tag action sign. more used in facet
Show more results sign. less used in tag cloud
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20. Graz University of Technology
Results: Usage
Question 4: Does tag grouping by semantic category affect the usage of
these categories?
50.00%
Baseline
45.00%
Tag Cloud
40.00%
Facet
35.00%
30.00%
25.00%
20.00%
Answer: Yes,
15.00%
We found sign. differences
10.00%
between the baseline and the
5.00%
faceted tag cloud interface
0.00%
who where when what other
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21. Graz University of Technology
Results: Participants„ perception of the
interfaces
Question 5: What was the perception of the particpants regarding the interfaces?
Post-questionaires after each interface
Scale: 1=very bad….5=very good
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22. Graz University of Technology
Results: Preference and Rating
Question 6: What was the preference of the users?
• Post-questionair was handed out to the subjects with overall 7 questions.
Question 7: How are the interfaces rated?
Scale: 1 = very bad….5=very good
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23. Graz University of Technology
Questions?
Why did people like the tag cloud interfaces more than the
baseline?
Why was the tag cloud interface better rated than the
faceted tag cloud interface?
Why did people recommend the faceted tag cloud interface
even if they rated the tag cloud interface higher?
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Results: Comment Analysis (1/3)
Why did people like the tag cloud interfaces?
“The tag cloud provided more information than
search only interface”
“I like tag cloud because it gives me new
ideas and it is easier to use”
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25. Graz University of Technology
Results: Comment Analysis (1/3)
Why did people prefer the faceted tag cloud
interface?
“It is easy to find the tags that I needed in faceted
tag cloud”
“I like faceted tag cloud interface, because
the interface is clearer and I always know
where to find the tag”
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26. Graz University of Technology
Results: Comment Analysis (2/3)
Why did people prefer the tag cloud interface over
the faceted tag cloud interface?
“The facet did not seem to identify tags well”
“I think the categorization was not good, it was
not relevant to the task”
?
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27. Graz University of Technology
Conclusions of this work
In general, tags are useful in search interfaces
They help the user to find information faster
Less clicks, less search time
They give the users hints
They make the user happier
However, depending on the tag
interface design different results Take home message
can be observed…
Not always the most advanced
interface design is the best
choice…
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28. Graz University of Technology
End of Presentation
Thank you!
Christoph Trattner
ctrattner@iicm.edu
Graz University of Technology, Austria
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