This document proposes a Benchmark for End-User Structured Data User Interfaces (BESDUI) to evaluate and compare semantic and relational data exploration tools from a user perspective. The benchmark includes a set of 12 representative user tasks on a sample dataset, along with metrics to measure tools' capabilities, efficiency, and time performance in completing the tasks. The document describes applying BESDUI to four different tools and finding one tool performs most efficiently based on the metrics. It encourages the community to adopt and contribute to BESDUI to help drive research on improving semantic search and exploration user interfaces.
Real-time Recommendations for Retail: Architecture, Algorithms, and DesignJuliet Hougland
Users are constantly searching for new content and to stay competitive organizations must act immediately based on up-to-date data. Outdated recommendations decrease the likelihood of presenting the right offer and make it harder to maintain customer loyalty. In order to provide the most relevant recommendations and increase engagement, organizations must track customer interactions and re-score recommendations on the fly.
Data sources have expanded dramatically to include a wealth of historical data and a constant influx of behavior data. The key to moving from predictive models, applied in batch, to models that provide responses in real time, is to focus on the efficiency of model application. The speed that recommendations can be served is influenced by:
Architecture of the recommendation serving platform
Choice of recommendation algorithm
Datastore access patterns
In this presentation, we’ll discuss how developers can use open source components like HBase and Kiji to develop low-latency recommendation models that can be easily deployed by e-commerce companies. We will give practical advice on how to choose models and design data stores that make use of the architecture and quickly serve new recommendations.
Modern Perspectives on Recommender Systems and their Applications in MendeleyKris Jack
Presentation given for one of Pearson's Data Research teams. It motivates the use of recommender systems, describes common approaches to building and evaluating them and gives examples of how they are used in Mendeley. Thanks to Maya Hristakeva for creating some of the slides.
How to Build a Recommendation Engine on SparkCaserta
How to Build a Recommendation Engine on Spark was a presentation given by Joe Caserta, CEO and founder of Caserta Concepts, at @AnalyticsWeek in Boston.
Boston's Data AnalyticsStreet Conference is a 2 day packed event with thought provoking keynotes, knowledge filled sessions, intense workshops, insightful panels, and real-world case studies - engaging analytics community with latest methodologies and trends. The conference encompasses largest Speaker-to-Attendee ratio for unmatched networking and learning opportunity.
For more information on the services and solutions Caserta Concepts offers, visit our website at http://casertaconcepts.com/.
Generally in recommendation engines, user's past history on engagements with different items is a key input. However, in many situations in an enterprise’s business cycle, it is necessary to generate recommendations based on user activity in real time. In this Big Data Cloud's meetup on April 3, 2014, we discussed how to decipher real time click streams into meaningful recommendations in real time.
Pranab Ghosh discussed the real time recommendations feature of Sifarish, which is an open source project built on Hadoop, Storm and Redis.
Sifarish is a recommendation engine that does content based recommendation as well as social collaborative filtering based recommendation.
Real-time Recommendations for Retail: Architecture, Algorithms, and DesignJuliet Hougland
Users are constantly searching for new content and to stay competitive organizations must act immediately based on up-to-date data. Outdated recommendations decrease the likelihood of presenting the right offer and make it harder to maintain customer loyalty. In order to provide the most relevant recommendations and increase engagement, organizations must track customer interactions and re-score recommendations on the fly.
Data sources have expanded dramatically to include a wealth of historical data and a constant influx of behavior data. The key to moving from predictive models, applied in batch, to models that provide responses in real time, is to focus on the efficiency of model application. The speed that recommendations can be served is influenced by:
Architecture of the recommendation serving platform
Choice of recommendation algorithm
Datastore access patterns
In this presentation, we’ll discuss how developers can use open source components like HBase and Kiji to develop low-latency recommendation models that can be easily deployed by e-commerce companies. We will give practical advice on how to choose models and design data stores that make use of the architecture and quickly serve new recommendations.
Modern Perspectives on Recommender Systems and their Applications in MendeleyKris Jack
Presentation given for one of Pearson's Data Research teams. It motivates the use of recommender systems, describes common approaches to building and evaluating them and gives examples of how they are used in Mendeley. Thanks to Maya Hristakeva for creating some of the slides.
How to Build a Recommendation Engine on SparkCaserta
How to Build a Recommendation Engine on Spark was a presentation given by Joe Caserta, CEO and founder of Caserta Concepts, at @AnalyticsWeek in Boston.
Boston's Data AnalyticsStreet Conference is a 2 day packed event with thought provoking keynotes, knowledge filled sessions, intense workshops, insightful panels, and real-world case studies - engaging analytics community with latest methodologies and trends. The conference encompasses largest Speaker-to-Attendee ratio for unmatched networking and learning opportunity.
For more information on the services and solutions Caserta Concepts offers, visit our website at http://casertaconcepts.com/.
Generally in recommendation engines, user's past history on engagements with different items is a key input. However, in many situations in an enterprise’s business cycle, it is necessary to generate recommendations based on user activity in real time. In this Big Data Cloud's meetup on April 3, 2014, we discussed how to decipher real time click streams into meaningful recommendations in real time.
Pranab Ghosh discussed the real time recommendations feature of Sifarish, which is an open source project built on Hadoop, Storm and Redis.
Sifarish is a recommendation engine that does content based recommendation as well as social collaborative filtering based recommendation.
This tutorial gives an overview of how search engines and machine learning techniques can be tightly coupled to address the need for building scalable recommender or other prediction based systems. Typically, most of them architect retrieval and prediction in two phases. In Phase I, a search engine returns the top-k results based on constraints expressed as a query. In Phase II, the top-k results are re-ranked in another system according to an optimization function that uses a supervised trained model. However this approach presents several issues, such as the possibility of returning sub-optimal results due to the top-k limits during query, as well as the prescence of some inefficiencies in the system due to the decoupling of retrieval and ranking.
To address this issue the authors created ML-Scoring, an open source framework that tightly integrates machine learning models into Elasticsearch, a popular search engine. ML-Scoring replaces the default information retrieval ranking function with a custom supervised model that is trained through Spark, Weka, or R that is loaded as a plugin in Elasticsearch. This tutorial will not only review basic methods in information retrieval and machine learning, but it will also walk through practical examples from loading a dataset into Elasticsearch to training a model in Spark, Weka, or R, to creating the ML-Scoring plugin for Elasticsearch. No prior experience is required in any system listed (Elasticsearch, Spark, Weka, R), though some programming experience is recommended.
RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning...S. Diana Hu
Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
NoSQL Simplified: Schema vs. Schema-lessInfiniteGraph
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UI Dev in Big data world using open sourceTech Triveni
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Key points to consider while choosing any open-source framework/library for the big data world.
Do you need to write a custom framework or use ready-made open source, when what to choose?
How dev can leverage open source frameworks like Angular, REACT to making big data apps faster?
How you can extend open-source BI tools like Kibana, superset graphana to make UI development tool?
How to show network big data using open source graph libraries?
How to deal with real-time data in Big data UI?
Why use & contribute to open source?
Design UI for future as in Big data world customer problems keep changing with time. Showcasing demo for our real customer's problems, how we achieved using these open source libraries.
A presentation detailing a Library Management System (LMS) Project for a Medical Research Council. The function of the Library is to organize and account for all the materials (Books, Journals, Magazines, Publications and Thesis) in the Library.
The system makes use of a Bar coding system to identify materials; used when checking in items.
Martins Jr.
Webinar: Increase Conversion With Better SearchLucidworks
Hear from IBM Product Line Manager Iris Yuan & Lucidworks VP of Partner Engineering Sarath Jarugula for a deep discussion into how improving ecommerce search can drive conversions and increase revenue.
The goal of this presentation is to give attendees a deeper understanding of usability testing so they can leverage it in their own work. The material will shed light on what is important to the research buyer and will help the research provider to better understand how to plan, moderate, and report on a usability study. It will also provide information on where they can go to learn more about this very practical qualitative method.
Kay will cover what a usability test is and when to use it, the key planning steps, the language around it, and the unique insights this method produces. She will also discuss the various approaches a market researcher can take when running a usability study at different points in a product’s development (e.g., concept, early prototype, released product).
WSO2 Data Analytics Server is a comprehensive enterprise data analytics platform; it fuses batch and real-time analytics of any source of data with predictive analytics via machine learning.
How To Implement Your Online Search Quality Evaluation With KibanaSease
Online testing represents a fundamental method to assess the performance of a ranking model in practical applications, providing the information needed to improve and better understand its behavior. Despite the advantages, the currently available evaluation tools have certain limitations. For this reason, we will present an alternative and customized approach to evaluate ranking models using Kibana. The talk will begin with an overview of online testing, including its benefits and drawbacks. Then, we will provide an in-depth exploration of our Kibana implementation, detailing the reasons behind our approach. Attendees will learn about the various tools provided by Kibana, and with practical examples, we will show how to create visualizations and dashboards, complete with queries and code, to compare different rankers. Attending this presentation will provide participants with valuable knowledge on how to leverage Kibana for the purpose of evaluating ranking models on custom metrics and on specific contexts such as the most popular and “populous” queries.
Crowdsourced query augmentation through the semantic discovery of domain spec...Trey Grainger
Talk Abstract: Most work in semantic search has thus far focused upon either manually building language-specific taxonomies/ontologies or upon automatic techniques such as clustering or dimensionality reduction to discover latent semantic links within the content that is being searched. The former is very labor intensive and is hard to maintain, while the latter is prone to noise and may be hard for a human to understand or to interact with directly. We believe that the links between similar user’s queries represent a largely untapped source for discovering latent semantic relationships between search terms. The proposed system is capable of mining user search logs to discover semantic relationships between key phrases in a manner that is language agnostic, human understandable, and virtually noise-free.
Fusion 3.1 comes with exciting new features that will make your search more personal and better targeted. Join us for a webinar to learn more about Fusion's features, what's new in this release, and what's around the corner for Fusion.
Search Solutions 2011: Successful Enterprise Search By DesignMarianne Sweeny
When your colleagues say they want Google, they don’t mean the Google Search Appliance. They mean the Google Search user experience: pervasive, expedient and delivering the information that they need. Successful enterprise search does not start with the application features, is not part of the information architecture, does not come from a controlled vocabulary and does not emerge on its own from the developers. It requires enterprise-specific data mining, enterprise-specific user-centered design and fine tuning to turn “search sucks” into search success within the firewall. This presentation looks at action items, tools and deliverables for Discovery, Planning, Design and Post Launch phases of an enterprise search deployment.
CopyrightLY: Blockchain and Semantic Web for Decentralised Copyright ManagementRoberto García
CopyrightLY focuses on building an authorship and rights management layer that provides a set of services to claim authorship, on both content and data. Moreover, it also makes it possible to attach reuse terms to these claims, which state the conditions to reuse the associated data or content. This authorship and rights management layer will constitute the foundation for future services built on top of it, like social media copyright management or media monetisation through NFTs.
Facilitating an agricultural data ecosystem- The EU Code of conduct on agric...Roberto García
To facilitate the creation of a data ecosystem in the agricultural sector that allows the realisation of its full potential, existing barriers that complicate data collection, integration and exploitation should be lowered. Codes of conduct, such as that of the European Union, are aimed at these difficulties. The experience gained during the development of the Global Forest Biodiversity Initiative data portal shows that following the recommendations of the code of conduct facilitates the emergence of a community of data providers and an ecosystem for its exploitation.
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This tutorial gives an overview of how search engines and machine learning techniques can be tightly coupled to address the need for building scalable recommender or other prediction based systems. Typically, most of them architect retrieval and prediction in two phases. In Phase I, a search engine returns the top-k results based on constraints expressed as a query. In Phase II, the top-k results are re-ranked in another system according to an optimization function that uses a supervised trained model. However this approach presents several issues, such as the possibility of returning sub-optimal results due to the top-k limits during query, as well as the prescence of some inefficiencies in the system due to the decoupling of retrieval and ranking.
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Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
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Key points to consider while choosing any open-source framework/library for the big data world.
Do you need to write a custom framework or use ready-made open source, when what to choose?
How dev can leverage open source frameworks like Angular, REACT to making big data apps faster?
How you can extend open-source BI tools like Kibana, superset graphana to make UI development tool?
How to show network big data using open source graph libraries?
How to deal with real-time data in Big data UI?
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A presentation detailing a Library Management System (LMS) Project for a Medical Research Council. The function of the Library is to organize and account for all the materials (Books, Journals, Magazines, Publications and Thesis) in the Library.
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Kay will cover what a usability test is and when to use it, the key planning steps, the language around it, and the unique insights this method produces. She will also discuss the various approaches a market researcher can take when running a usability study at different points in a product’s development (e.g., concept, early prototype, released product).
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The amount of media in the Web poses many scalability issues and among them copyright management. This problem becomes even bigger when not just the copyright of pieces of content has to be considered, but also media fragments. Fragments and the management of their rights, beyond simple access control, are the centrepiece for media reuse. This can become an enormous market where copyright has to be managed through the whole value chain. To attain the required level of scalability, it is necessary to provide highly expressive rights representations that can be connected to media fragments. Ontologies provide enough expressive power and facilitate the implementation of copyright management solutions that can scale in such a scenario. The proposed Copyright Ontology is based on Semantic Web technologies, which facilitate implementations at the Web scale, can reuse existing recommendations for media fragments identifiers and interoperate with existing standards. To illustrate these benefits, the papers presents a use case where the ontology is used to enable copyright reasoning on top of DDEX data, the industry standard for information exchange along media value chains.
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1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
# Internet Security: Safeguarding Your Digital World
In the contemporary digital age, the internet is a cornerstone of our daily lives. It connects us to vast amounts of information, provides platforms for communication, enables commerce, and offers endless entertainment. However, with these conveniences come significant security challenges. Internet security is essential to protect our digital identities, sensitive data, and overall online experience. This comprehensive guide explores the multifaceted world of internet security, providing insights into its importance, common threats, and effective strategies to safeguard your digital world.
## Understanding Internet Security
Internet security encompasses the measures and protocols used to protect information, devices, and networks from unauthorized access, attacks, and damage. It involves a wide range of practices designed to safeguard data confidentiality, integrity, and availability. Effective internet security is crucial for individuals, businesses, and governments alike, as cyber threats continue to evolve in complexity and scale.
### Key Components of Internet Security
1. **Confidentiality**: Ensuring that information is accessible only to those authorized to access it.
2. **Integrity**: Protecting information from being altered or tampered with by unauthorized parties.
3. **Availability**: Ensuring that authorized users have reliable access to information and resources when needed.
## Common Internet Security Threats
Cyber threats are numerous and constantly evolving. Understanding these threats is the first step in protecting against them. Some of the most common internet security threats include:
### Malware
Malware, or malicious software, is designed to harm, exploit, or otherwise compromise a device, network, or service. Common types of malware include:
- **Viruses**: Programs that attach themselves to legitimate software and replicate, spreading to other programs and files.
- **Worms**: Standalone malware that replicates itself to spread to other computers.
- **Trojan Horses**: Malicious software disguised as legitimate software.
- **Ransomware**: Malware that encrypts a user's files and demands a ransom for the decryption key.
- **Spyware**: Software that secretly monitors and collects user information.
### Phishing
Phishing is a social engineering attack that aims to steal sensitive information such as usernames, passwords, and credit card details. Attackers often masquerade as trusted entities in email or other communication channels, tricking victims into providing their information.
### Man-in-the-Middle (MitM) Attacks
MitM attacks occur when an attacker intercepts and potentially alters communication between two parties without their knowledge. This can lead to the unauthorized acquisition of sensitive information.
### Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
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2. Authors
Roberto García
GRIHO - HCI & Data Integration
Research Group
Universitat de Lleida, Spain
Eirik Bakke
Computer Science and Artificial
Intelligence Laboratory
MIT, USA
Rosa Gil
GRIHO - HCI & Data Integration
Research Group
Universitat de Lleida, Spain
David R. Karger
Computer Science and Artificial
Intelligence Laboratory
MIT, USA
Juan Manuel Gimeno
GRIHO - HCI & Data Integration
Research Group
Universitat de Lleida, Spain
3. Motivation
• Inability to reach users traditionally alleged as
one of the main barriers for Semantic Web
uptake
• No killer app for the Semantic Web?
Desired outcome?
• Client applications should hide the complexities
of semantic technologies
• For specific tasks, task-specific user interfaces
better satisfy user needs without breaking user
experience
4. Motivation
• Anyway, opportunity for Semantic Web user interfaces:
datasets without dedicated user interface
• New data collections or rarely used
• Combination of existing datasets
• Provide users power of Web-wide connected data to
explore and discover unforeseen connections…
• Semantic Web killer app?
• Current proposals:
• Linked Data browsers, Controlled Natural Language query
engines, faceted browsers,…
• Difficult to compare from the user perspective
• What ways of exploring the data they provide?
• How efficient they are from a Quality in Use perspective?
5. Proposal
• Benchmark for comparing user interfaces
• Set of typical user tasks
• Procedure for measuring performance per task
• Low cost and easy to apply, not requiring the
intervention of real users
• For UI tools based on semantic or relational data
• Longer term
• Trigger a community discussion leading to a
framework for comparing, measuring,…
…encourage better semantic search/exploration tools
6. User Tasks
• Criteria:
• Avoid introducing bias from our a priori conception
of the problem or experience developing our own
tools
• Looked outward to find sets of typical end-user tasks related
to structured data exploration
• Applicable both to relational and semantic data
• Somewhere to start:
• Berlin SPARQL Benchmark (BSBM), Explore Use Case
• Intended for measuring the computational performance but
based on a set of realistic queries inspired by common
information needs
7. User Tasks
1. BSBM-1 Find products for a given set of generic features COMBINED
2. ADDED Find products for a given set of alternative features
3. BSBM-2 Retrieve basic information about a specific product for display purposes
4. BSBM-3 Find products having some specific features and not having one feature
5. BSBM-4 Find products matching two different sets of features
6. BSBM-5 Find product that are similar to a given product
7. BSBM-6 Find products having a label name that contains a specific string some text
8. BSBM-7 Retrieve in-depth information about a specific product including offers
and reviews
9. BSBM-8 Give me recent reviews in English for a specific product
10.BSBM-9 Get Information about a reviewer
11.BSBM-10 Get offers for a given product which fulfill specific requirements
BSBM-11 Get all information about an offer
12.BSBM-12 Export the chosen offer into another information system which uses a
different schema
8. User Tasks
• BESDUI includes for each Task, considering the
sample dataset:
• Information need:
• “List products of type sheeny with product features
stroboscopes OR gadgeteers, and a
productPropertyNumeric1 greater than 450”
• Expected output:
• “aliter tiredest”, “auditoriums reducing pappies”,
“boozed”, “byplay”, “closely jerries”
9. User Tasks
• Set of tasks is not closed, work in progress,
contributions appreciated
• However, quite complete.
References for evaluation:
• Information Seeking Strategies (Belkin et al., 1995)
• All dimensions covered by the current tasks
• Method of Interaction:
Searching (known item) / Scanning (unknown)
• Goal of Interaction:
Learning / Selecting (for retrieval)
• Mode of Retrieval:
Recognition (by association) / Specification (identified items)
• Resource Considered:
Information / Meta-information
10. User Tasks
• Frameworks of Information Exploration - Towards the
Evaluation of Exploration Systems (Nunes & Schwabe,
2016)
• Work in progress…
but complete for some operations and criteria
• Boolean Expressivity
• Conjunction values Same Relation and Different Relations
Product feature “A” and feature “B”
Product feature “A” and price “100”
• Disjunction values Same Relation and Different Relations
Product feature “A” or feature “B”
Product feature “A” or price “100”
• Negation
12. Metrics
BESDUI
Alpha Frontal Asymmetry
related to Valence (Pleasure)
“Method for Improving EEG
Based Emotion Recognition…”
(López-Gil et al., 2016)
“Using SWET-QUM to Compare the Quality in Use of
Semantic Web Exploration Tools”
(González et al., 2013) http://rhizomik.net/swet-qum/
13. Metrics
• Effectiveness
degree to which users can achieve the tasks with precision and completeness
• BESDUI Metric:
Capability: Is performing the task possible with the given system?
0% No – 100% Yes (50% if task has 2 parts)
• Efficiency
degree to which users can achieve tasks investing appropriate amount of resources
• BESDUI Metrics:
Operation Count: How many basic steps
(mouse clicks, keyboard entry, scrolling)
must be performed to carry out the task?
Time: How quickly can these steps be
executed? Map operations to time using
Keystroke Level Model (Card et al, 1980)
Time Efficiency: capability / time,
“goals per second” measure
KLM Operator
Time
(secs.)
K: button press or keystroke 0.2
P: pointing to a target on a
display with a mouse
1.1
H: homing the hand(s) on the
keyboard or other device
0.4
14. Applying BESDUI
1. Anyone, but preferably an experienced tool
user, loads the dataset and performs the 12
Tasks
2. For each one, record if the tool capable of
completing it. If so, detail all interaction steps
required
3. Map interaction steps to task time (using
provided spreadsheet)
15. Applying BESDUI
• Task 1:
“Look for products of type sheeny with product features stroboscopes
AND gadgeteers, and a productPropertyNumeric1 greater than 450”
• Tools
• Rhizomer:
• Capability: 0%
no support for conjunction of values same property
• Virtuoso FCT (Faceted Browser):
• Capability: 100%
16. Virtuoso FCT – Task 1
1. Type “sheeny” and “Enter”, then click “ProductType10”.
2. Click “Go” for “Start New Facet”, then click “Options”.
3. For “Interence Rule” Click and Select rules graph then “Apply”.
4. Click “Attributes”, then “productFeature” and “stroboscopes”.
5. Click “Attributes”, then “productFeature” and “gadgeteers”.
6. Click “Attributes” and “productPropertyNumeric1”.
7. Click “Add condition: None” and select “>”.
8. Type “450” and click “Set Condition”.
9K, 2P, 3H
2K, 2P
2K, 2P
3K, 3P
3K, 3P
2K, 2P
2K, 2P
5K, 2P, 2H
17. Applying BESDUI
• Task 2:
“Look for products of type sheeny with product features stroboscopes
OR gadgeteers, and a productPropertyNumeric1 greater than 450”
• Tools
• Rhizomer:
• Capability: 100%
• Virtuoso FCT:
• Capability: 100%
18. Rhizomer – Task 2
1. Click menu “ProductType” and then “Sheeny” submenu.
2. Click “Show values” for facet “Product Feature”.
3. Click facet value “stroboscopes”.
4. Type in input “Search Product Feature” “gad...”
5. Select “gadgeteers” from autocomplete
6. Set left side of “Product Property Numeric1”slider to “450”.
2K, 2P, 1H
1K, 1P
1K, 1P
4K, 1P, 1H
1K, 1P, 1H
1K, 2P
20. Results
• Currently, BESDUI applied to:
• Rhizomer
a semantic data exploration tool with facets and pivoting
• Virtuoso FCT
the faceted browser for the Virtuoso RDF data store
• Sieuferd
a general-purpose user interface for relational databases
• PepeSearch
a search interface for querying SPARQL endpoints
21. Results & Conclusions
• Sieuferd the most capable but less performant,
most complex user interface
• PepeSearch the less capable but more performant,
less complex user interface
• Rhizomer best effectiveness/efficiency ratio,
more “goals per second”
Averages
per Tool
Capability
K
(0.2s)
P
(1.1s)
H
(0.4s)
Operator
Count
Time
Time Efficiency
(Capability/Time)
Rhizomer 58% 15.9 10.9 2.6 29.3 16.1 3.60
Virtuoso FCT 54% 20.4 12.7 3.0 36.1 19.3 2.80
Sieuferd 96% 48.7 19.7 2.9 71.3 32.6 2.94
PepeSearch 25% 10.3 5.3 5.3 21.0 10.1 2.48
22. Conclusions
• Importance of benchmarks to drive research in a
domain
• Simple benchmark (too much?) but adoption key
• BSBM useful source of tasks and data
• Synthetic nature results in funny product names like
“waterskiing sharpness horseshoes”
…but no significant impact (no real users)
• Measure UI without having to involve users
• Less reliable but cheaper
• Ideal during early dev stages or to compare tools
23. Future Work
• Continue tasks review and extend set of users tasks
• Consider additional tools:
• Direct manipulation (Explorator, Tabulator,…)
• Interactive Query Building (YASGUI, iSPARQL…)
• Relational data (Cipher, BrioQuery,…)
• …
• Improve metrics to consider users mental effort
• SPARQL command line best UI from a KLM point of view
• Considering GOMS, includes cognitive and perceptual operators
• Compare results with real users tests
• Available as GitHub repository: http://w3id.org/BESDUI
• Please, FORK and CONTRIBUTE!
24. Thank you for your attention
Questions?
rgarcia@diei.udl.cat
http://rhizomik.net/~roberto/
BESDUI Persistent URI:
http://w3id.org/BESDUI
Editor's Notes
Les 11 persones son:
Roberto, Rosa, Marta, Toni, Montse, Paco
Juan M
Afra, Llúcia, Josep Mª
David
Andres, Yenny