The document appears to be a long search query string containing many Danish terms related to education such as "gymnasium", "student", "school", and subject areas. It is searching across various fields and sources for information related to Danish secondary education.
Extraction and reflection: early evolution of the Hymenoptera Anatomy OntologyKatja C. Seltmann
10 min presentation from the 2009 Entomological Society of America Meeting describing the extraction of classes and labels from OCRed journal articles found on the Biodiversity Heritage Library and the building of the Hymenoptera Anatomy Ontology (http://hymao.org)
Andreas Zeller's keynote at the 1st Intl Fuzzing workshop 2022 at NDSS: https://fuzzingworkshop.github.io/program.html
Do you fuzz your own program, or do you fuzz someone else's program? The answer to this question has vast consequences on your view on fuzzing. Fuzzing someone else's program is the typical adverse "security tester" perspective, where you want your fuzzer to be as automatic and versatile as possible. Fuzzing your own code, however, is more like a traditional tester perspective, where you may assume some knowledge about the program and its context, but may also want to _exploit_ this knowledge - say, to direct the fuzzer to critical locations.
In this talk, I detail these differences in perspectives and assumptions, and highlight their consequences for fuzzer design and research. I also highlight cultural differences in the research communities, and what happens if you submit a paper to the wrong community. I close with an outlook into our newest frameworks, set to reconcile these perspectives by giving users unprecedented control over fuzzing, yet staying fully automatic if need be.
Bio: Andreas Zeller is faculty at the CISPA Helmholtz Center for Information Security and a professor for Software Engineering at Saarland University, both in Saarbrücken, Germany. His research on automated debugging, mining software archives, specification mining, and security testing has won several awards for its impact in academia and industry. Zeller is an ACM Fellow, an IFIP Fellow, an ERC Advanced Grant Awardee, and holds an ACM SIGSOFT Outstanding Research Award.
The Hidden Empires of Malware with TLS Certified Hypotheses and Machine LearningRyan Kovar
The “threat hunting” landscape has drastically changed due to the increase in encrypted transport layer security (TLS) Internet traffic. The days of adversaries registering domains with their given names are gone, and malicious actors increasingly use malware that takes advantage of TLS encryption to hide their tracks. Yet, even in this brave new world of altered tactics, techniques, and procedures, adversaries leave clues that can expose their infrastructure. To find these clues, however, blue teams need to learn some new tricks. This talk focuses on expanding on techniques that have been researched and presented at various conferences by Mark Parsons, and specifically on his methods for using TLS certificates to find malicious malware infrastructure. We will build on Parsons’ body of work and show how his approach to malware certificate hunting can be expanded to detect instances of PowerShell Empire servers that have self-generated SSL certifications on port 443 and 8080. These certificates have a unique fingerprint that can be detected by leveraging tools like zmap/zgrep, python, and statistics/machine learning. The results of this research will show how network defenders can find previously unknown instances of malicious infrastructure communicating with their network and prevent them in the future. Finally, we will discuss our creation of hypotheses, codes and techniques, and methods of validation for verification. We’ll then release our tools and methodology for use by the community to explore other potential “hidden empires” of malware
The discussion on automated tests is hot topic. The approach has same number of advocates and skeptics. More and more tools eases testing, but also introduces a fundamental question: what, when and how to test? Practise and experience let's answer those questions or guide in the right direction.
In this talk usage examples of unit, functional and behavioral tests will be shown. Importance of properly handling dependencies and mocking them will be discussed as well. But most of important part will be hints on how to write code, that could be tested automaticaly.
Slides are available in interactive mode here: http://tdd.sznapka.pl/
Extraction and reflection: early evolution of the Hymenoptera Anatomy OntologyKatja C. Seltmann
10 min presentation from the 2009 Entomological Society of America Meeting describing the extraction of classes and labels from OCRed journal articles found on the Biodiversity Heritage Library and the building of the Hymenoptera Anatomy Ontology (http://hymao.org)
Andreas Zeller's keynote at the 1st Intl Fuzzing workshop 2022 at NDSS: https://fuzzingworkshop.github.io/program.html
Do you fuzz your own program, or do you fuzz someone else's program? The answer to this question has vast consequences on your view on fuzzing. Fuzzing someone else's program is the typical adverse "security tester" perspective, where you want your fuzzer to be as automatic and versatile as possible. Fuzzing your own code, however, is more like a traditional tester perspective, where you may assume some knowledge about the program and its context, but may also want to _exploit_ this knowledge - say, to direct the fuzzer to critical locations.
In this talk, I detail these differences in perspectives and assumptions, and highlight their consequences for fuzzer design and research. I also highlight cultural differences in the research communities, and what happens if you submit a paper to the wrong community. I close with an outlook into our newest frameworks, set to reconcile these perspectives by giving users unprecedented control over fuzzing, yet staying fully automatic if need be.
Bio: Andreas Zeller is faculty at the CISPA Helmholtz Center for Information Security and a professor for Software Engineering at Saarland University, both in Saarbrücken, Germany. His research on automated debugging, mining software archives, specification mining, and security testing has won several awards for its impact in academia and industry. Zeller is an ACM Fellow, an IFIP Fellow, an ERC Advanced Grant Awardee, and holds an ACM SIGSOFT Outstanding Research Award.
The Hidden Empires of Malware with TLS Certified Hypotheses and Machine LearningRyan Kovar
The “threat hunting” landscape has drastically changed due to the increase in encrypted transport layer security (TLS) Internet traffic. The days of adversaries registering domains with their given names are gone, and malicious actors increasingly use malware that takes advantage of TLS encryption to hide their tracks. Yet, even in this brave new world of altered tactics, techniques, and procedures, adversaries leave clues that can expose their infrastructure. To find these clues, however, blue teams need to learn some new tricks. This talk focuses on expanding on techniques that have been researched and presented at various conferences by Mark Parsons, and specifically on his methods for using TLS certificates to find malicious malware infrastructure. We will build on Parsons’ body of work and show how his approach to malware certificate hunting can be expanded to detect instances of PowerShell Empire servers that have self-generated SSL certifications on port 443 and 8080. These certificates have a unique fingerprint that can be detected by leveraging tools like zmap/zgrep, python, and statistics/machine learning. The results of this research will show how network defenders can find previously unknown instances of malicious infrastructure communicating with their network and prevent them in the future. Finally, we will discuss our creation of hypotheses, codes and techniques, and methods of validation for verification. We’ll then release our tools and methodology for use by the community to explore other potential “hidden empires” of malware
The discussion on automated tests is hot topic. The approach has same number of advocates and skeptics. More and more tools eases testing, but also introduces a fundamental question: what, when and how to test? Practise and experience let's answer those questions or guide in the right direction.
In this talk usage examples of unit, functional and behavioral tests will be shown. Importance of properly handling dependencies and mocking them will be discussed as well. But most of important part will be hints on how to write code, that could be tested automaticaly.
Slides are available in interactive mode here: http://tdd.sznapka.pl/
Hebrew Bible as Data: Laboratory, Sharing, LessonsDirk Roorda
Recently, the Hebrew Bible has been published online as a database. We show what you can do with it, and how to share your results with others. Work by the Amsterdam scholars of the Eep Talstra Centre for Bible and Computer, supported by CLARIN-NL.
Talk to me – Chatbots und digitale Assistenteninovex GmbH
Menschliche Kommunikation folgt zwar einer ganzen Reihe von Regeln, diese lassen sich aber schwer formalisieren. Nicht zuletzt deshalb, weil in unseren Interaktionen immer auch eine Fülle von Welt- und implizitem Kontextwissen eine Rolle spielt. Rein regelbasierte Chatbots sind daher nicht nur äußert komplex in der Programmierung, sondern stoßen in vielen Anwendungsbereichen schnell an ihre Grenzen.
In diesem Vortrag gab Anna Weißhaar einen Überblick über die aktuellen Lösungen und Herausforderungen im Bereich digitale Assistenten. Der Fokus lag dabei auf Ansätzen, die Chatbots „chatty“ machen, sie also möglichst adäquat auf im Voraus unbekannte Nutzereingaben reagieren zu lassen.
Event: inovex Meetup: Das Unvorhersehbare vorhersagen: Zeitreihen und Chatbots, 26.03.2019
Speaker: Anna Weißhaar (inovex)
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
PyLadies Talk: Learn to love the command line!Blanca Mancilla
This talks aims to uncover some of the magic powers of scripting and the command line.
I'll share with you some of my experience using the shell to schedule backups of a git repository or to find strings in files of unknown name and location.
And then you might see that it is a tough love!
Collabnix Community conduct webinar on regular basis. Swapnasagar Pradhan, an engineer from VISA delivered a talk on Traefik this January 11th 2020. Check this out.
Blueprint Series: Banking In The Cloud – Ultra-high Reliability ArchitecturesMatt Stubbs
Data architecture for a challenger bank.Speaker: Jason Maude, Head of Technology Advocacy, Starling BankSpeaker Bio: Jason Maude is a coder, coach, and public speaker. He has over a decade of experience working in the financial sector, primarily in creating and delivering software. He is passionate about explaining complex technical concepts to those who are convinced that they won't be able to understand them. He currently works at Starling Bank as their Head of Technology Advocacy and host of the Starling podcast.Filmed at Skills Matter/Code Node London on 9th May 2019 as part of the Big Data LDN Meetup Blueprint Series.Meetup sponsored by DataStax.
Speed Up Your Apache Cassandra™ Applications: A Practical Guide to Reactive P...Matt Stubbs
Speaker: Cedrick Lunven, Developer Advocate, DataStax
Speaker Bio: Cedrick is a Developer Advocate at DataStax where he finds opportunities to share his passions by speaking about developing distributed architectures and implementing reference applications for developers. In 2013, he created FF4j, an open source framework for Feature Toggle which he still actively maintains. He is now contributor in JHipster team.
Talk Synopsis: We have all introduced more or less functional programming and asynchronous operations into our applications in order to speed up and distribute treatments (e.g., multi-threading, future, completableFuture, etc.). To build truly non-blocking components, optimize resource usage, and avoid "callback hell" you have to think reactive—everything is an event.
From the frontend UI to database communications, it’s now possible to develop Java applications as fully reactive with frameworks like Spring WebFlux and Reactor. With high throughput and tunable consistency, applications built on top of Apache Cassandra™ fit perfectly within this pattern.
DataStax has been developing Apache Cassandra drivers for years, and in the latest version of the enterprise driver we introduced reactive programming.
During this session we will migrate, step by step, a vanilla CRUD Java service (SpringBoot / SpringMVC) into reactive with both code review and live coding. Bring home a working project!
Filmed at Skills Matter/Code Node London on 9th May 2019 as part of the Big Data LDN Meetup Blueprint Series.
Meetup sponsored by DataStax.
Blueprint Series: Expedia Partner Solutions, Data PlatformMatt Stubbs
Join Anselmo for an engaging overview of the new end-to-end data architecture at Expedia Group, taking a journey through cloud and on-prem data lakes, real-time and batch processes and streamlined access for data producers and consumers. Find out how the new architecture unifies a complex mix of data sources and feeds the data science development cycle. Expedia might appear to be a market-leading travel company – in reality, it’s a highly successful technology and data science company.
Blueprint Series: Architecture Patterns for Implementing Serverless Microserv...Matt Stubbs
Richard Freeman talks about how the data science team at JustGiving built KOALA, a fully serverless stack for real-time web analytics capture, stream processing, metrics API, and storage service, supporting live data at scale from over 26M users. He discusses recent advances in serverless computing, and how you can implement traditionally container-based microservice patterns using serverless-based architectures instead. Deploying Serverless in your organisation can dramatically increase the delivery speed, productivity and flexibility of the development team, while reducing the overall running, DevOps and maintenance costs.
Big Data LDN 2018: DATABASE FOR THE INSTANT EXPERIENCEMatt Stubbs
Date: 14th November 2018
Location: Customer Experience Theatre
Time: 12:30 - 13:00
Speaker: David Maitland
Organisation: Redis Labs
About: This session will cover the technology underpinning at the software infrastructure level required to deliver the instant experience to the end user and enterprises alike. Use cases and value derived by major brands will be shared in this insightful session based the world's most loved database REDIS.
Big Data LDN 2018: BIG DATA TOO SLOW? SPRINKLE IN SOME NOSQLMatt Stubbs
Date: 14th November 2018
Location: Customer Experience Theatre
Time: 11:50 - 12:20
Speaker: Perry Krug
Organisation: Couchbase
About: Who wants to see an ad today for the shoes they bought last week? Everyone knows that customer experience is driven by data: don't waste an opportunity to get them the right data at the right time. Real-time results are critical, but raw speed isn't everything: you need power and flexibility to react to changes on the fly. Come learn how market-leading enterprises are using Couchbase as their speed layer for ingestion, incremental view and presentation layers alongside Kafka, Spark and Hadoop to liberate their data lakes.
Big Data LDN 2018: ENABLING DATA-DRIVEN DECISIONS WITH AUTOMATED INSIGHTSMatt Stubbs
Date: 13th November 2018
Location: Customer Experience Theatre
Time: 11:50 - 12:20
Speaker: Charlotte Emms
Organisation: seenit
About: How do you get your colleagues interested in the power of data? Taking you through Seenit’s journey using Couchbase's NoSQL database to create a regular, fully automated update in an easily digestible format.
Big Data LDN 2018: DATA MANAGEMENT AUTOMATION AND THE INFORMATION SUPPLY CHAI...Matt Stubbs
Date: 14th November 2018
Location: Governance and MDM Theatre
Time: 10:30 - 11:00
Speaker: Mike Ferguson
Organisation: IBS
About: For most organisations today, data complexity has increased rapidly. In the area of operations, we now have cloud and on-premises OLTP systems with customers, partners and suppliers accessing these applications via APIs and mobile apps. In the area of analytics, we now have data warehouse, data marts, big data Hadoop systems, NoSQL databases, streaming data platforms, cloud storage, cloud data warehouses, and IoT-generated data being created at the edge. Also, the number of data sources is exploding as companies ingest more and more external data such as weather and open government data. Silos have also appeared everywhere as business users are buying in self-service data preparation tools without consideration for how these tools integrate with what IT is using to integrate data. Yet new regulations are demanding that we do a better job of governing data, and business executives are demanding more agility to remain competitive in a digital economy. So how can companies remain agile, reduce cost and reduce the time-to-value when data complexity is on the up?
In this session, Mike will discuss how companies can create an information supply chain to manufacture business-ready data and analytics to reduce time to value and improve agility while also getting data under control.
Date: 13th November 2018
Location: Governance and MDM Theatre
Time: 12:30 - 13:00
Organisation: Immuta
About: Artificial intelligence is rising in importance, but it’s also increasingly at loggerheads with data protection regimes like the GDPR—or so it seems. In this talk, Sophie will explain where and how AI and GDPR conflict with one another, and how to resolve these tensions.
More Related Content
Similar to Big Data LDN 2016: Making Sense of Big Data with Open Source Search
Hebrew Bible as Data: Laboratory, Sharing, LessonsDirk Roorda
Recently, the Hebrew Bible has been published online as a database. We show what you can do with it, and how to share your results with others. Work by the Amsterdam scholars of the Eep Talstra Centre for Bible and Computer, supported by CLARIN-NL.
Talk to me – Chatbots und digitale Assistenteninovex GmbH
Menschliche Kommunikation folgt zwar einer ganzen Reihe von Regeln, diese lassen sich aber schwer formalisieren. Nicht zuletzt deshalb, weil in unseren Interaktionen immer auch eine Fülle von Welt- und implizitem Kontextwissen eine Rolle spielt. Rein regelbasierte Chatbots sind daher nicht nur äußert komplex in der Programmierung, sondern stoßen in vielen Anwendungsbereichen schnell an ihre Grenzen.
In diesem Vortrag gab Anna Weißhaar einen Überblick über die aktuellen Lösungen und Herausforderungen im Bereich digitale Assistenten. Der Fokus lag dabei auf Ansätzen, die Chatbots „chatty“ machen, sie also möglichst adäquat auf im Voraus unbekannte Nutzereingaben reagieren zu lassen.
Event: inovex Meetup: Das Unvorhersehbare vorhersagen: Zeitreihen und Chatbots, 26.03.2019
Speaker: Anna Weißhaar (inovex)
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
PyLadies Talk: Learn to love the command line!Blanca Mancilla
This talks aims to uncover some of the magic powers of scripting and the command line.
I'll share with you some of my experience using the shell to schedule backups of a git repository or to find strings in files of unknown name and location.
And then you might see that it is a tough love!
Collabnix Community conduct webinar on regular basis. Swapnasagar Pradhan, an engineer from VISA delivered a talk on Traefik this January 11th 2020. Check this out.
Blueprint Series: Banking In The Cloud – Ultra-high Reliability ArchitecturesMatt Stubbs
Data architecture for a challenger bank.Speaker: Jason Maude, Head of Technology Advocacy, Starling BankSpeaker Bio: Jason Maude is a coder, coach, and public speaker. He has over a decade of experience working in the financial sector, primarily in creating and delivering software. He is passionate about explaining complex technical concepts to those who are convinced that they won't be able to understand them. He currently works at Starling Bank as their Head of Technology Advocacy and host of the Starling podcast.Filmed at Skills Matter/Code Node London on 9th May 2019 as part of the Big Data LDN Meetup Blueprint Series.Meetup sponsored by DataStax.
Speed Up Your Apache Cassandra™ Applications: A Practical Guide to Reactive P...Matt Stubbs
Speaker: Cedrick Lunven, Developer Advocate, DataStax
Speaker Bio: Cedrick is a Developer Advocate at DataStax where he finds opportunities to share his passions by speaking about developing distributed architectures and implementing reference applications for developers. In 2013, he created FF4j, an open source framework for Feature Toggle which he still actively maintains. He is now contributor in JHipster team.
Talk Synopsis: We have all introduced more or less functional programming and asynchronous operations into our applications in order to speed up and distribute treatments (e.g., multi-threading, future, completableFuture, etc.). To build truly non-blocking components, optimize resource usage, and avoid "callback hell" you have to think reactive—everything is an event.
From the frontend UI to database communications, it’s now possible to develop Java applications as fully reactive with frameworks like Spring WebFlux and Reactor. With high throughput and tunable consistency, applications built on top of Apache Cassandra™ fit perfectly within this pattern.
DataStax has been developing Apache Cassandra drivers for years, and in the latest version of the enterprise driver we introduced reactive programming.
During this session we will migrate, step by step, a vanilla CRUD Java service (SpringBoot / SpringMVC) into reactive with both code review and live coding. Bring home a working project!
Filmed at Skills Matter/Code Node London on 9th May 2019 as part of the Big Data LDN Meetup Blueprint Series.
Meetup sponsored by DataStax.
Blueprint Series: Expedia Partner Solutions, Data PlatformMatt Stubbs
Join Anselmo for an engaging overview of the new end-to-end data architecture at Expedia Group, taking a journey through cloud and on-prem data lakes, real-time and batch processes and streamlined access for data producers and consumers. Find out how the new architecture unifies a complex mix of data sources and feeds the data science development cycle. Expedia might appear to be a market-leading travel company – in reality, it’s a highly successful technology and data science company.
Blueprint Series: Architecture Patterns for Implementing Serverless Microserv...Matt Stubbs
Richard Freeman talks about how the data science team at JustGiving built KOALA, a fully serverless stack for real-time web analytics capture, stream processing, metrics API, and storage service, supporting live data at scale from over 26M users. He discusses recent advances in serverless computing, and how you can implement traditionally container-based microservice patterns using serverless-based architectures instead. Deploying Serverless in your organisation can dramatically increase the delivery speed, productivity and flexibility of the development team, while reducing the overall running, DevOps and maintenance costs.
Big Data LDN 2018: DATABASE FOR THE INSTANT EXPERIENCEMatt Stubbs
Date: 14th November 2018
Location: Customer Experience Theatre
Time: 12:30 - 13:00
Speaker: David Maitland
Organisation: Redis Labs
About: This session will cover the technology underpinning at the software infrastructure level required to deliver the instant experience to the end user and enterprises alike. Use cases and value derived by major brands will be shared in this insightful session based the world's most loved database REDIS.
Big Data LDN 2018: BIG DATA TOO SLOW? SPRINKLE IN SOME NOSQLMatt Stubbs
Date: 14th November 2018
Location: Customer Experience Theatre
Time: 11:50 - 12:20
Speaker: Perry Krug
Organisation: Couchbase
About: Who wants to see an ad today for the shoes they bought last week? Everyone knows that customer experience is driven by data: don't waste an opportunity to get them the right data at the right time. Real-time results are critical, but raw speed isn't everything: you need power and flexibility to react to changes on the fly. Come learn how market-leading enterprises are using Couchbase as their speed layer for ingestion, incremental view and presentation layers alongside Kafka, Spark and Hadoop to liberate their data lakes.
Big Data LDN 2018: ENABLING DATA-DRIVEN DECISIONS WITH AUTOMATED INSIGHTSMatt Stubbs
Date: 13th November 2018
Location: Customer Experience Theatre
Time: 11:50 - 12:20
Speaker: Charlotte Emms
Organisation: seenit
About: How do you get your colleagues interested in the power of data? Taking you through Seenit’s journey using Couchbase's NoSQL database to create a regular, fully automated update in an easily digestible format.
Big Data LDN 2018: DATA MANAGEMENT AUTOMATION AND THE INFORMATION SUPPLY CHAI...Matt Stubbs
Date: 14th November 2018
Location: Governance and MDM Theatre
Time: 10:30 - 11:00
Speaker: Mike Ferguson
Organisation: IBS
About: For most organisations today, data complexity has increased rapidly. In the area of operations, we now have cloud and on-premises OLTP systems with customers, partners and suppliers accessing these applications via APIs and mobile apps. In the area of analytics, we now have data warehouse, data marts, big data Hadoop systems, NoSQL databases, streaming data platforms, cloud storage, cloud data warehouses, and IoT-generated data being created at the edge. Also, the number of data sources is exploding as companies ingest more and more external data such as weather and open government data. Silos have also appeared everywhere as business users are buying in self-service data preparation tools without consideration for how these tools integrate with what IT is using to integrate data. Yet new regulations are demanding that we do a better job of governing data, and business executives are demanding more agility to remain competitive in a digital economy. So how can companies remain agile, reduce cost and reduce the time-to-value when data complexity is on the up?
In this session, Mike will discuss how companies can create an information supply chain to manufacture business-ready data and analytics to reduce time to value and improve agility while also getting data under control.
Date: 13th November 2018
Location: Governance and MDM Theatre
Time: 12:30 - 13:00
Organisation: Immuta
About: Artificial intelligence is rising in importance, but it’s also increasingly at loggerheads with data protection regimes like the GDPR—or so it seems. In this talk, Sophie will explain where and how AI and GDPR conflict with one another, and how to resolve these tensions.
Big Data LDN 2018: REALISING THE PROMISE OF SELF-SERVICE ANALYTICS WITH DATA ...Matt Stubbs
Date: 13th November 2018
Location: Governance and MDM Theatre
Time: 11:50 - 12:20
Speaker: Mark Pritchard
Organisation: Denodo
About: Self-service analytics promises to liberate business users to perform analytics without the assistance of IT, and this in turn promises to free IT to focus on enhancing the infrastructure.
Join us to learn how data virtualization will allow you to gain real-time access to enterprise-wide data and deliver self-service analytics. We will explore how you can seamlessly unify fragmented data, replace your high-maintenance and high cost data integrations with a single, low-maintenance data virtualization layer; and how you can preserve your data integrity and ensure data lineage is fully traceable.
Big Data LDN 2018: TURNING MULTIPLE DATA LAKES INTO A UNIFIED ANALYTIC DATA L...Matt Stubbs
Date: 13th November 2018
Location: Governance and MDM Theatre
Time: 11:10 - 11:40
Organisation: TIBCO
About: The big data phenomenon continues to accelerate, resulting in multiple data lakes at most organisations. However, according to Gartner, “Through 2019, 90% of the information assets from big data analytic efforts will be siloed and unusable across multiple business processes.”
Are you ready to unleash this data from these silos and deliver the insights your organisation needs to drive compelling customer experiences, innovative new products and optimized operations? In this session you will learn how to apply data virtualisation to: - Access, transform and deliver data from across your lakes, clouds and other data sources - Empower a range of analytic users and tools with all the data they need - Move rapidly to a modern and flexible data architecture for the long run In addition, you will see a demonstration of data virtualisation in action.
Big Data LDN 2018: CONSISTENT SECURITY, GOVERNANCE AND FLEXIBILITY FOR ALL WO...Matt Stubbs
Date: 14th November 2018
Location: Data-Driven Ldn Theatre
Time: 12:30 - 13:00
Organisation: Cloudera
About: The growth of public cloud is reinforcing the need to think more carefully about taking a consistent approach to data governance as technology teams build out a flexible and agile infrastructure to meet the demands of the business.
Join this session to learn more about Cloudera's recommended approach for enterprise-grade security and governance and how to ensure a consistent framework across private, public and on-premises environments.
Big Data LDN 2018: MICROLISE: USING BIG DATA AND AI IN TRANSPORT AND LOGISTICSMatt Stubbs
Date: 14th November 2018
Location: Data-Driven Ldn Theatre
Time: 11:10 - 11:40
Organisation: Microlise
About: Microlise are a leading provider of technology solutions to the transport and logistics industry worldwide. Discover how, with over 400,000 connected assets generating billions of messages a day, Microlise is evolving its platform to bring real-time analytics to its customers to improve safety, security and efficiency outcomes.
Big Data LDN 2018: EXPERIAN: MAXIMISE EVERY OPPORTUNITY IN THE BIG DATA UNIVERSEMatt Stubbs
Date: 14th November 2018
Location: Data-Driven Ldn Theatre
Time: 10:30 - 11:00
Speaker: Anna Matty
Organisation: Experian
About: Today there is a widespread focus on the 'how' in relation to problem solving. How can we gain better knowledge of what consumers want, or need? How can we be more efficient, reduce the cost to serve, or grow the lifetime value of a customer? But, how do you move to a place where you are not only solving a problem, you are redesigning the entire strategic potential of that problem? You are being armed with insight on what the problem is.
Data and innovation offer huge potential to revolutionise all markets. There is an opportunity to be one step ahead of the need, to redesign journeys and enhance enterprise strategies. To do this you need access to the most advanced analytics but also the best quality, including variations and types of data, and then the technology that can act on this insight. Data science can present a unique opportunity for uncovered growth and accelerate your business through strategic innovation – fast. In this session you will hear more about how today's analytics can move from a single task, to an ongoing strategic opportunity. An opportunity that helps you move at the speed of the market and helps you maximise every opportunity.
Big Data LDN 2018: A LOOK INSIDE APPLIED MACHINE LEARNINGMatt Stubbs
Date: 13th November 2018
Location: Data-Driven Ldn Theatre
Time: 13:10 - 13:40
Speaker: Brian Goral
Organisation: Cloudera
About: The field of machine learning (ML) ranges from the very practical and pragmatic to the highly theoretical and abstract. This talk describes several of the challenges facing organisations that want to leverage more of their data through ML, including some examples of the applied algorithms that are already delivering value in business contexts.
Big Data LDN 2018: DEUTSCHE BANK: THE PATH TO AUTOMATION IN A HIGHLY REGULATE...Matt Stubbs
Date: 13th November 2018
Location: Data-Driven Ldn Theatre
Time: 12:30 - 13:00
Speaker: Paul Wilkinson, Naveen Gupta
Organisation: Cloudera
About: Investment banks are faced with some of the toughest regulatory requirements in the world. In a market where data is increasing and changing at extraordinary rates the journey with data governance never ends.
In this session, Deutsche Bank will share their journey with big data and explain some of the processes and techniques they have employed to prepare the bank for today’s challenges and tomorrow’s opportunities.
Brought to you by Naveen Gupta, VP Software Engineering, Deutsche Bank and Paul Wilkinson, Principal Solutions Architect, Cloudera.
Big Data LDN 2018: FROM PROLIFERATION TO PRODUCTIVITY: MACHINE LEARNING DATA ...Matt Stubbs
Date: 14th November 2018
Location: Self-Service Analytics Theatre
Time: 13:50 - 14:20
Speaker: Stephanie McReynolds
Organisation: Alation
About: Raw data is proliferating at an enormous rate. But so are our derived data assets - hundreds of dashboards, thousands of reports, millions of transformed data sets. Self-service analytics have ensured that this noise is making it increasingly hard to understand and trust data for decision-making. This trust gap is holding your organisation back from business outcomes.
European analytics leaders have found a way to close the gap between data and decision-making. From MunichRe to Pfizer and Daimler, analytics teams are adopting data catalogues for thousands of self-service analytics users.
Join us in this session to hear how data catalogues that activate data by incorporating machine learning can:
• Increase analyst productivity 20-40%
• Boost the understanding of the nuances of data and
• Establish trust in data-driven decisions with agile stewardship
Big Data LDN 2018: DATA APIS DON’T DISCRIMINATEMatt Stubbs
Date: 13th November 2018
Location: Self-Service Analytics Theatre
Time: 15:50 - 16:20
Speaker: Nishanth Kadiyala
Organisation: Progress
About: The exploding API economy, combined with an advanced analytics market projected to reach $30 billion by 2019, is forcing IT to expose more and more data through APIs. Business analysts, data engineers, and data scientists are still not happy because their needs never really made it into the existing API strategies. This is because most APIs are designed for application integration, but not for the data workers who are looking for APIs that facilitate direct data access to run complex analytics. Data APIs are specifically designed to provide that frictionless data access experience to support analytics across standard interoperable interfaces such as OData (REST) or ODBC/JDBC (SQL). Consider expanding your API strategy to service the developers with open analytics in this $30 billion market.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Big Data LDN 2016: Making Sense of Big Data with Open Source Search
1. Charlie Hull - Managing Director, Flax
3rd
November 2016
Big Data London
charlie@flax.co.uk
www.flax.co.uk/blog
+44 (0) 8700 118334
Twitter: @FlaxSearch
Making sense of Big Data
with open source search
2. We build, tune and support fast, accurate and highly scalable
search, analytics and Big Data applications
We use (and create) open source software
We're independent, honest and have 15+ years experience
We also:
– Run and attend many events & conferences
– Write extensively about search & related matters
– Train and mentor
3.
4. Why
A shared history
Choosing a technology
How
Things to consider
Something clever
Conclusions
Making sense of Big Data
@FlaxSearch
7. What's the use of Big Data?
We're all searchers now...
Why?
@FlaxSearch
8. What's the use of Big Data?
We're all searchers now...
Analytics built on search
Why?
@FlaxSearch
9. What's the use of Big Data?
We're all searchers now...
Analytics built on search
Sorting the wheat from the chaff
Why?
@FlaxSearch
10. Search comes from Web search (the original Big Data?)
A shared history
@FlaxSearch
11. Search comes from Web search (the original Big Data?)
A shared history
@FlaxSearch
1999
Doug Cutting
12. Search comes from Web search (the original Big Data?)
A shared history
@FlaxSearch
1999 2003
Doug Cutting
13. Search comes from Web search (the original Big Data?)
A shared history
@FlaxSearch
1999 2003 2011
Doug Cutting
14. Search comes from Web search (the original Big Data?)
A shared history
@FlaxSearch
1999 2003 2011
Doug Cutting
15. You could use (if you like writing code)
Choosing a technology
@FlaxSearch
16. You could use (if you like writing code)
Apache
– Mature & stable & open development
– Huge list of features, plugins, integrations, resources...
Choosing a technology
@FlaxSearch
17. You could use (if you like writing code)
Apache
– Mature & stable & open development
– Huge list of features, plugins, integrations, resources...
– Newer & funkier & open-ish
– Easier to get started with & an integrated stack
Choosing a technology
@FlaxSearch
18. You could use (if you like writing code)
Apache
– Mature & stable & open development
– Huge list of features, plugins, integrations, resources...
– Newer & funkier & open-ish
– Easier to get started with & an integrated stack
The secret? Solr & Elasticsearch are pretty similar
Choosing a technology
@FlaxSearch
19. Build it yourself
– Get trained
– Read some books & websites
– Go to a Meetup (plug!)
How?
@FlaxSearch
20. Build it yourself
– Get trained
– Read some books & websites
– Go to a Meetup (plug!)
Get some help
– Employ an expert (good luck with that)
– Talk to people like us
How?
@FlaxSearch
21. Build it yourself
– Get trained
– Read some books & websites
– Go to a Meetup (plug!)
Get some help
– Employ an expert (good luck with that)
– Talk to people like us
Buy a product or subscription
How?
@FlaxSearch
22. Build it yourself
– Get trained
– Read some books & websites
– Go to a Meetup (plug!)
Get some help
– Employ an expert (good luck with that)
– Talk to people like us
Buy a product or subscription
How?
@FlaxSearch
27. Sizing & scale
– The 50m rule of thumb
– 'It depends'
– Test & prototype
Things to consider
@FlaxSearch
28. Sizing & scale
– The 50m rule of thumb
– 'It depends'
– Test & prototype
Gotchas
– ES-Hadoop
– Fast moving ES APIs
– Memory & JVM tuning
– Distributed systems are hard
Things to consider
@FlaxSearch
29. When real time isn't
– 'real time' often means 'faster indexing'
– Most streaming architectures end in a relatively static
index
Things to consider (2)
@FlaxSearch
30. When real time isn't
– 'real time' often means 'faster indexing'
– Most streaming architectures end in a relatively static
index
Queries considered harmful
– Remember it's a search engine
– A bad query can kill your cluster
– Boolean that isn't
– Some big queries
Things to consider (2)
@FlaxSearch
31. ((((aymnasoesoeekzueazez* OR 'aymnasoeeleveenes lanssueaanosazoun*' OR 'aymnasoelæeeenes oseæzslæeeefueenona*' OR 'aymnasoeskuleenes oseæzslæeeefueenona*' OR 'aymnasoeskuleenes læeeefueenona*' OR 'aymnasoeenes mazemazoklæeeefueenona*' OR aymnasoeeefuem* OR xeasona:unasumsussannelse* OR
((aymnasoum* OR aymnasoee*) NEAR/15 (kaeakzeefukus* OR elevzal*)) OR (((aymnasoe* OR unasumsussannelse* OR 'aymnasoal* ussannelse*' OR aymnasoum) NEAR/14 (kaeakzeekeav* OR asaanaskeav* OR beuaeebezalona* OR besqae* OR nesskæe*)) AND aeuuqfoels:z_ms_lanss) OR
xeasona:unasumsussannelse* OR (aymnasoe* NEAR/9 valafaa*) OR (xeasona:~Gymnasoum) OR (aymnasoee* NEAR/9 xanselsskule*) OR ((~HF OR aymnasoe* OR aymnasoum* OR unasumsussannelse*) NEAR/9 lekzoe*) OR xeasona:~Gymnasoum OR (((aymnasoez* OR aymnasoum*) NEAR/14 unasumsussannelse*) AND
(aeuuqfoels:z_ms_lanss OR aeuuqfoels:z_ms_eea)) OR ('~Danske ~Gymnasoee' AND aeuuqfoels:z_ms_lanss) OR(('~Danske ~Gymnasoee' AND unasumsussannelse*) AND aeuuqfoels:z_ms_lanss) OR ((((aymnasoe* OR aymnasoum* OR unasumsussannelse*) NEAR/14 kaeakzeeskala*) NOT (aeuuqfoels:z_mu_web_1))) OR
((aymnasoe* OR aymnasoum*) NEAR/9 (zaxamezee*)) OR ((unasumsussannelse*) NEAR/15 (ussannelsesumeåse*)) OR (('feemzosens aymnasoum' OR 'sez almene aymnasoum*') AND aeuuqfoels:z_ms_lanss) OR ((((aymnasoe* OR aymnasoum* OR unasumsussannelse* OR 'aymnasoal ussannelse*') NEAR/15
(szusenzeeeksamen* OR kaeakzeeee* OR suqqleeonasfaa* OR suqqleeonaskuesus*)) AND aeuuqfoels:z_ms_lanss)) OR (szusenzeeeksamen* NEAR/19 ('aymnasoale faa' OR suqqleeonasfaa* OR suqqleeonaskuesus*)) OR ((aymnasoe* AND kaeakzeekeav*) AND aeuuqfoels:z_ms_lanss)
OR (aymnasoe* NEAR/14 asaanaskeav*) OR '~EYES DK' OR 'nyz aymnasoum' OR (('uno c' OR 'uno cs' OR ('sanmaeks oz cenzee' NEAR/4 ussannelse*) OR (~EMU NEAR/9 (unseevosnona* OR *quezal* OR xjemmesose* OR websoze*)) OR 'emu.sk*' OR ~SkuleInzea* OR '%læeeeonzea' OR uno?c OR easy?a OR 'sez
szusoeasmonoszeazove syszem*' OR (~SIS NEAR/15 (szusoeuesnona* OR szusoequezal OR 'szusoe onfuemazoun' OR 'szusoe onfuemazoun* syszem*')) OR 'elevqlan.sk' OR elevqlan?sk OR (emu NEAR/15 ('elekzeunosk møseszes unseevosnona*' OR unseevosnona* OR elekzeunosk* OR unseevosnonasquezal* OR
'unseevosnona? quezal*')) OR 'elekzeunosk møseszes fue unseevosnona*' OR 'elekzeunosk møseszes unseevosnona*' OR fuesknonasnezzez* OR 'fuesknonasnezzez.sk' OR fuesknonasnezzez?sk OR mazeeoaleqlazfuem* OR 'uqzaaelse.sk*' OR uqzaaelse?sk* OR 'qeakzokqlassen.sk*' OR qeazokqlassen?sk* OR sekzuenez* OR
sunsxesssazanez* OR ((luaon OR seevee*) NEAR/4 uno) OR ~SkuleInzea* OR 'ussannelsesszazoszok.sk*' OR sekzuenez* OR 'sekzue nez' OR skulekum OR ~SkuleKum* OR ~SkuDa* OR (skusa* NEAR/15 'skuleenes sazabase*') OR ~SkulePeu* OR ussannelsesfueum* OR 'ussannelsesszazoszok.sk*' OR mazeeoaleqlazfuem*
OR 'uno seevee*' OR unoseevee* OR ~Szusoeqlan*) NEAR/14 (*aymnasoez* OR *aymnasoum* OR aymnasoal* OR ~HF OR xf OR xxx OR xanselsaymnas* OR 'zeknosk aymnaso*' OR xanselsaymnaso* OR xanselsaymnaso*)) OR xeasona:unasumsqaelamenz* OR subxeasona:unasumsqaelamenz* OR
(((*aymnaso* OR *aymnasoum* OR aymnasoal* OR ~HF OR xf OR xxx OR xanselsaymnas* OR 'zeknosk aymnaso*' OR xanselsaymnaso* OR købmanssskule OR xanselsskule OR xanselsaymnaso* OR 'noels beuck*' OR zoezaenskulen*) NEAR/14 læeee) NEAR/14 ('uffenzloa* ansaz*' OR aebejssmoljø* OR aebejssvolkåe* OR
*uveeenskumsz* OR løn OR lønfuexanslona*)) OR (aymnasoelæeee* NEAR/14 ('uffenzloa* ansaz*' OR aebejssmoljø* OR aebejssvolkåe* OR *uveeenskumsz* OR løn OR lønfuexanslona*)) OR (allxeasonas:((aymnasoelevee* OR aymnasoum* OR aymnasoum*))) OR ('sansk onszozuz' NEAR/9 aymnasoeqæsaauaok*) OR
(aymnaso* NEAR/4 (xf*)) OR (aymnaso* NEAR/9 (sannelse OR almensannelse OR 'ubloaazueosk faa' OR valafaa OR feemmessqeua* OR qensum OR ((onnuvazoun* OR onnuvaz* OR ovæeksæzzee* OR ovæeksæzzeeo) NEAR/14 unseevosnona))) OR (aymnaso* NEAR/9 (nesskæeona OR sqaee OR unasumsbueameszee OR
ussannelsesbueameszee)) OR (([allxeasonas,subxeasona]:((*aymnasoez* OR *aymnasoum* OR aymnasoal* OR ~HF OR xf OR xxx OR xanselsaymnas* OR 'zeknosk aymnaso*' OR xanselsaymnaso* OR købmanssskule OR xanselsskule OR xanselsaymnaso* OR 'noels beuck' OR zoezaenskulen))) AND ussannelse*) OR
(((*aymnasoez* OR *aymnasoum* OR aymnasoal* OR ~HF OR xf OR xxx OR xanselsaymnas* OR 'zeknosk aymnaso*' OR xanselsaymnaso* OR købmanssskule OR xanselsskule OR xanselsaymnaso* OR 'noels beuck' OR zoezaenskulen) NEAR/10 eekzue*) AND (aymnasoelevee*)) OR (((*aymnasoez* OR *aymnasoum* OR
aymnasoal* OR ~HF OR xf OR xxx OR xanselsaymnas* OR 'zeknosk aymnaso*' OR xanselsaymnaso* OR købmanssskule OR xanselsskule OR xanselsaymnaso* OR 'noels beuck' OR zoezaenskulen) NEAR/10 (lekzoe* OR kaeakzeeee* OR åeskaeakzee)) AND (aymnasoelevee*)) OR ((*aymnasoez* OR *aymnasoum* OR
aymnasoal* OR ~HF OR xf OR xxx OR xanselsaymnas* OR 'zeknosk aymnaso*' OR xanselsaymnaso* OR købmanssskule OR xanselsskule OR xanselsaymnaso* OR 'noels beuck' OR zoezaenskulen) NEAR/10 ('mubol lab*' OR 'eneeao xuesens*' OR eexveevslov* OR voeksumxesee* OR unseevosnonasmon* OR selveje* OR
fonansluv* OR 'uffenzloa* sekzue*' OR eeaoun OR szeukzuekummossoun* OR kummuneeefuem* OR kummunaleefuem* OR szeukzueeefuem* OR faafueenona* OR faafuebuns* OR zollosseeqeæsenzanz* OR aebejsszos* OR klassekvuzoenz* OR *feavæe* OR feafals* OR feafalssqeucenz* OR aennemføesel* OR
eekeuzzeeona* OR zollæaslosze* OR qjæk* OR læsnona* OR læseqlan* OR unseevosnona* OR faaloaxes OR 'faaloa* noveau*' OR valafaa* OR fællesfaa* OR zolvala* OR nazuevosenskab* OR samfunssvosenskab* OR oseæz* OR eksame* OR 'uqeeazoun saasvæek')) OR (xeasona:aymnasoe* AND aeuuqfoels:z_ms_lanss)
OR (subxeasona:aymnasoe* AND aeuuqfoels:z_ms_lanss) OR zuqocs:'aymnasoale ussannelsee' AND *aymnaso*[3..]) AND (ueoaonazue:=aae OR ueoaonazue:=bny OR ueoaonazue:=bee OR ueoaonazue:=bya OR ueoaonazue:=buu OR ueoaonazue:=ccw OR ueoaonazue:=sea OR ueoaonazue:=vvs OR ueoaonazue:=sku OR
ueoaonazue:=sob OR ueoaonazue:=soo OR ueoaonazue:=sju OR ueoaonazue:=slm OR ueoaonazue:=efs OR ueoaonazue:=elc OR ueoaonazue:=eex OR ueoaonazue:=faa OR ueoaonazue:=fmc OR ueoaonazue:=fuz OR ueoaonazue:=fus OR ueoaonazue:=fuq OR ueoaonazue:=fuk OR ueoaonazue:=foz OR ueoaonazue:=ful OR
ueoaonazue:=ffu OR ueoaonazue:=fes OR ueoaonazue:=feo OR ueoaonazue:=fys OR ueoaonazue:=aym OR ueoaonazue:=xsk OR ueoaonazue:=xsn OR ueoaonazue:=xkq OR ueoaonazue:=xko OR ueoaonazue:=xkl OR ueoaonazue:=kum OR ueoaonazue:=xku OR ueoaonazue:=xkl OR ueoaonazue:=xkv OR ueoaonazue:=xke
OR ueoaonazue:=xku OR ueoaonazue:=xkz OR ueoaonazue:=xks OR ueoaonazue:=xuj OR ueoaonazue:=ona OR ueoaonazue:=jmo OR ueoaonazue:=juu OR ueoaonazue:=lav OR ueoaonazue:=lbf OR ueoaonazue:=lbn OR ueoaonazue:=lbs OR ueoaonazue:=lbu OR ueoaonazue:=mum OR ueoaonazue:=mma OR
ueoaonazue:=esn OR ueoaonazue:=suq OR ueoaonazue:=suc OR ueoaonazue:=sql OR ueoaonazue:=luu OR ueoaonazue:=mmm OR ueoaonazue:=uns OR ueoaonazue:=ve2 OR ueoaonazue:=vej OR ueoaonazue:=ueu OR ueoaonazue:=kmu OR ueoaonazue:=aeb OR ueoaonazue:=bza OR ueoaonazue:=bes OR
ueoaonazue:=bma OR ueoaonazue:=eks OR ueoaonazue:=onf OR ueoaonazue:=jyq OR ueoaonazue:=kes OR ueoaonazue:=waa OR ueoaonazue:=loc OR ueoaonazue:=efl OR ueoaonazue:=qul OR ueoaonazue:=eel OR ueoaonazue:=bew OR ueoaonazue:=bex OR ueoaonazue:=eoz OR ueoaonazue:=nqq OR ueoaonazue:=skf
OR suuecename:ST 'alzonaez.sk' OR ueoaonazue:=4a2 OR ueoaonazue:=4a1 OR ueoaonazue:=skw OR ueoaonazue:=4a5 OR ueoaonazue:=4a7 OR ueoaonazue:=4a9 OR ueoaonazue:=4ab OR aeuuqfoels:z_ms_eea OR aeuuqfoels:z_ms_uae OR ueoaonazue:='bu+' OR ueoaonazue:='sy+' OR ueoaonazue:='jv+' OR
ueoaonazue:='sa+' OR ueoaonazue:='nu+' OR ueoaonazue:='ue+' OR ueoaonazue:=uek OR ueoaonazue:='24+' OR ueoaonazue:='mx+' OR ueoaonazue:='sj+' OR ueoaonazue:='nv+')) NOT ((xeasona:(('nyz jub' OR 'nuzee: onslans' OR 'nuzee: uslans' OR 'kuez ua ausz' OR 'eunsz o saa' OR 'eunsz o mueaen' OR 'eunsz o
uveemueaen' OR onsbeus* OR luqqemaekes* OR 'åbenz xus*' OR zyveeo OR 'nyz o nuzee' OR 'saaen o saa' OR føsselssaa OR *beylluq* OR 'kaeeoeee kuez' OR cozazxoszueoe OR 'nyxesee fea uslansez o kuez fuem'))) OR (aeuuqfoels:z_ms_eea AND wuescuunz:<100) OR xeasona:=valakalensee OR xeasona:=søse OR
(xeasona:((squezmaszee* OR musokaeuqqe* OR 'xae o xøez' OR ~SOSU OR qæsaauasemonae* OR 'åes szusenzeejubolæum*'))) OR (xeasona:=%navne OR xeasona:='%akzuelle %navne' OR xeasona:=%mæekesaae OR xeasona:=%føsselssaae OR xeasona:=%navnenyz OR xeasona:=%søse OR xeasona:=%søs OR
xeasona:=%ansæzzelse OR xeasona:=%ansæzzelsee OR xeasona:=%feazeæselse OR xeasona:=%feazeæselsee OR xeasona:=%nyføsz OR xeasona:=%nyføsze OR xeasona:=%baenesåb OR xeasona:=%søbz OR xeasona:=%kunfoemazoun OR xeasona:=%kunfoemazounee OR xeasona:=%kunfoemansee OR xeasona:=
%beylluqssaa OR xeasona:=%beylluqssaae OR xeasona:=%kubbeebeylluq OR xeasona:=%sølvbeylluq OR xeasona:=%eubonbeylluq OR xeasona:=%aulsbeylluq OR xeasona:=%soamanzbeylluq OR xeasona:=%keunsoamanzbeylluq OR xeasona:=%jeenbeylluq OR xeasona:=%beylluq OR xeasona:=%uesenee OR xeasona:=
%mesalje OR xeasona:=%uslæez OR xeasona:=%svenseqeøve OR xeasona:=%jubolæum OR xeasona:=%jubolæee OR xeasona:=%szusenzee OR xeasona:=%ausoens OR xeasona:=%søssfals OR xeasona:=%ussannelse OR xeasona:=%usnævnz OR xeasona:=%usnævnelse OR xeasona:=%'fylsee åe'OR xeasona:(%'navnloa
navne' OR %'saaens navne' OR %'nyz um navne' OR %'øveoae navne' OR %'navne o nuzee' OR %'navne o saa' OR %'ansee navne' OR %'lukale navne' OR %nyansæzzelse OR "Nyz jub" OR %'eunse saae' OR %'eunse åe' OR %'eunsz o saa' OR %'eunsz o mueaen' OR %'eunse føsselssaae' OR %'eunse zal o mueaen' OR
%'eunse zal o saa' OR %'eunsz zal o mueaen' OR %'eunsz zal o saa' OR %'eunsz sønsaa' OR %'euns saa' OR %'eunse saae' OR %'føsselssaa o saa' OR %'føsselssaa o mueaen' OR %'osaa fylsee' OR %'o mueaen fylsee' OR %'bosæzzelsee ua beaeavelsee' OR %'beaeavelsee ua bosæzzelsee') OR
(qaaename:(navne OR menneskee) AND (xeasona:(usnævnelse OR %jubnyz OR voelse OR beylluq OR velsoanelse OR jubolæum OR juboleeee OR %eeceqzoun OR %uesenee OR %efzeeløn OR %søssfals OR %'ee søs' OR %nekeulua OR %monseues OR %leaaz OR monseleaaz* OR fueskeeleaaz* OR æeesleaaz* OR qeos OR
qeosvonsee* OR %bosæzzelsee OR %beaeavelsee)
OR xeasona:(%nuze AND (%navne OR %mz OR %eksamen OR %szusenzee OR %nyussannese OR %ussannez OR %svenseqeøve)) OR (xeasona:%svense AND svenseqeøve)
OR xeasona:='%nye %assoszenzee' OR xeasona:='%nye %cxaufføeee' OR xeasona:='%nye %elekzeokeee' OR xeasona:='%nye %aaezneee' OR xeasona:='%nye %xjælqeee' OR xeasona:='%nye %xånsvæekeee' OR xeasona:='%nye %onaenoøeee' OR xeasona:='%nye %labueanzee' OR xeasona:='%nye %lansmæns' OR
xeasona:='%nye %læaee' OR xeasona:='%nye %læeeee' OR xeasona:='%nye %maleee' OR xeasona:='nye mekanokeee' OR xeasona:='%nye %meszee' OR xeasona:='%nye %munzøeee' OR xeasona:='nye %mueeee' OR xeasona:='%nye %uqeeazøeee' OR xeasona:='%nye %qeæszee' OR xeasona:='%nye %eåsaoveee' OR
xeasona:='%nye %qæsaauaee' OR xeasona:='%nye %slaazeee' OR xeasona:='%nye %smese' OR xeasona:='%nye %syaeqlejeeskee' OR xeasona:='%nye %zeknokeee' OR xeasona:='%nye %zeeaqeuzee' OR xeasona:='%nye %zømeeee' OR xeasona:='%nye %økunumee')) OR xeasona:=%'?0 åe' OR xeasona:=%'?5 åe' OR
xeasona:="I DAG" OR xeasona:="I MORGEN" OR xeasona:="DAGEN I DAG" OR xeasona:ST [%monseues, %føsselssaa] OR %'xus seunnona maeaeezxe qå' OR %'fue sen kunaeloae belønnonasmesalje' OR %'følaense zakkese fue usnævnelse zol' OR xeasona:=%'se zakkese seunnonaen' OR xeasona:=%'xus seunnonaen' OR
xeasona:(%'o ausoens' AND seunnona) OR xeasona:=%'o ausoens xus seunnonaen' OR xeasona:=%'o ausoens' OR xeasona:=%ausoens OR xeasona:=%'seunnonaen zua omus' OR xeasona:=%'zak fue uesenee ua mesaljee' OR (seunnona AND (afskessausoens OR 'zolselona af eosseekuesez' OR 'usnævn* zol eossee af
sannebeua' OR 'zak fue usnævnelsen zol' OR 'xus seunnonaen fue az zakke fue' OR 'zakkese fue eosseekuesez af')) OR q1:(%seunnonaen AND fuezjenszmesalje)) OR (xeasona:=%'sez skee' OR xeasona:=%'sez skee:' OR xeasona:=%buakalensee OR xeasona:=%kunsz OR xeasona:(ST %'sez skee' AND (%mansaa OR
%zoessaa OR %unssaa OR %zuessaa OR %feesaa OR %løesaa OR %sønsaa)) OR xeasona:(%'sez skee' AND ("AUGUSTENBORG" OR "GRÅSTEN" OR "SØNDERBORG")) OR
xeasona:(ST %sez AND (%'sez skee o' OR %'sez skee nezuq nu' OR %'sez skee uae' OR %'sez skee qå' OR %'sez skee lukalz' OR %'sez skee o saa' OR %'skansonavoen o næsze uae' OR %'kalnsee fue koeke')) OR xeasona:(ST zos AND (zos NEAR/2 szes)) OR (ueoaonazue:ST jv* AND (xeasona:=%'o saa' OR xeasona:=%'o
mueaen')) OR xeasona:=%aeeanaemenzee OR xeasona:=%'fasze aeeanaemenzee' OR xeasona:=%'kummense aeeanaemenzee' OR xeasona:=%kalensee OR xeasona:ST "Kalensee" OR xeasona:ST "KALENDER:" OR xeasona:=%kalenseeen OR xeasona:ST %qlakazen OR xeasona:ST %kulzuekalensee OR xeasona:
(%'qlakazen feesaa' OR kulzuekalensee*) OR xeasona:=%kalenseekloq OR xeasona:(%'squez o weekensen' OR %'zee zona o weekensen') OR (xeasona:ST uaens AND xeasona:'uaens folm o') OR (xeasona:ST %uaen AND xeasona:%'uaen see kummee') OR (xeasona:ST saa AND xeasona:%'saa fue saa') OR
(ueoaonazue:=BMA AND xeasona:ST %'xuls øje mes' AND seczounname:'auk.sk') OR xeasona:=%åbnonaszosee OR xeasona:=%usszollona OR xeasona:=%usszollonaen OR xeasona:=%usszollonaee OR (xeasona:ST %usszollonaee AND xeasona:%'usszollonaee o') OR ((xeasona:ST %akzuelle OR xeasona:ST %saaens OR
xeasona:ST %uaens OR xeasona:ST %månesens) AND xeasona:(%usszollona OR %usszollonaee OR %fuzuusszollona OR %fuzuusszollonaee)) OR xeasona:=%kulzueuaen OR xeasona:=%zeazee OR (xeasona:ST %saaens AND xeasona:(%'saaens folm' OR %'saaens kunceez' OR %'saaens zeazee' OR %'saaens zoq' OR
%'saaens usszollona')) OR (xeasona:ST %weekensens AND xeasona:%'weekensens folm') OR (xeasona:ST uaens AND xeasona:%'uaens usvalaze' AND xeasona:(%kunceezee OR %kunsz OR %scene)) OR (xeasona:ST %lukal AND xeasona:(%'lukal kunsz o' OR %'lukal kunsz fea' OR %'lukal kunsz xus' OR 'lukal kunsz qå')) OR
(xeasona:ST %kunsz AND xeasona:(%'kunsz o' OR %'kunsz xus' OR %'kunsz fea' OR %'kunsz qå')) OR xeasona:=%folm OR (xeasona:ST %folm AND xeasona:(%'folm o' OR %'folm füe senoueen')) OR (xeasona:ST %zv AND xeasona:(%'zv o saa' OR %'zv-fueumzale' OR %'zv-umzale')) OR xeasona:=%kunceez OR
(xeasona:ST %kunceez AND xeasona:(%'kunceez o' OR '%kunceez mes' OR %'kunceez qå' OR %'kunceez ves' OR %'kunceez fue' OR %'kunceezee klassosk' OR %'kunceezee eyzmosk')) OR (xeasona:ST %uqeea AND xeasona:(%'uqeea o' OR %'uqeea qå' OR %'uqeea mes'
OR %'uqeea ves' OR %'uqeea fue')) OR xeasona:ST %eevy AND xeasona:(%'eevy o' OR %'eevy qå')) OR (xeasona:ST %zeazee AND xeasona:(%'zeazee qå' OR %'zeazee fea' OR %'zeazee o' OR %'zeazee fue' OR %'zeazee um')) OR (xeasona:ST %'auose:' AND
(suuecename:'obyen.sk' OR xeasona:%weekens)) OR xeasona:=%ausszjeneszee OR xeasona:=ausszjenesze OR xeasona:('%saaens ausszjeneszee' OR 'ausszjenesze o' OR 'ausszjenesze qå' OR 'ausszjenesze sønsaa' OR %'ausszjenesze fue' OR 'ausszjenesze mes' OR
ausszjeneszelosze* OR 'onaen ausszjenesze' OR 'sønsaaens ausszjeneszee') OR 'see ee aeazos asaana zol kunceezen' OR %'see ee aeazos asaana zol aeeanaemenzez' OR %'see ee aeazos asaana zol fueeseaaez' OR %'see ee aeazos asaana zol museez' OR %'see ee
aeazos asaana zol fueeszollonaen' OR %'see ee aeazos asaana zol usszollonaen') OR v_emnee:folm OR v_emnee:musok OR xeasona:=leaaz OR aeuuqfoels:z_ms_uae OR v_emnee:feasuez_squez OR (qlaces:uslans NOT (qlaces:sanmaek OR ueaanosazouns:[Djøf,
'euskolse unoveesozez', 'kummuneenes lanssfueenona', fuebeuaeeumbussmansen, 'szazsfænaslez o veossløselolle', fulkezonaez, 'moljø- ua føsevaeemonoszeeoez', 'Nuezxsose feszoval', qeessenævnez, 'euskolse feszoval', HK, 3F, FOA, 'sez ezoske eås', eoasxusqozalez,
'nuesosk wolm', 'købenxavns unoveesozez', kummune, eezzen, byeez, qulozo, 'wulkeskulen (sanmaek)', eeaoun, lansseez, 'syssansk unoveesozez', 'sanmaeks zeknoske unoveesozez', 'Dez nazounale wuesknonascenzee wue velwæes', 'aela wuuss', 'alzeenazovez (qaezo)',
'købenxavns wunssbøes', 'szazens seeum onszozuz', asvukazsamwunsez, 'onszozuz wue menneskeeezzoaxesee', 'aalbuea unoveesozez', 'sez kunaeloae boblouzek', 'Heelev xusqozal', 'sanske eeaounee', 'sanske qazoenzee', wuebeuaeeeåsez, 'wuesokeona & qensoun',
læaewueenonaen, eneeaoszyeelsen, bulous, sunsxessszyeelsen, 'xvosuvee xusqozal', 'szazens nazuexoszueoske museum', wøsevaeeszyeelsen, 'sansk onsuszeo', 'lansbeua & wøsevaeee', 'Aaexus unoveesozez', DR, 'bøene- ua unasumsqæsaauaeenes lansswuebuns',
'sanmaeks eejsebueeau wueenona', 'aaexus unoveesozezsxusqozal', 'sansk aebejssaoveewueenona', 'Sø- ua xanselseezzen', søwaezsszyeelsen, szazsmonoszeeoez, 'Købenxavns byeez', baamanssqulozoez, wøzex, 'bæeesyazoaz lansbeua', 'usense
unoveesozezsxusqozal', 'syssansk musokkunseevazueoum', 'købenxavns xuvesbaneaåes', venszee, sucoalsemukeazeene, 'lobeeal alloance', 'sansk wulkeqaezo', 'sez kunseevazove wulkeqaezo', enxessloszen, alzeenazovez, 'easokale venszee', 'sanmaeks szazoszok',
'eexveevs ua vækszmonoszeeoez', 'sanmaeks mesoe ua juuenaloszxøjskule', xanselsskule, zeazee, muesaaaes, luuosoana, 'szazens museum wue kunsz', 'zxuevalssens museum', 'ny caelsbeea alyqzuzek', 'nazuexoszueosk museum', 'sez nazounalxoszueoske museum',
'øszee aasvæek', 'saxu wield', 'leau aeuuq', 'euyal unobeew', TDC, 'sanske maeozome', yuusee, 'maeesk lone', numa, 'xuzel s?analezeeee', lunsbeckwunsen, cequs, 'Wolloam semanz', 'sez kunaeloae zeazee*', wuseeszuwwen, Bueaeeseevoce, aeunswus, 'Danosx ceuwn',
omeecu, 'onaenoøewueenonaen IDA', 'ughannelses- ua wuesknonasmonoszeeoez', Cuwo, 'H. lunsbeck', 'Aebejseebevæaelsens eexveevseås', 'eåsez wue sucoalz ughazze', 'kløwzen weszoval', 'seb qensoun', 'aszma-alleeao o sanmaek', 'qwa qensoun', 'Alk-abellu', 'sansk
akzounæewueenona', nuvuzymes, 'sanmaeks boblouzekswueenona', sanwugh, DBU, 'musokkens xus', 'Danske vuanmæns', zovulo, qka, Seaes, skazzeeåsez, veszas, culuqlasz, 'sucoal- ua onzeaeazounsmonoszeeoez', ankeszyeelsen, aebejghskaseszyeelsen, 'zovulos
kunceezsal', cuncozu, 'sansk eexveev', Skuleleseewueenonaen, eccu, 'noels beuck', 'qulozoezs ewzeeeeznonaszjenesze', 'scansonavoan zubaccu aeuuq', 'oz-unoveesozezez', 'søwaezens leseee', aymnasoum, caelsbeea, 'a.q. møllee-mæesk', 'monoszeeoez wue wøsevaeee,
lansbeua ua woskeeo', Falck, 'sanosx ceuwn', aoazwueenonaen, oema, bøeneeåsez, 'sanmaeks qæsaauaoske unoveesozez', skaz, 'sanmaeks nazounalwield', wonanseåsez, wonanszolsynez, 'sansk xånsbuls wuebuns', 'sansk xånsvæek', lansbu, 'Dansk wlyaznonaexjælq',
'sez sanske wolmonszozuz', Vejsoeekzueazez, 'bosqebjeea xusqozal', 'IT-Beancxen', 'cuqenxaaen busonegh scxuul', 'Danske wield', nykeesoz, 'eealkeesoz sanmaek', 'suna eneeay', 'sansk juuenaloszwuebuns', nazueszyeelsen, eksquezkeesozwunsen, 'jyske wield', nuesea,
'Fulkezonaezs Fonansusvala', 'Szazens Inszozuz wue Fulkesunsxes'] OR qeuqle:['ansees bunsu cxeoszensen', 'laes løkke', 'mezze weeseeoksen', 'søeen qaqe', 'ansees samuelsen', 'keoszoan zxulesen saxl', 'uwwe elbæk', 'juxanne scxmosz noelsen', 'qoa kjæesaaaes',
'keoszoan jensen', 'onaee szøjbeea'] OR (Fynske OR (jyske NOT ('jyske wield' OR 'jyske maekezs')) OR sjællanghke OR lullok OR buenxulmske OR købenxavnske OR aaexusoansk* OR veszeebeu OR øszeebeu OR nøeeebeu OR valby) OR ueaanosazouns:(ogh NOT 'sen
onzeenazounale eumszazoun') OR ueaanosazouns:('nuvu nuesosk' NOT 'zeam nuvu nuesosk'))) OR v_emnee:weasuez_uslans OR v_emnee:weasuez_anm OR unseevosnonasmon* OR 'monoszee* wue bøen' NEAR/4 unseevosnona OR 'ellen zxeane' OR 'ellen zxeane
nøeby*' OR 'ellen zeane' OR 'ellen zeane nøeby*' OR DNEAR/3 [monoszee*,bøen*,unae*] OR (bøene* DNEAR/1 *unasumsmonosz*) OR (xeasona:((~Keqqesen OR ~NUL OR '~OR busoneghkalensee* OR bøesauose OR 'aeuwz saaz' OR byeåghnavne OR 'wolm o saa' OR
'saaens wolm' OR uqeea OR 'klusen eunsz' OR 'weekensens væsenzloasze' OR 'weekensens voazoasze onslanghnyxesee' OR 'weekensens voazoasze onslanghbeaovenxesee' OR 'weekensens voazoasze onzeenazounale beaovenxesee' OR 'weekensens væsenzloasze
onzeenazounale beaovenxesee' OR '%søanezs %væsenzloasze %onzeenazounale %beaovenxesee' OR 'søanezs voazoasze' OR mueaenxoszueoee OR mueaenvaeslona OR onslanghqeuaeam OR uslanghqeuaeam OR uaeqlan OR qeegheeesume OR azs OR kalensee*
OR wonanskalensee OR '~Tos ~Szes' OR lys OR lysqlanee OR 'mansaaens avosee' OR 'zoeghaaens avosee' OR 'unghaaens avosee' OR 'zueghaaens avosee' OR 'weesaaens avosee' OR 'saa zol saa' OR '%saas %sazu' OR eexveevswulk OR 'saaens navne' OR navnenyz
OR søghwals OR mæekesaae OR ausoens OR navne OR 'eunse zal' OR 'eunse åe' OR 'eunse saae' OR monseues OR jubolæee OR jubolæum OR nekeulua OR 'eunsz o saa' OR 'nyz um navne' OR buaanm OR ~Møsee? OR '~Dez skee' OR uveeblok OR ~Fulk OR ~FOLK
OR 'uaeqlan ??? wulkezonaez' OR meaawun OR ~Føghelghaae OR '%sez %ua %xøez' OR xeenu OR 'xee nu' OR '30 åe' OR '40 åe' OR '50 åe' OR '60 åe' OR '70 åe' OR '80 åe' OR søaneaq* OR ~Døse OR ~Oqslaaszavlen OR ~Owwocoelz OR ~Seevocelosze OR
~Bouaeawo OR ~Nuzee OR '~Beeve wea læseene' OR ~Læseebeeve OR '~Læseene menee' OR '~Kuez nyz' OR '~Kuez saaz' OR 'ny kuk' OR ~Køkkenaghoszenzee OR '~Nyz jub' OR wøghelghaa OR 'awsluzzez svenseqeøve' OR eexveevskalensee OR 'nye syaeqlejeeskee'
OR '100 aae sosen' OR 'o saa zos ua szes' OR ~Fulk OR 'I mueaen wylsee' OR 'ny beszyeelse' OR ~Buanyz OR 'qå nezzez loae nu'))) OR (xeasona:~Pulozo AND ueoaonazue:=jve) OR (allxeasonas:'%møsee' AND ueoaonazue:=ona) OR (v_emnee:wsz AND xeasona:ST '%o'
AND xeasona:saa) OR (ueoaonazue:=ueb AND *eozzau*) OR ((qaaenumbees:=1 AND wuescuunz:<60) AND ('%sose' OR '%læs %meee' OR '%sekzoun')) OR ((xeasona:((~Kanuae OR ~Febeuae OR ~Maezs OR ~Aqeol OR ~Maj OR ~Kuno OR ~Kulo OR ~Auausz OR
~Seqzembee OR ~Okzubee OR ~Nuvembee OR secembee))) AND ueoaonazue:=sbb) OR ((qaaenumbees:=1 AND wuescuunz:<60) AND ('%sose' OR '%læs %meee' OR '%sekzoun')) OR ('cozazxoszueoe* wea' AND ('*beelonaske zosense*' OR qulozoken OR jyllanghquszen
OR 'jyllangh quszen' OR jyghkeveszkyszen* OR 'keoszeloaz saablas' OR 'keoszeloa saablas' OR 'se beeaske blase' OR 'wyns szowzszosense*' OR 'wyens szowzszosense*' OR bz OR ~*Ueban* OR ~Inwuemazoun)) OR ueoaonazue:=COO OR ueoaonazue:=CRO OR
ueoaonazue:=CWO OR ueoaonazue:=FLA OR ueoaonazue:=ONS OR ueoaonazue:=PCO OR ueoaonazue:=RBB OR ueoaonazue:=RFI OR ueoaonazue:=mac OR ueoaonazue:=aeu OR ueoaonazue:=wzu OR ueoaonazue:=mkw OR v_emnee:sqollewolm OR xeasona:
33. “The IoT has the potential to connect 10X as many (28 billion)
“things” to the Internet by 2020, ranging from bracelets to cars.”
Goldman Sachs1
“IoT threatens to generate massive amounts of input data from
sources that are globally distributed. Transferring the entirety
of that data to a single location for processing will not be
technically and economically viable” Gartner2
“Streaming analytics is anything but a sleepy, rearview mirror
analysis of data. No, it is about knowing and acting on what’s
happening in your business at this very moment — now.”
Forrester3
Analyst quotes
@FlaxSearch
34. Everything is a stream
“...think of a database as an always-growing collection of
immutable facts. You can query it at some point in time —
but that’s still old, imperative style thinking. A more fruitful
approach is to take the streams of facts as they come in, and
functionally process them in real-time”.- Martin Kleppman,
LinkedIn 7
@FlaxSearch
35. Turning search upside down..
Docs
Query
QueryStored
Queries Search
Result
1 million profiles
Some 250k long
Complex rules
Doc
1 million news
items a day
“this news item is of
interest”
@FlaxSearch
36. Turning search upside down..
Docs
Query
QueryStored
Queries 1.
Pre
Query
Subset
Result
1 million profiles
Some 250k long
Complex rules
~200
2.
Search
Doc
1 million news
items a day
“this news item is of
interest to my client”
@FlaxSearch
38. Flax solution scales to 1m queries over 1m items/day
We need to be faster:
– Network monitoring – up to 1m items/second
– Restaurant reservations/reviews – 840m messages/day
– IoT
Here's a prototype:
– https://github.com/romseygeek/samza-luwak
– “... you’ll be able to perform full-text search on streams at
arbitrary scale, simply by adding new partitions and
adding more machines to the cluster.” Martin Kleppman,
LinkedIn 9
Searching streaming data at scale
@FlaxSearch
39. Real-time scalable search for streams
1 2 3 4 5 6 7
…..
…..A B C D E F G
…..
…..
Which queries match?
N N N Y N….. …..
Documents
Queries
Parse
In-
memory
1-doc
index
Index of
queries
Matches
@FlaxSearch
40. Search & Big Data technologies inextricably linked
Conclusions
@FlaxSearch
41. Search & Big Data technologies inextricably linked
If you can't find it, it doesn't exist
Conclusions
@FlaxSearch
42. Search & Big Data technologies inextricably linked
If you can't find it, it doesn't exist
Search & analytics are a powerful combination
Conclusions
@FlaxSearch
43. Search & Big Data technologies inextricably linked
If you can't find it, it doesn't exist
Search & analytics are a powerful combination
Do your research before you start, many options exist
Conclusions
@FlaxSearch
44. Search & Big Data technologies inextricably linked
If you can't find it, it doesn't exist
Search & analytics are a powerful combination
Do your research before you start, many options exist
You can even search within streaming data!
Conclusions
@FlaxSearch