Search Analytics at Enterprise Search Summit Fall 2011
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This presentation describes what Search Analytics is, what value it brings to the table, how it can be used, what additional functionality and values can be build with search data, etc.
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Presentation by Meshlabs at Zensar #TechShowcase - An iSPIRT ProductNation initiative.. Bangalore based firm; has a text analytics platform. Listens to all stake holders and unlocks the hidden value via text analytics.
Move Beyond ETL: Tapping the True Business Value of Hadoop
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Clicks, Conversions and Crawls
In this webinar hosted by DeepCrawl, we take a look at how clickstream data - from the SERPs through to checkout - can be analyzed to form predictions around the optimal customer journey. We dig into how the predictions can be utilized to optimize sites and apps to more fluidly guide customers from point of entry to conversion. We also review how understanding your crawl budget and the factors that impact which of your site's pages are indexed are all critical to creating a valid model to optimize your content for increased conversions.
Big Data Serving with Vespa - Jon Bratseth, Distinguished Architect, Oath
Offline and stream processing of big data sets can be done with tools such as Hadoop, Spark, and Storm, but what if you need to process big data at the time a user is making a request? Vespa (http://www.vespa.ai) allows you to search, organize and evaluate machine-learned models from e.g TensorFlow over large, evolving data sets with latencies in the tens of milliseconds. Vespa is behind the recommendation, ad targeting, and search at Yahoo where it handles billions of daily queries over billions of documents.
5733 a deep dive into IBM Watson Foundation for CSP (WFC)
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Big data on_aws in korea by abhishek sinha (lunch and learn)
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Search Analytics For Content Strategists @CSofNYC
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The Value of Key Performance Indicators: Unlock Your Data
Learn about KPIs, the process recommended to select relevant measures and tips on presentation that counts.
Modern Data Architectures for Business Insights at Scale
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Web analytics
What comes to your mind when you hear the word Analytics?
What exactly does it mean?
How it is that the Web Analytics is done & why use it?
What for & to what Capacity is it used?
How Companies are using StratApps Analytics to Outsmart Competition
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Big data and Marketing by Edward Chenard
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#MarketingShake - Edward Chenard - Descubrí el poder del Big Data para Transf...
Big data and marketing is becoming an important tool for companies. The document discusses how big data can be used for personalization, listening to customers, and responding to better serve their needs. It outlines the key steps in the process from data collection and analysis to insights and actions. Various big data tools and techniques are mentioned to understand customer behavior and trends in order to tailor marketing and customer experiences. The importance of data visualization to tell the story of patterns and create useful insights for businesses is also highlighted.
Watching websites
Presentation on monitoring the web, including synthetic, UEUM, web analytics, interaction analysis. Given at www.meshconference.com/meshu on May 20, 2008
Designing Outcomes For Usability Nycupa Hurst Final
MarkoHurst.com :: My topic of discussion at the Feb 17 2009 NYC UPA.
Even as the pace of society, business, and the Internet continue to increase, many budgets and time lines continue to decrease. To compound this issue, there is a serious disconnect between business goals, user goals, and what visitors actually do on your site. UX practitioners need a simple and efficient way to reconcile these diverse needs while taking action on their data. Join us to learn about a new method for incorporating quantitative data such as web analytics and business intelligence into your qualitative user experience deliverables: personas, wireframes, and more. This presentation will include discussions of online business models, feedback loops for ensuring cross-discipline collaboration, and ongoing revisions.
Government Web Analytics
The document discusses how to implement web analytics in government, noting that it is challenging due to a lack of direct profit motivation and responsibilities being spread across different teams. It provides an overview of collecting behavioral and outcomes data, measuring the visitor experience, and analyzing social media, while emphasizing the importance of tying analytics to an organization's goals. Common challenges include different groups not seeing analytics as their core responsibility and a need to build cross-functional collaboration.
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Search analytics what why how - By Otis Gospodnetic
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Search analytics what why how - By Otis Gospodnetic
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Data Driven Design: Using Web Analytics to Improve Information Architectures
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Presentation by Meshlabs at Zensar #TechShowcase - An iSPIRT ProductNation in...
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Move Beyond ETL: Tapping the True Business Value of Hadoop
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5733 a deep dive into IBM Watson Foundation for CSP (WFC)
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Big data on_aws in korea by abhishek sinha (lunch and learn)
Big data on_aws in korea by abhishek sinha (lunch and learn)
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This talk was given during Activate Conference 2019. Lucene has a lot of options for configuring similarity, and Solr inherits them. Similarity makes the base of your relevancy score: how similar is this document to the query? The default similarity (BM25) is a good start, but you may need to tweak it for your use-case. In this session, you will learn how BM25 works and how you may want to change its parameters. Then, we'll move to other similarity classes: DFR, DFI, IB and LM. You will learn the thinking behind them, how that thinking translates to the similarity score, and which parameters allow you to tweak how score evolves based on things like term frequency or document length. By the end, you’ll have a good understanding of which similarity options are likely to work well for your use-case. You'll know which tunables are available and whether you need to implement a custom similarity class. As an example, we’ll focus on E-commerce, where you often end up ignoring term frequency altogether.
Key Takeaway
1) What are the built-in Lucene/Solr similarities and what they do
2) Which similarity to use for which use-case
3) How to use a custom similarity class in Solr
Learn more about search relevance and similarity: sematext.com/blog/search-relevance-solr-elasticsearch-similarity
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Is observability good for your brain?
This talk was given during Monitorama EU 2018.
Observability, like other ops practices, has hard and soft benefits. No logs - no root cause, that’s a hard benefit. A soft benefit is when we have more confidence in an observable system. Then we can be more productive in developing it. The trouble with soft benefits like confidence, is how to measure them. Does observability actually make us more productive? How about other activities, such as post-mortems? Why is alert fatigue so bad? Turns out, there are plenty of studies about the impact of such activities on our brain, our behavior, our productivity. In this session, we’ll explore what [neuro]science says about such practices so that:
We turn soft benefits into hard benefits
We can encourage a culture where we get the benefits and avoid the traps
Be prepared for surprises, as some “best practices” aren’t “best” at all.
Introducing log analysis to your organization
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Solr Search Engine: Optimize Is (Not) Bad for You
This talk was given during Lucene Revolution 2017.
They say optimize is bad for you, they say you shouldn't do it, they say it will invalidate operating system caches and make your system suffer. This is all true, but is it true in all cases?
In this presentation we will look closer on what optimize or better called force merge does to your Solr search engine. You will learn what segments are, how they are built and how they are used by Lucene and Solr for searching. We will discuss real-life performance implications regarding Solr collections that have many segments on a single node and compare that to the Solr where the number of segments is moderate and low. We will see what we can do to tune the merging process to trade off indexing performance for better query performance and what pitfalls are there waiting for us. Finally, at the end of the talk we will discuss possibilities of running force merge to avoid system disruption and still benefit from query performance boost that single segment index provides.
Solr on Docker - the Good, the Bad and the Ugly
The document summarizes the good, bad, and ugly aspects of using Solr on Docker. The good is the orchestration and ability to dynamically allocate resources which can deliver on the promise of development, testing, and production environments being the same. The bad is that treating instances as cattle rather than pets requires good sizing, configuration, and scaling practices. The ugly is that the ecosystem is still young, leading to exciting bugs as Docker is still the future.
Introduction to solr
Radu Gheorghe gives an introduction to Solr, an open source search engine based on Apache Lucene. He discusses when Solr would be used, such as for product search, as well as when it may not be suitable, such as for sparse data. The presentation covers how Solr works with inverted indexes and scoring documents, as well as features like facets, streaming aggregations, master-slave and SolrCloud architectures. A demo is offered to illustrate Solr functionality.
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Elasticsearch for Logs & Metrics - a deep dive
Filled with tips for successfully using Elasticsearch for logs and metrics. From Velocity Conference, Amsterdam, 2016.
How to Run Solr on Docker and Why
Docker is all the rage these days. While one doesn't hear much about Solr on Docker, we're here to tell you not only that it can be done, but also share how it's done.
We'll quickly go over the basic Docker ideas - containers are lighter than VMs, they solve "but it worked on my laptop" issues - so we can dive into the specifics of running Solr on Docker.
We'll do a live demo showing you how to run Solr master - slave as well as SolrCloud using containers, how to manage CPU assignments, constraint memory and use Docker data volumes when running Solr in containers. We will also show you how to create your own containers with custom configurations.
Finally, we'll address one of the core Solr questions - which deployment type should I use? We will demonstrate performance differences between the following deployment types:
- Single Solr instance running on a bare metal machine
- Multiple Solr instances running on a single bare metal machine
- Solr running in containers
- Solr running on virtual machine
- Solr running on virtual machine using unikernel
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Tuning Solr & Pipeline for Logs
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We'll start by looking at how to scale SolrCloud through a hybrid approach using a combination of time- and size-based indices, and also how to divide the cluster in tiers in order to handle the potentially spiky load in real-time. Then, we'll look at tuning individual nodes. We'll cover everything from commits, buffers, merge policies and doc values to OS settings like disk scheduler, SSD caching, and huge pages.
Finally, we'll take a look at the pipeline of getting the logs to Solr and how to make it fast and reliable: where should buffers live, which protocols to use, where should the heavy processing be done (like parsing unstructured data), and which tools from the ecosystem can help.
Top Node.js Metrics to Watch
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Large Scale Log Analytics with Solr (from Lucene Revolution 2015)
In this talk from Lucene/Solr Revolution 2015, Solr and centralized logging experts Radu Gheorghe and Rafal Kuć cover topics like: flow in Logstash, flow in rsyslog, parsing JSON, log shipping, Solr tuning, time-based collections and tiered clusters.
From Zero to Production Hero: Log Analysis with Elasticsearch (from Velocity ...
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This talk covers the basics of centralizing logs in Elasticsearch and all the strategies that make it scale with billions of documents in production. Topics include:
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- Different node tiers to de-couple reading from writing, heavy traffic from low traffic
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- Configuring tools such as logstash and rsyslog to maximize throughput and minimize overheadDocker Logging Webinar
Sematext's DevOps Evangelist, Stefan Thies (@seti321), takes a Docker Logging tour through the different log collection options Docker users have, the pros and cons of each, specific and existing Docker logging solutions, tooling, the role of syslog, log shipping to ELK Stack, and more. Q&A session at end.
Docker Monitoring Webinar
For the Docker users out there, Sematext's DevOps Evangelist, Stefan Thies, goes through a number of different Docker monitoring options, points out their pros and cons, and offers solutions for Docker monitoring. Webinar contains actionable content, diagrams and how-to steps.
Metrics, Logs, Transaction Traces, Anomaly Detection at Scale
This document discusses Sematext's monitoring and logging products and services. It introduces Sematext, which is headquartered in Brooklyn and has employees globally. It then discusses why performance monitoring, log searching, and anomaly alerting are needed capabilities (Why). The document proceeds to describe Sematext's SPM and Logsene products, which provide these capabilities using open source technologies like OpenTSDB, Elasticsearch, and Kafka. It covers how the SPM agent collects metrics and traces and how Logsene ingests and analyzes logs at scale.
Side by Side with Elasticsearch & Solr, Part 2
This document compares the performance and scalability of Elasticsearch and Solr for two use cases: product search and log analytics. For product search, both products performed well at high query volumes, but Elasticsearch handled the larger video dataset faster. For logs, Elasticsearch performed better by using time-based indices across hot and cold nodes to isolate newer and older data. In general, configuration was found to impact performance more than differences between the products. Proper testing with one's own data is recommended before making conclusions.
Tuning Elasticsearch Indexing Pipeline for Logs
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BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
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Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
Search Analytics at Enterprise Search Summit Fall 2011
- 1. Search Analytics What ? Why ? How ? Otis Gospodneti ć – Sematext International @otisg ◦ @sematext ◦ sematext.com sematext.com/search-analytics
- 7. Why search users search providers search experience
- 8. Why Oh Why search providers search experience This search sucks! It takes 17 tries to find anything here! F!?@#$%^&?!? search users Cool, the latest search tweaks made our site really sticky! Awesome!
- 9. Fill in the Missing Piece Search Analytics Performance Monitoring Quality Assurance Tuning UI
- 10. Blind Leading the Blind
- 22. Watching Latency
- 23. Search Analytics & Measuring If you can't measure it, you can't fix it! You can't measure it if you don't have Analytics
- 27. Closing the Loop search users search providers search experience
Editor's Notes
- 10 days of data (5K/min)
- 10 days of data (5K/min)