The document discusses Oracle Stream Analytics (OSA), a platform for processing streaming data in real-time. It describes OSA's capabilities like filtering, aggregating, complex event processing, predictive analytics, and integrating with downstream systems. It then provides examples of how OSA could be used in various industries like retail, banking, transportation, and healthcare for applications such as fraud detection, recommendations, predictive maintenance, and personalization.
Medea: Scheduling of Long Running Applications in Shared Production ClustersPanagiotis Garefalakis
MEDEA: Scheduling of Long Running Applications in Shared Production Clusters
EuroSys'18
https://lsds.doc.ic.ac.uk/sites/default/files/medea-eurosys18.pdf
Improving Traffic Prediction Using Weather Datawith Ramya RaghavendraSpark Summit
As common sense would suggest, weather has a definite impact on traffic. But how much? And under what circumstances? Can we improve traffic (congestion) prediction given weather data? Predictive traffic is envisioned to significantly impact how driver’s plan their day by alerting users before they travel, find the best times to travel, and over time, learn from new IoT data such as road conditions, incidents, etc. This talk will cover the traffic prediction work conducted jointly by IBM and the traffic data provider. As a part of this work, we conducted a case study over five large metropolitans in the US, 2.58 billion traffic records and 262 million weather records, to quantify the boost in accuracy of traffic prediction using weather data. We will provide an overview of our lambda architecture with Apache Spark being used to build prediction models with weather and traffic data, and Spark Streaming used to score the model and provide real-time traffic predictions. This talk will also cover a suite of extensions to Spark to analyze geospatial and temporal patterns in traffic and weather data, as well as the suite of machine learning algorithms that were used with Spark framework. Initial results of this work were presented at the National Association of Broadcasters meeting in Las Vegas in April 2017, and there is work to scale the system to provide predictions in over a 100 cities. Audience will learn about our experience scaling using Spark in offline and streaming mode, building statistical and deep-learning pipelines with Spark, and techniques to work with geospatial and time-series data.
Medea: Scheduling of Long Running Applications in Shared Production ClustersPanagiotis Garefalakis
MEDEA: Scheduling of Long Running Applications in Shared Production Clusters
EuroSys'18
https://lsds.doc.ic.ac.uk/sites/default/files/medea-eurosys18.pdf
Improving Traffic Prediction Using Weather Datawith Ramya RaghavendraSpark Summit
As common sense would suggest, weather has a definite impact on traffic. But how much? And under what circumstances? Can we improve traffic (congestion) prediction given weather data? Predictive traffic is envisioned to significantly impact how driver’s plan their day by alerting users before they travel, find the best times to travel, and over time, learn from new IoT data such as road conditions, incidents, etc. This talk will cover the traffic prediction work conducted jointly by IBM and the traffic data provider. As a part of this work, we conducted a case study over five large metropolitans in the US, 2.58 billion traffic records and 262 million weather records, to quantify the boost in accuracy of traffic prediction using weather data. We will provide an overview of our lambda architecture with Apache Spark being used to build prediction models with weather and traffic data, and Spark Streaming used to score the model and provide real-time traffic predictions. This talk will also cover a suite of extensions to Spark to analyze geospatial and temporal patterns in traffic and weather data, as well as the suite of machine learning algorithms that were used with Spark framework. Initial results of this work were presented at the National Association of Broadcasters meeting in Las Vegas in April 2017, and there is work to scale the system to provide predictions in over a 100 cities. Audience will learn about our experience scaling using Spark in offline and streaming mode, building statistical and deep-learning pipelines with Spark, and techniques to work with geospatial and time-series data.
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights.
Video: https://www.sumologic.com/online-training/#start
Sumo Logic exposes the Search Job API for access to resources and log data from third-party scripts and applications.
Targeting experienced Sumo Administrators, this webinar shows you how to leverage the Search Job API to interact with the Sumo Logic service. Everyone attending should be familiar with the concepts of RESTful web services and JSON. Through theory and demo, this webinar covers:
Creating a Search Job
Checking Status of a Search Job
Paging through messages and records
Unified Data Processing with Apache Flink and Apache Pulsar_Seth WiesmanStreamNative
Come learn how the combination of Apache Pulsar and Apache Flink is making stateful stream processing even more expressive and flexible to support applications in streaming that were previously not considered streamable. The new world of applications and fast data architectures has broken up the database: Raw data persistence comes in the form of event logs, and the state of the world is computed by a stream processor. Apache Pulsar provides a strong solution for the event log, while Apache Flink forms a powerful foundation for the computation over the event streams.
We will discuss the key concepts behind Apache Flink's approach to stream processing and how it is a powerful abstraction for stateful event-driven applications. We will then see how to use Flink in conjunction with Apache Pulsar to creates a unified data processing platform.
Using AWS to Build a Scalable Big Data Management & Processing Service (BDT40...Amazon Web Services
By turning the data center into an API, AWS has enabled Sumo Logic to build a very large scale IT operational analytics platform as a service at unprecedented scale and velocity. Based around Amazon EC2 and Amazon S3, the Sumo Logic system is ingesting many terabytes of unstructured log data a day while at the same time delivering real-time dashboards and supporting hundreds of thousands of queries against the collected data. When co-founder and CTO Christian Beedgen started Sumo Logic, it was obvious that the service would have to scale quickly and elastically, and AWS has been providing the perfect infrastructure for this endeavor from the start.
In this talk, Christian dives into the core Sumo Logic architecture and explains which AWS services are making Sumo Logic possible. Based around an in-house developed automation and continuous deployment system, Sumo Logic is leveraging Amazon S3 in particular for large-scale data management and Amazon DynamoDB for cluster configuration management. By relying on automation, Sumo Logic is also able to perform sophisticated staging of new code for rapid deployment. Using the log-based instrumentation of the Sumo Logic codebase, Christian will dive into the performance characteristics achieved by the system today and share war stories about lessons learned along the way.
Sumo Logic QuickStart Webinar - Dec 2016Sumo Logic
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights.
Video: https://www.sumologic.com/online-training/#start
Analyze a SVC, STORWIZE metro/ global mirror performance problem-v58-20150818...Michael Pirker
Latency problem was reported for VDisk CA-CL1-Disk04-N at 02/05/15 8:09,
The environment are two clusters connected with Metro Mirror. The first aim of this document is to show how we found the root cause of this problem in the link between the two clusters.
The second aim of this document is to describe how the root cause for this problem was found by using the BVQ structured performance problem analysis method. It demonstrates that successful analysis work needs a structured method and also a tool which supports this method and delivers the needed technical insight. We have the concept that everybody should be able to conduct a performance analysis. This is important because the level of service is lowered day by day and especially small customers are more and more reliant on their own skills or on the skills of their partners. This is a common problem occurring at all vendors!
Time Series Anomaly Detection with .net and AzureMarco Parenzan
If you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
Detailed presentation on how queries and updates behave on updateable secondary. A few nuggets of best practices to make sure your HDR configuration works well.
Bringing complex event processing to Spark streamingDataWorks Summit
Complex event processing (CEP) is about identifying business opportunities and threats in real time by detecting patterns in data and taking appropriate automated action. Example business use cases for CEP include location-based marketing, smart inventories, targeted ads, Wi-Fi offloading, fraud detection, churn prediction, fleet management, predictive maintenance, security incident event management, and many more. While Spark Streaming provides a distributed resilient framework for ingesting events in real time, effort is still needed to build CEP applications. This is because CEP use cases require correlation of events, which in turn requires us to treat every incoming event as a discrete occurrence in time. Spark Streaming treats the entire batch of events as single occurrence. Many CEP use cases also require alerts to be fired even when there is no incoming event. An example of such use case is to fire an alert when an order-shipped event is NOT received within the SLA times following an order-received event. At Oracle we have adopted a few neat techniques like running continuous query engines as long running tasks, using empty batches as triggers, etc. to bring complex event processing to Spark Streaming.
Join us to learn more on CEP for Spark, the fastest growing data processing platform in the world.
Speakers
Prabhu Thukkaram, Senior Director, Product Development, Oracle
Hoyong Park, Architect, Oracle
Dashboards are fantastic, but how do I get notified of critical events? This webinar will cover how to create alerts that will allow your team to effectively monitor business-critical events. Alert channels include email or webhooks into Slack, PagerDuty, DataDog, ServiceNow, or any other webhook you want to develop. What about running custom scripts triggered from alerts? Let's do it.
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights.
Video: https://www.sumologic.com/online-training/#start
Sumo Logic exposes the Search Job API for access to resources and log data from third-party scripts and applications.
Targeting experienced Sumo Administrators, this webinar shows you how to leverage the Search Job API to interact with the Sumo Logic service. Everyone attending should be familiar with the concepts of RESTful web services and JSON. Through theory and demo, this webinar covers:
Creating a Search Job
Checking Status of a Search Job
Paging through messages and records
Unified Data Processing with Apache Flink and Apache Pulsar_Seth WiesmanStreamNative
Come learn how the combination of Apache Pulsar and Apache Flink is making stateful stream processing even more expressive and flexible to support applications in streaming that were previously not considered streamable. The new world of applications and fast data architectures has broken up the database: Raw data persistence comes in the form of event logs, and the state of the world is computed by a stream processor. Apache Pulsar provides a strong solution for the event log, while Apache Flink forms a powerful foundation for the computation over the event streams.
We will discuss the key concepts behind Apache Flink's approach to stream processing and how it is a powerful abstraction for stateful event-driven applications. We will then see how to use Flink in conjunction with Apache Pulsar to creates a unified data processing platform.
Using AWS to Build a Scalable Big Data Management & Processing Service (BDT40...Amazon Web Services
By turning the data center into an API, AWS has enabled Sumo Logic to build a very large scale IT operational analytics platform as a service at unprecedented scale and velocity. Based around Amazon EC2 and Amazon S3, the Sumo Logic system is ingesting many terabytes of unstructured log data a day while at the same time delivering real-time dashboards and supporting hundreds of thousands of queries against the collected data. When co-founder and CTO Christian Beedgen started Sumo Logic, it was obvious that the service would have to scale quickly and elastically, and AWS has been providing the perfect infrastructure for this endeavor from the start.
In this talk, Christian dives into the core Sumo Logic architecture and explains which AWS services are making Sumo Logic possible. Based around an in-house developed automation and continuous deployment system, Sumo Logic is leveraging Amazon S3 in particular for large-scale data management and Amazon DynamoDB for cluster configuration management. By relying on automation, Sumo Logic is also able to perform sophisticated staging of new code for rapid deployment. Using the log-based instrumentation of the Sumo Logic codebase, Christian will dive into the performance characteristics achieved by the system today and share war stories about lessons learned along the way.
Sumo Logic QuickStart Webinar - Dec 2016Sumo Logic
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights.
Video: https://www.sumologic.com/online-training/#start
Analyze a SVC, STORWIZE metro/ global mirror performance problem-v58-20150818...Michael Pirker
Latency problem was reported for VDisk CA-CL1-Disk04-N at 02/05/15 8:09,
The environment are two clusters connected with Metro Mirror. The first aim of this document is to show how we found the root cause of this problem in the link between the two clusters.
The second aim of this document is to describe how the root cause for this problem was found by using the BVQ structured performance problem analysis method. It demonstrates that successful analysis work needs a structured method and also a tool which supports this method and delivers the needed technical insight. We have the concept that everybody should be able to conduct a performance analysis. This is important because the level of service is lowered day by day and especially small customers are more and more reliant on their own skills or on the skills of their partners. This is a common problem occurring at all vendors!
Time Series Anomaly Detection with .net and AzureMarco Parenzan
If you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
Detailed presentation on how queries and updates behave on updateable secondary. A few nuggets of best practices to make sure your HDR configuration works well.
Bringing complex event processing to Spark streamingDataWorks Summit
Complex event processing (CEP) is about identifying business opportunities and threats in real time by detecting patterns in data and taking appropriate automated action. Example business use cases for CEP include location-based marketing, smart inventories, targeted ads, Wi-Fi offloading, fraud detection, churn prediction, fleet management, predictive maintenance, security incident event management, and many more. While Spark Streaming provides a distributed resilient framework for ingesting events in real time, effort is still needed to build CEP applications. This is because CEP use cases require correlation of events, which in turn requires us to treat every incoming event as a discrete occurrence in time. Spark Streaming treats the entire batch of events as single occurrence. Many CEP use cases also require alerts to be fired even when there is no incoming event. An example of such use case is to fire an alert when an order-shipped event is NOT received within the SLA times following an order-received event. At Oracle we have adopted a few neat techniques like running continuous query engines as long running tasks, using empty batches as triggers, etc. to bring complex event processing to Spark Streaming.
Join us to learn more on CEP for Spark, the fastest growing data processing platform in the world.
Speakers
Prabhu Thukkaram, Senior Director, Product Development, Oracle
Hoyong Park, Architect, Oracle
Dashboards are fantastic, but how do I get notified of critical events? This webinar will cover how to create alerts that will allow your team to effectively monitor business-critical events. Alert channels include email or webhooks into Slack, PagerDuty, DataDog, ServiceNow, or any other webhook you want to develop. What about running custom scripts triggered from alerts? Let's do it.
AlgoAnalytics is the “one stop AI shop”. We are the best organization in India as far as applied machine learning expertise is considered. We aim to be the one of the best in the world.
We work at the intersection of mathematics, computer science and specific domain knowledge like finance, retail, healthcare, manufacturing and others. We have developed expertise in handling structured/numerical, image and text data and integrating the intelligence gathered from heterogeneous data which is combination of structured and un-structured.
We integrate the cutting edge tools and technologies with our strong domain expertise to design predictive analytics solutions for businesses.We are proficient in classical as well as deep learning methodologies. In AlgoAnalytics we extensively use tools like R-Caret, Scikit-learn, Tensorflow, Theano and Microsoft Cognitive toolkit (CNTK).
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdasProf Dr Mehmed ERDAS
Big Data Analytics for TELCOs Customer Experience Management Permission Based Marketing for Location and Movement Data Data Modelling Business Use Cases Data Mining BSS OSS COTS OTT Churm Modeling Markov Processes HANA HADOOP INtegration Video Streaming Test cases
Spumaint is a generic software to service any industrial sector. Has Asset Maintenance Module to record any number of machinery with spares inventory,special tools, etc. Preventive Maintenance Module for scheduling multiple time pop-ups as well as condition based. Analytical tools also found in breakdown n reporting module
Top industry use cases for streaming analyticsIBM Analytics
Organizations need to get high value from streaming data to gain new clients and capitalize on market opportunities. Discover how IBM Streams is best suited for use cases that has the need for high speed and low latency.
Executive Briefing: What Is Fast Data And Why Is It ImportantLightbend
[About This Webinar]
Streaming data systems, so called Fast Data, promise accelerated access to information, leading to new innovations and competitive advantages. These systems, however, aren’t just faster versions of Big Data; they force architecture changes to meet new demands for reliability and dynamic scalability, more like microservices.
This means new challenges for your organization. Whereas a batch job might run for hours, a stream processing application might run for weeks or months. This raises the bar for making these systems resilient against traffic spikes, hardware and network failures, and so forth. The good news is that there is a strong history of facing these demands in the world of microservices.
In this webinar by Dr. Dean Wampler, VP of Fast Data Architecture at Lightbend, Inc., we will cut through the buzz around Fast Data and explore how to successfully exploit this new opportunity for innovation in how your organization leverages data. Specifically, Dean will review:
* The business justification for transitioning from batch-oriented big data to stream-oriented fast data
* The architectural and organizational changes that streaming systems require to meet their higher demands for reliability, resiliency, dynamic scalability, etc.
* How some of these requirements can be met by leveraging what your organization already knows about microservice architectures
ADV Slides: Data Curation for Artificial Intelligence StrategiesDATAVERSITY
This webinar will focus on the promise AI holds for organizations in every industry and every size, and how to overcome some of the challenge today of how to prepare for AI in the organization and how to plan AI applications.
The foundation for AI is data. You must have enough data to analyze and build models. Your data determines the depth of AI you can achieve — for example, statistical modeling, machine learning, or deep learning — and its accuracy. The increased availability of data is the single biggest contributor to the uptake in AI where it is thriving. Indeed, data’s highest use in the organization soon will be training algorithms. AI is providing a powerful foundation for impending competitive advantage and business disruption.
Apache Flink: Real-World Use Cases for Streaming AnalyticsSlim Baltagi
This face to face talk about Apache Flink in Sao Paulo, Brazil is the first event of its kind in Latin America! It explains how Apache Flink 1.0 announced on March 8th, 2016 by the Apache Software Foundation (link), marks a new era of Big Data analytics and in particular Real-Time streaming analytics. The talk maps Flink's capabilities to real-world use cases that span multiples verticals such as: Financial Services, Healthcare, Advertisement, Oil and Gas, Retail and Telecommunications.
In this talk, you learn more about:
1. What is Apache Flink Stack?
2. Batch vs. Streaming Analytics
3. Key Differentiators of Apache Flink for Streaming Analytics
4. Real-World Use Cases with Flink for Streaming Analytics
5. Who is using Flink?
6. Where do you go from here?
I am Tyler Walsh. I love exploring new topics. Academic writing seemed an exciting option for me. After working for many years with databasehomeworkhelp.com, I have assisted many students with their homework. I can proudly say, each student I have served is happy with the quality of the solution that I have provided. I acquired my master's from the University of Queensland.
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