After a long day at work, you are dazed but at the same time determined to watch the newest episodes of your favourite series. And just when you’re in the nail-biting scene, the video has paused, then begins to buffer and never resumes! After a few frustrating seconds later, the video is at its lowest resolution, hardly showing clarity.

How did an exhilarating affair end into an arduous one?

Say Hello, to Video Streaming challenges. Honestly, This is not how a video should be streaming. That is when the potential game-changers like AI and Deep Learning comes into action. They can ensure Zero-delay, High Definition, enhanced streaming OTT making the user experience more enjoyable and worthy.

Adaptive BitRate

OTT or Over the Top content makers, as well as the providers, are launching intriguing content every single week. But the Quality of experience which is generally referred to as QoE is one main issue that’s not been able to tackle well enough by the OTT platforms.

QoE is about a video’s overall resolution, startup time, stalls and pauses that happen through the relay. Seamless delivery of video is highly reliable on internet speed or the so-called bandwidth which can drastically affect the user experience. That is when this ABR comes into play tackling the challenges with an innovative algorithm.

The parameters of a video have been “adapted” dynamically by the performance of your network. Therefore, compromising the quality of the video. But, it is still dependent on internet speed. It is 2021, and a lot of talks is happening around Deep Learning.

Intelligent Solutions

Videos have been the new type of data communication, literally an extension to an email or text. This has created a necessity for developments in video streaming with optimization of network and data. Most of the happening these days are unperceptive to the content it holds. The bitrate of a scene where the characters are just having a conversation differs from that of an action scene. Imagine seeing the wrinkles on Dumbledore’s face when he raises his wand, but unable to see clearly when Harry is fighting Voldemort.

And that needs an answer to the following questions

• What if a scene’s content could be comprehended?
• What if streaming algorithms learn the QoE by every scene?
• What if the encoding is instructed with relative importance to all frames?
• What if the streaming is friendly on any device?

The best possible answer is Deep Learning.

Deep Learning

Artificial Intelligence and Deep Learning are advancing technologies to improve video content and user’s QoE. Content conscious AI can remarkably improve the viewing experience making it personalized, immersive and novel. DNN (Deep Neutral Network) can be made device-aware, dynamically computing it with the strategic resources to scale up the performance of streaming.

An intelligent and notable method to enhance QoE is when the encoder looks at the entire video than looking at the pixels in individual frames. This helps for

• A better understanding of content
• Delivering the highest quality stream
• Identifying the content redundancies.

How can the impact of Deep Learning take place?

Device Aware

The device quality plays a major role in the entire process of decoding. Making it device friendly by all means, says that people can watch it anytime, anywhere and by any device. A click on the app of their smartphones. Because smartphones are the new laptops. Having the track on which device is targeted by the user can make his/her personal experience most engaging. The unique potential of a device can be utilized as its soul ability to redefine the entire streaming.

Context-Aware 

It doesn’t end there at being device-aware. Deep Learning and Artificial Intelligence can be made to pay specific attention to the entire context of the video, search preferences, genre interest. For instance, a user is streaming a particular series every week. Deep learning can use this data to show the user the preference show, the similar genres, other shows from the director and cast. This not only improves the QoE of the user but also makes it a Win-Win for the OTT providers as well.

Understanding the business of Streaming  

One might wonder why an OTT platform has to invest in such user engaging technologies? The answer to this is a recent survey done across 1000 customers who are using the OTT streaming in Urban India. As many as 62% of people viewing through the OTT face issues like buffering, pausing and hang of the app whilst used on travelling.

More than ever, In recent times, the customers are making it clear by immediate feedbacks on official and social media sites regarding any dissatisfaction While using the OTT. This shows a clear intolerance in regards to lower QoE which can affect the revenue. Long load times, buffering, pixelation and stalled videos should no longer be seen to sustain this business.

The emerging trends, the pandemic situation has given new dynamics to online streaming. The limitations to short films or fiction videos have widened up to live stream of sports, gaming and releasing movies. Thereby generating more revenue.

Gone were the days of gaming with Personal Computers. But, games like PUBG having pulled greater crowds just from a smartphone. The new gaming platform from Google, stadia gave an announcement stating as low bandwidth as 25mbps is enough for exceptional streaming.

This tells us how cost-effective ideas are emerging to change the phase of this business using AI and deep learning. A recent survey showed that 53.98% of viewers were willing to upgrade to premium subscriptions for intriguing content that has minimal streaming challenges.

Finally, The big picture of delivering high-quality videos at lower cost subscriptions to generate greater revenues is in view. The considerable benefits obtained from the usage of AI and the impact of deep learning in creating a greater streaming experience has solved many practical conditions. The biggest innovations of all this process are DNN based advancements. So, very soon this is going to change all the demographics to matchless QoE and incredible video consumption.

The post Deep learning impact on video streaming challenges appeared first on Artificial Intelligence.

Real-time analytics has become the most crucial term in Big data analytics for enterprises. This enables enterprises to use all available data as real-time analytics big data. This means with real-time analytics enterprises can generate analytics reports as and when the data is received. It ideally takes a minute. Furthermore, using real-time analytics, enterprises can receive fresh and contextual analytics reports. This gives close relevance to market trends. Real-time analysis happens through continuous querying. Streaming analytics or Real-time analytics enables applications to integrate with external data sources to application flow. Otherwise, it updates an external database with already processed information. This in other term is known as stream processing.

In Real-time analytics, while the stream of data moves continuously, it calculates statistical analytics on the live streaming data. Thus, it allows the monitoring and management of live streaming data. So, the business can at upon events happening at any given moment before the data loses its value.

Why is Real-time Analytics Important?

Real-time analytics allows organizations to analyze data as soon as it becomes available. Hence, it allows for analyzing risks before they occur. So, the business can find new opportunities easily which may result in an increase in profits, improved customer service and new customer ventures. A Streaming Analytics or real-time analytics platform can process millions of events per second. “Because data in a Streaming Analytics environment is processed before it lands in a database, the technology supports much faster decision making than possible with traditional data analytics technologies,” Philip Howard of Bloor Research said in a recent Datamation interview.  Since using real-time analytics companies can detect different security threat patterns and risks, it helps in security protection and monitoring of physical as well network.

Types of Real-Time Data Analytics

There are two types of real-time analytics:

• On-demand real-time analytics — This is a reactive analysis approach where the user processes a request through query and then delivers the result as analytics. For example, web site analytics is a kind of on-demand real-time analytics where an analyst monitors site traffic to resist a potential crash of the website.
• Continuous real-time analytics — This is a proactive analysis approach where users are continuously updated with alerts in real-time. For example, stock market tracking with various visualization representations is this type of analytics.

What’s so realabout Real-Time Analytics?

Real-time means at the very moment. Hence, real-time analytics is capable to process data at the moment it arrives in the system. So, there is no possibility of batch processing or future processing of data. Not to mention, it enhances the ability to make better decision making and performing meaningful action on a timely basis. So, real-time analytics combines and analyzes data at the right place and at the right time. Thus, it generates value from disparate data.

Advantages of Streaming Analytics

• Data visualization on a real-time basis provides Deeper Insight: 

To make a key performance on a daily basis, KPI or key performance indicator plays a vital role for companies. And Visualization is a key ingredient for KPIs. As the companies can view KPI data on a real-time basis, they can get the granular view of business data at any given point of time. This data can improve sales, identify errors, reduce costs, and provide information to react faster to risks to mitigate them. Real-time Analytics accelerates decision-making along with providing access to business metrics and reporting.

• Customer Behaviour insights: 

As real-time analytics provide real-time insights on customer data like what they are buying, their preferences, likes, and dislikes, it gives companies to retain customers as well as generate extra profits. Additionally, companies can rapidly respond to customer needs which helps in increasing revenues through cross-selling and up-selling of services and goods.

• Remain Competitive:

Real-time analytics helps to become companies more innovative and remain them competitive by strengthening the band. With real-time visualization reports it is easy to identify trends, develop use cases, white papers, and generate forecasts. This not only reduces internal and external threats but also provides advance views on industry changes.

Disadvantages of Streaming Analytics

• Lack of Experts: Though streaming analytics is a happening field, there is a lack of availability of experts in the field. The main reason behind it is the small number of Data Scientists. Since real-time analytics is still a recent technology and it shows a slow adoption by most developers due to their lack of expertise. “The streaming application programming model is unfamiliar to most application developers,” wrote Forrester analysts Mike Gualtieri and Rowan Curran in a Q3 2014 Forrester report on Big Data and Streaming Analytics.

• Perform Risk Analysis: One of the main features of Streaming analytics is it shows the analyzed result of the latest industry and media news. This helps companies to keep updated on the latest development amidst high competition. Along with that, since with real-time analytics data on vendors and customers are now in hand, it helps to take action against specific risks or events.
• Securing Data by threat analysis: WithStreaming Analytics companies now can identify internal and external threats that may affect the company or industry. Identifying sensitive information that is not protected is at fingertips now with streaming data analytics. So, whether it is federal, state or regulatory information, protecting them is easy with streaming data analytics.

Conclusion

This is a real-time society and to tap into the power of data, real-time analytics is a powerful tool. Today data is considered not as valuable but also as a commodity. Nowadays, the need of the companies is to expect immediate access to the information they are seeking. This information while experimented with applications brings new insights which allow them to make decisions on the next action items with the data.

The post WHAT IS STREAMING BIG DATA ANALYTICS OR REAL-TIME ANALYTICS AND ITS BENEFITS? appeared first on Artificial Intelligence.

In our latest post, we investigate the impact of today’s data pipelining challenges and explore how increased automation of stream processing frameworks such as Spark and Flink can help to yield better performance for telecom operators.

It’s been so long since we wrote our blog series (apache-storm-vs-spark-streaming and apache-storm-performance-tuners) on stream processing frameworks, and we thought to share our recent views on the current stream processing engines and their applicability towards 5G and IoT use cases.

On the journey of preparing Ericsson to expand its origin to 5G and IoT use cases, Ericsson has studied various scalable and flexible streaming processing frameworks to solve the data pipelining issues and its impact on the overall performance. There is also a rise in the need for automation in every domain, that’s achieved using machine learning on streaming data which aids in adaptive learning and intelligent decision making. It’s indeed a great challenge to incrementally learn models and gain information from the streaming data using machine learning algorithms.

In this post, we will talk about the challenges of AI in streaming data and how stream processing frameworks, primarily the Spark Streaming framework, can be used to solve those problems.

Spark Streaming framework

The following contents in the blog have been discussed as Input, Process (ETL and ML) and Output phases. We also talk about various machine learning and data analysis techniques that are used at stream processing frameworks to enable efficient control and optimization.

Input Phase:

Even though, there are different input sources like file, databases and various end-points, the interesting development in the current setup is how efficiently we can use Apache Kafka with the Spark Streaming Platform. In addition to the default receiver-based approach, there has been an inclusion of a “direct” technique where the performance and duplication issues have been resolved. In our telco domain, as we need to handle data rates of 1TB/sec from our network probes, this “direct” approach has been a precise technique to apply. In addition to the performance efficiency, we also need a simple approach to maintain the distribution technique in our complex telco systems. The telecom domain also requires the accuracy of 99.9999% which lays a tremendous need for us to handle failure scenarios. This “direct” technique also reduces the complexity of handling those failures and maintain less number for replicated data across the system.

Process Phase:

Extraction, transformation and loading (ETL):

In the old days, when we practiced stream processing, we usually talked about Bolts which run simultaneously on executors and our main task was to determine the deployment topology to have good distribution and maximum usage of available resources. Then, we started talking about micro-batches and how effective and fault-tolerant they were, compared to the pure stream processing setup. We also used to talk about Lambda architecture, combining both the batch and stream processing in a single query. At present, due to increased popularity of the Spark Streaming framework, industries have started shifting towards Structured Stream Querying where even flat tables have been treated as streaming data and incrementally processed. Structured Stream Querying allows us to process the newly arrived data with more priority to answer the streaming query while compared to processing of historic data.

In our telecom world, we have various transformations such as number mapping, cleaning, replacing null values, variable transformations, etc. To perform all these operations, which can be handled in a pure streaming manner, we use Apache Flink as there is no micro-batch concept. While for operations such as replacing missing values, mean of last N values, etc. – anything which requires historic data – we use Spark Streaming with Structural Querying as our preferred approach.

Machine Learning (ML):

For our telecom domain, we need to create both trained models and test data in a streaming manner. We have tried various approaches to update the model when new data points stream in, and found hierarchical models were much easier to perform the incremental model updates. These hierarchical data models can be easily deployed using the Spark Streaming framework as it internally supports micro-batch processing for these kinds of model preparation. We also understood that with the flexibility and pure streaming nature of Apache Flink, the implementation of reinforcement learning can be easily realized and the performance metrics for these implementations are quite competitive compared to the other frameworks.

Sink Phase:

After the data processing layer, we can store data into various options such as permanent data store, or in a distributed memory, or back to a message bus, or just visualize the data points. In our internal study, we have stored the processed data in Cassandra, which is a No-SQL data store given importance to availability when there is failure in partition tolerance. Working with Apache Cassandra in telecom applications for some years, we have found it very useful for fine-tuning to achieve consistency and availability. Even though I get my hands dirty on Hadoop ecosystem on a regular basis, I got inspired by the fact that we can play with consistency levels in Cassandra while I was not able to change availability levels in HBase.

And we also need to store the data in the “best” site. The resource may be created by the executer on site A in storage A, but the client application always queries it from site B, which would require us to determine where to better store the resource on site B, ensuring data locality. There was internal optimization which is done at Sink Level to ensure this data locality

In this post, I hopefully addressed some of the concerns in stream processing frameworks and the best ways of working. This intro on data pipelining on streaming systems should help to tune your systems to yield good performance results. As to how streaming engines can support 5G Network Slicing and IoT data, that will all be covered in our next blog. Stay Tuned!

In the meantime, recap by reading our other blogs about Apache Storm Performance Tuners and a comparison of Apache Storm vs. Spark Streaming.

The post Applying the Spark Streaming framework to 5G appeared first on Artificial Intelligence.