Big Data: A game changer for public service media?

Anderson, M. Araujo Carranza, E. Brynjolfsson, E. Buckles, S. Curtis, S. Ehrenberg, B. Advancing the Internet of Things in Europe. Digitising European Industry.

Urban Big Data Centre

Last year we migrated to a new analytics provider and we took this as an opportunity to re-architect our distributed monolith. We will share the lessons learnt from operating it for nearly 3 years, how we designed our new microservices architecture so that it is easier to test, scale to cater for increasing demand, keep track of the message flow and replay errors without stopping the rest of the messages from being processed. We will also discuss the ideas behind the tooling we have developed which helps us operate our pipeline and has helped new members of the team share the understanding required to troubleshoot problems.

We have been in production for over a year and as demand from our big data platform increases we are beginning to discuss what our platform may look like in the future and the steps we will go through to achieve it. What this means is that when you sign up with an account with the BBC, then you enter all this personalization features and you can get show recommendations, you can follow shows, stay up to date by receiving notifications when things that you are interested in are becoming available and things like that.

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Here is the podcast link. Hugo: Such a pleasure to have you on the show. And we’re here today to talk about your work as head of Data Science and Architecture at the BBC, how you’re thinking about democratizing and spreading machine learning through the organization, how you think about data products, machine learning as a service, and content recommendation. All of these incredibly exciting and modern things, but before we get into all of that I’d like to find out a bit about you.

As we all know, the BBC is a huge organization, and I’m sure there are a lot of opinions about what the head of data science’s job actually is. So I’d like to know what you do, but before that I’d really like to know what your colleagues say or think that you do. Gabriel: That’s actually a really good question. I think as any large organization, and the BBC has about 20, people that work there, there’s quite a difference understanding of machine learning and data science.

So probably if you ask some people they would not know at all what it means, some people would probably assume that it’s something related to understanding audiences, so the kind of stuff that we potentially might want to call analytics, and then some people who have a bit more of a detailed understanding would probably tell you that a lot of the work that we do is around building recommendation engines and other kind of algorithms that help improve the audience experience.

And actually, one of the big challenges that we have is how do we educate enough of the organization so that everyone has a certain amount of understanding of the hopes, and the hypes of machine learning, so that from a journalistic perspective we can properly educate our audiences, and from a technology perspective we can actually take proper advantage of this technology.

Hugo: That’s great. Now, I love this idea of the hopes and the hypes, because we’re also talking about constraints and what ML can do, machine learning can do, and what it can’t do, and what data is good for and what it isn’t, because there’s so much hype around this space that I think a lot of people think data and AI are capable of anything, right?

‘Mandatory’ big London date for Big Data

Scrapping viewership is one area of web scrapping, but perhaps you might be interested in doing sentiment analysis on content. So we want to extract the contents of the web pages rather than number of times someone viewed the web page. If we can have two data tables that have at least one column with the same name, then we can merge them together. Again, with a bit of data manipulation, we can merge the data table that contains the longitude and latitude information together with the funding data across different states.

Aiden Loe.

The Study on the Ethics of Big Data commissioned by the EESC. Can Get You Locked up, Available at important example is online dating, which can include the use of algorithms to help.

Broadly the term covers projects that use data to do one or more of the following:. Enable a reader to discover information that is personally relevant. These categories may overlap and in an online environment can often benefit from some level of visualization. On the BBC News website we have been using data to provide services and tools for our users for well over a decade.

The most consistent example, which we first published in , is our school league tables , which use the data published annually by the government. Readers can find local schools by entering a postcode, and compare them on a range of indicators. Education journalists also work with the development team to trawl the data for stories ahead of publication. When we started to do this there was no official site that provided a way for the public to interrogate the data.

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Ethics, moral responsibility, societal impact, education, Big Data governance. On 21 September Facebook (BBC, ). Some might up-to-date. Many books.

Blanca Garcia Gil talks about how BBC re-architected a distributed monolith and shares the lessons learnt from operating it for nearly three years, how they designed their new microservices architecture so that it is easier to test, scale to cater for increasing demand, keep track of the message flow and replay errors without stopping the rest of the messages from being processed.

She currently works on a team whose aim is to provide a reliable platform at petabyte scale for data engineering and machine learning. She provides leadership on ensuring that the development team has the correct infrastructure and tooling required for the entire delivery and support cycles of the project. Software is changing the world. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community.

A practitioner-driven conference, QCon is designed for technical team leads, architects, engineering directors, and project managers who influence innovation in their teams.

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Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many cases rows offer greater statistical power , while data with higher complexity more attributes or columns may lead to a higher false discovery rate.

Big data was originally associated with three key concepts: volume , variety , and velocity. When we handle big data, we may not sample but simply observe and track what happens. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value.

As well as providing ways to explore large data sets, we have also had success your number? published to coincide with the official date at which the world’s.

James A. High-throughput experiments, observatories and archives have begun to generate Big Data for the sciences, social sciences and humanities in recent years, but this has made the conserved stock of intelligent questions a bottleneck. Where do scientific and scholarly questions in come from? And can we leverage the answer to generate bigger, high-throughput questions equal to our data; questions that accelerate science by helping us overcome missed opportunities, update distorted intuitions, tune objectives, and steer the high-throughput engines of Big Data?

I explore this possibility in the context of modern biomedical, physical, social and humanistic sciences. I demonstrate how rich data can be extracted from the enormous published record to reveal its own history. I then show how we can infer institutions from that history that limit the power of research, which machines can account for in analysis and subsequent experiment. For example, I show how biomedical science shifts incrementally from questions asked in one year to those addressed in the next, and the liability of this pattern for collective discovery and some of the outcomes we most demand from science e.

I show other dynamics in other fields, and explore how we can we shift from the age of Big Data to a better era of Big Questions and Big Answers. His research focuses on the collective system of thinking and knowing, ranging from the distribution of attention and intuition, the origin of ideas and shared habits of reasoning to processes of agreement and dispute , accumulation of certainty and doubt , and the texture—novelty, ambiguity, topology—of human understanding.

Evans is especially interested in innovation—how new ideas and practices emerge—and the role that social and technical institutions e. Much of Evans work has focused on areas of modern science and technology, but he is also interested in other domains of knowledge—news, law, religion, gossip, hunches and historical modes of thinking and knowing.

Bbc Big Data Dating

Crunching Big Data may seem like a child of the twenty first century, but you would be mistaken to think that the concept itself is newly born. In World War II, code decipherers successfully worked day and night studying cryptic messages to eventually cut an estimated 2 years off the length of the war, in turn saving thousands of lives.

Teams of analysts then constantly assess this data in order to predict any potential threats or problems, and solve them before they happen.

Keep up to date on the latest industry trends with Charles’ blogs, Smart cities: Big Data’s Watching You – BBC Interview From Shenzhen to Toronto, city streets are becoming a valuable source of big data, so should we.

As of 10 May , there have been more than 4. Similar to mainland China, South Korea also has managed to flatten the curve quickly after seeing the initial outbreak. Commonalities of these successful responses include swift and decisive interventions to promote or impose social distancing, active case detection and prompt isolation of all cases, government responsibility for all associated costs, relentless public messaging about containment measures and the wide use of big data to trace individuals who may have come into contact with infected individuals.

South Korea first developed tools for aggressive testing and contact tracing during the MERS outbreak. To contain the outbreak, the Chinese government has implemented large-scale social distancing policies, including quarantine, isolation and travel restrictions to limit cross-regional population movement and minimize non-essential social contacts. Health Barcode was quickly expanded and subsequently adopted by national authorities affecting over million residents by the end of February.

Complex and sophisticated artificial intelligence AI and machine learning algorithms are then employed to retrace the movement of the infected person and all persons in close contact, feeding into individual risk assessment of three levels—low, medium and high.

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A list of BBC episodes and clips related to “Big data”.

Databrokers compare your data to the data of people they know more about. By comparing the patterns they try to guess the likelihood of thousands of details that you may never have disclosed. These are actual examples:. And that these algorithms are often biased , and built on bad data. More and more people feel this pressure, and they are starting to apply self-censorship. When doctors in New York were given scores this had unexpected results. Doctors that tried to help advanced cancer patients had a higher mortality rate, which translated into a lower score.

Doctors that didn’t try to help were rewarded with high scores, even though their patients died prematurely.

BBC’s Bang Goes the Theory: Big Data

The Border Watch in South Australia has become the country’s latest print casualty, closing at three days’ notice after years’ publication. Hundreds of UK national, regional and local titles have been working together in an ‘all in, all together’ campaign to deliver the COVID message. Two stories in the past week from New York focus the story of what’s happening to newsmedia

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Holden detailed how BBC currently is using the approximately one billion online data points collected daily, providing insights into the future of creating personalised content through myBBC. How is BBC using data now? Reaching out to more audience segments. Traditionally, BBC was traditionally accessed by more men than women. The use of Big Data has allowed the company to identify this trend and subsequently launch initiatives to target more women.

Measuring how successful these initiatives are, including the effectiveness of the use of social platforms such as Facebook. Assisting the BBC marketing team, such as looking at how to best caption social media posts. Providing real-time data to give journalists better understanding about the content they are produce. Apart from using off-the-shelf tools such as ChartBeat, staff also has developed solutions internally such as Telescope, which brings in data from different sources to journalists and shows how engagement changes throughout the day.

Producing content within the newsroom, such as explainer videos for younger audiences. Despite the extensive use of data to increase engagement, Holden emphasised that as a public service, BBC is not audience-led, and data should not replace newsroom decision making. Improved quality of data granularity and timeliness.

BBC to spaff £18 MILLION of licence fee cash… on BIG DATA

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Filter by date May (5) · March International Women’s Day – Researchers taking on big data to create new mental health solutions Ieso Digital Health features on BBC TV Look North, Monday 20th November Ieso Digital.

Often people purchase a particular platform because of the killer apps that run on it. Many NoSQL-based applications fall into the killer app category. These applications could not have become a reality using existing relational database technologies. Apache Cassandra was created by Facebook to power their Inbox. It did this for a number of years. Cassandra worked by doing the following:. The user ID was the primary key.

Each term became a super column, and the message IDs were the column names. Cassandra provided the ability to list all messages sent to and from a particular user. Here the user id was the primary key, the recipient IDs were the super columns, and the message IDs were the column names.

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