Customers in the UK will soon find out. Recent reports suggest that three of the country's largest supermarket chains are rolling out surge pricing in select stores. This means that prices will rise and fall over the course of the day in response to demand. Buying lunch at lunchtime will be like ordering an Uber at rush hour. This may sound pretty drastic, but far more radical changes are on the horizon.
It is often desirable to extract structured information from raw web pages for better information browsing, query answering, and pattern mining. many such Information Extraction (IE) technologies are costly and applying them at the web-scale is impractical. In this paper, we propose a novel prioritization approach where candidate pages from the corpus are ordered according to their expected contribution to the extraction results and those with higher estimated potential are extracted earlier. Systems employing this approach can stop the extraction process at any time when the resource gets scarce (i.e., not all pages in the corpus can be processed), without worrying about wasting extraction effort on unimportant pages. More specifically, we define a novel notion to measure the value of extraction results and design various mechanisms for estimating a candidate page’s contribution to this value. We further design and build the Extraction Prioritization (EP) system with efficient scoring and scheduling algorithms, and experimentally demonstrate that EP significantly outperforms the naive approach and is more flexible than the classifier approach.
As sales of sugary, fizzy drink products have declined in recent years Coca Cola has also hooked into data to help produce and market some of its healthier options, such as orange juice, which the company sells under a number of brands around the world (including Minute Maid and Simply Orange). The company combines weather data, satellite images, information on crop yields, pricing factors and acidity and sweetness ratings, to ensure that orange crops are grown in an optimum way, and maintain a consistent taste. The algorithm then finds the best combination of variables in order to match products to local consumer tastes in the 200-plus countries around the world where its products are sold. Augmented reality (AR) where computer graphics are overlaid on the user's view of the real world, using glasses or a headset, is being trialed in a number of the company's bottling plants around the world. This allows technicians to receive information about equipment they are servicing, and get backup from experts at remote locations who can see what they are seeing and help to diagnose and solve technical problems.
If you have read some of my posts in the past, you know by now that I enjoy a good craft beer. I decided to mix business with pleasure and write a tutorial about how to scrape a craft beer dataset from a website in Python. This post is separated in two sections: scraping and tidying the data. In the first part, we'll plan and write the code to collect a dataset from a website. In the second part, we'll apply the "tidy data" principles to this freshly scraped dataset.
With over 500 soft drink brands being sold to customers in more than 200 countries, the Coca-Cola Company is the largest beverage company in the world. Every day, thirsty consumers drink more than 1.9 billion servings of Coca-Cola products. An operation on this scale clearly generates a lot of data – whether that's from the production and distribution processes, sales, customer feedback or any other part of the process. One of the reasons the company has remained at the top for over 130 years is its ability to embrace innovation and new technology, including Big Data technology. Coca-Cola has a solid data-driven strategy underpinning decisions right across the business, and it's known to have invested extensive resources into research and development in areas like artificial intelligence (AI) to make the most of the data it collects.