Oracle's gargantuan $28.3 billion acquisition of health care data company Cerner, the largest deal in its 44-year history, is not just about electronic patient records. From algorithmic systems that predict the likelihood a patient will contract sepsis to tech that tracks hospital bed capacity, Cerner will bring an array of cloud-based data analytics and AI technologies to Oracle as it competes with Amazon Web Services, Google, IBM and others to serve the health care industry's data and AI needs. In fact, the deal is poised to shift some business away from AWS, which Cerner named as its preferred cloud partner in 2019. Oracle's acquisition of Cerner, a company that got its start in health care IT in 1979, is expected to close in 2022. The all-cash deal is also expected to improve Oracle's bottom line in its first year, the company said in a press release.
The ubiquitous availability of computing devices and the widespread use of the internet have generated a large amount of data continuously. Therefore, the amount of available information on any given topic is far beyond humans' processing capacity to properly process, causing what is known as information overload. To efficiently cope with large amounts of information and generate content with significant value to users, we require identifying, merging and summarising information. Data summaries can help gather related information and collect it into a shorter format that enables answering complicated questions, gaining new insight and discovering conceptual boundaries. This thesis focuses on three main challenges to alleviate information overload using novel summarisation techniques. It further intends to facilitate the analysis of documents to support personalised information extraction. This thesis separates the research issues into four areas, covering (i) feature engineering in document summarisation, (ii) traditional static and inflexible summaries, (iii) traditional generic summarisation approaches, and (iv) the need for reference summaries. We propose novel approaches to tackle these challenges, by: i)enabling automatic intelligent feature engineering, ii) enabling flexible and interactive summarisation, iii) utilising intelligent and personalised summarisation approaches. The experimental results prove the efficiency of the proposed approaches compared to other state-of-the-art models. We further propose solutions to the information overload problem in different domains through summarisation, covering network traffic data, health data and business process data.
I t wasn't long ago that technology was a topic only discussed among techies. In fact, technology was an elective course in many graduate school programs until very recently. Today, technology is part of our daily lives so it's not surprising that technology is very much a part of any industry. It's also not surprising to see the direction technology has taken. It has evolved from a way to communicate with each other and store important information, to a way to interact with each other, express ourselves and manage our lives. The drive to monetize our personal information for the purpose of creating the latest and greatest target marketing algorithm has paved the way for artificial intelligence or AI. Google was a pioneer and early adopter of this type of AI, gathering information about our interest based on our searches and pairing businesses and products we would likely use. It is this type of AI that brings customers to businesses like an arranged marriage. Collection of data through cloud-based applications originally created for business solutions slowly evolved for consumer convenience for everything from banking to entertainment. Amassing raw data to create solutions for everyday activities helped to speed the process of AI for the birth of AI. Had we not partaken in taking information once only saved on our desktops and placing it on cloud servers, AI may not have evolved into the presence of daily life today. Years ago, reluctance and lack of understanding of how digital information is used kept many people who are not computer savvy from partaking in this community. Today, thanks to companies like Facebook and Amazon, people readily share their information with companies with a basic trust that the information will only be used for the purpose intended. This is why, even though the information is occasionally breached, we are so willing to join communities like Citizens app and Waze which use crowd sourcing for the collective purpose of helping each of its participants. Crowd sourcing applications can then place ads as a form of revenue, though not all do. This rather invasive, though passive, business model hones in on our inherent need to share information in order to benefit from the information shared by others.