Big data refers to large and complex information in electronic format that is difficult to manage using conventional software, hardware, or data management tools. This information is tremendous in terms of its volume, variety, and velocity. In the healthcare industry, this includes a wide variety of data such as patient data in electronic medical records; clinical data; data from sensors monitoring vital signs; and emergency care data for news feeds. It also includes social media data from Facebook, Twitter, and blogs, and data from clinical decision support systems such as medical imaging, pharmacies, labs, prescriptions, and insurance records.

The growing potential of the IT services market in the Nordic region is attracting investments from global tech players. A significant number of start-up tech companies are emerging in the region. The readiness of IT companies to embrace new technological advances is driving the marketgrowth.The Nordic council of ministers have taken several initiatives to strengthen academia collaboration to drive innovation and promote start-ups and SMEs in the Nordic countries in order to strengthen the region's economy. For instance, Norway has been generating predominant revenues from the country's SMEs, and Finland has well-established SMEs in the high-technology sectors. These pose a positive scenario for IT services demand in Nordic.

ERP software is a business management solution that enables organizations to integrate all business operations, including product planning, product development, manufacturing processes, inventory control, and distribution for optimal management.

Online video platforms are software as a service (SaaS)-based. They host, manage,encode, and customize video streaming services for end-users.

A fundamental problem in computing is that processors cannot access memory fast enough to stay fully utilized. Architecture features like cache, prefetching, out-of-order execution, and multiprocessing only benefit software with temporal or spatial locality, or instruction-level or task-level parallelism. Software that relies on fine-grained access to data with structural locality, such as pointer-based data structures, derives little benefit from these features. The importance of these data structures motivates a new approach to improve memory performance. A hardware data structure (HWDS) implements a data structure with operations that leverage parallelism and structural locality to reduce data structure access times, but only supports an exclusive data structure small enough to fit the capacity of the HWDS. This thesis proposes operating system (OS) support for HWDSs so that applications can use and share a HWDS even when its capacity is less than the data structure's size.

No abstract available.

The new generation of microprocessors incorporates a huge number of cores on the same chip. Graphics processing units are an example of this kind of architectures. We analyze these architectures from a theoretical point of view using the K-model to estimate the complexity of a given algorithm defined on this computational model. To this end, we use the K-model to derive an efficient realization of two popular algorithms, namely prefix sum and sorting.

A wave of parallel processing research in the 1970s and 1980s developed various techniques for concurrent task scheduling, including work-stealing scheduling and lazy task creation, and various ideas for supporting speculative computing, including the sponsor model, but these ideas did not see large-scale use as long as uniprocessor clock speeds continued to increase rapidly from year to year. Now that the increase in clock speeds has slowed dramatically and multicore processors have become the answer for increasing the computing throughput of processor chips, increasing the performance of everyday applications on multicore processors by using parallelism has taken on greater importance, so concurrent task scheduling techniques are getting a second look. Work stealing and lazy task creation have now been incorporated into a wide range of systems capable of "industrial strength" application execution, but support for speculative computing still lags behind. This paper traces these techniques from their origins to their use in present-day systems and suggests some directions for further investigation and development in the speculative computing area.