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To support the growing demand for data-intensive and low-latency IoT applications, Multi-Access Edge Computing (MEC) is emerging as an effective edge-computing approach enabling the execution of delay-sensitive processing tasks close to…

Context: While there are many success stories of achieving high reuse and improved quality using software platforms, there is a need to investigate the issues and challenges organizations face when transitioning to a software platform…

Given a set of deep learning models, it can be hard to find models appropriate to a task, understand the models, and characterize how models are different one from another. Currently, practitioners rely on manually-written documentation to…

[Context & motivation] Driven by the need for faster time-to-market and reduced development lead-time, large-scale systems engineering companies are adopting agile methods in their organizations. This agile transformation is challenging and…

Fog computing extends the cloud computing paradigm by allocating substantial portions of computations and services towards the edge of a network, and is, therefore, particularly suitable for large-scale, geo-distributed, and data-intensive…

The increasing use of statistical data analysis in enterprise applications has created an arms race among database vendors to offer ever more sophisticated in-database analytics. One challenge in this race is that each new statistical…

With the general trend towards data-driven decision making (DDDM), organizations are looking for ways to use DDDM to improve their decisions. However, few studies have looked into the practitioners view of DDDM, in particular for agile…

Data commons collate data with cloud computing infrastructure and commonly used software services, tools and applications to create biomedical resources for the large-scale management, analysis, harmonization, and sharing of biomedical…

Data analytics stands to benefit from the increasing availability of datasets that are held without their conceptual relationships being explicitly known. When collected, these datasets form a data lake from which, by processes like data…

Huge amounts of data being generated continuously by digitally interconnected systems of humans, organizations and machines. Data comes in variety of formats including structured, unstructured and semi-structured, what makes it impossible…

Nowadays, many scientific areas share the same need of being able to deal with massive and distributed datasets and to perform on them complex knowledge extraction tasks. This simple consideration is behind the international efforts to…

We consider how underused computing resources within an enterprise may be harnessed to improve utilization and create an elastic computing infrastructure. Most current cloud provision involves a data center model, in which clusters of…

This paper presents the design of an autonomic, resource-aware distributed database which enables data to be backed up and shared without complex manual administration. The database, H2O, is designed to make use of unused resources on…

The advent of the digital age has led to a rise in different types of data with every passing day. In fact, it is expected that half of the total data will be on the cloud by 2016. This data is complex and needs to be stored, processed and…

We increasingly live in a data-driven world, with diverse kinds of data distributed across many locations. In some cases, the datasets are collected from multiple locations, such as sensors (e.g., mobile phones and street cameras) spread…

The recently proposed Data Lakehouse architecture is built on open file formats, performance, and first-class support for data transformation, BI and data science: while the vision stresses the importance of lowering the barrier for data…

Traditional data lakes provide critical data infrastructure for analytical workloads by enabling time travel, running SQL queries, ingesting data with ACID transactions, and visualizing petabyte-scale datasets on cloud storage. They allow…

Machine learning is now used in many applications thanks to its ability to predict, generate, or discover patterns from large quantities of data. However, the process of collecting and transforming data for practical use is intricate. Even…

Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using…

Data intensive applications often involve the analysis of large datasets that require large amounts of compute and storage resources. While dedicated compute and/or storage farms offer good task/data throughput, they suffer low resource…