Related papers: Literature Study on Operational Data Analytics Fra…
The term Data Space, understood as the secure exchange of data in distributed systems, ensuring openness, transparency, decentralization, sovereignty, and interoperability of information, has gained importance during the last years.…
Computing is bottlenecked by data. Large amounts of application data overwhelm storage capability, communication capability, and computation capability of the modern machines we design today. We argue that an intelligent architecture should…
Big data analytics has gathered immense research attention lately because of its ability to harness useful information from heaps of data. Cloud computing has been adjudged as one of the best infrastructural solutions for implementation of…
Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of…
Abstract--- With the rapid growth of the Internet of Things (IoT), current Cloud systems face various drawbacks such as lack of mobility support, location-awareness, geo-distribution, high latency, as well as cyber threats. Fog/Edge…
Recent years have seen an increased interest in large-scale analytical dataflows on non-relational data. These dataflows are compiled into execution graphs scheduled on large compute clusters. In many novel application areas the predominant…
Traditional data processing pipelines are typically static and handcrafted for specific tasks, limiting their adaptability to evolving requirements. While general-purpose agents and coding assistants can generate code for well-understood…
As deep neural networks develop significantly more diverse and complex, achieving high performance and efficiency on complicated DNN models faces pressing challenges. Modern DNN workloads are increasingly diverse in operation types, tensor…
Traditional enterprise warehouse solutions center around an analytical database system that is monolithic and inflexible: data needs to be extracted, transformed, and loaded into the rigid relational form before analysis. It takes years of…
The computing systems used by LHC experiments has historically consisted of the federation of hundreds to thousands of distributed resources, ranging from small to mid-size resource. In spite of the impressive scale of the existing…
Many organizations routinely analyze large datasets using systems for distributed data-parallel processing and clusters of commodity resources. Yet, users need to configure adequate resources for their data processing jobs. This requires…
Data quality assessment and data cleaning are context-dependent activities. Motivated by this observation, we propose the Ontological Multidimensional Data Model (OMD model), which can be used to model and represent contexts as logic-based…
We analyze a recently published dataset of 396 real-world cloud architectures deployed on AWS, from companies belonging to a wide range of industries. From this dataset, we identify those architectures that contain HPC or edge components…
The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are bringing more…
This report has two objectives. First, we describe a set of the production distributed infrastructures currently available, so that the reader has a basic understanding of them. This includes explaining why each infrastructure was created…
In recent years, cloud service providers have been building and hosting datacenters across multiple geographical locations to provide robust services. However, the geographical distribution of datacenters introduces growing pressure to both…
Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube such that each cell contains one or more measures…
Data science pipelines commonly utilize dataframe and array operations for tasks such as data preprocessing, analysis, and machine learning. The most popular tools for these tasks are pandas and NumPy. However, these tools are limited to…
The proliferation of SQL for data processing has often occurred without the rigor of traditional software development, leading to siloed efforts, logic replication, and increased risk. This ad-hoc approach hampers data governance and makes…
While manufacturers have been generating highly distributed data from various systems, devices and applications, a number of challenges in both data management and data analysis require new approaches to support the big data era. These…