Related papers: A Distributed Process Infrastructure for a Distrib…
An essential part of building a data-driven organization is the ability to handle and process continuous streams of data to discover actionable insights. The explosive growth of interconnected devices and the social Web has led to a large…
The Semantic Web drives towards the use of the Web for interacting with logically interconnected data. Through knowledge models such as Resource Description Framework (RDF), the Semantic Web provides a unifying representation of richly…
Digital Twins (DTs) represent digital counterparts of physical systems, assets, or processes, referred to as the actual twin (AT). DTs integrate heterogeneous data, models, and semantic technologies to support monitoring, simulation,…
The need to federate repositories emerges in two distinctive scenarios. In one scenario, scalability-related problems in the operation of a repository reach a point beyond which continued service requires parallelization and hence…
Massive RDF data sets are becoming commonplace. RDF data is typically generated in social semantic domains (such as personal information management) wherein a fixed schema is often not available a priori. We propose a simple Three-way…
Modern distributed data processing systems struggle to balance performance, maintainability, and developer productivity when integrating machine learning at scale. These challenges intensify in large collaborative environments due to high…
The recent developments and research in distributed ledger technologies and blockchain have contributed to the increasing adoption of distributed systems. To collect relevant insights into systems' behavior, we observe many evaluation…
Data-intensive platforms such as Hadoop and Spark are routinely used to process massive amounts of data residing on distributed file systems like HDFS. Increasing memory sizes and new hardware technologies (e.g., NVRAM, SSDs) have recently…
Parallel dataflow systems are a central part of most analytic pipelines for big data. The iterative nature of many analysis and machine learning algorithms, however, is still a challenge for current systems. While certain types of bulk…
The importance of geo-spatial data in critical applications such as emergency response, transportation, agriculture etc., has prompted the adoption of recent GeoSPARQL standard in many RDF processing engines. In addition to large…
Increasing amounts of scientific and social data are published in the Resource Description Framework (RDF). Although the RDF data can be queried using the SPARQL language, even the SPARQL-based operation has a limitation in implementing…
Multi-dimensional discrete Fourier transforms (DFT) are typically decomposed into multiple 1D transforms. Hence, parallel implementations of any multi-dimensional DFT focus on parallelizing within or across the 1D DFT. Existing DFT packages…
We address the problem of compactly storing a large number of versions (snapshots) of a collection of keyed documents or records in a distributed environment, while efficiently answering a variety of retrieval queries over those, including…
The data science community today has embraced the concept of Dataframes as the de facto standard for data representation and manipulation. Ease of use, massive operator coverage, and popularization of R and Python languages have heavily…
Many fields of science rely on relational database management systems to analyze, publish and share data. Since RDBMS are originally designed for, and their development directions are primarily driven by, business use cases they often lack…
Developments in the context of Open, Big, and Linked Data have led to an enormous growth of structured data on the Web. To keep up with the pace of efficient consumption and management of the data at this rate, many data Management…
Distributed systems can be very large and complex. The various considerations that influence their design can result in a substantial specification, which requires a structured framework that has to be managed successfully. The purpose of…
Due to Variety, Web data come in many different structures and formats, with HTML tables and REST APIs (e.g., social media APIs) being among the most popular ones. A big subset of Web data is also characterised by Velocity, as data gets…
Centralized and distributed systems are two main approaches to organizing ICT infrastructure, each with its pros and cons. Centralized systems concentrate resources in one location, making management easier but creating single points of…
In parallel with big data processing and analysis dominating the usage of distributed and cloud infrastructures, the demand for distributed metadata access and transfer has increased. In many application domains, the volume of data…