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Existing feature filters rely on statistical pair-wise dependence metrics to model feature-target relationships, but this approach may fail when the target depends on higher-order feature interactions rather than individual contributions.…
This paper presents an architecture, based on Distributed Ledger Technologies (DLTs) and Decentralized File Storage (DFS) systems, to support the use of Personal Information Management Systems (PIMS). DLT and DFS are used to manage data…
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…
It allows any two parties that are either both on the same network or connected via the internet to transfer the contents of a file based on a particular sequence of words. Peer discovery happens via multicast DNS if both peers are on the…
The rise of the Internet of Things and edge computing has shifted computing resources closer to end-users, benefiting numerous delay-sensitive, computation-intensive applications. To speed up computation, distributed computing is a…
Recently, a new generation of P2P systems capable of addressing data integrity and authenticity has emerged for the development of new applications for a "more" decentralized Internet, i.e., Distributed Ledger Technologies (DLT) and…
The proliferation of resourceful mobile devices that store rich, multidimensional and privacy-sensitive user data motivate the design of federated learning (FL), a machine-learning (ML) paradigm that enables mobile devices to produce an ML…
Modern malware can take various forms, and has reached a very high level of sophistication in terms of its penetration, persistence, communication and hiding capabilities. The use of cryptography, and of covert communication channels over…
In traditional SaaS enterprise applications, microservices are an essential ingredient to deploy machine learning (ML) models successfully. In general, microservices result in efficiencies in software service design, development, and…
The distribution of files using decentralized, peer-to-peer (P2P) systems, has significant advantages over centralized approaches. It is however more difficult to settle on the best approach for file sharing. Most file sharing systems are…
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…
Centralized version control systems (VCS) are vital for software development but pose risks of data loss and ownership disputes. While blockchain offers a decentralized alternative, existing solutions are often hindered by high latency,…
Performance modeling can help to improve the resource efficiency of clusters and distributed dataflow applications, yet the available modeling data is often limited. Collaborative approaches to performance modeling, characterized by the…
Metadata hotspots remain one of the key obstacles to scalable Input/Output (I/O) in both High-Performance Computing (HPC) and cloud-scale storage environments. Situations such as job start-ups, checkpoint storms, or heavily skewed namespace…
This paper presents an architecture-friendly k-means clustering algorithm called SIVF for a large-scale and high-dimensional sparse data set. Algorithm efficiency on time is often measured by the number of costly operations such as…
The InterPlanetary File System (IPFS) is a peer-to-peer network for storing data in a distributed file system, hosting over 190,000 peers spanning 152 countries. Despite its prominence, the privacy properties that IPFS offers to peers are…
Heterogeneous information networks (HINs) represent different types of entities and relationships between them. Exploring, analysing, and extracting knowledge from such networks relies on metapath queries that identify pairs of entities…
The following paper presents various methods and implementation techniques used to harvest metadata efficiently from a Kademlia Distributed Hashtable (DHT) as used in the BitTorrent P2P network to build an index of publicly available files…
Scientific applications in HPC environment are more com-plex and more data-intensive nowadays. Scientists usually rely on workflow system to manage the complexity: simply define multiple processing steps into a single script and let the…
Data-intensive applications often require exploratory analysis of large datasets. If analysis is performed on distributed resources, data locality can be crucial to high throughput and performance. We propose a "data diffusion" approach…