The Workshop on Analytics, Telemetry, and Cybersecurity for High-Performance Computing and Communications (HPCC) is newly launched and takes place in Taipei, Taiwan, on October 17, 2025, in conjunction with the ACM Conference on Computer and Communications Security (CCS'25). As its title suggests, the workshop centers on strengthening resilience and security in HPCC applications and infrastructures by leveraging leading technologies, including data-driven methodologies and machine intelligence techniques. The primary objective of this workshop is to provide a dedicated platform for researchers, practitioners, and industry experts to engage in discussions on cutting-edge topics in analytics, telemetry, and cybersecurity for HPCC. This year's call for contributions welcomes both full research papers and work-in-progress submissions, resulting in the acceptance of five full-length research papers. In addition, the workshop features a distinguished keynote presentation by Dr. Hsu-Chun Hsiao, Associate Professor in the Department of Computer Science and Information Engineering and the Graduate Institute of Networking and Multimedia at National Taiwan University. The WATCH'25 complete workshop proceedings can be found at: https://dl.acm.org/citation.cfm?id=3733826.

HPC and distributed systems are the driving force for the advancement of many emerging technologies, such as exascale systems, quantum machines, terabit networking, 5G/6G wireless, and cloud/edge computing. The tasks of systems and network telemetry are a key element for effective operations and management of the advancement of many emerging systems and technologies, and require more scalable telemetry and analysis techniques for comprehensive monitoring and analysis. Various input sources such as end systems, switches, firewalls, intrusion sensors and the emerging network elements speaking with different syntax and semantics make organizing and incorporating the generated data challenging for the quantitative and qualitative analysis. This workshop looks for new approaches and methods at the intersection of HPC systems and data sciences to address these difficult challenges of emerging technologies from the diverse angles of systems/network performance, availability, reliability, and security.

Abstract We present afqn (Approximate Fast Qn), a novel algorithm for approximate computation of the Qn scale estimator in a streaming setting, in the sliding window model. It is well-known that computing the Qn estimator exactly may be too costly for some applications, and the problem is a fortiori exacerbated in the streaming setting, in which the time available to process incoming data stream items is short. In this paper we show how to efficiently and accurately approximate the Qn estimator. As an application, we show the use of afqn for fast detection of outliers in data streams. In particular, the outliers are detected in the sliding window model, with a simple check based on the Qn scale estimator. Extensive experimental results on synthetic and real datasets confirm the validity of our approach by showing up to three times faster updates per second. Our contributions are the following ones: (i) to the best of our knowledge, we present the first approximation algorithm for online computation of the Qn scale estimator in a streaming setting and in the sliding window model; (ii) we show how to take advantage of our UDDSketch algorithm for quantile estimation in order to quickly compute the Qn scale estimator; (iii) as an example of a possible application of the Qn scale estimator, we discuss how to detect outliers in an input data stream.

We aim at contributing to the reliability of the phase scintillation index on Global Navigation Satellite System (GNSS) signals at high-latitude. To the scope, we leverage on a recently introduced detrending scheme based on the signal decomposition provided by the fast iterative filtering (FIF) technique. This detrending scheme has been demonstrated to enable a fine-tuning of the cutoff frequency for phase detrending used in the phase scintillation index definition. In a single case study based on Galileo data taken by a GNSS ionospheric scintillation monitor receiver (ISMR) in Concordia Station (Antarctica), we investigate how to step ahead of the cutoff frequency optimization. We show how the FIF-based detrending allows deriving adaptive cutoff frequencies, whose value changes minute-by-minute. They are found to range between 0.4 and 1.2 Hz. This allows better accounting for diffractive effects in phase scintillation index calculation and provides a GNSS-based estimation of the relative velocity between satellite and ionospheric irregularities.

We present parallel algorithms for mining Correlated Heavy Hitters from a two-dimensional data stream. In particular, we design and implement a message-passing, a shared-memory and a hybrid algorithm. To the best of our knowledge, these are the first parallel algorithms solving the problem. We show, through experimental results, that our algorithms provide very good scalability, whilst retaining the accuracy of their sequential counterpart.

The Rheticus® cloud-based platform provides continuous monitoring services of the Earth's surface. One of the services provided by Rheticus® is the Displacement Geoinformation Service, which offers monthly monitoring of millimetric displacements of the ground surface, landslide areas, the stability of infrastructures, and subsidence due to groundwater withdrawal/entry or from the excavation of mines and tunnels. To provide this information, the Rheticus® platform processes a large amount of Geospatial Big Data. In particular, Rheticus® is capable to process Synthetic Aperture Radar images acquired by the X-band COSMO-SkyMed constellation, as well as satellite Open Data provided by Copernicus Sentinels, and it is capable to integrate local INSPIRE data sources. In this paper, we summarize the main features of the Rheticus® services and we provide examples of the detection and monitoring of geohazard and infrastructure instabilities through Multitemporal InSAR techniques. Furthermore, we outline the porting activity and the efficient implementation of the most time-consuming algorithmic kernels in the GPGPU environment.

Summary Given an array of n elements and a value 2≤k≤n, a frequent item or k‐majority element is an element occurring in more than n/k times. The k‐majority problem requires finding all of the k‐majority elements. In this paper, we deal with parallel shared‐memory algorithms for frequent items; we present a shared‐memory version of the Space Saving algorithm, and we study its behavior with regard to accuracy and performance on many and multi‐core processors, including the Intel Phi accelerator. We also investigate a hybrid MPI/OpenMP version against a pure MPI‐based version. Through extensive experimental results, we prove that the MPI/OpenMP parallel version of the algorithm significantly enhances the performance of the earlier pure MPI version of the same algorithm. Results also prove that for this algorithm the Intel Phi accelerator does not introduce any improvement with respect to the Xeon octa–core processor.

Preserving data confidentiality in clouds is a key issue. Secret Sharing, a cryptographic primitive for the distribution of a secret among a group of n participants designed so that only subsets of shareholders of cardinality 0 <; t ≤ n are allowed to reconstruct the secret by pooling their shares, can help mitigating and minimizing the problem. A desirable feature of Secret Sharing schemes is cheater detection, i.e., the ability to detect one or more malicious shareholders trying to reconstruct the secret by obtaining legal shares from the other shareholders while providing them with fake shares. Verifiable Secret Sharing schemes solve this problem by allowing shareholders verifying the others' shares. We present new verification algorithms providing arbitrary secret sharing schemes with cheater detection capabilities, and prove their space efficiency with regard to other schemes appeared in the literature. We also introduce, in one of our schemes, the Exponentiating Polynomial Root Problem (EPRP), which is believed to be NP-Intermediate and therefore difficult.

The access control problem in a hierarchy can be solved by using a hierarchical key assignment scheme, where each class is assigned an encryption key and some private information. A formal security analysis for hierarchical key assignment schemes has been traditionally considered in two different settings, i.e., the unconditionally secure and the computationally secure setting, and with respect to two different notions: security against key recovery (KR-security) and security with respect to key indistinguishability (KI-security), with the latter notion being cryptographically stronger. Recently, Freire, Paterson and Poettering proposed strong key indistinguishability (SKI-security) as a new security notion in the computationally secure setting, arguing that SKI-security is strictly stronger than KI-security in such a setting. In this paper we consider tthehe unconditionally secure setting for hierarchical key assignment schemes. In such a setting the security of the schemes is not based on specific unproven computational assumptions, i.e., it relies on the theoretical impossibility of breaking them, despite the computational power of an adversary coalition. We prove that, in this setting, SKI-security is not stronger than KI-security, i.e., the two notions are fully equivalent from an information-theoretic point of view.

The sharing and integration of health care data such as medical history, pathology, therapy, radiology images, etc., is a key requirement for improving the patient diagnosis and in general the patient care. Today, many EPR (Electronic Patient Record) systems are present both in the same or different health centers and record a huge amount of data regarding a patient. In most cases the care treatment of a patient involves different healthcare facilities, including the cares provided by the family doctors. Managing these data, typically petabytes or terabytes in size, and optimizing the applications (image analysis, data mining, etc.) for these architectures is one of the challenges that must be tackled. Therefore, there is a clear need for the design and implementation of new scalable approaches to deal with the associated information overload and cognitive complexity issues. A possible solution involves considering a simplification of data coming from different EPRs, in a structured schema, typically called a meta-EPR. Owing to the security of patient data, each health center manages its own meta-EPR whereas a framework integrates these data among different sites. This work addresses the issue of sharing and integrating health care data, proposing a meta-EPR, based on Peer-to-peer (P2P) technology for data fusion. We describe an implementation of a distributed information service, that shares meta-EPRs and provides aggregation of relevant clinical information about patients based on a structured P2P overlay.

Abstract We present a deterministic parallel algorithm for thek‐majority problem, that can be used to find in parallel frequent items, i.e. those whose multiplicity is greater than a given threshold, and is therefore useful to process iceberg queries and in many other different contexts of applied mathematics and information theory. The algorithm can be used both in the online (stream) context and in the offline setting, the difference being that in the former case we are restricted to a single scan of the input elements, so that verifying the frequent items that have been determined is not allowed (e.g. network traffic streams passing through internet routers), while in the latter a parallel scan of the input can be used to determine the actualk‐majority elements. To the best of our knowledge, this is the first parallel algorithm solving the proposed problem. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract We present two deterministic parallel Selection algorithms for distributed memory machines, under the coarse‐grained multicomputer model. Both are based on the use of two weighted 3‐medians, that allows discarding at least 1/3 of the elements in each iteration. The first algorithm slightly improves the current experimentally fastest algorithm by Saukas and Song where at least 1/4 of the elements are discarded in each iteration, while the second one is a fast, special purpose algorithm working for a particular class of input, namely an input that can be sorted in linear time using RadixSort. Copyright © 2009 John Wiley & Sons, Ltd.

Workshop on Workflow Management and Applications in Grid Environments (

Data grid management systems are becoming increasingly important in the context of the recently adopted service oriented science paradigm. The Grid Relational Catalog (GRelC) project is working towards ubiquitous, integrated, seamless and comprehensive data grid management solutions to fully address application specific requirements. This paper describes a GRelC based environment for bioinformatics and its underlying data grid services allowing scientific users (by means of a customized grid portal) to manage data, handle, share and publish metadata, perform search and discovery activities, etc.

With a growing trend towards grid-based data repositories and data analysis services, scientific data analysis often involves accessing multiple data sources, and analyzing the data using a variety of analysis programs. A strictly related critical challenge is the fact that data sources often hold the same type of data in a number of different formats; moreover, the formats expected and generated by various data analysis services are often distinct. In bioinformatics the data are often stored in flat files, therefore accessing them to retrieve a subset of records determined by constraints, is slower with respect to other approaches such as relational DBMS. We have developed a data grid system, built on top of specific biological data sources in flat file format, which carries out the ingestion into a relational DBMS for data integration reducing the data redundancy present in the biological flat files. In this work, we describe the prototype for the ingestion in a relational DBMS of the Swiss-2D PAGE flat file.

In a growing number of scientific disciplines, large data collections are emerging as important community resources. Data and metadata management exploiting the data grid paradigm is becoming more and more important as the number of involved data sources is continuously increasing and decentralizing. Efficient grid data access services are perceived as mandatory components for data management. In the grid data management area the GRelC Project has been addressing efficiency, transparency, interoperability and security issues, providing grid enabled solutions and proposing a set of data access and integration/federation services. In this paper we present the advances related to the GRelC Data Access, highlighting differences and innovations w.r.t. previous work. Basic foundations about the grid-enabled queries provided by the GRelC DAS and experimental results related to a bioinformatics international testbed on the GILDA t-Infrastructure are also reported and discussed.

A large-scale simulation in e-science experiments can be modeled by using a workflow. The ProGenGrid workflow management system is being developed at the University of Salento in Lecce since 2004 and consists of an editor for designing the experiment and an engine for scheduling the jobs in a computational grid. The initial version was based on wrapping the bioinformatics tools as Web services and scheduling job execution on the grid by using an opportune engine. Then the engine was optimized to support batch, parameter and MPI jobs by using the Globus Toolkit. This component was developed as part of the grid resource broker project. In this paper, we present the latest advances regarding the editorpsilas new features such as on line monitoring, and the enginepsilas support for scheduling jobs on other grid middleware such as gLite and Unicore.

Abstract Increasingly, complex scientific applications are structured in terms of workflows. These applications are usually computationally and/or data intensive and thus are well suited for execution in grid environments. Distributed, geographically spread computing and storage resources are made available to scientists belonging to virtual organizations sharing resources across multiple administrative domains through established service‐level agreements. Grids provide an unprecedented opportunity for distributed workflow execution; indeed, many applications are well beyond the capabilities of a single computer, and partitioning the overall computation on different components whose execution may benefit from runs on different architectures could provide better performances. In this paper we describe the design and implementation of the Grid Resource Broker (GRB) workflow engine. Copyright © 2008 John Wiley & Sons, Ltd.

Data grids are middleware systems that offer secure shared storage of massive scientific datasets over wide area networks. In this paper we describe the GReIC Data Storage, a novel grid storage service which has been developed within the Grid Relational Catalog (GReIC) Project. The aim of this service is to manage efficiently, securely and transparently collections of bioinformatics data concerning "in silico" experiments on the grid promoting flexible, secure and coordinated storage resource sharing and publication across virtual organizations, taking into account current grid standards and specifications.

Abstract This paper describes the Grid Resource Broker (GRB), a Grid portal built leveraging a set of high‐level, Globus‐Toolkit‐based Grid libraries called GRB libraries. The portal leverages the Liferay framework to provide users with an intuitive, highly customizable Web GUI. The underlying GRB middleware allows trusted users seamless access to their computational Grid environments. Copyright © 2007 John Wiley & Sons, Ltd.

Currently several applications produce huge amount of data making them available for post-processing operations in order to infer new knowledge. Main issues of these applications are the need for efficient mechanisms to access data and high performance computing to obtain the results in an acceptable time. Wrapping the applications as Web services allows interoperability with others tools and in particular with grid computing environments exploiting a large set of resources through a standard interface, to support the requirements of so-called "data intensive" applications that handle large amounts of data. This paper presents the architecture of a complex data managements system leveraging the grid computing paradigm, exploiting existing middleware developed at the University of Lecce within the ProGenGrid, GReIC and GRB projects to support high throughput applications. This architecture has been specialized for a bioinformatics domain and a case study of the use of a biological application will be also described

We describe the ProGenGrid (Proteomics and Genomics Grid) Workflow system, developed by the CACT/ISUFI at the University of Lecce which aims at providing a tool that e-scientists can utilize to simulate biological experiments, compose existing analysis and visualization tools, monitor their execution, store the intermediate and final output and finally, if needed, save the model of the experiment for updating or reproducing it. The tools that we are considering are software components wrapped as Web services and composed through a workflow. Since bioinformatics applications need to use high performance machines or many workstations to reduce the computational time, we are exploiting a Grid infrastructure for interconnecting wide-spread tools and hardware resources.

Given the heterogeneous nature of biological data and their intensive use in many tools, in this paper we propose a semantic data access and integration (DAI) service, based on the grid paradigm, for the bioinformatics domain. This service uses ontologies for correlating different data sets. The DAI proposed in this work is a fundamental component of the ProGenGrid system, a grid-enabled platform, which aims at the design and implementation of a virtual laboratory where e-scientists could simulate complex "in silico" experiments, composing some popular analysis and visualization tools (e.g. Blast and Rasmol) available as Web services, into a workflow. The main goal of the DAI is to provide bioinformatics tools with advanced functionalities and data integration services for heterogeneous biological data banks, such as PDB and Swiss-Prot. A case study of our specialized data access service for locating similar protein sequences is presented.

Abstract This paper describes the Grid Resource Broker (GRB) portal, a Web gateway to computational Grids in use at the University of Lecce. The portal allows trusted users seamless access to computational resources and Grid services, providing a friendly computing environment that takes advantage of the underlying Globus Toolkit middleware, enhancing its basic services and capabilities. Copyright © 2002 John Wiley & Sons, Ltd.

The Testbed and Applications working group of the European Grid Forum (EGrid) is actively building and experimenting with a grid infrastructure connecting several research-based supercomputing sites located in Europe. The paper reports on our first feasibility study: running a self-migrating version of the Cactus simulation code across the European grid testbed, including "live" remote data visualization and steering from different demonstration booths at Supercomputing 2000, in Dallas, TX. We report on the problems that had to be resolved for this endeavour and identify open research challenges for building production-grade grid environments.