Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis

Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no longer be completed in a few seconds and data exploration is severely hampered. This article describes a novel computation paradigm called Progressive Computation for Data Analysis or more concisely Progressive Analytics, that brings at the programming language level a low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory data analysis systems. This article describes the new paradigm through a prototype implementation called ProgressiVis, and explains the requirements it implies through examples.Comment: 10 page

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

A multidimensional control architecture for combined fog-to-cloud systems

The fog/edge computing concept has set the foundations for the deployment of new services leveraging resources deployed at the edge paving the way for an innovative collaborative model, where end-users may collaborate with service providers by sharing idle resources at the edge of the network. Combined Fog-to-Cloud (F2C) systems have been recently proposed as a control strategy for managing fog and cloud resources in a coordinated way, aimed at optimally allocating resources within the fog-to-cloud resources stack for an optimal service execution. In this work, we discuss the unfeasibility of the deployment of a single control topology able to optimally manage a plethora of edge devices in future networks, respecting established SLAs according to distinct service requirements and end-user profiles. Instead, a multidimensional architecture, where distinct control plane instances coexist, is then introduced. By means of distinct scenarios, we describe the benefits of the proposed architecture including how users may collaborate with the deployment of novel services by selectively sharing resources according to their profile, as well as how distinct service providers may benefit from shared resources reducing deployment costs. The novel architecture proposed in this paper opens several opportunities for research, which are presented and discussed at the final section.This work was supported by the H2020 EU mF2C project, ref. 730929 and for UPC authors, also by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under contract RTI2018-094532-B-I00.Peer ReviewedPostprint (author's final draft

Tensor Computation: A New Framework for High-Dimensional Problems in EDA

Many critical EDA problems suffer from the curse of dimensionality, i.e. the very fast-scaling computational burden produced by large number of parameters and/or unknown variables. This phenomenon may be caused by multiple spatial or temporal factors (e.g. 3-D field solvers discretizations and multi-rate circuit simulation), nonlinearity of devices and circuits, large number of design or optimization parameters (e.g. full-chip routing/placement and circuit sizing), or extensive process variations (e.g. variability/reliability analysis and design for manufacturability). The computational challenges generated by such high dimensional problems are generally hard to handle efficiently with traditional EDA core algorithms that are based on matrix and vector computation. This paper presents "tensor computation" as an alternative general framework for the development of efficient EDA algorithms and tools. A tensor is a high-dimensional generalization of a matrix and a vector, and is a natural choice for both storing and solving efficiently high-dimensional EDA problems. This paper gives a basic tutorial on tensors, demonstrates some recent examples of EDA applications (e.g., nonlinear circuit modeling and high-dimensional uncertainty quantification), and suggests further open EDA problems where the use of tensor computation could be of advantage.Comment: 14 figures. Accepted by IEEE Trans. CAD of Integrated Circuits and System

Evaluating mobile health applications as digital therapeutical products

The emergence of new technological advancements and the unprecedented expansion of mobile phone usage has led to the exponential growth of Mobile Health Applications (mHealth apps) development and implementation in the global markets. mHealth apps have created innovative channels to diagnose, treat, monitor, and engage with patients in various healthcare settings, and therefore, it is an important exploration in the fields of information technology, healthcare, and cognitive behavioural sciences. However, a significant portion of mHealth apps has been identified to be developed without scientific or clinical evidence. The objective of implementing the proposed “mHealth App Evaluation Tool” and its validation of the perceived usefulness of the tool from clinicians, mHealth app developers and end-users is to provide a solution for addressing the current gap in evaluating the efficacy of unregulated mHealth apps. An extensive review of the literature from 2010 to 2022 was conducted in three separate phases, gathering and synthesising the core concepts of the mHealth app landscape, proposed frameworks and parameters, the evolution and construction of unidimensional and multidimensional scales and the use of multi-stakeholder participation for a holistic evaluation process. The proposed mHealth app evaluation tool was developed on the foundation of six design drivers: modifiability, scalability, multi-stakeholder involvement, simultaneous management of multiple evaluation projects, ease of use and accessibility. The development of the tool utilised the RestFul API pattern, leveraging Laravel PHP and Vue.js frameworks. The data collection process was completed in two separate phases. The first phase involved the data obtained from the participant’s evaluation of the WYSA app using the proposed mHealth App Evaluation Tool. The system auto-generated an associated average score out of 5 against each evaluation. The second phase involved the data collection during the 30 minutes interview session. Due to the ever-changing nature of software applications, it is inevitable that the elements of mHealth app evaluation will continue to evolve and change over time. What is deemed to be necessary and critical in evaluating mHealth apps today may not be so in years to come. The mHealth App Evaluation tool addresses the need for future criteria modifications, scalability, and the necessity to obtain expert knowledge from multiple stakeholders for a holistic mHealth app evaluation