Spectral acceleration of parallel iterative eigensolvers for large scale scientific computing

The computation of a number of the smallest eigenvalues of large and sparse matrices is crucial in various scientific applications, as the Finite Element solution of PDEs, electronic structure calculations or Laplacian of graphs, to mention a few. We propose in this contribution a parallel algorithm that is based on the spectral low-rank modification of a factorized sparse approximate inverse preconditioner (RFSAI) to accelerate the Newton-based iterative eigensolvers. Numerical results onto matrices arising from various realistic problems with size up to 5 million unknowns and 2.2 x 10^8 nonzero elements account for the efficiency and the scalability of the proposed RFSAI-updated preconditioner

From the Thirties to post-war reconstruction. The Land Reclamation Consortia and rural architecture in Italy.

This investigation highlights a new conception of design space in architecture, in the relationship between settlement and land, rooted in architectural historical studies and research on rural and agrarian economy and unlocks a potential regeneration and restoration of the rural villages of Italy’s cultural heritage. In Italy, the theme of rural architecture has gained momentum ever since the spread of the Modern Movement, reviving settlement and spatial principles as a moral lesson for the general development of new aesthetics and a new society. Innovative concepts inspired by Arrigo Serpieri such as “Integral Land Reclamation”, and long-standing institutions such as the Land Reclamation Consortia, became official law in 1933, and played a crucialrole in this process, particularly in consolidating new architectural thinking that was to endure up to post-war reconstruction and beyond, until our own times. Paradoxically, ideologically opposing phenomena, settlements related to the extensive land reclamation of the Fascist period and the rural redevelopment of the Fifties, were somehow based on comparable theoretical and operational aspects. We can recognize these ideas by looking at the most interesting experiments developed in these two periods: the city of Sabaudia designed by Piccinato, and the village of La Martella at Matera designed by Quaroni (and sponsored by Adriano Olivetti). The quest for a new “moral aesthetic” of architecture undertaken by leading representatives of Italian Rationalism was to re-emerge in the neorealism of post-war reconstruction

MOMIS Dashboard: a powerful data analytics tool for Industry 4.0

In this work we present the MOMIS Dashboard, an interactive data analytics tool to explore and visualize data sources content through several kind of dynamic views (e.g. maps, bar, line, pie, etc.). The software tool is very versatile, and supports the connection to the main relational DBMS and Big Data sources. Moreover, it can be connected to MOMIS, a powerful Open Source Data Integration system, able to integrate heterogeneous data sources as enterprise information systems as well as sensors data. MOMIS Dashboard provides a secure permission management to limit data access on the basis of a user role, and a Designer to create and share personalized insights on the company KPIs, facilitating the enterprise collaboration. We illustrate the MOMIS Dashboard efficacy in a real enterprise scenario: a production monitoring platform to analyze real-time and historical data collected through sensors located on production machines that optimize production, energy consumption, and enable preventive maintenance

Entity Resolution On-Demand

Entity Resolution (ER) aims to identify and merge records that refer to the same real-world entity. ER is typically employed as an expensive cleaning step on the entire data before consuming it. Yet, determining which entities are useful once cleaned depends solely on the user's application, which may need only a fraction of them. For instance, when dealing with Web data, we would like to be able to filter the entities of interest gathered from multiple sources without cleaning the entire, continuously-growing data. Similarly, when querying data lakes, we want to transform data on-demand and return the results in a timely manner---a fundamental requirement of ELT (Extract-Load-Transform) pipelines. We propose BrewER, a framework to evaluate SQL SP queries on dirty data while progressively returning results as if they were issued on cleaned data. BrewER tries to focus the cleaning effort on one entity at a time, following an ORDER BY predicate. Thus, it inherently supports top-k and stop-and-resume execution. For a wide range of applications, a significant amount of resources can be saved. We exhaustively evaluate and show the efficacy of BrewER on four real-world datasets

BigDedup: a Big Data Integration toolkit for Duplicate Detection in Industrial Scenarios

Duplicate detection aims to identify different records in data sources that refers to the same real-world entity. It is a fundamental task for: item catalogs fusion, customer databases integration, fraud detection, and more. In this work we present BigDedup, a toolkit able to detect duplicate records on Big Data sources in an efficient manner. BigDedup makes available the state-of-the-art duplicate detection techniques on Apache Spark, a modern framework for distributed computing in Big Data scenarios. It can be used in two different ways: (i) through a simple graphic interface that permit the user to process structured and unstructured data in a fast and effective way; (ii) as a library that provides different components that can be easily extended and customized. In the paper we show how to use BigDedup and its usefulness through some industrial examples

bigbench workload executed by using apache flink

Abstract Many of the challenges that have to be faced in Industry 4.0 involve the management and analysis of huge amount of data (e.g. sensor data management and machine-fault prediction in industrial manufacturing, web-logs analysis in e-commerce). To handle the so-called Big Data management and analysis, a plethora of frameworks has been proposed in the last decade. Many of them are focusing on the parallel processing paradigm, such as MapReduce , Apache Hive , Apache Flink . However, in this jungle of frameworks, the performance evaluation of these technologies is not a trivial task, and strictly depends on the application requirements. The scope of this paper is to compare two of the most employed and promising frameworks to manage big data: Apache Flink and Apache Hive , which are general purpose distributed platforms under the umbrella of the Apache Software Foundation. To evaluate these two frameworks we use the benchmark BigBench , developed for Apache Hive . We re-implemented the most significant queries of Apache Hive BigBench to make them work on Apache Flink , in order to be able to compare the results of the same queries executed on both frameworks. Our results show that Apache Flink , if it is configured well, is able to outperform Apache Hive