AMANDA Algorithmics for MAssive and Networked DAta View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2012-2015

FUNDING AMOUNT

334276 EUR

ABSTRACT

Large amounts of data are generated generously by different types of applications, which often experience serious scalability problems. Several recent articles on international newspapers (eg NY Times, IBM: Big Data, Big Patterns, 02.15.2012) show that many organizations need to process and analyze large-scale data and that a crucial Many large companies are to identify meaningful patterns in the huge amount of data available. Exploring hidden properties in large data sets therefore raises complex problems of essentially algorithmic nature and, by accident, the word "algorithm" often appears in the above-mentioned articles. In addition, the sets of data to be analyzed often contain a strong interconnection structure that makes them modelable through networks, that is graphs, constituted by entity and by relationships (implicit or explicit) between them. Description of the Search The AMANDA project deals with algorithmic problems concerning large data bases. On the one hand, the project studier emerging and realistic calculation models and general design techniques of algorithms; On the other hand, it focuses on specific algorithmic aspects of network set data sets. Pursuing these goals requires some particularly ambitious research challenges. In fact, the size of the data, their dynamic nature and the interconnection structure between them require a jump in quality in the design and engineering of the algorithms. These challenges will be addressed in two workparts (WPs) each consisting of two tasks and based on an appropriate combination of theoretical analysis and experimental validation. The WPs are summarized below (see Section 12). WP1 Large Dimension Databases aims to develop innovative algorithmic methodologies to handle large amounts of data. On the one hand, aspects such as error tolerance, asset-performance tradeoff, streaming graphing, and tablet data structures will be explored. Particular emphasis will be placed on emerging paradigm-based computing models for processing large data bases (eg map-reduce). On the other hand, we focus on specific data-intensive applications by designing new algorithms for some biomedical data mining issues and developing tools to analyze the dynamic behavior of software systems that handle massive data sets. These objectives will be pursued in two tasks: Task 1.2. Algorithms for Emerging Computational Models and Task 2.2. Algorithms for Data-Intensive Calculation. WP2 Massive and Dynamic Data on Networks focuses on two aspects: the calculation of structural properties of large networks, which are instrumental in the development of methodologies for the rapid mining of information from data, and the design of ad-hoc algorithms and human interfaces -macchina to support the mining process. This is particularly demanding when the data does not come fully into memory and when it changes dynamically over time. Our approach is schematized in the sequence: Massive and Dynamic Data on Networks-> Structural Property Computation -> Algorithms and Interfaces Project for Networks Mining. With reference to this schema, WP2 is structured into two tasks, which will integrate into specific application domains and benefit from extensive experimental activity on data available to proposers: Task 2.1. Algorithms for Structural Property Computation of Networks and Task 2.2. Algorithms and Visual Interfaces for Networks Mining. We believe AMANDA contributes to maintaining a relevant position in Italian algorithmic research in the world and contributing to European scientific excellence. In addition, some of AMANDA's expected results can be exploited by companies, providing them with support to face the challenge of big data, while other results (e.g., Those related to biomedical applications) will have a predictable social impact (see Section 13). Consortium AMANDA is proposed by a consortium of 6 units: Padua (PD), Perugia (PG), Pisa (PI), Rome Sapienza (RM1), Roma Tor Vergata (RM2) and Roma Tre (RM3) In the algorithmic search (see Section 11). Their high scientific profile is attested by a large number of publications of high impact, from the presence in the editorial / scientific committees of magazines and prestigious congresses, and by the success in obtaining funding. In addition, the 6 units have complementary skills and a long tradition of cooperation, which are crucial to achieving project goals. So AMANDA's activities will be carried out by an integrated and highly qualified consortium. More... »

URL

http://cercauniversita.cineca.it/php5/prin/cerca.php?codice=2012C4E3KT

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