Cerimonia di Conferimento del Dottorato di Ricerca Honoris Causa in Information and Communication Technologies al Prof. Carlo Zaniolo, Martedì 28 maggio 2019, ore 15.00 – Sala “University Club”, Unical

Martedì 28 maggio 2019, ore 15.00 – Sala “University Club”, Unical

PROGRAMMA

Ore 15.00 Apertura della Cerimonia

                    Prof. Gino Mirocle Crisci Magnifico Rettore dell’Università della Calabria

Ore 15.15  Motivazioni del Conferimento

                     Prof. Luigi Palopoli Direttore del DIMES dell’Università della Calabria

Ore 15.30  Laudatio del Prof. Carlo Zaniolo

                      Prof. Sergio Greco Ordinario di Sistemi di Elaborazione delle Informazioni, Unical

Ore 16.00  Lectio Magistralis: “Declarative Languages and Algorithms for BigData Applications

                     Prof. CARLO ZANIOLO  Full Professor – University of California, Los Angeles (UCLA)

Ore 17.00   Conferimento del Dottorato di Ricerca Honoris Causa in Information and Communication Technologies

 

Lectio Magistralis: Declarative Languages and Algorithms for BigData Applications

Prof. Carlo Zaniolo

The critical importance of advanced knowledge-based applications made possible by BigData underscores the need for high-level declarative languages providing ease-of-use, portability, scalability and performance on such applications. Researchers have pursued this ambitious goal by extending the enabling technologies of Relational Databases and Logic Programming, making great progress that benefited their fields and commercial systems. Yet, progress has been hampered by non-monotonic reasoning issues that have limited our ability to express complex algorithms using negation and aggregates in recursive queries. Major progress on this front has recently been achieved with the introduction of the concept of Pre-Mappability (PreM) that makes possible to view recursive programs with aggregates as stratified programs—stratification is a simple syntactic notion that assures declarative semantics and portability. PreM is a very general notion that applies to diverse constraints and languages and also dove-tails with map-reduce and data streams. In fact, Apache-Spark SQL extended with PreM outperforms on graph applications special-purpose database systems created to exploit this area of weakness of commercial SQL systems. In our seminar, we will finally discuss how easily classical algorithms can be expressed concisely in Datalog with aggregates, and how PreM in these algorithms can be proven using the simple methods we discovered.

 

Prof. Carlo Zaniolo: short biography

Carlo Zaniolo was born in Vicenza, Italy. He received an E.E. Engineer degree at Padua University in 1968, and M.S. and Ph.D. degrees in Computer Science at UCLA in 1970 and 1976, respectively. After working at Bell Laboratories, Murray Hill, NJ, and MCC in Austin Texas, Prof. Zaniolo joined the UCLA CS Department in 1991 as a Full Professor of Computer Science, and was awarded the N.E. Friedmann Chair in Knowledge Science. At UCLA, he is now the co-director of the Scalable Analytics Institute. Prof. Zaniolo’s interests include big data and knowledge based systems, non-monotonic and temporal reasoning, internet information systems, answering questions, queries and searches in knowledge base. Prof. Zaniolo has published more than 300 papers in different areas but his fame is primarily due to his contributions to Database technology:  his discovery of Multivalued Dependencies that he introduced in his PhD thesis; his work on null values, and on algorithms for relational schema design including the simplified definition of Third Normal Form(3NF) used in all textbooks; his work on a model called GEM, which extends the relational model with object-oriented features; his seminal contributions on Datalog and non-monotonic reasoning focused on stable models and their connection to choice models; his work on supporting and optimizing regular expressions in query languages. A most enduring theme of his esearch has been taming the non-monotonic nature of aggregate functions to let them serve as the basis for powerful queries, declarative algorithms, and knowledge-based applications (including declarative BigData applications).