M
Maria Luisa Sapino
Researcher at University of Turin
Publications - 137
Citations - 2055
Maria Luisa Sapino is an academic researcher from University of Turin. The author has contributed to research in topics: Cluster analysis & Tensor. The author has an hindex of 19, co-authored 135 publications receiving 1982 citations. Previous affiliations of Maria Luisa Sapino include Telecom Italia & Arizona State University.
Papers
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Journal ArticleDOI
Flexible support for multiple access control policies
TL;DR: A unified framework that can enforce multiple access control policies within a single system and be enforced by the same security server is presented, based on a language through which users can specify security policies to be enforced on specific accesses.
Proceedings ArticleDOI
A multi-similarity algebra
TL;DR: This work introduces a similarity algebra that brings together relational operators and results of multiple similarity implementations in a uniform language and provides a generic cost model for evaluating cost of query plans in the similarity algebra and query optimization methods based on this model.
Journal ArticleDOI
Recommending multimedia visiting paths in cultural heritage applications
Ilaria Bartolini,Vincenzo Moscato,Ruggero G. Pensa,Antonio Penta,Antonio Picariello,Carlo Sansone,Maria Luisa Sapino +6 more
TL;DR: A general recommendation framework able to uniformly manage heterogeneous multimedia data coming from several web repositories and to provide context-aware recommendation techniques supporting intelligent multimedia services for the users—i.e. dynamic visiting paths for a given environment is presented.
Journal ArticleDOI
The role of abduction in database view updating
TL;DR: The role of abduction is investigated in view updating, singling out similarities and differences between view updating and abduction, and providing a formal result showing the correctness of the approach.
Book
Data Management for Multimedia Retrieval
TL;DR: This textbook on multimedia data management techniques offers a unified perspective on retrieval efficiency and effectiveness and presents data structures and algorithms that help store, index, cluster, classify, and access common data representations.