DidaWiki - dm

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 13:21:18 +0000

Privacy is an important problem in our society: the lack of trustable privacy safeguards in many current services and devices is at the basis of a diffusion that is often more limited than expected. Moreover, people feel reluctant to provide true personal data, if not absolutely necessary. Thus, privacy is becoming a fundamental aspect to be took into account when one wants to use, publish and analyze data involving sensitive information. Many recent research works have focused on the study of p…

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 07:22:48 +0000

The biology community is collecting a large amount of raw data, such as the genome sequences of organisms, microarray data, interaction data such as gene-protein interactions, protein-protein interactions and so on. This volume is rapidly increasing and the process of understanding the data is lagging behind the process of acquiring it. An inevitable first step towards making sense of the data is to study their regularities focusing on the patterns, which are not random structures appearing surp…

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 07:45:58 +0000

Delays in biological systems may be used to model events for which the underlying dynamics cannot be precisely observed, or to provide abstraction of some behavior of the system resulting in more compact models. Deterministic modeling of biological systems with delays is usually based on Delay Differential Equations (DDEs), an extension of ordinary ones where the derivative of the unknown function depends on past-states of the system. Stochastic modeling is done by using non- Markovian stochas…

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 07:42:58 +0000

The growing diffusion of computational entities and their composition in networks has made security a critical issue for every phase of the software lifecycle. Indeed, the modern scenarios present a complexity degree that only few years ago was unpredictable. The internet represents the clearest example. It offers a panorama of services that seems to be unlimited, also involving the client side in several ways. From the mere exchange of messages and contents sharing to the creation of distribute…

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 15:06:24 +0000

In recent times we are witnessing a steep increase in the availability of data coming from real--life environments. Nowadays, virtually every one connected to the Internet may have instant access to a tremendous amount of data from satellite elevation maps, airborne time-of-flight scanners and digital cameras, street

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 13:32:18 +0000

We propose a bio-inspired automata-based formalism for the description of biological systems that allows properties of the system to be verified in a modular way. The properties expressed in the universal fragment of temporal logic CTL can be verified on a portion of the model, rather than on the whole model. We show an application to a case study from systems biology. We discuss further extensions of the approach, including dynamicity an reasoning with instances. Finally, we sketch the implemen…

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 12:58:42 +0000

Abstract. The management of the operations acting over distributed and virtual resources plays an important role to achieve the success of modern distributed systems such as Cloud Computing. In this paper, we advocate a notion of resources as entities with their own local policies and containing their global interactive properties and suggest a variant of pi-calculus with primitives to abstractly represent resources and their behaviors. We argue that this calculus provides a suitable model for C…

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 07:37:41 +0000

The new generation of wireless networks (e.g., 802.16/WiMAX, etc.) offers different kind of services (e.g., VoIP, videophone, audio and video streaming, etc.), having different requirements of quality of service, but the wireless system resource, the bandwidth, is limited and must be shared among many users. Therefore, these networks need to adopt both sophisticated transmission techniques, such as the OFDMA one, and proper packet scheduling algorithms that allocates the bandwidth to many user t…

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 11:27:33 +0000

Pervasive embedded P2P systems running on (low capacity) devices that connect ad-hoc as participants of infrastructure-less mobile ad-hoc networks (MANET1), bring new service discovery challenges to service-based application developers. (c1) A distributed implementation of the service discovery protocol should aim at saving the resource consumption of low capacity devices, which could otherwise consume all their resources by participating in the discovery protocol, and then become unavailable t…

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 07:30:50 +0000

Grid is a computational environment where heterogeneous resources are virtualized and outsourced to multiple users across the Internet. The in- creasing popularity of the resources visualization is explained by the emerg- ing suitability of such technology for automated execution of heavy parts of business and research processes. Efficient and flexible model for access and usage control over computational Grid resources is a prominent challenge. This talk will outline the novel access and usage …

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 12:40:20 +0000

Adaptivity is an essential feature whose importance has been assessed in many research fields (e.g. parallel programs, mobile computing and web applications). Especially for high-performance processing, this feature is of special interest in order to properly and promptly respond to time- variable QoS requirements, to react to uncontrollable environmental effects, and to efficiently deal with highly irregular parallel problems. In this scenario the Structured Parallel Programming approach is a c…

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 13:17:14 +0000

The widespread use of positioning technologies ranging from GSM and GPS to WiFi devices tend to produce large-scale datasets of trajectories, which represent the movement of travelling entities. Several application domains, such as recreational area management, may benefit from analysing such datasets. However, analysis results only become truly useful and meaningful for the end user when the intrinsically complex nature of the movement data in terms of context is taken into account during the k…

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 23:27:04 +0000

Succinct data structures are theoretical and practical data structures based on compressed data. Their main goal is to obtain data compression close to the best and add little extra data to support operations on the underlying data while keeping them in compressed format. They lie at the base of more complex data structures that find application in massive data sets analysis, text and web indexing, and more.

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 07:32:59 +0000

In the thesis proposal we intended to analyze a new framework for nearest neighbor search in high-dimensional Euclidean spaces and its applications, but the initial work did not give the results we had hoped for. The work on NNS has been temporarily put on hold for an internship at Microsoft Bing in London which focused on algorithm engineering of succinct data structures. The work resulted in an implementation of a structure for prefix search with precomputed results which is competitive in ti…

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 07:40:04 +0000

In Systems Biology, spatial modelling allows an accurate description of phenomena whose behaviour is influenced by the spatial arrangement of the elements. We present various modelling formalisms with spatial features, each using a different abstraction level of the real space.

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 07:25:51 +0000

In a relational database, a view is a query that is stored in the database and it is queried as if it were a stored relation. In some situations, the user is allowed to perform updates on the view and these updates are reflected back to the query source; this process is called view update.

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 15:18:37 +0000

In population ecology, biologists zoologists and natural scientists study how a large number of individual (a population) over a large territory behave over time. The focus is often to model some of the characteristic of the environment, such as resource availability, temperature or water level, and to predict how the population evolves, both in the short term with individual animals moving and migrating from place to place and over long term, with a possible genetic drift of the overall p…

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 12:44:51 +0000

The tracking of moving events is an important task of wireless sensor networks in many applicative fields. However, current approaches either focus on vertical solution to tracking of specific events, or use generic query languages that are not specifically built to manage events mobility. We propose a new query language and query processing solutions that enable the definition of queries that track and gather information from events. Furthermore, differently than TinyDB and similar languages, t…

anonymous@undisclosed.example.com (Anonymous) — Tue, 11 Jan 2011 13:02:58 +0000

We discuss some techniques for deriving labelled transition systems (LTSs) and bisimulation congruences for graphical encoding of process calculi. We consider graphical encodings (over graphs equipped with suitable interfaces) such that the structural congruence of processes is captured by graph isomorphism, and the reduction semantics is modeled by a set of graph transformation rules, specified using the DPO approach. These graphs are then amenable to the borrowed contexts (BCs) mechanism…

Appello 1 giugno 2010: Risultati

anonymous@undisclosed.example.com (Anonymous) — Thu, 10 Jun 2010 09:49:50 +0000

Appello 1 giugno 2010: Risultati Cognome e Nome Matricola Voto (per lo scritto) Moscatelli Alessandro 246503 24 Zaccagnini Elisa 305168 21

Appello 3 settembre 2009: Risultati

anonymous@undisclosed.example.com (Anonymous) — Wed, 09 Sep 2009 16:56:13 +0000

Appello 3 settembre 2009: Risultati Cognome e Nome Matricola Voto parte A Voto parte B Voto finale Bianchi Francesco 271133 - 28 - De Gregorio Antonio 448413 27 30L 30 Pisu M. Francesca 445796 24 22 (*) 23 (*) Voto della sessione precedente. La prova qui svolta non era compatibile con le regole dell'esame, quindi non è stata valutata.

Appello 9 settembre 2010: Risultati

anonymous@undisclosed.example.com (Anonymous) — Wed, 15 Sep 2010 08:28:51 +0000

Appello 9 settembre 2010: Risultati Cognome e Nome Matricola Voto (per lo scritto) Diana Nicholas 256804 24 Mazzei Maurizio 265253 30 Novara Dario 244364 25 Orrù Linda 431345 26

Appello 12 giugno 2009: Risultati

anonymous@undisclosed.example.com (Anonymous) — Mon, 29 Jun 2009 11:22:11 +0000

Appello 12 giugno 2009: Risultati Cognome e Nome Matricola Voto parte A Voto parte B Bertini Elisabetta 33940 30L Franchini Simone 251920 24 Genovese Gaetano 300726 21 23 Gugliotta Giuditta 301419 23 Isetto Francesco 251825 24

Appello 13 luglio 2010: Risultati

anonymous@undisclosed.example.com (Anonymous) — Tue, 20 Jul 2010 20:06:18 +0000

Appello 13 luglio 2010: Risultati Cognome e Nome Matricola Voto (per lo scritto) Piepoli Gianluca 247445 27 Torsello Ernesto 252487 25 Viggiano Valentino 251693 24

Risultati scritto del 16.01.2012

anonymous@undisclosed.example.com (Anonymous) — Thu, 19 Jan 2012 21:07:10 +0000

Risultati scritto del 16.01.2012 * Bertolini Alessandro: 18 * Galatolo Gabriele: 22 * Vaira Francesco: 27 * Xhagjika Vamis: 18

Appello 18 gennaio 2010: Risultati

anonymous@undisclosed.example.com (Anonymous) — Sat, 13 Feb 2010 14:45:40 +0000

Appello 18 gennaio 2010: Risultati Cognome e Nome Matricola Voto parte A Voto parte B Voto finale (per lo scritto) Buoncristinani Diego 226230 17 30 24 Fontana Francesco 435524 32 27 30 Licari Daniele 19 INS* 18 Mastrini Luca 273378

Appello 18 febbraio 2010: Risultati

anonymous@undisclosed.example.com (Anonymous) — Sun, 07 Mar 2010 16:22:46 +0000

Appello 18 febbraio 2010: Risultati Cognome e Nome Matricola Voto parte A Voto parte B Voto finale (per lo scritto) Brunetti Alessandro 25 22 24 Ceccarelli Enrico 415468 27 29 28 Ciampicali Alessio 445826 27 29 28 Di Matteo Sara

Appello 21 luglio 2009: Risultati

anonymous@undisclosed.example.com (Anonymous) — Sun, 30 Aug 2009 14:37:45 +0000

Appello 21 luglio 2009: Risultati Cognome e Nome Matricola Voto parte A Voto parte B Voto finale Lamacchia Claudio 347919 26 28 27 Montauti Francesco 302106 27 27 27 Urciuoli Ilaria Clara 244217 29 29 29

Appello 22 giugno 2010: Risultati

anonymous@undisclosed.example.com (Anonymous) — Mon, 28 Jun 2010 16:37:12 +0000

Appello 22 giugno 2010: Risultati Cognome e Nome Matricola Voto (per lo scritto) Casciola Valentina 276458 27 Di Matteo Sara 444484 24 Di Meo Roberta 432256 27 Morelli Davide 225784 30 Urciuoli Ilaria Clara 244217 29 Zaccagnini Elisa

Appello 30 giugno 2009: Risultati

anonymous@undisclosed.example.com (Anonymous) — Mon, 20 Jul 2009 11:32:21 +0000

Appello 30 giugno 2009: Risultati (I voti tra parentesi si riferiscono a esami sostenuti durante appelli o verifiche precedenti) Cognome e Nome Matricola Voto parte A Voto parte B Voto finale Fastame Luca 243038 (27) 22 25 Genovese Gaetano

Argomenti complementari per l'esame di DM II, AA 2011-2012

anonymous@undisclosed.example.com (Anonymous) — Thu, 31 May 2012 11:52:15 +0000

Argomenti complementari per l'esame di DM II, AA 2011-2012 Come parte integrante dell'esame, si richiede ad ogni studente di scegliere e studiare uno dei seguenti articoli/testi, che saranno argomento di discussione durante l'orale. La valutazione terrà conto del grado di comprensione ed il livello approfondimento mostrati, ovviamente rapportati alla quantità e alla complessità del materiale studiato (le diverse alternative proposte qui sotto variano molto in lunghezza e livello tecnico).

F.A.Q. Progetti Data Mining

anonymous@undisclosed.example.com (Anonymous) — Wed, 28 May 2008 15:28:02 +0000

F.A.Q. Progetti Data Mining Domande generali * Non riesco ad aprire il dataset con Excel: Excel, a parte le versioni più recenti, ha un limite nel numero di righe gestibili. Si consiglia di utilizzare un DBMS (Access, SQL Server, MySQL) su cui importare il database. Se le operazioni che si intende fare sono realizzabili già dagli strumenti di data mining scelti, è possibile caricare i dati direttamente dai file forniti, senza passare dal database.

Big Data Analytics

anonymous@undisclosed.example.com (Anonymous) — Thu, 09 Jan 2020 17:59:15 +0000

Big Data Analytics * Anna Monreale Università di Pisa, Knowledge Discovery and Data Mining Lab Textbooks * Slides (see Calendar). * Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, F. GUIDE TO INTELLIGENT DATA ANALYSIS. Springer Verlag, 1st Edition., 2010. ISBN 978-1-84882-259-7

Data Mining 2018

anonymous@undisclosed.example.com (Anonymous) — Fri, 12 Oct 2018 18:03:26 +0000

Data Mining 2018 * Anna Monreale Università di Pisa, Knowledge Discovery and Data Mining Lab News Goals Data mining and knowledge discovery techniques emerged as an alternative approach, aimed at revealing patterns, rules and models hidden in the data, and at supporting the analytical user to develop descriptive and predictive models for a number of business problems. This short course focusses on the main applications scenarios of data mining to challenging problems in…

Data Mining A.A. 2006/07

anonymous@undisclosed.example.com (Anonymous) — Fri, 22 Feb 2008 11:25:33 +0000

Data Mining A.A. 2006/07 Docenti: * Fosca Giannotti, * Mirco Nanni, * KDD LAB - Knowledge Discovery Laboratory, ISTI-CNR e Dipartimento di Informatica dell'Università di Pisa Obiettivi del corso La grande disponibilità di dati provenienti da database relazionali, dal web o da altre sorgenti motiva lo studio di tecniche di analisi dei dati che permettano una migliore comprensione ed un più facile util…

Data Mining A.A. 2007/08

anonymous@undisclosed.example.com (Anonymous) — Wed, 23 Dec 2009 14:38:20 +0000

Data Mining A.A. 2007/08 Docenti: * Fosca Giannotti, * Mirco Nanni, * KDD LAB - Knowledge Discovery Laboratory, ISTI-CNR e Dipartimento di Informatica dell'Università di Pisa News * Gli studenti che devono ancora sostenere l'esame (scritto e/o presentazione progetto) possono farlo su appuntamento. Si prega di contattare il docente.

Data Mining A.A. 2008/09

anonymous@undisclosed.example.com (Anonymous) — Wed, 23 Dec 2009 14:38:59 +0000

Data Mining A.A. 2008/09 Docenti: * Fosca Giannotti, * Mirco Nanni, * KDD LAB - Knowledge Discovery Laboratory, ISTI-CNR e Dipartimento di Informatica dell'Università di Pisa News * [30/08/2009] I risultati dell'appello del 21 luglio 2009 sono online.

Data Mining A.A. 2009/10

anonymous@undisclosed.example.com (Anonymous) — Tue, 16 Nov 2010 09:16:31 +0000

Data Mining A.A. 2009/10 Docenti: * Mirco Nanni * KDD Laboratory, ISTI - CNR, Pisa * Tel.: 050-3152843 * * News * [15/09/2010] I risultati dell'appello del 9 settembre 2010 sono online. * [20/07/2010] I risultati dell'appello del 13 luglio 2010 sono online.

Data Mining A.A. 2010/11

anonymous@undisclosed.example.com (Anonymous) — Tue, 20 Sep 2011 06:52:20 +0000

Data Mining A.A. 2010/11 Docenti: * Dino Pedreschi, Fosca Giannotti, Mirco Nanni * KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa * * * * Assistenti: * Anna Monreale, Salvo Rinzivillo, Diego Pennacchioli *

Data Mining A.A. 2011/12

anonymous@undisclosed.example.com (Anonymous) — Mon, 24 Sep 2012 12:19:29 +0000

Data Mining A.A. 2011/12 Docenti: * Dino Pedreschi, Fosca Giannotti, Mirco Nanni * KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa * * * * Assistenti: * Anna Monreale, Luca Pappalardo, Diego Pennacchioli *

Data Mining A.A. 2012/13

anonymous@undisclosed.example.com (Anonymous) — Fri, 11 Oct 2013 11:51:22 +0000

Data Mining A.A. 2012/13 Instructors - Docenti: * Dino Pedreschi, Fosca Giannotti, Mirco Nanni * KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa * * * * Teaching assistants - Assistenti: * Anna Monreale, Luca Pappalardo, Diego Pennacchioli

Data Mining A.A. 2013/14

anonymous@undisclosed.example.com (Anonymous) — Wed, 12 Nov 2014 12:51:56 +0000

Data Mining A.A. 2013/14 Instructors - Docenti: * Dino Pedreschi, Fosca Giannotti, Mirco Nanni * KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa * * * * Teaching assistant - Assistente: * Anna Monreale * KDD Laboratory, Università di Pisa and ISTI - CNR, Pisa

Data Mining A.A. 2014/15

anonymous@undisclosed.example.com (Anonymous) — Mon, 21 Sep 2015 11:17:19 +0000

Data Mining A.A. 2014/15 DM 1: Foundations of Data Mining Instructors - Docenti: * Dino Pedreschi, Fosca Giannotti * KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa * * * * Teaching assistant - Assistente: *

Data Mining A.A. 2015/16

anonymous@undisclosed.example.com (Anonymous) — Wed, 21 Sep 2016 22:12:02 +0000

Data Mining A.A. 2015/16 DM 1: Foundations of Data Mining Instructors - Docenti: * Dino Pedreschi, Anna Monreale * KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa * * * * Teaching assistant - Assistente: * Riccardo Guidotti

Data Mining A.A. 2016/17

anonymous@undisclosed.example.com (Anonymous) — Tue, 24 Sep 2019 10:09:23 +0000

Data Mining A.A. 2016/17 DM 1: Foundations of Data Mining Instructors - Docenti: * Dino Pedreschi, Anna Monreale * KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa * * * * Teaching assistant - Assistente: * Riccardo Guidotti

Data Mining A.A. 2017/18

anonymous@undisclosed.example.com (Anonymous) — Fri, 04 Nov 2022 12:13:48 +0000

Data Mining A.A. 2017/18 DM 1: Foundations of Data Mining (6 CFU) Instructors - Docenti: * Dino Pedreschi * KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa * * Teaching assistant - Assistente: * Riccardo Guidotti * KDD Laboratory, Università di Pisa and ISTI - CNR, Pisa

Data Mining A.A. 2018/19

anonymous@undisclosed.example.com (Anonymous) — Fri, 04 Nov 2022 12:13:57 +0000

Data Mining A.A. 2018/19 DM 1: Foundations of Data Mining (6 CFU) Instructors - Docenti: * Dino Pedreschi * KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa * * Teaching assistant - Assistente: * Riccardo Guidotti * KDD Laboratory, Università di Pisa and ISTI - CNR, Pisa

Data Mining A.A. 2019/20

anonymous@undisclosed.example.com (Anonymous) — Fri, 04 Nov 2022 12:14:07 +0000

Data Mining A.A. 2019/20 DM 1: Foundations of Data Mining (6 CFU) Instructors - Docenti: * Dino Pedreschi * KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa * * DM 2: Advanced Topics on Data Mining and Applications (6 CFU)

Data Mining A.A. 2020/21

anonymous@undisclosed.example.com (Anonymous) — Fri, 04 Nov 2022 12:14:18 +0000

Data Mining A.A. 2020/21 DM1 - Data Mining: Foundations (6 CFU) Instructors: * Dino Pedreschi * KDDLab, Università di Pisa * * * Mirco Nanni * KDDLab, ISTI - CNR, Pisa * * Teaching Assistant * Salvatore Citraro

anonymous@undisclosed.example.com (Anonymous) — Fri, 09 Sep 2022 22:50:36 +0000