1993
Other
Unknown
Testing equivalence in a Stream-based model
Latella D., Massink M., Pedreschi D.
An abstract is not avaiable
Latella D., Massink M., Pedreschi D.
An abstract is not avaiable
See at: CNR ExploRA
2004
Journal article
Unknown
Editorial activity - PKDD 2004
Boulicaut J. F., Esposito F., Giannotti F., Pedreschi D.
An abstract is not availableSource: Lecture notes in computer science 3202 (2004): v–vi.
Boulicaut J. F., Esposito F., Giannotti F., Pedreschi D.
An abstract is not availableSource: Lecture notes in computer science 3202 (2004): v–vi.
See at: CNR ExploRA
2004
Journal article
Unknown
ECML 2004
Boulicaut J., Esposito F., Giannotti F., Pedreschi D.
An abstract is not availableSource: Lecture notes in computer science 3201 (2004): I–XVIII.
Boulicaut J., Esposito F., Giannotti F., Pedreschi D.
An abstract is not availableSource: Lecture notes in computer science 3201 (2004): I–XVIII.
See at: CNR ExploRA
2008
Other
Unknown
EU FET GeoPKDD - Geographic Knowledge Discovery and Delivery
Giannotti F., Nanni M., Pedreschi D., Renso C.
An abstract is not available
Giannotti F., Nanni M., Pedreschi D., Renso C.
An abstract is not available
See at: CNR ExploRA
1998
Other
Unknown
DataSift: Uno strumento per l'analisi intelligente dei dati di vendita della grande distribuzione
Giannotti F., Manco G., Nanni M., Pedreschi D.
An abstract is not available.
Giannotti F., Manco G., Nanni M., Pedreschi D.
An abstract is not available.
See at: CNR ExploRA
1998
Other
Unknown
Uno strumento per l'analisi intelligente dei dati di vendita della grande distribuzione
Giannotti F., Manco G., Nanni M., Pedreschi D., Turini F.
An abstract is not available.
Giannotti F., Manco G., Nanni M., Pedreschi D., Turini F.
An abstract is not available.
See at: CNR ExploRA
2001
Conference article
Unknown
Adaptive web caching using decision trees
Bonchi F., Fenu R., Giannotti F., Gozzi C., Manco G., Nanni M., Pedreschi D., Renso C., Ruggieri S., Sannais L.
An abstract is not available.Source: SDM01 Workshop on Web Mining, Chicago, April 2001
Bonchi F., Fenu R., Giannotti F., Gozzi C., Manco G., Nanni M., Pedreschi D., Renso C., Ruggieri S., Sannais L.
An abstract is not available.Source: SDM01 Workshop on Web Mining, Chicago, April 2001
See at: CNR ExploRA
2006
Contribution to conference
Unknown
Is data mining dangerous?
Atzori M., Bonchi F., Giannotti F., Pedreschi D.
In this poster we discuss anonymity threats arising when data mining results are published. The architecture and the process of our framework to sanitize the data mining output is shown.Source: CERIAS Information Security Symposium, West Lafayette, Indiana, USA, 21-22/03/2006
Atzori M., Bonchi F., Giannotti F., Pedreschi D.
In this poster we discuss anonymity threats arising when data mining results are published. The architecture and the process of our framework to sanitize the data mining output is shown.Source: CERIAS Information Security Symposium, West Lafayette, Indiana, USA, 21-22/03/2006
See at: CNR ExploRA
2000
Report
Unknown
MineFAST: intelligent web caching based on data mining
Bonchi F., Fenu R., Giannotti F., Manco G., Nanni M., Pedreschi D., Renso C., Ruggieri S., Sannais L.
An abstract is not available.Source: Project report, MineFAST, pp.1–101, 2000
Bonchi F., Fenu R., Giannotti F., Manco G., Nanni M., Pedreschi D., Renso C., Ruggieri S., Sannais L.
An abstract is not available.Source: Project report, MineFAST, pp.1–101, 2000
See at: CNR ExploRA
2000
Other
Unknown
Semantics and expressive power of non-deterministic constructs in deductive databases
Giannotti F., Pedreschi D., Zaniolo C.
In this paper, we study the semantics and expressive power of the various non deterministic constructs proposed in the past, including various versions of the choice operator and the witness operator
Giannotti F., Pedreschi D., Zaniolo C.
In this paper, we study the semantics and expressive power of the various non deterministic constructs proposed in the past, including various versions of the choice operator and the witness operator
See at: CNR ExploRA
1999
Conference article
Unknown
Una metodologia basata sulla classificazione per la pianificazione degli accertamenti nel rilevamento di frodi
Bonchi F., Giannotti F., Mainetto G., Pedreschi D.
An abstract is not available.Source: &-mo Convegno Nazionale su Sistemi Evoluti per Basi di Dati, pp. 69–84, Villa Olmo (CO), 1999
Bonchi F., Giannotti F., Mainetto G., Pedreschi D.
An abstract is not available.Source: &-mo Convegno Nazionale su Sistemi Evoluti per Basi di Dati, pp. 69–84, Villa Olmo (CO), 1999
See at: CNR ExploRA
1998
Other
Unknown
Tecniche di data mining per la lotta all'evasione:un case-study basato su classificazione e regole d'associazione
Bonchi F., Giannotti F., Mainetto G., Manco G., Nanni M., Pedreschi D., Turini F.
An abstract is not available.
Bonchi F., Giannotti F., Mainetto G., Manco G., Nanni M., Pedreschi D., Turini F.
An abstract is not available.
See at: CNR ExploRA
2009
Report
Unknown
High Quality True-Positive Prediction for Fiscal Fraud Detection
Basta S., Fassetti F., Papi G. M., Pisani S., Spinsanti L., Giannotti F., Guarascio M., Manco G., Mazzoni A., Pedreschi D.
An abstract is not availableSource: ISTI Technical reports, 2009
Basta S., Fassetti F., Papi G. M., Pisani S., Spinsanti L., Giannotti F., Guarascio M., Manco G., Mazzoni A., Pedreschi D.
An abstract is not availableSource: ISTI Technical reports, 2009
See at: CNR ExploRA
2011
Journal article
Restricted
Privacy in mobility data mining
Gkoulalas-Divanis A., Saygin Y., Pedreschi D.
The invited papers that are published in this special issue cover different research directions in the area of privacy for mobility data. In what follows, we provide a concise summary of the content that is covered in each articleSource: SIGKDD explorations 13 (2011): 4–5. doi:10.1145/2031331.2031333
DOI: 10.1145/2031331.2031333
Project(s): MODAP
Metrics:
Gkoulalas-Divanis A., Saygin Y., Pedreschi D.
The invited papers that are published in this special issue cover different research directions in the area of privacy for mobility data. In what follows, we provide a concise summary of the content that is covered in each articleSource: SIGKDD explorations 13 (2011): 4–5. doi:10.1145/2031331.2031333
DOI: 10.1145/2031331.2031333
Project(s): MODAP
Metrics:
See at:
delivery.acm.org 
| ACM SIGKDD Explorations Newsletter | CNR ExploRA
2007
Contribution to book
Unknown
Mobility, Data Mining and Privacy: A vision of Convergence
Giannotti F., Pedreschi D.
A flood of data pertinent to moving objects is increasingly available, particularly due to the automated collection of privacy-sensitive telecom data from mobile phones and other location-aware devices. Such wealth of data, referenced both in space and time, may enable novel applications of high societal and economic impact, provided that the discovery of consumable knowledge out of these raw data is made possible.Source: Mobility, Data Mining and Privacy: Geographic Knowledge Discovery, edited by F. Giannotti, D. Pedreschi, pp. 1–11. Berlin: Springer-Verlag, 2007
Giannotti F., Pedreschi D.
A flood of data pertinent to moving objects is increasingly available, particularly due to the automated collection of privacy-sensitive telecom data from mobile phones and other location-aware devices. Such wealth of data, referenced both in space and time, may enable novel applications of high societal and economic impact, provided that the discovery of consumable knowledge out of these raw data is made possible.Source: Mobility, Data Mining and Privacy: Geographic Knowledge Discovery, edited by F. Giannotti, D. Pedreschi, pp. 1–11. Berlin: Springer-Verlag, 2007
See at: CNR ExploRA
2014
Report
Unknown
Valutazione del rischio di privacy nel processo di costruzione dei modelli di call habit che sottostanno al sociometro = Assessing the Privacy Risk in the Process of Building Call Habit Models that Underlie the Sociometer
Furletti B., Gabrielli L., Monreale A., Nanni M., Pratesi F., Rinzivillo S., Giannotti F., Pedreschi D.
The paper discusses in detail the problem of the privacy of the users of the original phone data, demonstrating the possibility to measure the risk of identification from the compact representation of the profiles.Source: ISTI Technical reports, 2014
Furletti B., Gabrielli L., Monreale A., Nanni M., Pratesi F., Rinzivillo S., Giannotti F., Pedreschi D.
The paper discusses in detail the problem of the privacy of the users of the original phone data, demonstrating the possibility to measure the risk of identification from the compact representation of the profiles.Source: ISTI Technical reports, 2014
See at: CNR ExploRA
2000
Other
Unknown
On temporal, nonmonotonic, nondeterministic logic databases
Giannotti F., Manco G., Nanni M., Pedreschi D.
We consider in this paper an extension of Datalog with mechanisms for temporal1 nonmonotonic and nondeterministic reasoning, which we refer to as Datalog++. We show, by means of examples, its fiexibility in expressing queries concerning aggregates and data cube. Also, we show how iterateci fìxpoint and stable model semantics can be combined to the purpose of clarifying the semantics of Datalog++ programs, and supporting their efficient execution. Finally, we provide a more concrete implementation strategy, on which basis the design of optimization techniques tailored for Datalog++ is addressed.
Giannotti F., Manco G., Nanni M., Pedreschi D.
We consider in this paper an extension of Datalog with mechanisms for temporal1 nonmonotonic and nondeterministic reasoning, which we refer to as Datalog++. We show, by means of examples, its fiexibility in expressing queries concerning aggregates and data cube. Also, we show how iterateci fìxpoint and stable model semantics can be combined to the purpose of clarifying the semantics of Datalog++ programs, and supporting their efficient execution. Finally, we provide a more concrete implementation strategy, on which basis the design of optimization techniques tailored for Datalog++ is addressed.
See at: CNR ExploRA
1999
Other
Unknown
Metodologia, tecniche e risultati di esperimenti di data mining nel contrasto dell'evasione fiscale
Bonchi F., Giannotti F., Mainetto G., Pedreschi D.
In this paper we present the metholodology, the techniques we have used for dealing with problems of fiscal frauds in Italy. We describe in details the results of our experiments, the problems we have solved, which are a subset of the set of problems which constitutes the large body of the fiscal fraud detection problem in Italy.
Bonchi F., Giannotti F., Mainetto G., Pedreschi D.
In this paper we present the metholodology, the techniques we have used for dealing with problems of fiscal frauds in Italy. We describe in details the results of our experiments, the problems we have solved, which are a subset of the set of problems which constitutes the large body of the fiscal fraud detection problem in Italy.
See at: CNR ExploRA
2019
Journal article
Open Access
The AI black box explanation problem
Guidotti R., Monreale A., Pedreschi D.
Explainable AI is an essential component of a "Human AI", i.e., an AI that expands human experience, instead of replacing it. It will be impossible to gain the trust of people in AI tools that make crucial decisions in an opaque way without explaining the rationale followed, especially in areas where we do not want to completely delegate decisions to machines.Source: ERCIM news (2019): 12–13.
Project(s): SoBigData
Guidotti R., Monreale A., Pedreschi D.
Explainable AI is an essential component of a "Human AI", i.e., an AI that expands human experience, instead of replacing it. It will be impossible to gain the trust of people in AI tools that make crucial decisions in an opaque way without explaining the rationale followed, especially in areas where we do not want to completely delegate decisions to machines.Source: ERCIM news (2019): 12–13.
Project(s): SoBigData
See at:
ercim-news.ercim.eu 
| ISTI Repository | CNR ExploRA
2005
Conference article
Unknown
k-anonymous patterns
Atzori M., Bonchi F., Giannotti F., Pedreschi D.
It is generally believed that data mining results do not violate the anonymity of the individuals recorded in the source database. In fact, data mining models and patterns, in order to ensure a required statistical significance, represent a large number of individuals and thus conceal individual identities: this is the case of the minimum support threshold in association rule mining. In this paper we show that this belief is ill-founded. By shifting the concept of k-anonymity from data to patterns, we formally characterize the notion of a threat to anonymity in the context of pattern discovery, and provide a methodology to efficiently and effectively identify all possible such threats that might arise from the disclosure of a set of extracted patterns.Source: European Conference on Principles and Practice of Knowledge, pp. 10–21, Porto, Portugal, 3-7 October 2005
Atzori M., Bonchi F., Giannotti F., Pedreschi D.
It is generally believed that data mining results do not violate the anonymity of the individuals recorded in the source database. In fact, data mining models and patterns, in order to ensure a required statistical significance, represent a large number of individuals and thus conceal individual identities: this is the case of the minimum support threshold in association rule mining. In this paper we show that this belief is ill-founded. By shifting the concept of k-anonymity from data to patterns, we formally characterize the notion of a threat to anonymity in the context of pattern discovery, and provide a methodology to efficiently and effectively identify all possible such threats that might arise from the disclosure of a set of extracted patterns.Source: European Conference on Principles and Practice of Knowledge, pp. 10–21, Porto, Portugal, 3-7 October 2005
See at: CNR ExploRA